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Mosquito-borne flaviviruses are among the most significant arboviral pathogens worldwide . Vaccinations and mosquito population control programs remain the most reliable means for flavivirus disease prevention , and live attenuated viruses remain one of the most attractive flavivirus vaccine platforms . Some live attenuated viruses are capable of infecting principle mosquito vectors , as demonstrated in the laboratory , which in combination with their intrinsic genetic instability could potentially lead to a vaccine virus reversion back to wild-type in nature , followed by introduction and dissemination of potentially dangerous viral strains into new geographic locations . To mitigate this risk we developed a microRNA-targeting approach that selectively restricts replication of flavivirus in the mosquito host . Introduction of sequences complementary to a mosquito-specific mir-184 and mir-275 miRNAs individually or in combination into the 3’NCR and/or ORF region resulted in selective restriction of dengue type 4 virus ( DEN4 ) replication in mosquito cell lines and adult Aedes mosquitos . Moreover a combined targeting of DEN4 genome with mosquito-specific and vertebrate CNS-specific mir-124 miRNA can silence viral replication in two evolutionally distant biological systems: mosquitoes and mouse brains . Thus , this approach can reinforce the safety of newly developed or existing vaccines for use in humans and could provide an additional level of biosafety for laboratories using viruses with altered pathogenic or transmissibility characteristics .
Mosquito-borne flaviviruses such as dengue , yellow fever , Japanese encephalitis and West Nile viruses ( genus , Flavivirus; family , Flaviviridae ) are among the most significant arboviral pathogens of humans and domestic animals in many regions of the world . Depending on the particular virus , clinical manifestations can vary from asymptomatic or self-limited flu-like illness to a life threatening jaundice , hemorrhagic fever and shock syndrome , or severe meningitis and encephalitis . Outbreaks and epidemics of flavivirus diseases typically coincide with warm rainy seasons , when the population of competent mosquito vectors reaches sufficient density for sustainable virus transmission between vertebrate hosts . Historically , mosquito control programs were considered as the most effective measure to prevent outbreaks and limit the spread of flavivirus diseases . However , reduction of funding allocated to mosquito control campaigns during the last decades , accompanied by an increase in human population density and global travel [1 , 2] resulted in an increase in the number of flavivirus-associated illnesses [1–3] . This emphasizes the need for development of effective vaccines or therapeutics as an alternative means for protection against flavivirus diseases . A number of approaches are being pursued for development of effective vaccine candidates against flaviviruses , including RNA , DNA , inactivated , and subunit vaccines as well as vaccine candidates based on single-round replicated viral particles ( reviewed in [4 , 5] ) . Although proven to be safe , they typically fail to provide long-lasting immunity after a single dose , and some may not be cost effective . This suggests a considerable advantage to vaccines based on live attenuated viruses that are relatively inexpensive to manufacture and provide a durable and potent immunity after a single dose of vaccination [6] . However , despite advances in developing live attenuated vaccine ( LAV ) candidates , a concern has been raised that they might not be safe in the environment due to their intrinsic genetic instability , potential reversion back to wild-type , and possible dissemination by mosquito vectors after feeding on vaccinees [7] . The possibility of mosquito transmission is further supported by the fact that many LAV candidates against diseases caused by mosquito-borne viruses can replicate in mosquito-derived cell lines and some are capable of infecting principle mosquito vectors in the laboratory [8–19] . For example , a vaccine strain of Venezuelan equine encephalitis virus ( arbovirus , genus Alphavirus , family Togaviridae ) was isolated outside its typical range from wild mosquitoes collected during a 1971 horse vaccination campaign in Louisiana [20] , highlighting the risk of introduction and dissemination of potentially dangerous viral strains in new geographic locations . Flaviviruses are enveloped single-stranded , positive-sense RNA viruses with genomes of approximately 11 , 000 nucleotide bases that contain 5’ and 3’-terminal non-coding regions ( NCR ) flanking a single open reading frame ( ORF ) encoding a polyprotein . During cap-dependent translation , the polyprotein is processed by viral and cellular proteases to yield three structural proteins ( capsid [C] , premembrane [prM] , and envelope [E] ) followed by at least seven nonstructural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) [21] . In recent years , an approach based on targeting of viral genomes for number of cellular microRNAs ( miRNAs ) has been proven to be a simple and efficient method to restrict replication and pathogenesis of DNA and RNA viruses in a cell- , tissue- or species-specific manner [22–33] . Moreover , we demonstrated that insertion of targets for several brain-specific microRNAs ( miRNAs ) into the 3’ NCR and/or ORF of neurotropic chimeric tick-borne encephalitis virus/dengue type 4 virus ( TBEV/DEN4 ) was sufficient to selectively inhibit viral replication in neurons and to constrain the development of lethal encephalitis in adult and newborn mice after intracerebral or intraperitoneal infection [34–36] . In this study using a similar miRNA-targeting approach , we sought to design a mosquito-borne flavivirus that would be selectively restricted for replication in its invertebrate host . As a model system to investigate the effect of miRNA targeting on flavivirus fitness in arthropod vector , we selected a dengue type 4 virus ( DEN4 ) that efficiently replicates in widely distributed Aedes ( A . ) aegypti and A . albopictus mosquitoes . DEN4 is not neuroinvasive in vertebrate hosts and is currently being used as a genetic background for development of live attenuated vaccines against neurotropic and non-neurotropic flaviviruses [8–12] . However , the DEN4-based chimeric viruses , as well as the DEN4 parent , are able to replicate in the central nervous system ( CNS ) of neonatal mice infected intracerebrally , causing lethal encephalitis . Therefore , we also explored if combined targeting of the DEN4 genome with mosquito-specific and mouse brain-expressed miRNAs can simultaneously restrict DEN4 infection and replication in the mosquito host and attenuate virus neurovirulence in newborn mice .
Recently , miRNA expression profiles have been identified for several mosquito species [37–39] . Based on this data , three mosquito-specific miRNAs ( mir-184 , mir-275 and mir-1 ) were selected for DEN4 genome targeting because they satisfy the following criteria: 1 ) they are highly expressed in different mosquito organs and mosquito-derived cell lines , and also remain abundant during flaviviruses infection [37]; 2 ) these miRNAs are evolutionarily conserved among insect species including mosquitoes , but they are different from their miRNA analogs in mammals . To investigate if miRNA targeting of DEN4 genome results in selective restriction of DEN4 replication in mosquitoes , a single copy of mir-184 , mir-275 , or mir-1 target sequence was introduced into the genome of DEN4 strain 814669 [40] ( abbreviated D4s ) between nucleotides ( nts ) 10277 and 10278 ( 15 nts downstream of the TAA stop codon preceding the 3’NCR ) . This particular strain of DEN4 was chosen because it has been extensively characterized in the laboratory and is currently being used as a genetic background for construction of LAV candidates against flaviviruses [8–12] . DEN4 viruses carrying miRNA target sequences were generated by electroporation of in vitro transcribed full-length genomic RNA into Vero cells . Parental and each modified DEN4 virus ( designated D4s , D4-184s , D4-275s , and D4-1s; Fig 1 ) were harvested on day 5 post-electroporation and amplified by one additional passage in Vero cells . Specific infectivity values of synthesized RNA and viral titers in Vero cells supernatants for D4-184s , D4-275s , and D4-1s were comparable to the unmodified D4s clone ( S1 Table ) , indicating that insertion of target sequences into the 3’NCR did not result in substantial attenuation of DEN4 in Vero cells . The effect of miRNA targeting on the DEN4 replication in mosquito cell lines was analyzed by comparing growth kinetics of viruses in Aag2 and C710 cells isolated from A . aegypti and A . albopictus mosquitoes , respectively . In both cell lines infected at multiplicity of infection ( MOI ) of 0 . 01 , parental D4s and D4-1s viruses replicated efficiently with nearly identical kinetics reaching titers of ~6 log10 pfu/ml by 3 dpi ( Fig 2A and 2B ) . In contrast , the replication of viruses D4-184s and D4-275s carrying a target for mir-184 or mir-275 was significantly impaired ( p<0 . 001; 2-way ANOVA ) . In both cell lines , these viruses exhibited a 1000-fold or higher reduction in virus titer at 3 days post-infection ( dpi ) compared to the D4s virus , correlating with mir-184 and mir-275 expression levels in Aag2 and C710 cell lines ( S1 Fig ) [37] . Based on these data , we selected D4-184s and D4-275s for further evaluation of their replication in adult A . aegypti mosquitoes . Female mosquitoes were infected intrathoracically with 100 pfu of D4s , D4-184s , or D4-275s virus . After 7 days of incubation , whole body homogenates of each mosquito were prepared individually , and titers were determined on Vero cell monolayers ( Fig 2C ) . Insertion of either miRNA target sequence in the 3’NCR of DEN4 genome led to a significant restriction of virus replication in mosquito bodies ( p< 0 . 001 , one-tailed t-test ) , however , it was not sufficient to completely suppress the DEN4 infection . Previously , we demonstrated that multiple miRNA-targeting of flavivirus genome for brain-specific miRNAs in the 3’NCR results in increased efficiency of miRNA-mediated virus suppression in the CNS of mice compared to that observed for viruses containing only a single copy of the miRNA target [34 , 35] . Accordingly , we reasoned that a similar strategy should be applied to achieve a more reliable attenuation for DEN4 virus replication in cell culture and adult mosquitoes . Two additional constructs were developed based on D4-275s that contained additional target sequence for either mir-184 or mir-275 at nt position 212 of DEN4 3’NCR ( Fig 1 ) . Unfortunately , titers that these viruses achieved after recovery were not sufficient for their biological evaluation in mosquitoes ( S1 Table ) . To improve virus recovery , we modified the DEN4 cDNA clone in the following ways: substituted the SP6 promoter ( for in vitro transcription ) with the eukaryotic RNA polymerase II cytomegalovirus ( CMV ) promoter; introduced two intron sequences at nt positions 3922 and 8836 of the DEN4 genome to minimize plasmid DNA toxicity during propagation in E . coli; inserted the hepatitis delta virus ribozyme sequence at the 3’-end of the DEN4 virus genome to ensure correct release of authentic 3’-terminated RNA during transcription; and introduced the previously identified Vero cell-adaptive mutation ( L122→F ) into the NS4B protein gene to enhance replication in Vero cells [41–44] . The modified parental and mir-184- or mir-275-targeted DEN4 viruses ( designated D4 , D4-184 , and D4-275; Fig 1 ) were re-generated by cDNA transfection into Vero cells ( S2 Table ) . Two additional viruses ( D4-275-184 and D4-275x2 ) were developed based on the D4-275 genome and contained a second target sequence for either mir-184 or mir-275 at nt position 212 of the 3’NCR ( Fig 1 ) . Viruses were biologically cloned by terminal dilution and amplified by one additional passage in Vero cells ( S2 Table ) . Viruses were genetically stable and contained the miRNA target insertions as assessed by sequence analysis of viral genomes after 5 additional passages in Vero cells . All viruses containing miRNA targets were significantly attenuated compared to D4 virus for growth in A . aegypti-derived Aag2 cells ( Fig 3A , p<0 . 001; 2-way ANOVA ) and in A . albopictus-derived C6/36 cells , which replaced the previously used C710 cells ( Fig 3B , p<0 . 001; 2-way ANOVA ) . As anticipated , the combined expression of miRNA targets at two distant positions of the 3’NCR ( D4-275-184 and D4-275x2 ) led to a more effective restriction of the virus replication in mosquito cells and resulted in at least a 3 . 5 or 6 . 0 log10 pfu/ml reduction in virus titer compared to D4 virus in Aag2 and C6/36 cells , respectively . In contrast , all viruses bearing one or two targets for mosquito-specific miRNAs replicated efficiently in Vero cells and the virus yield of each miRNA-targeted virus did not differ significantly from that of parental D4 virus ( Fig 3C ) . The maintenance of dengue viruses in the environment relies on the ability of the virus to infect , replicate , and develop a disseminated infection within two critical vectors: A . aegypti and A . albopictus mosquitoes . To investigate how targeting of the viral genome for mosquito-specific miRNA affects DEN4 fitness in female mosquitoes , A . aegypti and A . albopictus were fed with blood meals containing 7 . 1–7 . 4 log10 pfu/ml of recombinant DEN4 viruses . Viral titers in mosquito bodies , viral infectivity , and dissemination into heads of orally infected mosquitoes were assayed on day 14 after feeding . D4 virus infected 90% of both mosquito species by day 14 , reaching a mean virus titer of ~2 . 5 log10 pfu/mosquito body , and disseminated to the heads of 71% of A . aegypti and 55% of A . albopictus ( Fig 4 ) . Introduction of a single copy of miRNA target for either mir-184 or mir-275 miRNA resulted in a significant reduction of the DEN4 titer in mosquito bodies ( Fig 4A and 4B; p≤0 . 002 one-tailed Student's t-test ) in both mosquito species as well as viral infectivity and ability of virus to develop a disseminated infection in A . aegypti ( Fig 4C; p<0 . 05 one-tailed Fisher’s exact test ) . Infectivity of D4-184 and D4-275 viruses in A . albopictus were only slightly reduced as compared to D4 virus ( Fig 4D; p = 0 . 144 and p = 0 . 0728 ) , however both viruses were significantly attenuated in the ability to develop a disseminated infection ( Fig 4D; p<0 . 05 one-tailed Fisher’s exact test ) . All D4-184 and D4-275 viruses recovered from A . albopictus and the majority of viruses recovered from infected A . aegypti contained deletions or point mutations in the miRNA target sequences ( S2 Fig ) , indicating that targeting of the DEN4 genome by a single copy of miRNA target was not sufficient to prevent virus transmission by mosquitoes . In contrast , the D4-275-184 virus was unable to infect the midgut and thus failed to disseminate in both mosquito species indicating that a combined expression of mir-184 and mir-275 targets in D4-275-184 was sufficient to completely block DEN4 replication in the principal mosquito vectors ( Fig 4C and 4D , p<0 . 001; Fisher’s exact test ) . These results are consistent with our previous observations made for viruses targeted by brain-specific miRNAs [34–36] , suggesting that targeting of the viral genome in the same sites for two different miRNAs is a more efficient approach for flavivirus attenuation in the mosquitoes and the CNS of mice compared to a monotypic miRNA-targeting . Interestingly , a duplication of the target sequence for mir-275 miRNA alone resulted in a virus that was more infectious for A . aegypti ( but not to A . albopictus ) as compared to the D4-275-184 virus . Previous studies of miRNA-regulated gene expression have demonstrated that the majority of mRNAs are post-transcriptionally regulated by targeting in the 3’NCR , and although regulation of mRNAs through miRNA-targeting in the coding region is less frequent , it is well documented ( reviewed in [45 , 46] ) . Therefore , to explore if targeting of an ORF region of DEN4 by mosquito-specific miRNAs can result in specific viral attenuation in mosquitoes , targets for mosquito-expressed mir-184 and mir-275 as well as three targets for human neuron-specific mir-124 miRNA were introduced in the DEN4 genome between sequences encoding the two C-terminal stem-anchor domains of DEN4 E protein ( D4-E virus; Fig 1 ) . We designed these insertions of miRNA targets in this region using an approach that has been described previously [34 , 47] . As a control , we generated a D4-E* virus , in which mir-184 and mir-275 target sequences of D4-E were synonymously mutated in the third base position of each codon . The levels of replication for both control D4-E* virus and D4-E-mutant-bearing miRNA targets were similar in Vero cells indicating that the presence of miRNA targets or their scrambled sequences in the ORF of the viral genome does not affect viral fitness in cells that do not express the corresponding miRNAs ( Fig 5C ) . Genome regions containing miRNA targets in D4-E or scrambled sequences in D4-E* virus remained stable for at least 5 subsequent passages in Vero cells as demonstrated by sequence analysis ( S3 Fig ) . Replication of D4-E , but not D4-E* , was strongly attenuated in both Aag2 and C6/36 cells as compared to the D4 virus ( Fig 5A and 5B , p<0 . 001; 2-way ANOVA ) , but the level of a residual replication of D4-E in either cell line was higher than that observed for the D4-275-184 virus carrying miRNA targets in the 3’NCR ( Figs 3 and 5 ) . Studies in orally infected A . aegypti mosquitoes demonstrated that infection rates of D4-E but not D4-E* were significantly decreased as compared to D4 ( Fig 6B , p<0 . 001 and p = 0 . 151 , respectively; one-tailed Fisher’s exact test ) . Infectivity of D4-E to A . aegypti was also significantly lower than that of D4-E* ( 1/24 versus 11/24; p = 0 . 0009; Fisher’s exact test ) and none of the mosquitoes ( 0/24 ) had disseminated infection with D4-E virus compared to 20 . 1% ( 5/24 ) for D4-E* ( p = 0 . 025; Fisher’s exact test ) . These results indicate that miRNA targeting of the D4-E genome within the ORF can specifically attenuate the virus for both mosquito-derived cells and mosquitoes due to the presence of authentic miRNA target sequences . Even though a combined expression of mir-184 and mir-275 targets in the 3’NCR was sufficient to greatly restrict the D4-275-184 virus replication in mosquito cells and abolish infectivity in adult mosquitoes , an escape mutant lacking both authentic target sequences can theoretically emerge as a result of error prone flavivirus replication under miRNA-mediated selective pressure . To minimize the probability of such events , we generated a virus ( D4-E-NCR1; Fig 1 ) expressing mir-184 and mir-275 target sequences in both the ORF and 3’NCR of DEN4 genome . As expected , the resulting virus was genetically stable and replicated well in Vero cells , but failed to initiate a productive infection in mosquito Aag-2 and C6/36 cells ( Fig 5 ) . Moreover , none of the A . aegypti mosquitoes that fed on a blood meal containing 6 . 8 log10 pfu/ml of D4-E-NCR1 became infected , or developed a disseminated infection into mosquito heads as tested at 14 dpi ( Fig 6 ) . These findings indicate that combined co-targeting of the DEN4 genome in the ORF and 3’NCR does not result in target interference during miRNA mediated flavivirus attenuation in mosquitos . To explore if the miRNA targeting approach represents adequate means for simultaneous restriction of flavivirus replication in both the CNS of vertebrates and in mosquito vectors , we first evaluated neurovirulence of D4-E and D4-E-NCR1 viruses in a highly permissive animal model such as 3-day-old Swiss mice inoculated intracranially [34 , 36 , 42 , 44 , 48] . Both D4-E and D4-E-NCR1 viruses contained miRNA targets for mosquito-specific mir-184 and mir-275 and three copies of target sequences for vertebrate brain-specific mir-124 in the duplicated E/NS1 region ( Fig 1 ) . As a control virus for comparative assessment in the CNS of mice , we generated a D4-E** virus based on D4-E that contained synonymous mutations in the third base position of each codon of the CNS-specific mir-124 target sequences . The effect of mir-124 targeting in limiting neurotropism of flaviviruses has been extensively characterized in our laboratory previously [34–36] . Replication of D4-E and D4-E-NCR1 viruses in the mouse brain was strongly attenuated compared to that of D4 or D4-E** virus ( Fig 7A , p<0 . 001 for each of control viruses; 2-way ANOVA ) . Moreover , none of the mice infected with D4-E or D4-E-NCR1 died , whereas the mortality rate for parental D4 was 100% ( Fig 7B , p<0 . 001; log-rank test ) . This demonstrates that the miRNA targeting approach can be used for specific attenuation of flavivirus replication in more than one host or cell-type of interest . Interestingly , D4-E** virus with scrambled mir-124 target sequences in the ORF had a lower titer in the brain at each time point as compared with D4 virus ( Fig 7A , p<0 . 001; 2-way ANOVA ) and only 25% of mice infected with D4-E** virus died during a 21-days observation period ( Fig 7B , p = 0 . 0014; log-rank test ) . This likely reflects that insertions of heterologous sequences ( mir-275 and mir-184 targets ) in the ORF can result in partial attenuation of DEN4 replication in the brain of mice . Our previous studies [34 , 35] had demonstrated that increasing the number of miRNA targets in the 3’NCR of flavivirus genome significantly diminished virus replication in mouse brain and prevented virus escape from miRNA-mediated suppression . In order to minimize or reduce the emergence of DEN4 escape mutants during replication in the CNS , we developed an additional virus ( D4-E-NCR2 ) containing a combination of brain and mosquito specific miRNA target sequences in the ORF and 3’NCR ( Fig 1 ) . As expected , the resulting D4-E-NCR2 virus efficiently replicated in Vero cells , but not in mosquito Aag2 or C6/36 cells ( Fig 5; p<0 . 001; 2-way ANOVA ) . Moreover , no virus replication was detected in the brains of neonatal mice infected IC with D4-E-NCR2 ( Fig 7A , p<0 . 001; 2-way ANOVA ) , and no death or neurological signs were observed ( Fig 7B ) . In addition , the D4-E-NCR2 virus was unable to infect and generate disseminated infection in A . aegypti mosquitoes fed a blood meal containing 6 . 8 log10 pfu/ml of either virus ( Fig 6B , p<0 . 001; Fisher’s exact test ) . We concluded that D4-E-NCR1 and D4-E-NCR2 viruses potentially could be used as a genetic background for development of chimeric live attenuated vaccine candidate against neurotropic flaviviruses , or a similar strategy could be applied for targeted attenuation of other flavivirus vaccine platforms such as yellow fever virus ( YF ) 17D or dengue viruses [13–19] .
Here , for the first time we demonstrate that a combined targeting of the mosquito-borne flavivirus genome can silence viral replication in two evolutionally distant species: mosquitoes and mice . We believe that the miRNA co-targeting approach can be adapted to support the design of environmentally safe , live attenuated virus vaccines by restricting their ability to be introduced into nature during feeding of competent vectors on viremic vaccinees , thereby limiting the possibility of subsequent viral evolution and unpredictable consequences . Thus , miRNA-mediated silencing of virus replication in mice and mosquitoes can reinforce the safety of newly developed and existing vaccines for use in humans . This engineered host range restriction in both insect vector and vertebrate host by miRNA-mediated mechanisms represents an alternative to non-specific strategies for the rational control of viral tissue tropism and pathogenesis in the vertebrate host and replicative efficacy in permissive vectors .
Recombinant cDNA clone of dengue type 4 virus strain 814669 , referred to here as D4s ( GenBank access # AY648301 ) has been described previously [40] . To generate a modified D4 version of D4s clone , DNA fragments of viral genome were amplified from D4s using Phusion DNA polymerase ( New England Biolabs [NEB] , Ipswich , MA ) and cloned individually or in combinations into low copy number pACNR1181 plasmid vector [66] using conventional PCR-based methods [67] . Intron sequence [nt . 857–989 in HaloTag CMV-neo vector pHTC ( GenBank access # JF920305 ) ] was synthesized by GenScript Inc . ( Piscataway , NJ ) and was introduced at positions 3922 and 8836 of DEN4 genome at AGCT sites . cDNA of CMV promoter was amplified from pCMV-SPORT6 plasmid ( Invitrogen , Carlsbad , CA ) and was fused with 5' end of DEN4 genome using PCR-based techniques . The hepatitis delta ribozyme sequence was amplified from plasmid RBZ-17D/25C-GFP-FMDV2A-YFCO_full_prME ( generous gift of Dr . I . Frolov , University of Alabama ) , and fused with polyA signal/translation terminator sequence that was amplified from pTriEx1 . 1 ( Novagen , Germany ) . The resulting amplicon was fused with the 3’-end of DEN4 genome to assemble wild-type D4 cDNA clone . A Vero cell adaptive mutation L122→F [41–44] was introduced into NS4B protein gene to generate a D4 plasmid . Target sequences for mosquito specific mir-1 ( 5’-CTCCATACTTCTTTACATTCCA-3’ ) , mir-184 ( 5’-GCCCTTATCAGTTCTCCGTCCA-3’ ) and mir-275 ( 5’-GCGCTACTTCAGGTACCTGA-3’ ) or human brain-specific mir-124 ( 5’-GGCATTCACCGCGTGCCTTA-3’ ) were introduced into the 3’NCR of DEN4 genome between nts 10 , 277 and 10 , 278 ( position 1 , Fig 1 ) or 10 , 474 and 10 , 475 ( position 2 , Fig 1 ) ; these sites of target insertion are located 15 or 212 nts downstream of the TAA stop codon in the 3’NCR , respectively . To introduce miRNA targets in the ORF , we utilized the approach that has been described previously [34 , 47] . Specifically , the introduced sequence was inserted between nts 2451 and 2452 of DEN4 genome and contained five tandem targets for mir-124 , mir-184 and mir-275 that were followed by a duplicated DEN4 E/NS1 region ( nts from 2130 to 2451 of DEN4 genome ) encoding 98 amino acids from the C-terminal end of the DEN4 E protein and 7 amino acids from the N-terminal end of the NS1 protein ( Fig 1 ) . Each codon ( except for Met and Trp ) within the duplicated E/NS1 region was mutated to a synonymous codon to minimize nucleotide sequence homology between repeated DEN4 genome segments ( Fig 1 ) . Each plasmid DNA was sequenced to verify its integrity . Detailed information for all plasmids is available from the authors upon request . Vero cells ( African green monkey kidney ) were cultured in serum free Opti-Pro medium ( Invitrogen ) as previously described [42] . Mosquito C6/36 ( derived from A . albopictus; ATCC ) , C710 and Aag2 ( derived from A . albopictus and A . aegypti , respectively; generous gift from Dr . A . Fallon , University of Minnesota ) cells were maintained in Dulbecco minimal essential medium ( DMEM ) ( Invitrogen ) supplemented with 5% FBS ( Lonza ) , 1x penicillin-streptomycin-glutamine ( PSG ) solution ( Invitrogen ) , 1x MEM nonessential amino acids ( Cellgro , Swedesboro , NJ ) , 1 × MEM vitamin solution ( Invitrogen ) and 5 μg/L of gentamicin ( Invitrogen ) at 32°C in an atmosphere of 5% CO2 . The C6/36 cells were used in all but the initial experiments , because they exhibit superior viability in our experimental conditions compared to C710 cells . Galveston colony of A . albopictus ( generous gift of Dr . S . C . Weaver , UTMB ) and NIH strain of A . aegypti mosquitoes have been described earlier [53 , 68] . Both mosquito species were maintained in carton containers supplemented with 10% sucrose on cotton balls at 28°C , 80% humidity and with a 16-hr daylight cycle . To determine effect of miRNA targeting on DEN4 replication , miRNA targeted or control viruses were diluted in Opti-Pro medium supplemented with 2% FBS , 2 mM L-glutamine and were used to infect Vero , C6/36 , Aag2 or C710 cells at an MOI of 0 . 01 in duplicate wells of a 6-well plate for 1 h at 37°C ( Vero ) or 32°C ( mosquito cells ) . The cells were washed three times with Opti-Pro and 2 . 75 mL of fresh media was added . Aliquots of 0 . 25 mL were harvested daily and stored at −80°C until virus titration . Differences in virus replication kinetics were compared using 2-way ANOVA analyses implemented in Prism 6 software ( La Jolla , CA ) . Confluent monolayers of Vero cells in 25 cm2 flasks were infected at an MOI of 0 . 01 and cell culture supernatant was harvested at 3 dpi , diluted 1/10 with Opti-Pro medium , and 1 mL of inoculum was used to infect 25 cm2 flasks of fresh Vero cells . The cycle was repeated 5 times . At the end of the fifth passage , viral RNA was extracted from 0 . 14 mL of supernatant using the QIAamp Viral RNA Mini kit ( Qiagen ) according to manufacturer’s instructions . Genome regions flanking miRNA target sites were amplified using Titan One Tube RT-PCR kit ( Roche , Indianapolis , IN ) and sequenced . All animal experiments were done in compliance with the guidelines of the NIAID/NIH Institutional Animal Care and Use Committee . The NIAID DIR Animal Care and Use Program acknowledges and accepts responsibility for the care and use of animals involved in activities covered by the NIH IRP’s PHS Assurance #A4149-01 , last issued 6/11/2011 . D4 and miRNA targeted viruses were diluted to 5 log10 pfu/ml with L-15 medium supplemented with 1x SPG . Three-day-old Swiss Webster mice ( Taconic Farms ) in groups of 10 were inoculated intracranially ( IC ) with 10 μL ( 3 log10 pfu ) of virus and returned to the dam . For study of virus replication , the brains from three mice were harvested on 5 , 8 , 11 , and 14 days post-infection ( dpi ) . Each brain was weighted , and 10% homogenates were prepared using L-15 medium supplemented with 1x SPG , 0 . 05mg/mL of Ciprofloxacin , 0 . 06 mg/mL of Clindamycin and 0 . 0025 mg/mL of Amphotericin B . Virus titers in each brain suspension were determined by titration in Vero cells . Harvesting of brains from mice infected with D4 was not performed at 14 dpi due to earlier death of the animals . To study the effect of miRNA targeting on virus neurovirulence , 3-day-old Swiss Webster mice ( Taconic Farms ) were inoculated IC with 3 log10 pfu of parental D4 or its miRNA-targeted derivative and monitored for morbidity and mortality for 21 dpi . Mice that developed neurological signs ( paralysis ) were humanely euthanized . Differences in replication kinetics were compared using 2-way ANOVA and P-values were adjusted using Bonferroni correction method to account for multiple comparisons , and differences in survival were compared using Log-rank ( Mantel-Cox ) test implemented in Prism 6 software ( La Jolla , CA ) . The biotinylated probes complementary to mir-184 ( 5’Biotin-GCCCTTATCAGTTCTCCGTCCA-Biotin3’ ) , mir-275 ( 5’Biotin-GCGCTACTTCAGGTACCTGA-Biotin3’ ) , and mir-1 ( 5’Biotin-CTCCATACTTCTTTACATTCCA-Biotin3’ ) were synthesized by Bioresearch Technologies and used at 2–10 ng/mL . Ribo-oligonucleotides for artificial mir-184 ( 5’UGGACGGAGAACUGAUAAGGGC ) , mir-275 ( 5’UCAGGUACCUGAAGUAGCGC ) , and mir-1 ( 5’UGGAAUGUAAAGAAGUAUGGAG3’ ) were synthesized by Integrated DNA Technologies , and were used in northern blot as positive controls and molecular weight standards . Total RNA was isolated with TRIzol SL reagent ( Invitrogen ) from 75-cm2 flasks containing confluent monolayers of Aag2 , C6/36 , C710 , or Vero cells or from individual brains of 5-day old Swiss mice or from pools of 20 adult A . aegypti mosquitoes collected at 7 days post-emergence . miRNA detection was carried out by northern blot as described previously with minor modifications [70] . A 15 μg sample of total RNAs was mixed with 4 μL of 10X RNA Loading Solution ( Quality Biologicals ) and then mixed with an equal volume of formamide ( Sigma-Aldrich ) . The RNA was denatured at 90°C for 5 min followed by rapid cooling on ice . A total of 14 μg of denatured RNA was separated at 150 V for 1 h in a 15% polyacrylamide tris-borate-EDTA gel supplemented with 7M urea ( BioRad ) . The gels were washed and stained with 100 mL of 20 mM MOPS-NaOH and 0 . 5 μg/mL ethidium bromide buffer ( pH7 . 0 ) for 15 min . miRNAs was electroblotted to Amersham Hybond-NX membrane ( GE Healthcare ) at 20 V for 2 h in 10mM MOPS-NaOH buffer ( pH7 . 0 ) , followed by cross-linking to the membrane using 12 mL of 0 . 13 M 1-methylimidazole ( Sigma ) , 0 . 16 M 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( Sigma ) solution ( pH 8 . 0 ) at 60°C for 1h . Membranes were prehybridized in 25 mL of ULTRAhyb ultrasensitive hybridization buffer ( Ambion ) at 37°C for 1 h , followed by hybridization with biotinylated probes at 39°C overnight in ULTRAhyb Ultrasensitive Hybridization Buffer . Membranes were then washed twice with 1x Low Stringency Washing Solution #1 ( Ambion ) and miRNAs were detected using Chemiluminescent Nucleic Acid Detection Module kit ( Thermo scientific ) according to the manufacturer's instructions . | Despite advances in developing flavivirus live attenuated vaccine ( LAV ) candidates , a concern exists that they might not be safe in the environment due to their intrinsic genetic instability leading to potential reversion back to wild-type that could be associated with possible dissemination of these mutated viruses by mosquitoes . Here , we describe a miRNA targeting approach that can be adapted to support the design of environmentally-safe LAV restricted in their ability to infect and be transmitted by competent vectors , thereby limiting the possibility of subsequent viral evolution and unpredictable consequences . A combined co-targeting of flavivirus genome with mosquito- and vertebrate brain- specific miRNAs resulted in simultaneous restriction of dengue virus infection and replication in mosquitoes and in brains of newborn mice indicating that the miRNA-mediated approach for virus attenuation represents an alternative to non-specific strategies for the control of viral tissue tropism and pathogenesis in the vertebrate host and replicative efficacy in permissive vectors . | [
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| 2015 | Dual miRNA Targeting Restricts Host Range and Attenuates Neurovirulence of Flaviviruses |
CD200 receptor ( CD200R ) negatively regulates peripheral and mucosal innate immune responses . Viruses , including herpesviruses , have acquired functional CD200 orthologs , implying that viral exploitation of this pathway is evolutionary advantageous . However , the role that CD200R signaling plays during herpesvirus infection in vivo requires clarification . Utilizing the murine cytomegalovirus ( MCMV ) model , we demonstrate that CD200R facilitates virus persistence within mucosal tissue . Specifically , MCMV infection of CD200R-deficient mice ( CD200R-/- ) elicited heightened mucosal virus-specific CD4 T cell responses that restricted virus persistence in the salivary glands . CD200R did not directly inhibit lymphocyte effector function . Instead , CD200R-/- mice exhibited enhanced APC accumulation that in the mucosa was a consequence of elevated cellular proliferation . Although MCMV does not encode an obvious CD200 homolog , productive replication in macrophages induced expression of cellular CD200 . CD200 from hematopoietic and non-hematopoietic cells contributed independently to suppression of antiviral control in vivo . These results highlight the CD200-CD200R pathway as an important regulator of antiviral immunity during cytomegalovirus infection that is exploited by MCMV to establish chronicity within mucosal tissue .
CD200R is an Immunoglobulin superfamily family member that is expressed by hematopoietic cells , with notably high expression on myeloid cells [1] . The ligand of CD200R , CD200 ( OX2 ) , is broadly expressed by cells of hematopoietic and non-hematopoietic origins [2] . The primary function of the CD200R pathway is to limit immune reactivity . CD200-CD200R interactions induce a unidirectional inhibitory signal within CD200R-bearing cells that is mediated by tyrosine motifs in the cytoplasmic domain of CD200R that recruit DOK2 and RasGAP , resulting in inhibition of the ERK pathway [3–6] . The CD200R pathway negatively regulates myeloid cell homeostasis in the periphery [7] , and in the pulmonary [8] and , to a lesser extent , the intestinal [9] mucosa . CD200R signaling limits the rapid onset of experimental autoimmune encephalomyelitis [3 , 7] and restrains bacterial-induced inflammation [10] . Importantly , CD200R also restricts viral-induced inflammation during respiratory influenza infection [9 , 11] and herpes simplex virus ( HSV ) infection of the cornea [11] . However , CD200R also restricts IFN-dependent control of corona virus infection via regulation of TLR7 [12] and control of intracranial HSV infection [13] , demonstrating that this inhibitory receptor can impinge on protective antiviral immunity . During evolution , numerous herpesviruses have acquired proteins with the potential to induce immune inhibitory receptor signaling [14] . For example , human cytomegalovirus ( HCMV ) encodes a functional homolog of the inhibitory cytokine interleukin-10 ( IL-10 ) [15] . Rhesus CMV lacking its IL-10 homolog induces increased virus-specific immune responses [16] , and IL-10R signaling during murine cytomegalovirus ( MCMV ) infection antagonizes antiviral immunity and facilitates virus persistence [17–19] . Thus , these studies provide in vivo experimental evidence supporting a rationale for CMV exploitation of host immune regulatory pathways . Intriguingly HCMV UL119–121 proteins display homology to human CD200 [20] , although it is currently unknown whether they induce inhibitory signaling through CD200R . However , numerous herpesviruses are known to encode functional CD200 orthologs ( vCD200s ) implying that exploitation of this inhibitory pathway is potentially advantageous for herpesviruses . The most well-characterized vCD200 is the Kaposi’s sarcoma-associated herpesvirus ( KSHV ) protein K14 , which suppresses the activation of neutrophils [21] , basophils and NK cells [22] , T cells [23] and macrophages [24] in vitro . Furthermore , the English isolate of rat cytomegalovirus ( RCMV-E ) encodes a CD200 homolog ( e127 ) capable of binding CD200R [25 , 26] . Despite the possible importance of the CD200-CD200R pathway in modulating anti-CMV immunity , how it influences antiviral immune responses and virus replication during infection in vivo requires clarification . To investigate this , we studied MCMV infection in wild type mice and mice lacking CD200R . Experiments revealed a pivotal role for CD200R regulation of myeloid cell responses in limiting antiviral CD4 T cell responses . We provide evidence that MCMV exploits the CD200-CD200R pathway to facilitate persistent infection within mucosal tissue .
MCMV replicates in numerous organs , including the spleen , liver and lungs , during acute infection , prior to dissemination to the salivary glands ( SGs ) , in which MCMV replicates for 1–2 months [27 , 28] . We hypothesized that CD200R signaling may facilitate MCMV replication in vivo . To test this , wild type C57BL/6 ( wt ) and CD200R-/- mice were infected with MCMV and virus load measured . Peak acute MCMV replication at day 4 post-infection ( pi ) in the spleen ( Fig . 1A ) and liver ( Fig . 1B ) was unaltered by CD200R deficiency . However , CD200R-/- mice exhibited a reduced burden of replicating virus ( Fig . 1A ) in the spleen 7 days pi . We next investigated whether CD200R promoted MCMV persistence . In our model , replicating virus is first detectable in the SGs at day 7 pi . Virus load in wt and CD200R-/- mice day 7 pi was comparable ( Fig . 1C ) , suggesting that improved antiviral control in spleens of CD200R-/- mice ( Fig . 1A ) did not influence dissemination to the SGs and associated brown fat in which MCMV replicates at this time-point [29] . Crucially , however , CD200R-/- mice restricted persistent MCMV replication in the SGs 14 days pi , and more CD200R-/- mice cleared MCMV by day 33 pi as compared to wt controls ( Fig . 1C ) . Thus , intact CD200R during chronic infection promoted virus persistence within this mucosal organ . Consistent with biological impact of CD200R within the SGs , we observed significant CD200R expression by CD11c+MHC II+ salivary gland ( SG ) APCs ( referred to hereafter as SG-APCs , Fig . 1D&E ) , which are phenotypically indicative of tissue-resident macrophages [30] , and NK cells ( Fig . 1D ) but not CD4 and CD8 T cells ( Fig . 1D ) . CD200R expression by SG myeloid cells was notably higher than splenic counterparts ( Fig . 1E ) , demonstrating enhanced expression of CD200R in mucosal versus non-mucosal sites of MCMV infection . CD200R expression by myeloid cells in both compartments was relatively stable during infection , with a slight reduction in the intensity of CD200R expression 4 days pi prior to recovery to steady-state levels by 14 days ( Fig . 1E ) . Interleukin-10 ( IL-10 ) is expressed in the SGs in response to MCMV infection and promotes virus persistence [18 , 31] . Although IL-10 induces CD200R expression by macrophages in vitro [8] , MCMV-infected IL-10-/- mice exhibited no alterations in CD200R expression by myeloid cells during infection ( Fig . 1F ) . Thus , CD200R was expressed during infection but was not significantly upregulated in response to MCMV , by either an IL-10-dependent or independent mechanism . Unlike certain herpesviruses [14 , 24] , MCMV does not encode an obvious vCD200 [32] . Within infected SGs , CD200+ cells were predominantly large CD31+ cells ( Fig . 2A , isotype controls:S1A Fig . ) that were EpCAM- ( Fig . 2B ) , suggestive of endothelial cell origin , and not EpCAM+ acinar epithelial cells in which MCMV replicates during the persistent phase of infection [33] . CD200+ cells did not express alpha smooth muscle actin ( S1B Fig . ) , also demonstrating these cells were not myoepithelial cells . Interestingly , CD200+ cells were often observed in ring-like structures around acinar epithelial cells ( Fig . 2B ) , indicative of capillary networks that surround acini [34] . CD200+CD31+ cells were detectable in naïve SGs ( S1C Fig . ) , and we observed no notable increase in the intensity of CD200 expression by CD31+ cells within infected tissue . In addition to abundant CD200+CD31+ cells , a more scarce population of CD200+CD45+ cells was also detectable within the SGs indicating the presence of CD200 on a hematopoietic cell type ( s ) ( Fig . 2C ) . Analysis by flow cytometry revealed significant CD200 expression by APCs and T cells within the SGs and spleen ( Fig . 2D&E ) during MCMV infection . Interestingly , we noted that CD200 expression by APC populations in the SGs and spleen was induced above baseline upon infection ( Fig . 2D&E ) . We hypothesized that MCMV infection of myeloid cells may directly influence CD200 expression . We infected myeloid cell populations in vitro using a multiplicity of infection of 1 that leads to an infection efficiency of less than 60% ( see Fig . 3A for example ) , enabling us to compare surface CD200 protein levels on uninfected and infected cells from the same well of a tissue culture plate , as identified by flow cytometric detection of the intracellular MCMV m06 protein . Infection of bone marrow-derived macrophages ( BM-DM , Fig . 3A–C ) and splenic macrophages ( Fig . 3B ) up-regulated CD200 ( Fig . 3A–C ) . Importantly , we observed a marked increase in CD200 expression by infected ( m06+ ) as compared with uninfected ( m06- ) macrophages derived from the same wells ( Fig . 3A–C ) , suggesting that CD200 is preferentially up-regulated by macrophages in which MCMV is actively replicating . Infection of BM-DM with influenza did not trigger CD200 expression ( Fig . 3D ) , demonstrating that CD200 up-regulation is not a generic macrophage response to viruses . However , CD200 expression is induced by ligation of TLRs , including TLR3 and TLR9 [10]; both of which are triggered by MCMV [35 , 36] . In accordance , TLR3 ligation by PolyI:C induced substantial CD200 mRNA expression by macrophages in an IFNβ-dependent manner ( Fig . 3E ) . To investigate whether MCMV induction of Cd200 transcription required productive replication , we compared expression following macrophage infection with IE3 knockout replication-deficient MCMV ( ΔIE3 ) [37] and replication-sufficient wt MCMV ( pSM3fr ) . Macrophage exposure to ΔIE3 MCMV induced a small , transient induction of CD200 mRNA in an IFNβ-dependent manner ( Fig . 3F ) , consistent with TLR-mediated induction of CD200 triggered during incomplete MCMV replication , and the moderate CD200 protein expression by uninfected ( m06- ) macrophages derived from infected cell cultures ( Fig . 3C ) . In contrast , replicating MCMV induced substantial and prolonged CD200 mRNA expression independently of IFNβ ( Fig . 3G ) . Furthermore , inhibition of viral DNA polymerase with phosphonoacetic acid ( PAA ) antagonized MCMV-induced CD200 expression in BM-DMs ( Fig . 3H ) , again demonstrating the requirement for productive virus replication in this process . Importantly , we observed that SG-APCs did not support MCMV replication in vitro in accordance with the absence of detectable infection in vivo [38] , and MCMV infection of splenic DCs did not further induce CD200 expression ( Fig . 3B ) . Thus , these data suggested that myeloid cells up-regulated CD200 during MCMV infection and , in the case of macrophages in secondary lymphoid tissues , MCMV induces CD200 expression independently of TLR stimulation during productive replication . Given that CD200 expression by both hematopoietic and non-hematopoietic cells was observed in MCMV-infected mice , we sought to understand which cellular compartment was responsible for inhibiting antiviral immunity . We made bone marrow chimeras derived from wt mice or mice deficient of CD200 , generating mice lacking CD200 within the hematopoietic and/or radiation-resistant ( non-hematopoietic ) compartment . We then studied virus load in SGs 14 days post-MCMV infection . Interestingly , deleting CD200 from either compartment reduced virus load as compared to wt>wt mice ( Fig . 4 ) , demonstrating that CD200 expressed by hematopoietic and non-hematopoietic cells both delivered immune suppressive signals that promoted MCMV persistence . We assessed the impact of CD200R deficiency on virus-induced myeloid cell responses . Reduced MCMV persistence in CD200R-/- mice was accompanied by accumulation of splenic DCs 14 days pi ( Fig . 5A ) . The inability of SG-APCs to cross-present antigen to CD8 T cells ( in combination with MCMV down-regulation of MHC class I ) has been shown to be responsible for the lack of CD8 T cell-mediated control of MCMV replication in the SGs , demonstrating that local antigen-presenting function of SG-APCs is a critical determinant of protective T cell immunity during MCMV persistence [38] . Interestingly , SG-APC accumulation in infected CD200R-/- mice was substantially increased 14 days pi ( Fig . 5B&C ) . However , SG-APC numbers were comparable in wt and CD200R-/- mice following resolution of MCMV infection in our model ( 48 days pi , S2A Fig . ) . Thus , CD200R restricted mucosal myeloid cell accumulation during early time-points of SG infection rather than influencing myeloid cell turnover during the resolution phase of infection . Tissue resident macrophages proliferate in response to inflammatory stimuli [39 , 40] . Thus , we measured SG-APC proliferation before and after MCMV infection 7 days pi . Low levels of SG-APC homeostatic proliferation were measured in naïve wt and CD200R-/- mice ( Fig . 5D ) . However , infection-induced SG-APC proliferation was further elevated in CD200R-/- mice as compared to wt mice day 7 pi ( Fig . 5D&E ) , a time at which CD200R was expressed by these cells in wt mice ( Fig . 1D&E ) . This suggested that increased SG-APC accumulation in CD200R-/- mice was a consequence of heightened proliferation . Importantly , visualization of MHC II+ cells within the SGs revealed that MHC II+ cells were consistently located adjacent to large CD200+ cells 7 days ( Fig . 5F ) and 14 days ( S2B Fig . ) pi . In accordance with CD200 expression by CD31+ cells ( Fig . 2A ) , MHC II+ cells were also observed surrounding CD31+ vessels ( Fig . 5G ) , suggesting that tissue-resident MHC II+ SG-APC interactions with CD200-bearing endothelial cells restricts infection-induced cellular proliferation . In support of this conclusion , chimeric mice lacking CD200 only in non-hematopoietic cells exhibited increased SG-APC accumulation ( S2C Fig . ) . Furthermore , improved control of MCMV in these mice ( Fig . 4 ) in addition to the absence of an impact of non-hematopoietic cell-derived CD200 on splenic DC responses ( S2D Fig . ) points towards a role for local SG-APC expansion in determining control of MCMV replication in the mucosa . CD4 T cells are critical effector cells in the control of MCMV persistence that afford protection via expression of IFNγ [38 , 41] . Despite the absence of measurable T cell expression of CD200R ( Fig . 1D ) , SG-infiltrating CD4 T cells in CD200R-/- mice exhibited increased activation , indicated by CD69 and CD25 up-regulation 10 days pi ( Fig . 6A&B ) . Enrichment of CD25hi CD4 T cells were not observed in either wt or CD200R-/- mice ( Fig . 6A ) , consistent with the absence of regulatory T cell infiltration into the SGs in response to MCMV [31] . Importantly , IFNγ+ virus-specific CD4 T cell numbers were elevated in the SGs of CD200R-/- mice by 14 days pi ( Fig . 6C ) . In addition to activated T cells , CD4+ tissue-resident memory T cells also express CD69 [42] . Interestingly , whereas CD69 expression by SG CD4 T cells was elevated in CD200R-/- mice 14 ( Fig . 6D ) and 30 ( S3A Fig . ) days pi , elevated prolonged expression of CD25 by CD200R-/- SG CD4 T cells was not observed ( S3B Fig . ) , implying that CD200R may also restrict the accumulation and/or retention of CD4 T cells with a tissue-resident memory-like phenotype . Furthermore , MHC II+ SG-APCs that proliferate and accumulate to higher numbers in CD200R-/- mice ( Fig . 5B–E ) co-localized with CD4 T cells ( Fig . 6E ) , suggesting that elevated myeloid cell responses within the SGs of CD200R-/- mice enhanced mucosal CD4 T cell responses . In addition , elevated splenic DC numbers 14 days pi in CD200R-/- mice ( Fig . 5A ) were accompanied by an increase in virus-specific CD4 T cells in this organ at this time ( Fig . 6F ) . These data therefore suggested that CD200R restricted peripheral and mucosal CD4 T cell responsiveness during virus persistence through localized regulation of tissue APC accumulation . We next investigated whether elevated CD4 T cell responses restricted MCMV persistence in CD200R-/- mice . Depletion of CD4 T cells abrogated the improved control of MCMV in CD200R-/- mice ( Fig . 6G ) , which is consistent both with the established role for CD4 T cells in limiting MCMV persistence in the SGs [38 , 41] , and the conclusion that MCMV exploits CD200R to facilitate persistence predominantly by antagonizing proliferation and accumulation of MHC class II-bearing myeloid cells .
We demonstrate that MCMV exploits the CD200-CD200R pathway to restrict mucosal antiviral immunity in vivo to facilitate MCMV persistence in a secretory mucosal organ . Restriction of myeloid cell responses was central to the inhibitory action of CD200R . CD200R signaling limited accumulation of MHC class II-bearing APCs in both the periphery and mucosa thus restricting the ensuing virus-specific CD4 T cell response . CD200R restricted SG-APC responses by limiting virus-induced cellular proliferation . CD200R inhibition of this process has likely evolved to limit responses to harmless antigens that mucosal surfaces are continually exposed to . However our data demonstrate that MCMV benefits from this immune-regulatory pathway to persist within its mammalian host , and MCMV can actively induce CD200 expression during infection . SG-APCs proliferated in response to MCMV infection , consistent with the ability of tissue-resident macrophages to undergo a proliferative burst following inflammation [39 , 40 , 43] . Our data suggest that the interaction of SG-APCs with the basal surface of CD200-bearing endothelial cells limits this process , and implies that the large vascular network within the SGs may function not only as a blood supply but also to deliver inhibitory signals that , in the context of homeostatic conditions , functions to limit immune responsiveness . The identification of ring-like structures surrounding acini implies a scenario during MCMV infection in which CD200R-expressing myeloid cells situated close to or migrating towards infected cells may receive inhibitory signals from CD200-expressing vascular structures . Currently , there are no methodologies available to exclusively delete SG-APCs in vivo . Therefore we are unable to make definitive conclusions regarding the function of these cells in our experiments . However , our data supports a model in which CD200R-mediated restriction of SG-APC proliferation reduces CD4 T cell activation within the SGs , subsequently impairing CD4 T cell responsiveness and control of MCMV persistence . Our data also demonstrated that CD200R impaired the accumulation of virus-specific CD4 T cells in the periphery that was accompanied by reduced splenic DC accumulation . Thus , CD200R signaling impinges on antiviral protection from mucosal MCMV replication by restricting CD4 T cell activation and expansion both within the mucosa itself , but also in secondary lymphoid tissue . Intriguingly , persistent MCMV infection of CD200R-/- mice led to the enrichment of CD69+ CD4 T cells not expressing the activation marker CD25 . CD69 is expressed by tissue-resident memory CD4 T cells [42] . MCMV replication continued at the time-points at which CD69+ CD4 T cells were detected in our study , thus precluding definitive conclusions regarding bone fide tissue-resident memory cells . However , our data implies that CD200R may indirectly restrict the accumulation of CD4 T cells in the SGs that exhibit a phenotype indicative of tissue-resident memory CD4 T cells . CD200R facilitates early viral replication in acute MHV [12] and HSV [13] infections in vivo . In contrast , we observed that early control of MCMV was unaffected by CD200R . This may reflect in part that CD200R deficiency did not influence MCMV replication in macrophages ( S4A Fig . ) , unlike data reported in HSV infection [13] . Improved control of MHV infection in CD200-/- mice was associated with elevated type I IFN [12] . Type I IFN was not measured in our study and may not be altered in MCMV-infected CD200R-/- mice . Also , type I IFN exerts potent antiviral activity against MCMV in vivo in wt mice [44] and may therefore be produced at levels that exert maximal antiviral activity in our model irrespective of any impact of CD200R on cytokine expression . Instead , we show for the first time that CD200R signaling influences persistent virus replication in vivo . Improved control of MCMV replication in the SGs in CD200R-/-mice was intriguing given that MCMV does not encode an obvious CD200 homolog . This may be explained in part by the existence of a structural CD200 ortholog encoded by MCMV that lacks sufficient sequence similarity to be detected , or by the existence of other viral ligands for CD200R . Importantly however , experiments utilizing CD200-/- mice highlighted a role for cellular CD200 in dampening antiviral immunity . Cellular CD200 restricts virus-induced immune responses in acute virus infections [8 , 12 , 45] , and our data supports the conclusion that some viruses may exploit host CD200-CD200R interactions to establish persistence . Intriguingly , in vivo experiments investigating a functional role for CD200 orthologs expressed by RCMV [26] and Rhesus macaque rhadinovirus [46] failed to detect significant benefit of these vCD200s in promoting herpesvirus persistence in these experimental models . Our data suggest the benefit of herpesvirus exploitation of host CD200 expression , irrespective of whether the virus also encodes its own vCD200 protein . Results obtained from bone marrow chimeras demonstrate the importance of non-hematopoietic cell-derived CD200 in facilitating MCMV persistence , thus supporting an important role for endothelial cells in indirectly restricting antiviral CD4 T cell responses via regulation of myeloid cells . However , a significant role for hematopoietic cells in promoting virus persistence was also revealed in these experiments . Peripheral and mucosal myeloid cells expressed CD200 during MCMV infection . Although SG-APCs did not support MCMV replication , splenic macrophages up-regulated CD200 following direct MCMV infection in vitro . MCMV infection of wt and CD200R-/- bone marrow-derived macrophages resulted in comparable expression of MHC II ( S4B Fig . ) , suggesting that MCMV does not exploit macrophage expression of CD200 to deliver an autocrine inhibitory signal; a conclusion further supported by comparable MCMV replication in wt and CD200R-/- macrophages and consistent with the inability of CD200 to interact with CD200R in a cis-cellular fashion [47 , 48] . Instead our data suggest that a CD200-bearing myeloid cell may restrict antiviral immunity and that , in the case of peripheral infection , MCMV influences this process . CD200 may suppress myeloid cell activity and/or accumulation indirectly via an unknown CD200R-expressing cell subset , or by directly triggering CD200R signaling within a myeloid cell . T cells expressed CD200 during MCMV infection , implying that MCMV may also passively exploit a negative feedback loop by which CD200-bearing T cells deliver an inhibitory signal to CD200R-bearing myeloid cell with which they interact . Notably however , non-hematopoietic cell-derived CD200 restricted myeloid cell accumulation within the SGs , suggesting that T cells do not exert CD200-mediated inhibition of myeloid cell proliferation within this particular site of MCMV infection . Irrespective of the exact mechanism ( s ) , our data suggest that CD200 expressed by hematopoietic cells impacts on the development of antiviral immunity that subsequently allows virus persistence within the SGs , and that MCMV actively exploits this process . MCMV induced myeloid cell CD200 expression via two distinct mechanisms . Firstly , incomplete virus replication triggered TLR-induced IFNβ-dependent Cd200 gene expression . Importantly , replication-competent virus induced Cd200 expression in macrophages independently of this pathway , and CD200 induction was dependent upon viral DNA polymerase activity . The mechanism through which MCMV actively regulates CD200 is not clear . CMV infection induces profound alterations in host cell protein production and gene expression [49–52] . Concurrent analysis of Cd200 gene and surface protein expression highlighted that viral induction of CD200 occurred at the transcriptional level . The impact of PAA on virus-induced CD200 expression suggests the involvement of a gene product or products expressed during the latter stages of virus replication . However , this conclusion is guarded given that inhibition of viral DNA polymerase during HCMV infection also incompletely inhibits production of certain viral proteins expressed at early times during the virus life-cycle [53] . Whether a viral gene product ( s ) directly or indirectly induces CD200 expression and which viral protein is responsible remains to be elucidated . Influenza infection of macrophages did not trigger CD200 expression despite the CD200-CD200R pathway restricting influenza-induced T cell responses in vivo [8 , 12] . Thus , CD200 induction is not a generic response mounted by macrophages in response to viruses . Instead , our experiments demonstrate that MCMV gene expression is essential for this process and implies that CD200 up-regulation represents a previously unappreciated mechanism exploited by CMV , and perhaps other viruses , to antagonize host antiviral immunity . Collectively , our study highlights a central role for myeloid cells in modulating cytomegalovirus-specific T cell responses in mucosal tissue and the potential importance of regulation of tissue-resident macrophage proliferation in this process . Our study also points towards the manipulation of cellular CD200 expression as a mechanism through which herpesviruses evade host immunity , suggesting that MCMV exploits CD200R signaling to antagonize myeloid cell orchestration of antiviral immunity to promote persistence within and dissemination from the mucosa .
C57BL/6 experimental mice were obtained from Harlan UK . CD200R-/- mice were originally generated and provided by Reginald Gorczynski ( University Health Network , Toronto ) , and David Copland ( University of Bristol ) provided the OX-2-/- mice , with kind permission from Jonathon Sedgwick ( Eli Lilly , Indianapolis ) . IL-10-/- mice were purchased from Jackson Laboratories and maintained in-house . MCMV Smith strain ( ATCC ) was prepared in BALB/c salivary glands and purified over a sorbital gradient . Mice were infected by the intra-peritoneal route ( i . p ) with 3 x 104 PFU MCMV . Some mice were injected i . p with 200µg αCD4 antibody ( 100µg clone YTS191 , 100µg clone YTS3 ) on days 4 and 6 pi . To measure proliferation in vivo , mice were injected i . p with 1mg/mouse EdU ( Life Technologies ) at day 6 pi . To generate chimeric mice , recipients were irradiated at 2 x 550G , transfused intra-venous ( i . v ) with 1 x 106 bone marrow cells 24 hours later . Mice were then treated for 3 weeks with baytril-supplemented water . Mice were infected with MCMV 8 weeks after irradiation . All experiments were conducted according to the UK Home Office guidelines at the designated facility at Heath Park , Cardiff University under UK Home Office-approved project licenses PPLs 30/2442 and 30/2969 . SGs and spleens were surgically excised from mice that were euthanized with carbon dioxide . SGs were cut into small pieces and incubated in RPMI 1640 medium ( Invitrogen ) supplemented with 5 mM CaCl2 , 5% FCS ( Invitrogen ) , 1 mg/ml collagenase D ( Roche Diagnostics ) , and 10 mg/ml DNAse I ( Sigma ) at 37°C for 45 minutes , before passing through a cell strainer prior to red blood cell lysis . Leukocytes were then stained with Live/Dead ( Invitrogen ) prior to incubation with Fc block ( eBioscience ) . Lymphocytes were then stained with a combination of αCD3e-PerCP ( Clone 145 . 2C11 , Biolegend ) , αF4/80-Pacific-Blue ( Clone BM8; Biolegend ) , αIA/IE-PerCP-Cy5 . 5 ( Clone M5/114 . 15 . 2 , BioLegend ) , αCD11c-PeCy7 ( Clone N418 , Biolegend ) , αNK1 . 1-allophycocyanin ( Clone PK136 , BD Biosciences ) , αCD4-Pacific-blue ( Clone RM4 . 5 , BD Biosciences ) , αCD25-APC-Cy7 ( Clone PC61 , Biolegend ) and αCD69-FITC ( Clone H1 . 2F3 , eBioscience ) . To detect EdU incorporation , cells were stained as above , fixed with 4% PFA , permeabilized with Saponin buffer , and EdU was labeled with Alexa Fluor 647 using the Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Life Technologies ) as per manufacturer’s protocol . To detect MCMV-specific CD4 T cells , leukocytes were incubated with 3μg MCMV peptides ( Genscript ) listed in Figure legends for 6 hours , with BFA ( Sigma ) for the final 4 hours . CD4 T cells stained as above were permeabilized prior to staining with αIFNγ FITC ( clone XMG1 . 2 , eBioscience ) . All data were acquired on a BD FACS Canto II . Electronic compensation was performed with antibody-capture beads ( BD Biosciences ) . Data was analyzed using FlowJo software version 10 . 0 . 3 ( TreeStar Inc , Ashland , OR ) . Total numbers of different cell populations were calculated by multiplying % positive viable cells detected by flow cytometry x the total number of viable leukocytes ( assessed by trypan blue exclusion ) . Femurs were surgically excised from wt and CD200R-/- mice , sterilized in 70% ethanol and washed in PBS . Bone marrow was isolated , cells centrifuged , washed in RPMI and passed through a 40µM cell strainer . Cells were incubated at 2 x 105 cells/well in D10 media supplemented with 20ng/ml of M-CSF ( Peprotech ) for 7 days , replenishing M-CSF after 3 days . Spleens and SGs were processed as previously described , with an additional Percoll ( GE Healthcare ) purification step for SGs after processing . Bone-marrow derived macrophages were infected with MCMV or influenza ( PR8 ) at an MOI of 1 . Some cells were also incubated with 300μg/ml phosphonoacetic acid ( PAA , Sigma-Aldrich ) for 1 hour prior to infection . Splenocytes ( 2 x 105 cells/well ) and SG leukocytes ( 2 x 104 cells/well ) were infected in 48-well plates and infected with MOI 0 . 5 MCMV . After 24hrs , all macrophages were gently scraped gently off the bottom of the wells , stained with Live/Dead® fixable aqua dead cell stain ( Invitrogen ) and Fc block ( eBioscience ) , and surface stained with αCD200-PE ( Clone OX-90 , Biolegend ) , αCD80 Pacific blue ( Clone 16–10A1 , Biolegend ) , αCD86 FITC ( Clone GL-1 , BD Pharmingen ) , and αIA/IE PerCP/Cy5 . 5 ( Clone M5/114 . 15 . 2 , Biolegend ) prior to permeabilization and staining with anti-m06 antibody ( a kind gift from Stipan Jonjic , Rijeka ) conjugated with APC ( Innova Biosciences ) . SGs were frozen in OCT and 5μm thick sections fixed in acetone . Sections were blocked with Avidin/Biotin Blocking Kit ( Vectorlabs ) and then with 2 . 5% Normal Horse Serum ( Vectorlabs ) . Sections were incubated overnight at 4°C in the dark with CD31-Biotin ( Clone MEC 13 . 3 , BD Pharmingen ) or CD200-Biotin ( Clone OX-90 , BioLegend ) , and MHC II-FITC ( Clone M5/114 . 15 . 2 , BioLegend ) or EpCAM ( Clone E144 , AbCam ) . Alexa Fluor 488 anti-rabbit IgG ( Invitrogen ) and Streptavidin Alexa Fluor 555 conjugate ( Invitrogen ) were used as secondary stains for EpCAM , and CD200-Biotin and CD31-Biotin , respectively . Sections were counterstained with TOTO-3 ( Invitrogen ) , then fixed with 1% PFA and treated with 0 . 3M glycine . To investigate CD200 colocalization with CD31 or CD45 , and MHC II colocalization with CD4 , sections were incubated with CD45 ( Clone 30-F11 , Biolegend ) , CD31-FITC ( Clone 390 , eBioscience ) or CD4 ( Clone RM4–5 , Biolegend ) overnight at 4°C in the dark . Alexa Fluor 488 anti-rat IgG ( Life Technologies ) , FITC anti-rat IgG2b antibody ( Biolegend ) and Alexa Fluor 568 goat anti-rat ( Life Technologies ) were used as secondary stains for CD31-FITC , CD45 and CD4 , respectively . Sections were fixed in 1% PFA and treated with 0 . 3M glycine and then incubated with anti-CD200-Biotin for 2 hours at room temperature , followed by Streptavidin Alexa Fluor 555 conjugate ( Invitrogen ) , or MHC II-FITC ( without secondary antibody ) . Sections were counterstained with TOTO-3 ( Invitrogen ) and fixed . The following isotype controls were used: Rat IgG2a-Biotin ( BD Pharmingen ) for CD200-Biotin and CD31-Biotin , Rat IgG2a-FITC ( eBioscience ) for CD31-FITC , Rat IgG2b-FITC ( eBioscience ) for MHC II-FITC , Rabbit IgG ( AbCam ) for EpCam , Rat IgG2a ( eBioscience ) for CD4 , and Rat IgG2b ( BD Pharmingen ) for CD45 . Images were collected with a Zeiss Axioskop 2 FS mot confocal microscope . Images were assembled using ImageJ software . Wt and IFNβ1-/- bone marrow derived macrophages ( BM-DM ) were derived from C57/BL6 mice as previously described [54] and grown in 24 well plates . After 7 days of culture , BM-DM were infected with wt-MCMV , MCMVΔIE3 ( MOI = 1 ) or mock infected [55] . Cells were then harvested at 2 , 4 , 6 , 8 , 10 and 24 hours post-infection for the isolation of RNA using an RNeasy Mini kit ( Qiagen , UK ) according to manufacturer’s instructions . After QC using an Agilent Bioanalyzer , total RNA was labeled and hybridized to Mouse Gene 1 . 0ST microarrays ( Affymetrix , CA , USA ) according to manufacturer’s instructions using a WT Expression kit ( Ambion , UK ) . After data capture , quality control metrics were assessed using Affymetrix Expression Console software and then all arrays were imported into Partek Genomics Suite ( Partek , USA ) for downstream analysis . In brief , arrays were normalized using the gcRMA algorithm [56] . After normalization , to increase confidence in the genes taken forward to statistical analysis , data was filtered to include genes with at least 1 signal value of > = 150 across the time course . For viral load analysis , statistical significance was determined using the Mann-Whitney U test for paired groups . To analyze viral load data from bone marrow chimeras , linear regression analysis was utilized . Data were first subject to square-root transformation to introduce stability . We then fitted a linear model for covariates ( donor + recipient ) with and without the interaction term . Subsequent ANOVA analysis of these models showed the interaction term not to be significant ( p = 0 . 13 ) . However a model without any interactions is strongly significant ( p = 0 . 0015 ) and was therefore used . For paired analysis of flow cytometry data , the two-tailed Student’s t test was utilized . For bone marrow chimeras , linear regression of non-transformed data was used . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . mCD200–17470; mCD200R- 57781l; IFNγ- 15978; mIL-10–16153; mCD69–12515; mCD31/PECAM-1–18613; mIFNB1–15977; mCD80–12519 | Immune inhibitory receptors , including CD200 receptor ( CD200R ) , can limit immune responses in the mucosa to restrict reactivity to the plethora of harmless antigens that mucosal surfaces are continually exposed to . However , viruses may exploit these suppressive mechanisms to enable their persistence and spread . Many viruses , including herpesviruses , have acquired functional homologs of CD200 , the ligand of CD200R , implying that viral exploitation of this pathway is evolutionary advantageous . We now show that the β-herpesvirus murine cytomegalovirus ( MCMV ) takes advantage of the CD200R inhibitory pathway to persist within a mucosal site of MCMV persistence , the salivary glands . Mice deficient in CD200R mounted elevated antiviral immune responses that were driven by the increased division and accumulation of myeloid cells that function to orchestrate the generation of antiviral effector immune responses . Interestingly , MCMV infection of myeloid cells up-regulated CD200 expression . Thus , MCMV exploits the CD200 pathway to persist within mucosal tissue . | [
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| 2015 | CD200 Receptor Restriction of Myeloid Cell Responses Antagonizes Antiviral Immunity and Facilitates Cytomegalovirus Persistence within Mucosal Tissue |
Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells . The inverse problem , where stimulus is reconstructed from spikes , has received less attention , especially for complex stimuli that should be reconstructed “pixel-by-pixel” . We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs . We constructed nonlinear ( kernelized and neural network ) decoders that improved significantly over linear results . An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals , and responses at locally constant light driven by spontaneous-like activity . This improvement crucially depended on the precise , non-Poisson temporal structure of individual spike trains , which originated in the spike-history dependence of neural responses . We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains .
Decoding plays a central role in our efforts to understand the neural code [1–4] . While statistical analyses of neural responses can be used to directly estimate [5 , 6] or bound [7] the information content of spike trains , such analyses remain agnostic about what the encoded bits might mean or how they could be read out [8] . In contrast , decoding provides an explicit computational procedure for recovering the stimulus from recorded single-trial neural responses , allowing us to ask not only “how much” , but also “what” the neural system encodes [9] . This is particularly relevant when a rich stimulus is represented by a large neural population—a regime which is increasingly accessible due to recent experimental progress , and the regime that we explore here . Decoding from large populations presents a significant technical challenge due to its intrinsic high dimensionality . Past work has predominantly addressed this problem using two approaches . In the first approach , one only presents stimuli that have simple , low-dimensional representations , in order to turn decoding into a tractable fitting ( e . g . , angular velocity of a moving pattern [10] , luminance flicker [11] , 1D bar position [12] , etc . ) or classification problem ( e . g . , shape identity [13] , a small set of orientations or velocities [14] , etc . ) . It is unclear , however , how results for simple stimuli can be generalized to naturalistic stimuli even in principle , as the latter have no low-dimensional representation and , furthermore , the retinal responses are nonlinear . In the second approach , one first builds a probabilistic encoding model , followed subsequently by model-based inference of the most likely stimulus given the observed neural responses [15–21] . Theoretically , this procedure is possible for any stimulus , but in practice model inference is feasible only if it incorporates strong dimensionality reduction assumptions ( e . g . , that neurons respond to a linear projection of the stimulus ) . Here we demonstrate a third alternative , where a complex and dynamical stimulus is reconstructed from the output of the mammalian retina directly , by means of large-scale nonlinear regression . Retina is an ideal experimental system for such a study , because it permits stable recordings from large , diverse , local populations of neurons under controlled stimulation , where even simultaneous neural spiking events can be sorted reliably [22] . We start by performing linear decoding from the entire recorded retinal ganglion cell population , to separately reconstruct the temporal light intensity trace at each spatial location in the stimulus movie . When using sparse regularization , we extract and subsequently analyze “decoding fields , ” the decoding counterpart of the cells’ receptive fields . We next examine nonlinear decoding using kernel ridge regression ( KRR [23] ) and deep learning [24] , which provide a substantial increase in performance over linear decoding , and isolate spike train statistics that the nonlinear decoders are making use of . We conclude by examining how these statistics arise in generative models of spike trains and suggest that they might be essential for separating stimulus-driven from spontaneous activity .
Retinal tissue was obtained from adult ( 8 weeks old ) male Long-Evans rat ( Rattus norvegicus ) and continuously perfused with Ames Solution ( Sigma-Aldrich ) and maintained at 32 °C . Ganglion cell spikes were recorded extracellularly from a multi-electrode array with 252 electrodes spaced 60 μm apart ( custom fabrication by Innovative Micro Technologies , Santa Barbara , CA ) . Experiments were performed in accordance with institutional animal care standards . The microelectrode covered a total retinal area of ∼ 1 mm2 . For the rat this corresponds to 16-17 degrees of visual angle [25] . The spike sorting was performed with an in-house method based on [22] . The stimulus movie consisted of randomly moving dark discs ( r = 100 μm ) against a bright background ( 100% contrast , 2 ⋅ 1012 photons/cm2/s ) . The discs followed mutually avoiding trajectories generated through an Ornstein-Uhlenbeck process of the form: Δ v i Δ t = - 1 τ v i + f i + σ d W , ( 1 ) where vi is the velocity of the disc i , Δt = 0 . 01 is the integration timestep , τ = 0 . 8 is the damping time constant , σ = 0 . 5 is the random force magnitude , dW is a zero-mean , unit-variance Gaussian random variable , and fi is the hard-core central repulsive force between the discs and between each disc and the frame bounding box , with a decay of ∝ r−6 , where r is the distance between the disc centers or between the disc and closest point of the bounding box . The resulting distribution of disc speeds peaked at v ≈ 0 . 6 μm/ms and had a width of about σv ≈ 0 . 4 μm/ms . The discs covered the recorded area uniformly to a very good approximation , with occupancy deviations at different encoding sites of ∼ 3% . The movie was divided in segments of 1 , 2 , 4 and 10 discs , each 675 s long . Segments with increasing number of discs were presented sequentially and in total 3 segments of each type were shown , amounting to a total experiment time of 135 min . Each segment was regularly interspersed with 18 short ( 7 . 5 s ) clips of repeated stimulus: in sum , 54 repeated clips were shown for each stimulus with different number of discs . The stimulus was convolved with a bank of 400 spatial symmetric gaussian filters ( σ = 66 . 67 μm ) placed in a regular square 20x20 grid with d = 53μm spacing to produce local luminance traces . The filter normalization ensures the resulting traces are bounded in ( 0 , 1 ) . The width of the filters was selected in preliminary tests to optimize decoding performance; specifically , in preliminary tests we found that filter widths in range σ ∼ 50 − 100μm minimized the mean-squared-error of L1-regularized linear decoders trained for a subset of decoding sites . The movie stimulus was shown at a refresh rate of 80 Hz . The response spike trains were binned accordingly in bins of 12 . 5 ms , and time aligned to the stimulus . The spatio-temporal receptive fields of the retinal ganglion cells were obtained through reverse correlation to a flickering checkerboard stimulus . The checkerboard was constructed from squares of 130 μm that were randomly selected to be black or white at a rate of 40 Hz . Retinal spontaneous activity was recorded in full darkness ( blackout condition ) for 2 . 5 min . Let y → be a one-dimensional stimulus trace of length N time bins . In the linear decoding framework we assume that an estimate of the stimulus y → ^ can be obtained from the neural response Σ as y → ^ = Σ · L → , where L → is a linear filter . In this formulation , the response of the retina is represented by the matrix Σ ∈ R N × ( C × Δ T + 1 ) , where C is the number of cells and ΔT the size in bins of the time window we associate with a single point in y → ( for all analyses ΔT = 61 corresponding to a window stretching from -375 ms to 375 ms around the time bin of interest , i . e . , the decoding is performed using “acausal” filters ) . The extra dimension is a column of ones to account for the bias term in the decoding . Thus , the decoding filter L → is structured as L → = [ L 0 L → 1 L → 2 L → 3 … L → C ] , where L → i is the filter corresponding to cell i and L0 is the bias term . We learned the filters L → by minimizing the square error function with L1-regularization χ 2 = 1 N ( y → ^ - y → ) ⊤ ( y → ^ - y → ) + λ ‖ L → ‖ 1 . ( 2 ) To solve the minimization problem computationally we made use of the Lasso algorithm with the routines by Kim et al . [26] . Data was divided into training and testing sets ( 4 . 9⋅104 training points , 2 . 3⋅104 testing points ) . The filters were obtained from the training set and all measures of performance refer to the testing set . Regularization parameter λ was chosen through 2-fold cross-validation on the training set . The regularization term ensures the sparsity of the filters . Due to this sparsity some cells have negligible filter norms and therefore do not contribute to the decoding . This allows us to establish a hierarchy of cells by sorting them according to their filter norm ‖ L → i ‖ 1 . “Single-best cell” for every site refers to the cell with the largest norm . “Contributing cells” are the subset of cells with largest norm that jointly account for at least half of the total filter norm ∑ i ‖ L → i ‖ 1 . All the results in the paper use acausal filters . If the decoding filters are restricted to be causal , the decoding performance can be significantly decreased; for test sites where we explored this effect , the cross correlation between true and linearly decoded luminance trace could decrease from ≈ 0 . 8 to ≈ 0 . 6 . Given that our stimulus is a stochastic process and retinal processing necessarily entails some processing delay , this is not surprising . If instead of L1-regularization we enforce L2-regularization , the linear decoding filters can be obtained analytically through the normal equation L → = Σ ⊤ ( Σ Σ ⊤ + λ I ) - 1 y → . Thus , an estimate of the stimulus for some new data Σ ^ is given by y → ^ = Σ ^ · L → = Σ ^ Σ ⊤ ( Σ Σ ⊤ + λ I ) - 1 y → . Since this expression only depends on products of spike trains , we can make use of the kernel trick and substitute the usual scalar product by some appropriate nonlinear function k of the spike trains . In this way , we can express our nonlinear decoding problem as y → ^ = κ ⊤ ( K + λ I ) - 1 y → , ( 3 ) κ i j = k ( σ → ^ i , σ → j ) , ( 4 ) K i j = k ( σ → i , σ → j ) , ( 5 ) where σ → i ⊤ ∈ R 1 × ( C × Δ T + 1 ) is the ith row of matrix Σ . This is known as Kernel Ridge Regression [27 , 28] . For our analyses we have used the Gaussian kernel k ( σ → i , σ → j ) =exp ( - 1 2 s 2 ‖ σ → i - σ → j ‖ 2 2 ) . ( 6 ) Before computing the kernel , it is customary to turn the spike trains into smooth traces for the sake of performance [29] . We convolved our spike trains with a Gaussian filter of 3 time bins width . The data was divided into training and testing sets ( 9 . 8⋅103 training points , 2 . 3⋅104 testing points ) . The parameters s and λ were obtained through joint 3-fold cross-validation on the training set . Since decoding at different sites is independent , s and λ were chosen separately at each site ( likewise , L1 regularization strength for the linear decoder was also selected independently for each site ) . The performance of the nonlinear decoder depends on the set of cells considered . Contrary to the linear case where L1-regularization can effectively silence cells by setting their filters to zero , this nonlinear framework cannot ignore cells in a similar way . Therefore , including in the analysis non-informative cells can decrease the generalization performance of the decoder . To determine the best subset of cells for decoding we took advantage of the hierarchy of cells established by the linear L1-regularized decoding . We trained nonlinear decoders with progressively more cells ( best cell , best two cells , etc . ) and selected the subset of minimum decoding error on the training set ( S7 Fig ) . Effectively , we jointly cross-validated the three parameters s , λ , and the subset size . We trained a deep neural network on the decoding task . The architecture of the network is as follows: there are 5460 inputs ( activity of 91 cells and 60 time bins ) and two or three fully connected hidden layers followed by a fully connected linear output layer of 400 cells ( corresponding to the 20x20 grid ) , see S20 Fig . The hidden layers have each 150 units with tanh activation function . The loss is L2 loss on the regression error and L1 and L2 regularization on the weights ( Elastic Net type ) , more specifically: χ 2 ( θ ) = 1 N ∑ i = 1 N ( y ^ θ ( x i ) - y i ) 2 + ∑ k = 0 K λ ( | W ( k ) | 1 + | W ( k ) | 2 ) ( 7 ) where y ^ θ ( x i ) is the network output for input xi and W ( k ) is the weight matrix to layer k + 1 , θ = { W ( k ) } k = 0 K . The regularization improves generalization by making the network weights smaller and creating a sparse connection graph ( increases robustness to training set variations ) . The available data of 126360 input-output pairs was split into 79560 , 23400 , and 23400 points for training , validation and testing respectively ( same test set as for other methods ) . We trained for 2500 epochs ( each epoch trains on all training points once ) . To avoid that early during training many weights become zero because of the regularization we set λ = 0 for the first 100 epochs . We performed model selection by a grid search through the following hyperparameters: regularization constant: λ ∈ {5 ⋅ 10−7 , 7 . 5 ⋅ 10−7 , 1 ⋅ 10−6 , 2 . 5 ⋅ 10−6 , 5 ⋅ 10−6} , number of hidden layers K ∈ {2 , 3} , and optimization method ∈ { stochastic gradient decent ( sgd ) with learning rate 0 . 01 and momentum of 0 . 9 , Adam optimizer [30] with learning rate 0 . 005 and ϵ = 0 . 0001} . The hyperparameter setting with the smallest validation error where selected , resulting in: K = 3 , λ = 2 . 5 ⋅ 10−6 , and sgd . Interestingly , only around 42 units per hidden layer have non-zero connections after training . Although , if started with only 50 units we observed worse performance . The mean of the square test error ( over locations ) is 0 . 01387 with standard deviation 0 . 00304 . For classification purposes we assign each time bin to one of two classes: “fluctuating” or “constant” . “Fluctuating” corresponds to discs moving over the site of interest and decreasing the light intensity in that site , while “constant” refers to the constant illumination of the site when no discs are present . To label the time bins we use a simple cut-off criterion plus two further correcting steps to account for retinal adaptation effects . First we label as “fluctuating” every bin with stimulus intensity less than 0 . 99 . Then we apply these corrections: i ) Every identified “constant” segment shorter than 30 bins ( 375 ms ) is relabelled as “fluctuating , ” and ii ) The first 30 bins following a “fluctuating” segment are also labelled “fluctuating . ” In this way the stimulus at each site is divided in segments of fluctuating and constant intensity . We train both linear and nonlinear Support Vector Machine ( SVM ) classifiers to determine , from the spike train response , whether a given time bin is labelled as “constant” or “fluctuating” . Similarly to the decoding framework , to classify a given bin we consider a time window of ΔT = 61 bins around it in the response . For the nonlinear SVM we use the same gaussian kernel as in nonlinear decoding and the parameter values obtained when training the decoder . Note that this is not the optimal nonlinear classifier but allows us to evaluate the classifying power of the decoding kernel . Given a stimulus intensity trace y → and the corresponding decoding prediction y → ^ we define the decoding error as the Mean Squared Error MSE = N - 1 ( y → ^ - y → ) ⊤ ( y → ^ - y → ) . We also make use of the related Fraction of Variance Explained defined as FVE = 1 − ( MSE/Var ( y ) ) . To measure decoding performance from the fully decoded movie we build Receiver Operating Curves ( ROC ) . We threshold the decoded intensity trace at each site . If intensity is below threshold , the presence of a disc in the site is predicted . By comparing the prediction as a function of the threshold to the original stimulus frames ( where the center of the site can only be white when no disc is present , or black when the disc is present ) , we can evaluate the performance of the decoder as a balance between the True Positive ( TP ) and False Positive ( FP ) rates TPR = TP TP + FN , FPR = FP FP + TN . To assess the performance of the SVM classifiers we use the F1-score measure defined as F 1 = 2 P R P + R , where P is the Precision and R the Recall given by P = TP TP + FP , R = TP TP + FN . For the binary classification task , “fluctuating” is defined as the positive class . Unless otherwise stated , all of the statistical significance tests were performed with the Wilcoxon signed rank test . For each site s we determine the set of available cells whose receptive field centers were less than 300 μm distant from the center of the site . We call Cs the total number of available cells at site s . In general , Cs is the sum of ON and OFF subtype cells , C s = C s on + C s off . If , from the available cells at site s , we pick a random subset of size N = Non + Noff , the probability of choosing Noff cells is given by the hypergeometric distribution ( random draw without replacement ) p ( Noff|s , N ) = ( CsoffNoff ) ( Cs−CsoffN−Noff ) ( CsN ) . The average probability over all sites considered is p ( N off | N ) = 1 S ∑ s = 1 S p ( N off | s , N ) . Separately , for each site s we have established a hierarchy of cells from their decoding filter norms . Following the hierarchy we create decoding sets of different size N ( the best cell , the best two cells , etc ) and we count the number of OFF type cells Noff in them . We summarize this information in the histogram M ( Noff , N ) that counts the number of sites where the decoding set of size N contains Noff OFF cells . With this histogram we obtain an empirical probability p emp ( N off | N ) = M ( N off , N ) S , that we can compare with p ( Noff|N ) . In particular , the bias reported in S5 Fig is given by 100 · p emp ( N off | N ) - p ( N off | N ) p ( N off | N ) . Only sites with Noff , Non ≥ 2 were considered for the comparison ( n = 115 ) . We build an encoding model for a single cell , based on the standard GLM type model proposed by Pillow et al [15] . The cell spikes stochastically through a Poisson process with a time-dependent firing rate λ ( t ) given by λ ( t ) =fα ( ( k→Y→ ( t ) +αh→σ→ ( t ) ) where k → is a spatio temporal filter acting on stimulus Y → and h → is a temporal filter of the past spike history of the cell represented by σ → . The function f ( x ) is a rectifying nonlinearity of the log-exp form f ( x ) = a log ( b exp ( x + c ) ) . The stimulus filter k → factorizes into separate spatial and temporal filters . The spatial component is given by a balanced difference of gaussians , with widths σc = 35μm for the positive and σs = 100μm for the negative part , providing a symmetrical center-surround type filter . The temporal part of the filter is given by a single negative lobe of a sin-like function . The filter for the past spike history takes the form h ( t ) = Asin ( t + π 2 ) exp ( B ( - t + π 2 ) ) . This filter inhibits firing after a spike but , depending on the values of the parameters , it can have a positive lobe after the inhibitory part that tends to increase the firing rate . We consider a span of 250 ms ( 20 bins ) for both the past history filter and the temporal part of the stimulus filter . All elements of the filter are fixed except for the rectifying nonlinearity that is changed according to the value of α . Initially , the parameters of the nonlinearity fα = 1 ( x ) are adjusted to provide an average firing rate similar to that observed in real data . The α = 1 model is taken as the ground-truth and every time α changes , the nonlinearity fα ( x ) is fitted anew by maximizing the likelihood on α = 1 rasters , in order to reproduce the firing rate trace ( PSTH ) as closely as possible to the PSTH generated by α = 1 . The model neuron is stimulated with real data and the intensity trace at the central site of its receptive field is the stimulus considered for decoding . The model has been implemented using the Nonlinear Input Model toolbox [31] .
We recorded the spiking activity of C = 91 ganglion cells from a 1 mm2 patch of the rat retina , while presenting a complex and dynamical stimulus that consisted of 1 , 2 , 4 or 10 black discs on a bright background ( Fig 1A and Methods ) . The discs followed self-avoiding random motion , generated as described in the Methods section , which ( for decoder training ) was non-repeated; all decoding results are reported on withheld ( test ) segments of the stimulus that were not used during training . The stimulus also contained a segment of repeated trajectories that was randomly interspersed into the non-repeated part and used only to assess the role of noise correlations . Our goal was to reconstruct the light intensity as a function of time at a grid of 20 × 20 spatial positions ( “sites” ) uniformly tiling the stimulus frame . Specifically , at each site , we convolved the original movie with a small Gaussian filter ( see Methods ) , which defined the “luminance trace” at that site , to be decoded . Stimulus features ( here , disc size ) were smaller than the receptive field center of a typical recorded RGC , making the decoding task non-trivial . To estimate the luminance trace at any given time , we trained a separate sparse linear decoder for each site on a 750 ms sliding window of the complete spiking raster , shown in Fig 1B , and represented as spike counts in Δt = 12 . 5 ms time bins ( see Methods ) . The decoder minimized the square error between the true and estimated luminance trace at each site , using sparse ( L1 ) penalty on decoding weights , as specified by Eq ( 2 ) . While each decoder in principle had access to all neural responses , the sparse penalty ensured that the majority of the weights corresponding to redundant or non-informative neural responses for each site were zeroed out , yielding interpretable results which we describe in detail below . When trained on the 10-disc stimulus , this procedure predicted well the luminance traces across individual sites on withheld sections of the stimulus ( Fig 1C ) , allowing us to reconstruct the complete movie ( Fig 1D ) . We expected the performance of our decoder to depend strongly on local coverage , i . e . , on the number of recorded cells whose receptive field centers overlap a given site . Coverage amounted to about six cells on average and exhibited substantial spatial heterogeneity , as shown in Fig 1E . The quality of our movie reconstruction , measured locally by “fraction of variance explained” ( FVE , see Methods ) , showed similar spatial variation ( Fig 1F ) which correlated strongly with coverage ( Fig 1G ) , and saturated at ≥ 6 cells . In what follows , we restrict our analyses to sites with good coverage that pass a threshold of FVE ≥ 0 . 4 . Despite the high dimensionality of this regression problem ( decoders have ∼5 ⋅ 103 parameters per site ) , sparse regularization ensured uniformly good performance even when tested on out-of-sample stimuli with varying number of discs ( Fig 1H ) . To analyze how rich stimuli are represented by a population of ganglion cells with densely overlapping receptive fields , we examined the resulting decoding weights in detail . We found that stimulus readout was surprisingly local . As illustrated for two example sites in Fig 1I , only a few cells whose receptive field centers were in close proximity to the respective sites were assigned non-negligible decoding weights . This was true in general: on average 5 . 4 ± 2 . 8 cells , whose RF centers were all located within 200 μm of the decoded site , contributed to the luminance trace reconstruction; cells beyond this spatial scale contained no decodable information ( S1 and S2 Figs ) . Our framework also allowed us to construct a “decoding field” for every cell ( Fig 1J ) . A decoding field represents an impulse response of the decoder , i . e . , an additive contribution to the stimulus reconstruction for every spike emitted by a particular cell . While one can reasonably expect that the receptive and decoding fields overlap in location and spatial extent , there is no theoretical guarantee that this must happen , given that neural encoding is strongly nonlinear . We nevertheless confirm this expectation and observe a very good correspondence between the spatial locations and sizes of the decoding and receptive fields for all cells ( S3 Fig ) , with decoding fields also exhibiting a clear center-surround-like structure . We find that decoding filters shapes for all cells are highly stereotyped ( S4 Fig ) . We further find that the readout of retinal responses is local , in the sense that only cells with receptive field centers close to the decoding site contribute to the decoding ( Fig 1I , S6 Fig ) . Lastly , the readout is structured , in the sense that the cells that contribute to decoding at each site have a preferred ON vs OFF composition that favors recruiting OFF cells ( S5 Fig ) , most likely because the visual feature that moves in our stimulus is a dark disc on a bright background . Taken together , our results suggest that retinal responses to complex stimuli can be read out in a highly stereotyped , structured , and local manner . Could nonlinear decoding improve on these results ? We considered two nonlinear regression methods that can tractably be applied to our data: kernel ridge regression ( KRR ) and regression using deep neural networks . Kernel ridge regression is a well-known extension of linear regression into the nonlinear domain by means of the kernel trick . We used Gaussian kernels whose width was determined using cross-validatation ( see Methods ) [29] , as specified in Eqs ( 3 ) – ( 6 ) . Importantly , the success of this nonlinear decoder crucially depended on the proper selection of local groups of cells relevant for each site , as identified by linear decoding: its sparse ( L1 ) regularization acted as “feature selection” for the nonlinear problem ( Methods , S7 Fig ) . Nonlinear decoder could then make use of higher-order statistical dependencies within and between the selected spike trains to achieve high performance . We compared these results to regression using neural networks . An architecture that achieved good performance consisted of an input layer ( that received spiking rasters of the same dimension as the linear regression problem ) , followed by three fully-connected hidden layers with 150 sigmoidal neurons each , followed by a 20 × 20 output layer whose units correspond to the decoding sites of our movie; this architecture is schematized in S20 Fig . The network was trained by minimizing the squared reconstruction error , Eq ( 7 ) , using standard deep learning tools ( see Methods ) . Fig 2A shows a luminance trace at one of the example sites , together with its linear and nonlinear reconstruction . Nonlinear decoders track better the detailed structure of luminance troughs , which occur when discs cross the site , as well as exhibiting smaller fluctuations when no discs are crossing the site and the true luminance trace is therefore constant . This is reflected in a substantial overall increase in fraction of variance explained ( FVE ) across different sites , shown in Fig 2B . A kernelized nonlinear decoder using only two best cells per site outperforms , on average , the best sparse linear decoder constructed from the entire population; nonlinear performance saturates quickly with the number of cells and peaks when decoding from local ∼8-cell groups . The neural network decoder , which we train on the complete neural population , reaches a comparable performance to the best kernelized decoder . An alternative way to compare decoding performance is to threshold the sequence of decoded movie frames ( see S8 Fig and S1 Video ) , thereby assigning each site to a decoded dark disc ( “below threshold” ) or to the bright background ( “above threshold” ) . Decoded movie frames can then be compared to ground truth ( i . e . , the original movie frames which can only be either black or white at every location ) at each threshold using the receiver operator characteristic ( ROC curve ) , shown for all decoders in Fig 2C . In this metric , the performance of the kernelized and neural network decoders are nearly indistinguishable , and consistently outperform linear decoders . Excess nonlinear performance of between 15 and 20% of FVE was maintained even when decoders were trained on 10-disc stimulus and tested on stimuli with smaller number of discs ( Fig 2D ) . Excess nonlinear performance was also observed when decoding from a cell mosaic of a single functional type ( S9 Fig ) and on a repeat experiment ( S10 Fig ) . We note the surprising consistency between the kernelized decoding and neural network results . Despite the fundamental differences in the nature and application of these two regression methods—neural networks are universal approximators , use different regularization from the kernelized decoders , and have been trained on all cells simultaneously to decode at all sites simultaneously , in contrast to the kernelized decoders—their numerical measures of performance appear quantitatively consistent . While it is impossible to exclude the possibility that yet another type of decoder could yield much higher performance , it is also possible that both nonlinear decoders we tried managed to extract all available information about local luminance traces from the recorded spike trains . Another particularly striking feature of our results was the difficulty of the linear decoder to match the true ( constant ) luminance trace when no disc was crossing the corresponding site . Rat retinal ganglion cells are continuously active even when there are no coincident on-center luminance changes , with the activity likely resulting from stimulus changes in the surround , from long-lasting sustained responses to previous stimuli , from effective network coupling to cells that do experience varying input , or from true spontaneous excitation that would take place even in complete absence of stimuli [32–35] . Either way , activity of cells at constant local luminance presents a confound that is difficult for a generic linear mechanism to eliminate , which results in decoder fluctuations , or “hallucinations , ” of sizeable variance . To quantify this effect , we partitioned the luminance traces at every site into constant and fluctuating epochs by means of a simple threshold ( see Methods ) , and examined decoding errors separately during both epochs . The decrease in decoding error by using nonlinear decoders was similar in absolute terms in both epochs , but represented a much larger fractional decrease during constant epochs , suggesting that nonlinear decoders might specifically be better at suppressing their responses to spontaneous-like neural activity ( Fig 2E ) . We reasoned that this improvement comes , in part , from the ability of both nonlinear methods to recognize whether there are any on-site luminance fluctuations or not , from the spike trains alone . To test this idea , we trained linear and nonlinear kernelized classifiers , operating on identical inputs and with the same kernel parameters as the decoders , to best separate constant from fluctuating activity . Consistent with our expectations , nonlinear classifiers outperformed linear at every site , irrespectively of whether their input were the rasters of all local cells that contribute to the decoding , as shown in S11A Fig , or the raster of a single best cell at every site , as shown in S11B Fig . Next , we attempted to identify the statistics of spike trains that are necessary to explain the excess performance of nonlinear decoders . Our starting point was the following observation: the simplest nonlinear decoders that used a single best cell for each site , when interrogated with a test-set epoch of pure spontaneous activity ( i . e . , neural responses to a completely blank screen ) , yielded luminance traces with significantly smaller variance than their linear counterparts ( S12 Fig ) . Since the only structure in spike trains during spontaneous activity is , by definition , due to “noise correlations”—pairwise or higher-order dependencies between spikes within an individual spike train or across different spike trains—we hypothesized that certain noise correlations could be used by nonlinear decoders also during stimulus presentation to boost their decoding performance . To test this hypothesis , we made use of many identical repeats of a particular stimulus fragment embedded in our disc movie ( these repeats were used neither for training nor testing ) . Using the same decoders as above , we decoded the original response rasters corresponding to the repeated fragment , as well as rasters in which we shuffled the spikes to remove spike-history dependencies , or to remove cell-cell noise correlations , as shown in Fig 3A , to assess how decoding is impacted by the removal of certain types of correlation in the spike trains . Note that these manipulations left the firing rates of all cells intact , and thus preserved all correlations in the spike trains that are due to the neurons responding to a spatio-temporally correlated stimulus . Crucially , for our analysis we did not retrain our decoders on the shuffled spike trains , because we wanted to ask whether the same decoders that we trained to read the real ( unshuffled ) neural code can also read the modified neural code lacking various components of the noise correlations . If the decoder performance were unaffected by such removal , then noise correlations are not crucial for our decoder; in contrast , a drop in decoder performance would suggest that noise correlations may be necessary . Alternatively , if we were to retrain our decoders on the shuffled spike trains , we would be answering a different question: Is there any decoder that can read the shuffled neural code ? While interesting , ( i ) it is unclear what statements about the actual neural code such an analysis would provide ( since these decoders would be trained on synthetic , shuffled codes that only exist in our computer ) ; ( ii ) technically , the number of distinct training samples would be drastically too small to train such decoders , since the experiments impose a hard trade-off between the number of repeats and the duration of the repeated fragment , on which the decoder would have to be trained . For these scientific and methodological reasons , we performed the following analysis using decoders trained on actual ( unshuffled ) responses to unrepeated stimuli . Fig 3B shows a stimulus reconstruction at an example site by the nonlinear kernelized decoder , for original rasters as well as rasters with removed spike-history dependencies or cell-cell noise correlations . Removing cell-cell noise correlations leads to a small increase in the variance of the reconstructions across stimulus repeats , with only marginal differences in the mean reconstructed trace , compared to decoding from intact rasters . Surprisingly , removing spike-history dependencies leads to much worse reconstructions , whose mean is strongly biased and variance increased; as a result , the dynamic range of the decoded trace is substantially lower compared to decoding from intact rasters . These observations are summarized across sites in Fig 3C , which shows the increase in decoding error when spike-history dependencies or cell-cell noise correlations are removed . Removal of cell-cell noise correlations leads to small increases in error , roughly of the same magnitude for both linear and nonlinear decoders; in contrast , while removal of spike-history dependencies leads to increases in error for both decoders , the effect is two-to-three-fold larger for the two nonlinear methods . We emphasize that kernelized decoders and neural network are two fundamentally different regression methods , yet the removal of spike-history dependences strongly decreases the performance of both , suggesting that our observations are likely not a consequence of choosing a particular decoder type . Qualitatively similar conclusions hold for the classifiers trained to separate constant from fluctuating input epochs ( S13 Fig ) , as well as for decoders and classifiers trained on the single best cell per site ( S14 Fig ) . Having established that spike-history dependencies are crucial to the performance of the nonlinear decoders , we looked at the detailed statistical structure of individual spike trains . For each neuron that best decoded the luminance trace at a specified site , we focused on 250 ms ( 20 time bins ) response sequences and constructed a distribution over the number of occupied time bins ( “spike counts” ) , separately for epochs where the luminance trace was fluctuating or where it was constant . As shown in Fig 3D , these distributions differed significantly: the count distribution was much tighter in constant epochs , while the mean firing rate between the epochs did not change much . During fluctuating-input epochs , observing more spikes in a 250 ms window was more likely than at constant input , but—perhaps surprisingly—patterns with very low numbers of spikes ( e . g . , zero or one ) were also more likely during fluctuating-input epochs . The count distribution at fluctuating light was very similar to binomial ( and , at this temporal resolution , Poisson ) , while it was tighter at constant light . These changes could be summarized by a simple statistic , the variance-to-mean ratio F = ( variance in spike count ) / ( mean spike count ) . Note that unlike the standard Fano factor , our F is not computed across the repetitions of the same stimulus and thus measures the total variability in the response , which includes variance due to the changing stimulus . When we removed spike-history dependencies , the variance-to-mean ratio F increased for both distributions and they became harder to distinguish from each other . Fig 3E shows that this behavior was consistent across all sites , highlighting the very high regularity of neural spiking that resulted in sub-Poisson variance ( F substantially below 1 ) during epochs of constant luminance . How could spike-history dependencies help in stimulus decoding ? A possible scenario would involve the situation where decoders should “sum” multiple spikes from the same neuron in the recent past super-linearly , to optimally reconstruct the stimulus . In this case , without spike-history dependencies that are responsible for precise firing with sub-Poisson variance , the Poisson spiking in the absence of spike-history effects would cause large ( compared to linear decoder ) spurious variance in the decoder output . Adding spike-history dependencies would , in contrast , tighten the number of emitted spikes , giving the nonlinear decoder a reliable option to sum spike effects super-linearly without being swamped by spiking noise . We emphasize that this is only the simplest scenario we could think of as an example where spike-history dependencies could be beneficial; there are likely many others . Taken together , our results show that: ( i ) , spike-history dependencies within individual spike trains are crucial for nonlinear decoder performance; ( ii ) , these dependencies shape the distribution of spike counts on timescales relevant for decoding; ( iii ) , during constant local luminance , spiking activity is very regular ( and statistically similar to true spontaneous activity , see S15 Fig ) ; ( iv ) , a simple statistic , which summarizes the effects of spike-history dependencies in different epochs and their changes when the spike trains are shuffled , is the spike variance-to-mean ratio F . This does not imply that nonlinear decoders actually compute some version of a local estimate for F: they could be sensitive to other statistics , e . g . , the interspike interval distribution , which also differs substantially between the epochs , see S16 Fig . Because nonlinear decoders we use have no well-defined set of sufficient statistics , it is impossible to claim which precise statistic of the spike train they are sensitive to , beyond stating that they clearly are sensitive to the removal of spike-history dependencies . Note further that we can only establish clearly that nonlinear decoders that we trained are sensitive to the removal of spike-history dependencies; we , however , cannot exclude the option that there exist nonlinear decoders of the same class that reach similar performance as ours but are at the same time robust to the removal of spike-history dependencies . Nevertheless , subsequent analyses on synthetic data that we provide below , as well as the robustness of our observations with respect to the nonlinear method ( kernelized decoder and the neural network ) suggest that crucial decoding information really is present in the spike-history dependencies , and that the underlying reason for nonlinear decoder performance is its ability to recognize high regularity of spiking during epochs of constant local luminance . Can the observed spike-history dependencies , which enable successful nonlinear decoding , be generated by simple and generic neural encoding models ? To address this question , we made use of generalized linear models ( GLMs ) [36 , 37] , probabilistic functional models of spiking neurons that extend the paradigmatic linear-nonlinear ( LN ) framework by incorporating the recurrent feedback from neuron’s past spiking , as schematized in Fig 4A . Previously , GLMs have been successfully applied to responses of the mammalian retina [15 , 20] and in the cortex [38 , 39] , and also reproduced well the firing rates of cells recorded in our experiment on the repeated stimulus fragment ( S17 Fig ) . To link encoding models and decodability in a way that would generalize beyond the specifics of our dataset , we created the simplest stereotyped model cell , shown in Fig 4A . Crucially , we parametrized the magnitude of the self-coupling filter with α: α = 0 thus corresponded to a pure LN model , while increasing values of α made neural spike trains non-Poisson , progressively enforcing dependence on past spiking and consequently increasing the magnitude of the resulting temporal correlations . With this model in hand , we generated a “baseline” raster of repeated responses to a randomly moving disc stimulus at an initial value of α = 1 , which corresponds to the strength of spike-history dependence inferred from our data , as shown in Fig 4B . The average firing rate was chosen to be the typical rate of our recorded ganglion cells . We then systematically changed the value of α and , for each value , refitted the nonlinearity by maximizing the likelihood to the baseline raster at α = 1 ( see Methods ) . This procedure generated synthetic rasters that , to an excellent approximation , were matched in their peri-stimulus time histograms ( PSTH ) and stimulus preference , yet differed in the strength of spike-history dependencies . Following our previous analyses , we partitioned the luminance trace into constant and fluctuating epochs , and looked at the spiking statistics in 250 ms ( 20 time bin ) windows . Spike count variance-to-mean ratio F in constant epochs decreased as a function of α and dropped substantially below 1; in contrast , when on-center luminance was fluctuating , F behaved non-monotonically ( Fig 4C ) . In line with expectations and behavior observed in our data , F at constant luminance was always below F at fluctuating luminance . Having ensured that the statistics of synthetic rasters qualitatively agreed with the data for the range of α we examined , we asked about the performance of linear and nonlinear decoders , trained and tested at different values of α . Fig 4D plots the decoding error as a function of α; see S19 Fig for the separation into error in fluctuating vs constant epochs as a function of α . Overall , the error levels are in range of those observed for real data ( cf . Fig 1H ) , with nonlinear decoders outperforming linear by ∼10 − 30% . Interestingly , the minimal error for both decoders is achieved at an intermediate value of α* ≈ 0 . 4 , which also corresponds to the point where nonlinear decoders maximally outperform their linear counterparts . At α = 0 , where the encoding models are effectively LN neurons , the decoders differ only marginally in performance ( analogous results hold for the classifiers separating fluctuating from constant epochs , see S18 Fig ) . How close are real retinal ganglion cells to the value of α that permits best nonlinear reconstruction ? This question cannot be answered precisely with the toy models we use . Within the class of generalized linear models ( GLMs ) considered here , we can reliably show that nonlinear decoding performance significantly outperforms linear decoding performance for a broad range of α values that includes both α* ≈ 0 . 4 as well as α = 1 ( which , by our definition , corresponds to the best fit of GLM model to our data ) , and this effect is robustly true for all the cells that we examined . It is , however , likely that GLM models are too simplistic for the cells we are considering ( realistic models for rat ganglion cells may require two nonlinear stages of stimulus processing , i . e . , LNLN models [40] ) , if we wanted to make a quantitative statement about how close real cells are to the value of α that permits optimal nonlinear reconstruction . In these simulations we also haven’t modeled cell-to-cell noise correlations; further , it is likely that GLM does not capture all spike-history dependencies; and we decoded only the central pixel of the model’s receptive field . These differences between the simulations and the real experiment are likely responsible for the fact that the difference between nonlinear decoding performance from GLM-generated spike trains and same spike trains with shuffle-removed spike-history dependencies are much smaller than what we see in real data . Nevertheless , while the quantitative match between real neural data and GLM simulations is beyond the scope of this paper , we have shown qualitatively that in a generic class of encoding models that have been widely applied to both peripheral as well as central neural processing , there exists a non-trivial strength of spike-history dependence that facilitates nonlinear stimulus reconstruction . Intuitively , the existence of optimal α* > 0 can be explained as a trade-off between ensuring regularity of spiking during constant epochs , which the nonlinear decoder can make use of , while not impeding stimulus encoding during fluctuating epochs; during these epochs , stimulus-driven term should dominate over sensitivity to past spiking , otherwise excessive dependence on spiking history ( e . g . , α ≥ 1 in Fig 4B ) could perturb reliable locking to the stimulus .
Insights from decoding provide crucial constraints for theoretical models of neural codes . A large body of work dissects nonlinearities in stimulus processing , from nonlinear summation in the receptive field or during adaptation , to essential spike generation nonlinearities . Consequently , one would expect nonlinear decoding to outperform linear , but reports to that effect are scarce [11 , 41] . In theory the results of a nonlinear encoding process can be linearly decodable [42 , 43] , yet whether this is true of real neurons under rich stimulation is still unclear . What has been demonstrated to date is that certain low-level representations of simple stimuli—but not the full frame-by-frame movie—can be linearly decoded [12 , 44] . Another fundamental question concerns the stability of decoding transformations , which has recently received renewed attention in the context of efficient coding [45–47] . Finally , a number of studies , both theoretical [48] and data-driven [7 , 15 , 20 , 49–51] , focused on correlations in neural activity , especially those due to spike-history dependence and network circuitry ( “noise correlations” ) ; here , decoding provides a way to quantitatively ask about the functional contribution of such correlations to stimulus reconstruction . Approaching these issues empirically requires us to first construct high-quality decoders for complete stimulus movies—conceptually , doing the inverse of the state-of-the-art encoding models [15]—which remains an open challenge . Some of the above questions have been approached before using frame-by-frame decoding , with stimuli of varying complexity . Theoretical methods for such decoding—as well as several approximations to render these methods tractable—have been presented , mainly in the context of probabilistic-model-based decoding [16–19] , although they have generally not been applied to real recordings with rich stimuli . Linear decoding of natural scenes has been undertaken in the cat LGN with linear decoders ( but without sparse regularization ) [52] , and using Bayesian methods with strong natural movie priors from fMRI recordings of the visual cortex [53] . Generalized linear models ( GLMs ) have been used to model the neural responses ( e . g . , [15 , 20 , 21] ) , although full stimulus reconstruction was undertaken only in [15] for a binary checkerboard stimulus , whereas other works used the inferred probabilistic models to perform the easier tasks of stimulus classification or decoding from synthetically generated spike trains . This is , in part , because optimal ( Bayesian ) decoding of stimuli with complex prior statistical structure ( such as ours or natural movies ) is technically challenging . Furthermore , for many neural systems , including but not limited to the retina under natural or complex dynamical stimulation , we do not have adequate encoding models; consequently , optimal Bayesian inversion of poor encoding models does not represent a clearly interpretable benchmark for other decoding methods . We thus decided for an alternative , statistical approach of constructing nonlinear decoders directly and benchmarking them against an accepted common standard , the linear decoder . To this end , we used large-scale linear and nonlinear ( kernelized , neural network ) regressions to directly decode a complex stimulus movie from the output of many simultaneously recorded retinal ganglion cells . Importantly , we did not use any prior knowledge of recorded cells’ properties ( e . g . , their types or receptive fields ) , or any prior knowledge of the stimulus structure , to carry out the decoding; as a result , our decoding filters could , at least in principle , be used to decode any stimulus . A combination of sparse prior over decoding filter coefficients and a high-dimensional stimulus revealed a surprisingly local and stereotyped manner in which the retinal code could be read out . This is in stark contrast to previous work using simple stimuli where the readout was distributed and the resulting decoding filters had no general interpretation [12] . While our filters and consequently the “decoding fields” were recovered under a particular stimulus class and thus nominally depend on stimulus statistics , it is interesting to speculate whether the retina could adaptively change its encoding properties so as to keep the decoding representations constant , as has recently been suggested [12–14 , 54] . Similarity between decoding and receptive fields and generalization to stimuli with different number of discs provide limited circumstantial support for this idea , but a definite answer can only emerge from dedicated experiments that specifically test the stability of decoders under rich stimuli with different statistical structure . The performance of linear decoders was further improved by using nonlinear decoding . The improvement was significant , systematic , and reproducible: we observed it at nearly all sites , irrespectively of how many relevant cells we decoded from , when decoding from all recorded cells jointly or a mosaic of a single type , and also in a repeat experiment . Furthermore , a very different nonlinear regression method—a multi-layer neural network trained with standard deep learning tools—recapitulated quantitatively the results of kernelized decoding . The performance improvement of nonlinear methods is nontrivial , because the increased expressive power of nonlinear methods comes at a cost of potentially overfitting models to data; this was evident also in our failed first attempt to apply kernelized decoding to the whole recorded population , instead of only to the relevant cells selected by sparse linear decoder at every site . The performance improvement depended crucially on the spike-history dependence in individual spike trains but only slightly on cell-cell noise correlations . Previous work also explored the role of cell-cell noise correlations for decoding: while no impact of cell-cell noise correlations on decoding performance was found in mouse retinas exposed to white noise and natural scene stimulation [21] , Pillow and colleagues report that the inclusion of cell-cell noise correlations in model-based decoding increased the stimulus information by about ∼20% [15] . We also observe a significant , 10 − 20% decrease in decoding performance if cell-cell noise correlations are removed from the test-set spike trains , with decoders trained on intact rasters . Our largest effect , however , comes from spike-history dependencies . Short-term history dependence in ganglion cells is mostly due to refractoriness , and including spike-history dependences of up to 40 ms after the spike did not substantially change the decoding performance from primate parasol cells [15] . In contrast , our spike-history dependencies extend over much longer times and modulate spiking structure over 100 ms or more , in temporal windows relevant for decoding; removing these dependencies drastically decreased the performance of nonlinear decoders . Consistent and robust results using two entirely different nonlinear regression methods , backed by simulations using GLM-model neurons , provide compelling evidence that spike-history dependencies indeed enable low-error stimulus reconstruction . What are the methodological advances presented in our work ? While sparse and nonlinear regression methods used here are standard methods in statistics , they have typically not been applied to spiking neural data for complex stimulus reconstruction . Nevertheless , we show here that they should provide a tractable way of studying how rich signals are represented in other parts of the brain without making explicit assumptions about the encoding process , thereby providing a complementary , decoder-centric alternative to Bayes inversion of probabilistic encoding models . Second , even though the inner workings of nonlinear methods are notoriously difficult to interpret intuitively , our analysis suggests that controlled manipulations of spike train statistics can provide valuable insights into which spike train features matter for decoding and which do not . Finally , we suggest the “pixel-by-pixel” decoding approach as an alternative way to shed light on the functional contributions of different cell types to stimulus representation . While beyond the scope of this paper , one could decode stimuli from individual mosaics of the same type , or from their combinations , and compare the decoding performance ( and resulting errors ) to that of a complete population . What are the general implications of our results ? The high-dimensional nature of our stimulus forced us to decode the movie “pixel-by-pixel , ” rather than trying to decode its compact representation . This , in turn , focused our attention on the intermittent nature of signals to be decoded: at any given site , the luminance trace switched between epochs where nothing changed locally , and periods where the trace was fluctuating in time . Such intermittency is common to many natural stimuli across different sensory modalities [55 , 56] , and therefore must shape the way in which sensory information is encoded [57–59] . From the decoding perspective , it can , however , also pose a serious challenge: since neurons might be similarly active irrespective of whether the stimulus fluctuates locally or not , a downstream processing layer would have to suppress “hallucinations” in response to upstream network-driven or spontaneous activity ( cf . [60] ) . This issue could be especially acute in the sensory periphery . The retina is an information bottleneck that conveys the information to the central brain in an essentially feed-forward fashion . Spontaneous activity [32–35] thus appears problematic , since there is no clear “extra” signal that could tell the downstream processing whether the input received from the retina is spontaneous or stimulus-driven; we thus looked for an intrinsic signature in the spike trains themselves . In contrast , cortex , with its recurrent / feedback architecture clearly supports the notion of cortical states that could provide additional information on how activity from higher sensory areas should be interpreted ( e . g . , is it a reverberation or current , stimulus-driven activity ) . Indeed , spontaneous and persistent spiking is widespread in the cortex [61–64] and has even been documented to statistically mimic the structure of stimulus-evoked activity [65] . Here we proposed a simple mechanism to discriminate spontaneous from stimulus-driven activity using history dependence of neural spiking: because neuronal encoding is nonlinear , the effect of spike-history dependence on neural firing substantially differs between epochs in which the neuron also experiences a strong stimulus drive and epochs in which it does not . In such situations , nonlinear methods can discriminate between a true stimulus fluctuation and spontaneous-like firing from statistical structure intrinsic to individual spike trains , even when the mean firing rate doesn’t change appreciably between different epochs . This mechanism is not specific to the retina , and may well apply in other systems that display both stimulus-evoked and spontaneous activity . | Neurons in the retina transform patterns of incoming light into sequences of neural spikes . We recorded from ∼100 neurons in the rat retina while it was stimulated with a complex movie . Using machine learning regression methods , we fit decoders to reconstruct the movie shown from the retinal output . We demonstrated that retinal code can only be read out with a low error if decoders make use of correlations between successive spikes emitted by individual neurons . These correlations can be used to ignore spontaneous spiking that would , otherwise , cause even the best linear decoders to “hallucinate” nonexistent stimuli . This work represents the first high resolution single-trial full movie reconstruction and suggests a new paradigm for separating spontaneous from stimulus-driven neural activity . | [
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| 2018 | Nonlinear decoding of a complex movie from the mammalian retina |
A complex disease has , by definition , multiple genetic causes . In theory , these causes could be identified individually , but their identification will likely benefit from informed use of anticipated interactions between causes . In addition , characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease . Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction . As a consequence , there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power . Another issue is that , currently , the relation between different methods for interaction inference is in many cases not transparent , complicating the comparison and interpretation of results between different interaction studies . In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models ( GLMs ) . The tests can be applied for inference of single or multiple interaction parameters , but we show , by simulation , that jointly testing the full set of interaction parameters yields superior power and control of false positive rate . Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis . We investigate the impact of several assumptions commonly made when modeling interactions . We also show that , across the important class of models with a full set of interaction parameters , jointly testing the interaction parameters yields identical results . Further , we apply our method to genetic data for cardiovascular disease . This allowed us to identify a putative interaction involved in Lp ( a ) plasma levels between two ‘tag’ variants in the LPA locus ( p = 2 . 42 ⋅ 10−09 ) as well as replicate the interaction ( p = 6 . 97 ⋅ 10−07 ) . Finally , our meta-analysis method is used in a small ( N = 16 , 181 ) study of interactions in myocardial infarction .
Large data sets are vital to counter low statistical power due to low allele frequency , small effect sizes , and multiple testing . This has driven the GWAS field towards more collaborative efforts as well as meta-analyses . Fortunately , there exists a standardized statistical methodology that allows for reliability and comparability between different studies . In contrast , in association studies aiming at identifying interactions , or epistasis , there are multiple competing methodologies with unclear relationships . As a consequence , collaborative GWAS efforts have almost exclusively focused on single variant associations . Conceptually , interactions in association studies are generated when multiple genetic variants affect the dynamic , non-linear and inter-connected networks that underlie complex traits [1] . Candidate-based medical genetic studies established early that interaction may play an important role in complex diseases [2–7] . Consequently , over the last five years , substantial attention has been devoted to resolving the statistical and computational problems associated with large-scale studies of interactions [8–15] . However , the formulation of these methods is still not easily compared , and crucial differences between the underlying interaction models are not always transparent . This makes comparability between studies still a major concern and hampers opportunities for meta-analysis . There is , therefore , a need to harmonize interaction models and investigate the implications of their assumptions . From a statistical point of view it is non-trivial to define interaction [16] and it can often be unclear how different assumptions , e . g . , on the main effect of each variant , affect the definition of interaction . There has been some work aimed at a standardized description of interaction models [17 , 18] . However , these studies specifically targeted a class of models , in which any two models are related by a linear one-to-one transformation , which limits their applicability . In this paper , we present a more general framework that enables modeling and interpretation of genetic interactions in the context of any generalized linear model ( GLM ) . This can be applied to , in principle , any type of outcome ( e . g . , continuous , binary , factor or count phentoypes ) or model of interaction . We show how this new formulation can be used to analyze the relation between various interaction models . Multiple tests for interaction have been proposed for case-control data . However , these tests typically depend on strong assumptions about the main effects , marginal effects or LD to reduce the computational complexity [15 , 19 , 20] . Recently Yu et al . introduced a closed-form Wald test restricted to a specific parameterization of the logistic regression model [21] . Here , we introduce a general class of computationally efficient Wald tests , that enables analysis of case-control traits , quantitative traits , and in fact any trait modeled by a member in the exponential family . More importantly , these tests allow for any combination of parameterization and link function to be used , that is , it can be applied to all the models considered here . Moreover , we show that our Wald test can be applied in large-scale meta-analyses . A major complication in interpreting interactions is that they are inferred relative to a link function . This function determines the parameter subspace that belongs to the null model and is , in practice , unknown . Consequently , mis-specification of the link function causes an inflated error rate that increases with sample size , which cannot be resolved by replication in a separate cohort . Here we address this issue by testing interactions using a family of link functions . Specifically we use two families of link functions that has been proposed previously [22 , 23] . We also show that the previously suggested goodness-of-link test [24] is not appropriate for joint testing of interaction parameters . We implement these new tests in a GLM-based analysis tool for both case-control and quantitative data . We investigate the impact of different parameterizations on both the false positive rate and the statistical power . We finally apply our Wald tests in two genome-wide interaction analyses . Firstly , we study a continuous phenotype , Lp ( a ) , in the PROCARDIS cohort . Secondly , we perform a meta-analysis of myocardial infarction by combining results from the PROCARDIS cohort and the Myocardial Infarction Genetics Consortium cohort .
We will now introduce five different strategies for testing interactions . We will use these strategies together with the Wald test described in Section Fast estimation and testing of interaction in generalized linear models , above , in our investigations of statistical power and false positive rate . The first three are based on two different saturated parameterizations: G × G and AD × AD ( Fig 1 ) . The G × G test models is the standard approach for regression on discrete variables in the GLM literature , the AD × AD test model corresponds to the F∞ model discussed by [17] ( the careful reader will notice that , compared to [17] , we have changed the coding of the second column of AD from ( 0 , −1 , 1 ) to ( 0 , 1 , 2 ) to allow for easy comparison with G ) . A parameterization that features multiple interaction parameters can be tested either jointly or separately . Let the interaction parameter vector be denoted by δ = {δ11 , δ21 , δ12 , δ22 , } . A joint test evaluates the hypothesis that all interaction parameters are zero δ = 0 and a separate test evaluates the hypothesis that one or more interaction parameters are zero ∪h{δh = 0}; notice for the separate test that , while each test has a lower degree of freedom , the multiple testing burden will increase by a factor of 4 . In our first two strategies , the interaction parameters are tested by a joint test and we will refer to these tests as G × G joint and AD × AD joint , respectively , while the third strategy is based on a separate test and the G × G parameterization , which we refer to as G × G separate . For the next strategy , we introduce a non-saturated parameterization that assumes that the genotypic effect is completely additive in the number of minor alleles , and thereby ignores possible deviations . This corresponds to the Kronecker product of two additive-encoded uni-variant GLMs ( A ⊗ A ) . This model is often written g ( μ a b ) = α + a β 1 + b γ 1 + a b δ 11 This is a much more restricted test model and the interaction is now represented by a single parameter δ11 instead of four as in the previous models . We refer to this test as the A × A test . The last strategy , which has been applied in multiple studies of interactions [9 , 27] , is to first encode the genotypes into binary variables according to dominance or recessiveness . These binary variables are then analyzed separately in an interaction test . This encoding corresponds to the ( saturated ) parameterizations D × D , R × D , D × R and R × R: g ( μ a b ) = α + I ( a ≥ 1 ) β + I ( b ≥ 1 ) γ + I ( a ≥ 1 ) I ( b ≥ 1 ) δ g ( μ a b ) = α + I ( a ≥ 1 ) β + I ( b = 2 ) γ + I ( a ≥ 1 ) I ( b = 2 ) δ g ( μ a b ) = α + I ( a = 2 ) β + I ( b ≥ 1 ) γ + I ( a = 2 ) I ( b ≥ 1 ) δ g ( μ a b ) = α + I ( a = 2 ) β + I ( b = 2 ) γ + I ( a = 2 ) I ( b = 2 ) δ In each GLM , interaction is measured by a single parameter δ . In analogy with separate testing , we evaluate if δ in any of the test models deviate from zero , and the multiple testing burden will increase by a factor of 4 . We refer to this family of tests as R/D × R/D . We performed two experiments to investigate how potential model misspecifications affect the false positive rate ( FPR ) for the interaction tests described previously . In the first experiment we generated synthetic data from ( null ) models with no interaction , but where some assumptions of the evaluated tests fail ( specifically , the presence of recessive and dominance for the A × A test and the presence of an additive component for the R/D × R/D test ) ; we also tested whether linkage disequilibrium ( LD ) affects the FPR . The results in Fig 2 show that all tests controlled the error rate when there is no LD for a Normal dispersion distribution . However , for the Binomial dispersion distribution , the error rate of the A × A and R/D × R/D tests was inflated by the presence of dominant and recessive inheritance patterns . A second source of errors is LD , and the A × A test had a strongly inflated error rate when data was generated from R × A and R × D under both dispersion distributions . Moreover , also the R/D × R/D parameterization had an inflated error rate for all generative models under LD . The error rate was highest for data generated from R × A and a Binomial dispersion distribution . In general , the G × G joint , G × G separate and AD × AD joint tests are safe to use in the presence of LD , whereas the other tests must be treated with caution . In the second experiment , we investigated the impact of link function misspecification on the FPR . As discussed in Section Relating different GLMs , it is known that interactions inferred with one link function might be absent when another link function [25 , 26] is used , and vice versa . Here we test how often link misspecification introduce “false interactions” . We generated data from the A × A model with the log link function , whereas we performed each test using the identity link function . We measured the FPR as a function of the main effect of the second variant , while the main effect of the first variant is constant . Notice that when the second main effect is zero , the phenotype depends only on the first variant and , thus , no inflation of “false interactions” is expected . The results ( Fig 3 ) show that all tests were affected similarly by link misspecification , and the false positive rate quickly increased with the second main effect’s deviation from zero; this effect was slightly more pronounced for the A × A test . The effect became more pronounced as the sample size increased . This demonstrates , as previously predicted [25] , that use of an erroneous link function may lead to false inference of interaction . Designing a convincing and realistic experiment for measuring the expected statistical power is challenging . Ideally , we want to generate data from a biologically relevant model . However , neither the effect sizes , nor the structure of such a model are known . In addition , there is a large number of possible interaction models , even for single pair interactions where we have nine model parameters and two allele frequencies . In an attempt to balance efficiency and exhaustive exploration of this parameter space , we , perform two separate experiments . The first considers data generated from a small set of common interaction models , and a second , that considers data generated from a larger set of models , to give an overall picture of how different tests perform in relation to each other . In the first experiment , we estimated the statistical power on data generated from the A × A , A × Afailed , D × D , D × Dfailed , H × H , and R × D GLMs . The A × Afailed and D × Dfailed are AD × AD generative models that were designed to violate the assumptions of the A × A and D × D models , respectively ( see further Section Materials and methods ) . The results in Fig 4 shows that , for data generated from these specific models , there is no universal winning test strategy . However , both the G × G joint and AD × AD joint are generally among the best test under each generative model . Surprisingly , and in contrast to a single variant association test [28] , the A × A test generally has a large loss of power when the generative model underlying the data is not A × A . Separate testing of parameters in the G × G separate or R/D × R/D tests sometimes incur a small loss of power compared to the joint tests . This can be expected because the application of Bonferroni correction in the separate test implicitly assumes independence of the individual interaction parameters , while the joint test accounts for any correlation among them; this will , overall , result in a small power advantage for the joint test . In the second experiment , we investigate the statistical power for the same tests over a large number of randomly sampled generative models . The results in Fig 5 reinforce those from the first power experiment . The joint tests , G × G joint and AD × AD joint , consistently perform best , followed by the G × G separate and R/D × R/D tests ( where each parameter is tested separately ) , whereas the A × A test has on average 20% lower power . There is , as expected , no difference between the G × G joint and AD × AD joint tests . Despite the increased number of parameters of the joint tests these results holds also for lower sample sizes , see S3 Fig . However , we can not exclude that when many additional covariates are required , the tests with fewer parameter might gain statistical power . We applied the G × G joint test in two different association analyses aimed at the quantitative outcome blood concentration of Lp ( a ) lipo-protein particles [29] and a case-control outcome for myocardial infarction ( MI ) , respectively . Our results , above , show that joint tests using saturated models has the best power . Moreover , joint tests using the G × G and AD × AD parameterizations are equivalent; our choice to use the G × G is arbitrary , but perhaps it is more easy to directly relate to the actual genotypes . The link family test was used to test for link function invariance . As this test is quite computer-intensive , we apply the following strategy for each association analysis: We first perform the large-scale discovery analysis using the canonical link function in the respective analysis . The significant interactions from the discovery are then re-analyzed including the full test for link function invariance . The full link function invariance test is also applied in the replication analysis .
Our major contribution is computationally efficient tests for the complete family of GLMs , applicable to any combination of parameterization and link function . Our tests can be used for case-control studies as well as studies of quantitative traits . We have also shown how this methodology facilitates computationally efficient meta-analyses . The G × G and AD × AD tests clearly had the overall best power to detect interaction . However , for certain data sets generated from specific interaction models , the G × G separate test and the R/D × R/D test performed comparable to the joint saturated full GLMs . Interestingly , the commonly used additive test had the overall worst power performance among the evaluated tests . It had strikingly poor power in several experiments . Nevertheless , for low minor allele frequencies the difference in power was less pronounced . In conclusion , our empirical results together with the equivalence of full saturated models , suggest that tests using saturated full GLMs are superior for interaction studies in general . It is important that high power does not come at the expense of control of the false positive rate ( FPR ) . Our simulations show that , under certain circumstances , incorrect parameterization in unsaturated or constrained GLMs may lead to incorrect interaction inferences . Specifically , when the data includes LD , tests using the constrained R/D × RD or the unsaturated A × A GLMs failed to control FPR , while it was still controlled by the tests using saturated full GLMs . Moreover , also for data generated without LD , but then only when generated with a Binomial dispersion distribution , incorrect parameterization caused the A × A test to display substantial FPR inflation . We demonstrate the applicability of our method on experimental data aimed at Lp ( a ) . Levels of circulating Lp ( a ) have an inverse association with coronary heart and are predominantly regulated by genetic variation in proximity of the LPA gene [29] . Although the mechanism remains elusive , it has been hypothesized that the association between Lp ( a ) levels and CVD is indeed causal , in part because a number of genetic polymorphisms have effects on both LP ( a ) levels and risk of cardiovascular events . We discovered a new genome-wide significant interaction between two variants in proximity to the LPA locus . This could potentially reflect an interaction between AGPAT2 , involved in triglyceride metabolism , and SLC22A2 , a transmembrane transporter , which previously has been indicated as involved in lipoprotein metabolism [46] . However , as often is the case , we cannot exclude the possibility that the discovered interaction may be a proxy for an unsampled , either single variant or another interaction , association . We demonstrated the applicability of our meta-analysis method by analyzing two MI case-control cohorts . The genetic architecture of MI is known to be complex and single variant analysis has so far explained only a small fraction of its heritability . This motivated us to investigate the potential impact of interactions on MI . Our meta-analysis did not result in any interactions of genome-wide significance , which require passing the severe Bonferroni-corrected threshold p ≤ 5 . 72 ⋅ 10−12 . The link function , a . k . a . the “scale” , is a crucial component of GLMs that relates the linear predictors to the phenotype mean . Because the link function used in a test can have a considerable impact on the resulting FPR , it is of interest whether the interaction is invariant of the scale , and a few tests for this have been devised . We applied the link family test , which evaluates invariance across a family of link functions [22 , 23] . This test is unfortunately computationally demanding and can be restrictive for large-scale analyses . To circumvent this , we used the so-called canonical link function in the large-scale discovery phase , and performed the test for link function invariance only on those interactions that passed the discovery phase . Alvarez-Castro and Carlborg [18] derived conditions for when the parameterization is orthogonal in a linear model . Parameterization orthogonality facilitates decomposition of the variance into components corresponding to main and interaction effects . For a GLM the concept of orthogonality is complicated by the link function and the genotypic dependence of the variance ( see S1 Text ) . Nevertheless , our tests are applicable to both orthogonal and non-orthogonal parameterizations alike . Our test is currently limited to discrete genotype data , i . e . , directly genotyped or hard-called imputed data [47] . Meta-analysis of cohorts with non-overlapping genotype data requires imputed data . However , with currently available algorithms for incorporating imputation uncertainty in interaction tests , this would substantially compromise computational efficiency . Similarly , in the present implementation it is not obvious how to include covariates in the analysis . A solution that we are currently working on could be to use the GLM weights to model both imputation uncertainty and covariate adjustment . Ideally , this will retain the computational efficiency at the cost of statistical efficiency . However , for the time being , the simplest solution to the covariate issue is a two-step strategy , which , a posteriori to the initial analysis , fits a standard GLM including relevant covariates for the identified interactions and checks that the results still hold ( this approach was used in the Lp ( a ) replication analysis ) . If this causes too many pairs to be identified , an alternative that is commonly applied in univariate meta-analyses is to first regress out relevant covariates and then model the residuals . Because residuals are expected to be normally distributed for all GLM models this can be generally applied; however , some interpretation is lost .
We first considered two scenarios that may produce false positives: submodels in which one or more assumptions fails to hold , and tests where the incorrect link function is used . In both experiments , we evaluated the following five different tests: the G × G joint , AD × AD joint , G × G separate , A × A and R/D × R/D tests . In the first experiment we investigated FPR where the data violates two model assumptions , erroneous parameterization and presence of linkage disequilibrium ( LD ) . LD was measured by Lewontin’s D ( LD ) and we used two cases , high linkage ( LD = 0 ) and low linkage ( LD = 0 . 8 ) . The genotypes were generated according to the following probability distribution p 00 2 2 p 00 p 01 p 01 2 2 p 00 p 10 2 p 00 p 11 + 2 p 01 p 10 2 p 01 p 11 p 10 2 2 p 10 p 11 p 11 2 ( 4 ) where p 00 p 01 p 10 p 11 = ( 1 - r ) ( 1 - q ) + D r ( 1 - q ) - D ( 1 - r ) q - D r q + D ( 5 ) and q is the minor allele frequency of the first variant , and r is the minor allele frequency of the second variant . The parameter D = LD ⋅ Dmin where Dmin = min ( 1 − qr , 1 − ( 1 − q ) ( 1 − r ) , q ( 1 − r ) , ( 1 − q ) r ) . The minor allele frequency was set to q = r = 0 . 3 and the sample size to 4 , 000 . For each genotype , continuous phenotypes were then generated using six different null GLMs , obtained as the combinations of ( i ) a linear predictor from either of the A × A , R × A and R × D parameterizations , ( ii ) the identity link function , and ( iii ) either a Binomial and normal dispersal distribution ( parameters can be seen in S5 Table ) . For each parameter combination we generated 1 , 000 data sets and estimated the false positive rate as the average number of incorrectly identified pairs . In the second experiment , we investigated the false positive rate when the link function is misspecified . Specifically , we generated the phenotype using a log link function , while the tests were performed using the identity link function . The genotypes were generated under Hardy-Weinberg equilibrium with a minor allele frequency of 0 . 3 for both variants . We used a GLM with the A × A linear predictor , the identity link function and a normal dispersion distribution to generate phenotype from each genotype; we varied the size of the second variant’s main effect between −0 . 4 and 0 . 4 and the sample size over 2000 , 3000 and 4000 ( parameters are shown in S6 Table ) . For each parameter combination , we generated 1000 data sets and estimated the false positive rate as described above . We performed two different simulation experiments for statistical power; one smaller comprising a set of specific GLMs and one larger comprising a wide range of interaction models . In both experiments we generated a continuous phenotype from a Normal dispersion distribution using the identity link function . We evaluated the same tests as in the FPR experiments , described above . In the small power experiment we generated data from six different parameterizations , A × A , A × Afailed , R × D , D × D , H × H , and D × Dfailed . The A × Afailed and D × Dfailed are AD × AD models designed to violate the assumptions of the A × A and D × D , respectively , that is , in the A × Afailed , the values of the interaction parameters are switched with respect to those in a A × A , while in the D × Dfailed , half of the interaction parameters are in the opposite and wrong direction compared to D × D . We generated genotypes for 4000 individuals under Hardy-Weinberg equilibrium with a minor allele frequency 0 . 3 for both variants . For each genotype , we then generated phenotypes from each GLM . We varied the effect size between -1 . 0 to 1 . 0; depending on the selected parameterization , this required modification either of a single or multiple parameters in β ( parameters can be seen in S7 Table ) . For each effect size , we generated 1000 data sets and estimated the statistical power of each test as the average number of correctly identified pairs under Bonferroni correction assuming 1012 variant pairs . In the large power experiment , we focused on different G × G parameterizations from a range of parameter combinations that can be found in S8 Table—notice that , since this is a saturated full parameterization , this approach will cover a large number of other , unsaturated and saturated , constrained and full , parameterizations . The genotypes were generated under Hardy-Weinberg equilibrium and we varied the allele frequency between 0 . 2 , 0 . 3 and 0 . 4 ( for both variants ) , and the sample size between 2000 , 3000 and 4000 . For each genotype a phenotype was generated using a linear predictor from the G × G parameterizations described above , the identity link function and the Normal dispersion distribution . For each parameter combination we generated 1000 data sets and estimated the statistical power as described above ( using a Bonferroni correction assuming 1012 variant pairs ) . The PROCARDIS study was approved by the Regional Ethics Review Board at Karolinska Institutet , Stockholm in Sweden ( approval number 98-482 and 03-491 ) and by the Institutional Review Boards of the Mario Negri Institute , Milano in Italy , the University of Munster , Munster , in Germany , and the University of Oxford , Oxford , United Kingdom . The PROCARDIS study was supplemented with controls from the WTCCC study , UK . The WTCCC was approved by the relevant research ethics committees . All study participants provided their written informed consent to participate in the study , which was conducted in accordance with the Helsinki Declaration . The SCARF and SHEEP studies were approved by the local ethics committees at Karolinska Hospital , Stockholm ( approval number 95-397 and 02-091 ) , and Karolinska Insitutet , Stockholm ( approval number 01-097 ) , respectively . The MIGEN data was accessed from dbGAP ( access . nr . phs000294 . v1 . p1 ) [45]; all participants in the MIGEN studies gave written informed consent in accordance with the guidelines of local ethical committees . The PROCARDIS multicentre study was designed to investigate early onset CAD . Cases with a documented CAD event before the age of 66 years were collected from Sweden , the UK , Germany and Italy . The full PROCARDIS cohort comprise 8410 cases and 5188 matched controls free from CAD . Here we have used a subset of the PROCARDIS cohort previously genotyped with the Illumina Human1M Quad chip and the Illumina Human610K chip [33] . We only included unrelated individuals genotyped on these chips . The intersection of these chips contain 566 , 865 variants . The subset of PROCARDIS used in this study depends on the phenotype . Lp ( a ) plasma levels were measured in 3 , 741 individuals . For the meta-analysis aimed at MI , the PROCARDIS cohort was extended with 5 , 667 control samples from the Wellcome Trust Consortium ( WTCCC ) [44] . This resulted in a total of 2 , 809 cases and 7 , 330 controls for the MI disease phenotype . The SCARF [35] and SHEEP [34] cohorts included unrelated MI cases from the Stockholm region of Sweden , with age and sex-matched controls collected from the general population of the same region . The comparable design and demographics of the 2 cohorts means that they can be combined as one cohort . The SCARF-SHEEP cohort was previously genotyped with the CardioMetabo chip , a custom Illumina iSelect genotyping array that targets genetic variants likely to be involved in metabolic and cardiovascular disorders [48] . The chip contains 116 , 540 variants . Lp ( a ) levels were measured in 2 , 345 individuals . The MIGEN cohort is a case-control study aimed at investigating the genetic basis of MI [45] . The cohort contains samples from 6 collection sites: Boston , MA ( Masschusetts General Hospital Premature Coronary Artery Disease Study ) , Seattle , WA ( Heart Attack Risk in Puget Sound ) , Helsinki , Finland ( FINRISK ) , Malmö , Sweden ( Malmö Diet and Cancer Study ) , Barcelona , Spain ( REGICOR ) , and Milan , Italy ( Italian Atherosclerosis Thrombosis and Vascular Biology Working Group ) . The MIGEN cohort was previously genotyped with the Affymetrix Genome-wide Human SNP Array 6 . 0 . The data was approved by and downloaded from dbGAP with accession phs000294 . v1 . p1 . This data contains 3 , 068 cases and 2 , 957 controls . We performed multiple experiments on biological data: exhaustive interaction scan in Lp ( a ) , vGWAS scan in Lp ( a ) , and a exhaustive meta analysis of CAD . All interaction tests was performed using the G × G joint test . In the discovery phase , a canonical link function and the appropriate dispersion distribution was used in an initial analysis . The significant pairs was then tested using the same G × G joint test , but using the link family test to step through a family of link functions; the latter approach was also used in the replication phase . For the continuous Lp ( a ) phenotype , the Normal dispersion distribution was used first with the canonical identity link and then with the symmetric Box-Cox family . For the binary MI case-control data , the Binomial dispersion distribution was used first with the logit link and then with Pregibon link family . The Lp ( a ) phenotype was log-transformed and outliers were removed in both PROCARDIS and SCARF-SHEEP . For the exhaustive interaction scan we applied the following QC filters: minor allele frequency 0 . 05 , genotyping rate 0 . 1 . We then removed variants in linkage disequilibrium using the Plink “–indep-pairwise” option , using a window size of 100 , step size of 5 and a LD threshold of r2 < 0 . 5 ( we additionally check D′ for any significant interaction discoveries ) . This resulted in 180 , 947 variants , and we tested for interaction between all possible pairs of these . For the vGWAS scan , we applied the same QC filters as above; the resulting set of SNPs is called L . We then tested for variance heterogeneity of each variant with a Brown-Forsythe test [37] . The set of variants with p-value less than 10−4 is called V . We constructed all pairs of one variant from V and one variant from L ( 1 . 26 ⋅ 107 pairs ) and tested these for interaction . In the meta-analysis , we only considered variants that was genotyped both in MIGEN and PROCARDIS , this resulted in 132 , 181 variants . We applied the following QC filters: minor allele frequency > 0 . 05 , genotyping rate > 0 . 1 . We only tested pairs for which all genotypes had at least five samples in both cases and controls ( 8 . 17 ⋅ 109 pairs ) . For each analyzed cohort and phenotype combination we checked for potential p-value inflation ( genomic inflation ) due to population stratification . In all cases genomic inflation was low ( see S4 , S5 , S6 and S7 Figs ) . The QQ-plots for the two Lp ( a ) cohorts ( S6 and S7 Figs ) showed a surprisingly large deviatin from the diagonal line , expected under the null mode . After investigation , it became clear that this inflation was due to substantial LD at the previously identified strongly associated Lp ( a ) locus on chromosome 6 [29] . A c++ implementation of the software can be accessed at https://github . com/mfranberg/besiq . | Interaction between organic molecules forms the basis of all biological systems . The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals . For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases . However , there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction . In this work we provide theory that reduces the required computational resources , and enable multiple research groups to effectively combine their results . | [
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| 2017 | Fast and general tests of genetic interaction for genome-wide association studies |
Epstein-Barr virus ( EBV ) is a human cancer-related virus closely associated with lymphoid and epithelial malignancies , and EBV glycoprotein B ( gB ) plays an essential role in viral entry into both B cells and epithelial cells by promoting cell-cell fusion . EBV gB is exclusively modified with high-mannose-linked N-glycans and primarily localizes to the endoplasmic reticulum ( ER ) with low levels on the plasma membrane ( PM ) . However , the mechanism through which gB is regulated within host cells is largely unknown . Here , we report the identification of F-box only protein 2 ( FBXO2 ) , an SCF ubiquitin ligase substrate adaptor that preferentially binds high-mannose glycans and attenuates EBV infectivity by targeting N-glycosylated gB for degradation . gB possesses seven N-glycosylation sites , and FBXO2 directly binds to these high-mannose moieties through its sugar-binding domain . The interaction promotes the degradation of glycosylated gB via the ubiquitin-proteasome pathway . Depletion of FBXO2 not only stabilizes gB but also promotes its transport from the ER to the PM , resulting in enhanced membrane fusion and viral entry . FBXO2 is expressed in epithelial cells but not B cells , and EBV infection up-regulates FBXO2 levels . In summary , our findings highlight the significance of high-mannose modification of gB and reveal a novel host defense mechanism involving glycoprotein homeostasis regulation .
Epstein-Barr virus ( EBV ) belongs to the γ-herpesvirus family , and more than 90% of individuals worldwide are asymptomatically infected with EBV . As the first discovered tumor virus , EBV has been identified in Burkitt’s lymphoma , Hodgkin’s disease , a subset of gastric cancers and nearly all non-keratinizing nasopharyngeal carcinomas ( NPCs ) , the dominant histological subtype of NPC in endemic regions , including southern China and Southeast Asia [1–3] . EBV is only capable of infecting human B lymphocytes and epithelia . EBV adopts two lifestyles: latent and lytic phases . The current model of the EBV lifecycle proposes that EBV is transmitted in saliva and crosses the oral mucosal epithelium to infect B lymphocytes , where it establishes a lifelong latent infection . EBV then occasionally switches to a lytic replication phase in epithelial cells for shedding virions to saliva or for infection of more B cells to replenish the virus reservoir [4] . The entry of EBV into host cells is a complex process mediated by multiple viral envelope glycoproteins . Glycoprotein B ( gB ) , gH/gL , gp42 and gp350 are required for EBV entry . gp350 and gp42 determine cell tropism and function by receptor binding [5 , 6] . gB , gH and gL form the core viral fusogen complex and are essential for entry into all cell types . Besides , binding of gH/gL to integrin and ephrin receptor A2 has been proposed to be necessary for the infection of epithelial cells [7–9] . Viral fusogens mediate the fusion of the viral envelope and host membrane during entry and egress . It has been suggested that gB catalyzes fusion itself , whereas the heterodimer gH/gL plays a regulatory role . gB/gp110 , encoded by the BALF4 ORF in EBV , is expressed during the lytic phase [10] . gB is a type I single-pass membrane protein that exists as a trimer . It harbors a large N-terminal ectodomain , a transmembrane domain and a short C-terminal tail . Unlike gp350 , gH/gL and gp42 , which attach to host cells by binding to their respective receptors , gB exhibits inherent fusogenic properties . Structurally , herpesvirus gB adopts a similar “hairpin” conformation , including a trimeric fold and bipartite fusion loop [11] , which led to the classification of herpesvirus gB as a class III viral fusogen [12] . Based on the available post-fusion crystal structure of EBV gB and the pre- and post-fusion conformations of herpes simplex virus type 1 ( HSV-1 ) gB , it is proposed that gB undergoes dramatic prefusion to post-fusion conformation changes to insert fusion loops into target cell membranes and drive membrane fusion [13–16] . Despite the high conservation and structural similarities among herpesvirus gB [14 , 16] , EBV gB exhibits some unique properties . For example , gB of α-herpesviruses , such as HSV-1 and HSV-2 gB , are very abundant envelope proteins on virions [17 , 18] . In contrast , EBV gB is predominantly localized in the endoplasmic reticulum ( ER ) [19] and exhibits low levels of cell surface expression and virion incorporation , therefore the virion abundance of gB is an important virulence factor for EBV infection [20] . The difference in subcellular distribution reflects the different glycan types on these gBs . Viral envelope glycoproteins are processed in the secretory compartment of host cells , where they are decorated with various types of oligosaccharides . In the ER , the protein is modified with high-mannose oligosaccharides consisting of Man5-9GlcNAc2 structures on an Asn residue; once the proteins traffic to the Golgi , high-mannose glycans are further modified by the addition of various sugar residues to form hybrid and complex N-glycans [21] . Therefore , the ER-retained EBV gB is only modified with high-mannose N-linked oligosaccharides [10 , 22–25] , whereas HSV gBs are enriched in complex N-glycans [17 , 18] . Because viral fusion proteins usually exist on the viral membrane surface to promote membrane merging , how host cells limit the processing and maturation of gB remain elusive . In this study , we report the identification of FBXO2/Fbx2/Fbs1/OCP1 , a glycan-dependent E3 ubiquitin ligase component recognizing N-linked high-mannose oligosaccharides [26–30] , as a novel EBV gB-interacting protein that controls the level and membrane transportation of gB in epithelial cells to decrease the infectivity of progeny viruses .
To better understand the interplay between EBV gB and host proteins , tandem affinity purification ( TAP ) [31] of gB was performed in human cells . Following two-step purification of triple-tagged ( S-Flag-Streptavidin-binding peptide; SFB ) gB in HEK293T cells and two NPC cell lines , CNE2 and HK1 , proteins associated with gB were identified by mass spectrometry ( MS ) analysis ( Fig 1A ) . Interestingly , a significant number of peptides corresponding to an F-box protein named FBXO2 , also known as FBS1/FBX2/NFB42/OCP1 , were repeatedly identified in the TAP-MS data of the three cell lines stably expressing gB . FBXO2 is the substrate recognition component of the SCF ( SKP1/Cul1/F-box protein ) E3 ubiquitin ligase complex , and accordingly , SKP1 and Cullin-1 were also identified in gB immunocomplexes ( Fig 1B ) ( see S1–S3 Tables for the complete TAP-MS data ) . The association with SCFFBXO2 seems to be specific to gB because TAP of other EBV glycoproteins , such as gp350 , gH/gL and gp42 , did not identify SCFFBXO2 polypeptides but did capture their respective receptors , including CR2 , integrin and MHC-II ( S1A Fig ) . Thus , our TAP approach was applicable for studying the host proteins interacting with EBV glycoproteins . To verify the association between gB and FBXO2 , we precipitated the ectopically expressed gB , gp350 and gH/gL from the transfected HEK293T cells and examined the presence of FBXO2 in the viral envelope protein immunocomplex . We found that only gB , but not gp350 and gH/gL , strongly interacts with FBXO2 ( Fig 1C ) . Furthermore , reciprocal immunoprecipitation ( IP ) also confirmed the specific binding of gB to FBXO2 , whereas another F-box protein , FBXO3 , was unable to bind gB ( S1B and S1C Fig ) . Next , we sought to determine whether the interaction is direct . A GST pull-down experiment was performed using recombinant GST-FBXO2 or GST protein derived from E . coli and gB protein derived from mammalian cells , and the data revealed a strong interaction between gB and FBXO2 ( Fig 1D ) . As a substrate adaptor in the SCF complex , FBXO2 binds to SKP1 via an F-box domain and binds to substrates via the C-terminal substrate-binding domain , which is also termed the sugar-binding domain ( SBD ) because it recognizes sugar moieties on substrates [30] . To determine the region responsible for gB binding , two FBXO2 truncation mutants , FBXO2-N , which contains the PEST and F-box domains , and FBXO2-C which harbors the SBD domain , were generated ( Fig 1E ) . Co-IP experiments demonstrated that gB only precipitated full-length FBXO2 and FBXO2 SBD but not FBXO2-N ( Fig 1F ) , and reciprocal co-IP obtained similar results ( Fig 1G ) . These data suggest that gB may represent a potential substrate of SCFFBXO2 . FBXO2 was originally described as a brain-specific F-box protein [32–34] and has also been identified in cochlear cells [35]; accordingly , FBXO2-knockout mice develop age-related hearing loss [36] . Recently , FBXO2 was reported to be up-regulated in the livers of obese mice , and the insulin receptor was identified as a substrate of FBXO2 [37] . Thus , whether FBXO2 is expressed in EBV host cells , including epithelial cells of the nasopharynx , oral cavity and stomach , and B lymphocytes , needs to be determined . Interestingly , cells originating from the nasopharynx epithelium , including six NPC cell lines , two primary NPC cell lines , and two immortalized nasopharyngeal epithelial ( NPE ) cell lines , all expressed considerable amounts of FBXO2 , with the exception of HK1 , which is the only well-differentiated squamous carcinoma cell line and is less representative for NPC [38] . Besides , FBXO2 was highly expressed in oral cancer cell lines but absent in normal oral keratinocytes ( NOK ) . In contrast , FBXO2 was undetectable in four gastric cancer cell lines we examined , including EBV-positive AGS cell line , it might because of the different cancer types , as most gastric tumors are adenocarcinoma , while more than 90% of all oral cancers are squamous cell carcinoma , and the majority of NPCs are the undifferentiated carcinoma . On the other side , none of the B cell lines examined expressed FBXO2 , including the EBV-negative non-Hodgkin's lymphoma B cell lines DoHH2 and SU-DHL-2 , the EBV-positive Burkitt's lymphoma ( BL ) cell lines Raji and Akata and an EBV-negative Akata cell line , and the induction of EBV into lytic replication by IgG crosslinking did not induce FBXO2 expression in Akata-EBV+ cells ( Fig 2A ) . We next examined the FBXO2 levels in paired EBV-negative and EBV-positive NPC cell lines . Two typical NPC cell lines , CNE2 and HNE1 , were infected with recombinant EBV produced in Akata cells . C666-1 , the only native EBV-infected NPC cell line , was subjected to EBV genome destruction by CRISPR/Cas9-mediated EBNA-1 deletion as described previously [39] . Intriguingly , EBV infection profoundly increased FBXO2 protein levels in the three pairs ( Fig 2B , left ) . Similarly , activating EBV production by transfecting Zta into HEK293 producer cells carrying a recombinant M81/ΔZta episome [40] substantially up-regulated FBXO2 expression ( Fig 2B , right ) . Quantitative PCR revealed that all these EBV-positive cells had higher FBXO2 mRNA levels than their virus-free counterparts ( Fig 2C ) , suggesting that EBV infection stimulates FBXO2 transcription in NPC and HEK293 cells . Furthermore , immunohistochemistry ( IHC ) of xenograft tumors derived from EBV-infected and uninfected NPC cells also support the positive correlation between EBV status and FBXO2 expression ( Fig 2D ) . FBXO2 specifically binds high-mannose oligosaccharides through its SBD [30] , and EBV gB has been characterized as a high-mannose-containing glycoprotein [10 , 22 , 25] . The EBV gB gene encodes a mature protein of 836 amino acids , equivalent to a molecular weight of 93 kDa . Its apparent molecular mass is 110 kDa . Therefore , gB has also been designated gp110 . We first characterized the gB glycan type by biochemical analysis . Addition of an SFB tag ( 25 kDa ) resulted in a 135-kDa recombinant protein . Digestion of the SFB-tagged gB with endoglycosidase H ( Endo H ) , a glycosidase that cleaves only N-linked sugars containing more than three mannose moieties , removed a glycan mass of approximately 15 kDa from gB . Treatment with peptide N-glycosidase F ( PNGase F ) , which cleaves all types of N-glycans , or a deglycosylation mix that cleaves both N- and O-linked glycans did not cause a further reduction in molecular weight over that induced by Endo H , indicating a lack of complex N-linked and O-linked oligosaccharides on gB ( Fig 3A ) , consistent with previous reports [10 , 22] . We then treated gB-expressing cells with tunicamycin , a GlcNAc transferase inhibitor that blocks the first step of N-linked glycosylation . Western blotting analysis revealed that tunicamycin treatment resulted in a complete loss of glycosylated gB , as demonstrated by the reduction in molecular weight and abolishment of Concanavalin A ( Con A ) agarose binding . Con A is an α-mannose/α-glucose-binding lectin that binds high-mannose and hybrid N-glycans , but not complex N-glycans [41] ( Fig 3B ) . Therefore , we used tunicamycin to obtain de-glycosylated gB in cells . Co-IP experiments suggested that only fully glycosylated gB , but not de-glycosylated gB , associated with FBXO2 ( Fig 3C ) , and vice versa ( Fig 3D ) . Furthermore , GST-FBXO2 captured most gB from the lysates of gB-expressing cells , and when the flow-through ( F-T ) was subjected to a second round of pull-down by Con A agarose , less gB was obtained and migrated slower than that enriched by FBXO2 ( Fig 3E ) , suggesting that in addition to high-mannose glycans , hybrid N-glycans are also present on EBV gB , but to a much lesser extent . These results also indicated that FBXO2 exhibits a narrower binding range than Con A for N-glycans . We next aimed to identify the N-linked glycosylation sites on gB . EBV gB has nine putative N-glycosylation sites ( Asn-X-Ser/Thr; X denotes any amino acid except proline ) . To determine the glycosylation sites on gB , we purified ectopic gB from HEK293T cells and performed N-glycan MS analysis ( Fig 3E ) . MS identified five glycosylation sites on gB at N76 , N290 , N348 , N395 and N436 ( S2 Fig ) . In addition , previous structural analysis of gB revealed that N-acetyl glucosamine ( NAG ) molecules could be found at positions N163 , N290 and N629 [14]; thus , N163 and N629 were also included in our study ( Fig 3F ) . According to the crystal structure of the EBV gB ectodomain [14] , we modeled the locations of N-glycans . N163 and N290 lie in domain I , N76 and N348 lie in domain II , and N629 is located in domain V , and these glycan sites are distributed on the surface of the gB protein ( Fig 3G ) . The seven glycan sites are conserved among different EBV strains ( S3 Fig ) but most of them are not present in other herpesviruses ( S4 Fig ) . We next constructed seven N-glycosylation-site-defective mutants by substituting Asn with Gln , and named them N76Q , N163Q , N290Q , N348Q , N395Q , N436Q and N629Q . However , these single-site mutations did not cause appreciable changes in the molecular weight or Con A binding of gB ( Fig 3H ) , nor did they impede its interaction with FBXO2 ( S5A Fig ) , indicating that gB is glycosylated at multiple sites . To fully characterize the glycosylation sites , seven Asn residues were mutated to Gln sequentially from the N-terminus to the C-terminus of the gB ectodomain; these mutants were designated by the number of Asn residues mutated , from 1NQ to 7NQ ( Fig 3F ) . The electrophoretic migration patterns of the single to seven point mutants showed that the migration rate gradually increased as more Asn residues were mutated; accordingly , their binding to Con A agarose was gradually reduced , suggesting that all seven Asn residues were glycosylated in vivo ( Fig 3I ) . We next examined the interaction of FBXO2 with these glycosylation-defective mutants . Co-IP results suggested that mutation of the first two Asn residues ( N76 and N163 ) began to reduce the gB-FBXO2 association , and mutations of three or more Asn residues largely abolished their interactions ( Fig 3J and 3K ) , suggesting that FBXO2 association is dependent on the number of mannose moieties on gB . In summary , we identified the glycosylation sites on gB and revealed a glycosylation-dependent interaction between FBXO2 and gB . Given the known substrate specificity of FBXO2 for high-mannose-containing glycoproteins [26–28 , 30] and the high-mannose modification of gB , we hypothesized that gB is recognized and ubiquitinated by SCFFBXO2 for proteasome-mediated degradation . To test this idea , we examined the protein level of gB in the presence or absence of FBXO2 . However , overexpression of FBXO2 did not affect the total level of gB ( Fig 4A , left panel ) . Considering that only a portion of gB molecules with high-mannose modification associated with FBXO2 ( Fig 3E ) , we examined the level of glycosylated gB when FBXO2 was overexpressed . Con A agarose pull-down showed a dramatic reduction in bound gB when FBXO2 was present , while the two truncation mutants of FBXO2 had no effect , although the C-terminal SBD of FBXO2 bound to Con A at a rate comparable to that of the full-length protein ( Fig 4A , right panel ) . By contrast , FBXO2 did not suppress the association of the glycosylation-defective 7NQ gB mutant with Con A agarose , although 7NQ exhibited low affinity for Con A agarose ( Fig 4B ) . Conversely , depleting FBXO2 with three independent siRNAs greatly increased the level of glycosylated gB ( Fig 4C ) , indicating that FBXO2 specifically degrades glycosylated gB . To gain insight into the mechanism of gB degradation , we treated cells stably expressing gB with the proteasome inhibitor MG132 or the lysosome inhibitor bafilomycin A1 . Con A pull-down suggested that only proteasome inhibition prevents FBXO2-mediated degradation of glycosylated gB ( Fig 4D ) . To further confirm the involvement of the ubiquitin-proteasome system ( UPS ) in FBXO2-mediated gB degradation , HEK293T cells were transfected with constructs encoding His6-tagged ubiquitin ( Ub ) , FBXO2 and gB and subjected to an in vivo ubiquitination assay under denaturing conditions . As expected , FBXO2 overexpression resulted in the enrichment of a high-molecular-weight , ubiquitinated gB species by nickel column pull-down , and FBXO2 auto-ubiquitination was also readily detectable ( Fig 4E ) , These data suggested that FBXO2 promotes gB ubiquitination in vivo . Furthermore , we examined the half-life of glycosylated gB in the presence or absence of FBXO2 using a cycloheximide ( CHX ) chase assay . Glycosylated gB enriched by Con A agarose was stable up to 12 hours , whereas FBXO2 overexpression reduced the half-life of glycosylated gB to approximately 4 hours ( Fig 4F ) . These data indicate that FBXO2 targets glycosylated gB for ubiquitin-mediated degradation . Having demonstrated that FBXO2 is the E3 ligase for EBV gB , we further investigated whether FBXO2 affects gB intracellular trafficking . Consistent with previous reports , EBV gB is retained in the ER and nuclear envelope and barely detectable on the plasma membrane ( PM ) [19 , 25] in both NPC and oral cancer cell lines ( S5B and S5C Fig ) , while FBXO2 is a cytoplasmic protein ( S5B and S5D Fig ) . Strikingly , upon depletion of FBXO2 by siRNA , the majority of gB translocated to the PM of cells ( Fig 5A ) . The data indicate that loss of FBXO2 promotes gB translocation from the ER to the cell surface . gB plays an essential role in virus entry by promoting cell-cell fusion . We took advantage of an established virus-free cell-based quantitative fusion assay [42] to determine the role of FBXO2 in gB fusogenic function . In accordance with the membrane transportation of gB by FBXO2 loss , depletion of FBXO2 increased the fusion activity of gB to 1 . 5-fold of that observed in cells transfected with control siRNA ( Fig 5B ) . Next , we investigated the effect of FBXO2 on EBV entry . Using the established EBV producer cell line CNE2 carrying EBV-GFP , we produced EBV virions to infect the B cell lines Raji and Akata . Flow cytometry revealed that EBV-GFP virions originating from FBXO2-depleted CNE2 cells were more infectious than those originating from control cells ( Fig 5C and 5D ) . To exclude the possibility that the enhanced infectivity induced by FBXO2 depletion was due to increased viral production , we examined lytic cycle induction and EBV copy number in EBV producer cells with or without FBXO2 knockdown , and the data suggested that FBXO2 had little effect on EBV production ( S5E and S5F Fig ) . Collectively , these data suggest that loss of FBXO2 promotes gB maturation to enhance membrane fusion and viral entry .
In this study , we identified SCFFBXO2 as an E3 ubiquitin ligase targeting EBV envelope protein gB . In the SCF complex , adaptors are necessary to achieve substrate specificity . FBXO2 is unique among ~70 F-box proteins encoded by the human genome in its recognition for high-mannose glycans [26–30] , and it has been reported to mediate the ubiquitination and degradation of various cellular glycoproteins [32–34 , 43–45] . High-mannose glycans contain unsubstituted terminal mannose sugars and have been regarded as incomplete products of the N-glycosylation pathway; they are evolutionarily older than complex N-glycans and are the typical oligosaccharides in yeast and other fungi . In mammalian cells , most glycans will be further modified in the Golgi by addition of various sugar residues , gaining complexity in sugar types and branching structure [41] . Structural studies of FBXO2 and glycan profiling indicate that FBXO2 binds Man3GlcNAc2 , the common core pentasaccharide in N-linked glycans , and adding mannose residues did not affect the affinity [28–30] . Man3GlcNAc2 is the innermost moiety , which is difficult to reach in hybrid and complex N-glycans under native conditions; therefore , SCFFBXO2 is thought to degrade denatured N-linked glycoproteins nonspecifically . EBV gB is a unique viral glycoprotein in that its N-glycan modification is predominantly of the high-mannose type , regardless of the source of gB ( virion or expressed in mammalian cells ) [10 , 19 , 22 , 25] . All EBV gB glycans are sensitive to Endo H ( Fig 3A ) . N-glycan MS analysis and mutagenesis studies identified seven glycan sites on gB , and mutation of each site led to similar small reductions in molecular weight; thus , each site has a set of homogeneous high-mannose-type oligosaccharides that exactly match the structural requirement of FBXO2 . The exclusively mannose-terminated glycosylation pattern explains the substrate specificity of FBXO2 for gB , but not for gp350 and gH/gL ( Fig 1C and S1 Fig ) , as gp350 and gH/gL harbor heterogeneous glycans [19] , and sugar chains terminated with non-mannose glycans could not be reached by FBXO2 under native condition . Therefore , SCFFBXO2 acts as a specific E3 ligase for gB because of its unique sugar chain type . High-mannose N-glycans are a signature of proteins in the ER . In accordance with its sugar chain type , EBV gB is primarily located at the ER [19 , 25] . We did observe a predominant ER localization of gB in epithelial cells ( Fig 5A and S5B–S5D Fig ) ; in contrast , depletion of FBXO2 substantially changes the subcellular distribution of gB from the ER to the PM ( Fig 5A ) , suggesting that FBXO2 is a rate-limiting factor for the processing and maturation of gB in host epithelial cells . Because viral fusion proteins usually exist on the viral membrane surface to promote membrane merging , it is not surprising that the loss of FBXO2 promotes cell fusion and viral entry . Spatially , FBXO2 distributes at the cytoplasm , whereas gB normally resides in the ER as determined by immunofluorescence imaging and subcellular fractionation ( S5B and S5D Fig ) . As a type I membrane protein , the N-terminal ectodomain of gB faces the lumen of the ER , where all the glycosylation residues located; moreover , the 26 S proteasomes are present in the cytoplasm but absent in organelles such as ER . In this regard , FBXO2 is highly likely to degrade gB via ER-associated degradation ( ERAD ) , and the degradation of gB occurs in the ER via retrotranslocation . Taking together with the PM transportation of gB by depletion of FBXO2 , it is conceivable that gB is retained in the ER and undergoes rapid equilibration between folding and unfolding , with the unfolded gB retrotransported to the cytoplasm to be degraded by SCFFBXO2 . Depletion of FBXO2 shifts the equilibrium to refolding , and as a result , most gB is folded correctly and then translocated from the ER to the cell surface . Therefore , FBXO2 acts as a host restriction factor for gB maturation . gB exists in almost all herpesviruses . gBs of other γ-herpesviruses , such as murine gamma herpesvirus 68 ( MHV-68 ) , have also been demonstrated to be high-mannose N-linked glycoproteins retained in the ER [46] . In addition , β-herpesvirus cytomegalovirus ( HCMV ) is also exclusively modified with high-mannose N-glycans [47] . By contrast , gB of α-herpesviruses , such as HSV-1 and HSV-2 , are only partially susceptible to Endo H treatment and are transported through the Golgi to the membrane [17 , 18] . Sequence alignment indicates that although EBV gB shares homology with herpesvirus gB homologs , most glycan sites on EBV gB are not conserved in other herpesviruses; even in γ-herpesviruses , only two glycan sites ( N348 and N629 ) are conserved ( S4 Fig ) . It remains to be determined whether other β- and γ-herpesviruses gBs are also regulated by the SCFFBXO2 E3 ligase in their host cells . We demonstrated that the degradation of gB by SCFFBXO2 in epithelial cells impedes entry of the progeny virus into B cells . However , as gB is more essential for epithelial cell penetration than for B cell entry [20] , we could not exclude the possibility that FBXO2 also negatively regulates the spreading of EBV to surrounding epithelial cells . We are currently unable to address this issue because of the very low efficiency of EBV re-entry into epithelial cells , which is in part due to the high levels of gp42 on EBV virions originating in epithelial cells [48 , 49] . The significance of epithelial-specific expression and EBV-induction of FBXO2 is an unanswered question . Similar to other envelope proteins , gB is only expressed in the lytic cycle to release infectious viral particles . EBV replicates poorly in B cells and only occasionally reactivates from latency; in contrast , human pharyngeal epithelial cells are believed to be reservoirs of lytic EBV , in which lytic replication directly follows viral entry . In this regard , anti-EBV lytic infection mechanisms appear to be more important for oral and nasopharyngeal epithelial cells than for B cells . We demonstrate that EBV infection stimulates FBXO2 expression in NPC cells , however the scenario did not happen in the BL cell lines and gastric carcinoma cell line AGS ( Fig 2 ) , the underlying mechanism of FBXO2 regulation by EBV is still elusive . In turn , FBXO2 degrades the newly synthesized gB , which forms a negative feedback loop to attenuate the infectivity of progeny viruses ( Fig 6 ) . To our knowledge , this is one of very few examples of how epithelial cells defend against EBV invasion . This finding might help gain a deeper understanding of the interplay between host and virus .
Raji is an EBV-positive BL cell line , and Akata-EBV is an Akata BL cell line carrying the Akata bacterial artificial chromosome ( BAC ) ; both were cultured in RPMI 1640 medium with 10% fetal bovine serum ( FBS ) . The NPC cell lines CNE1 , CNE2 , HNE1 , HONE1 , HK1 , SUNE1 and C666-1 , oral cancer cell lines SCC-15 , SCC-25 , UM1 , TSCCa , Tca8113 and CAL 27 , and gastric cancer cell lines AGS , AGS-EBV , MGC803 and MKN-45 were cultured in Dulbecco’s modified Eagle medium ( Gibco ) with 10% FBS . Normal oral keratinocyte NOK , immortalized nasopharyngeal epithelium cell lines NPEC2-Tert and NPEC5-Tert were cultured in keratinocyte serum-free growth medium supplemented with 5 μg/L EGF and 50 mg/L Bovine Pituitary Extract ( Gibco ) . EBV-positive CNE2 and HNE1 cell lines are parental cell lines carrying Akata-EBV-GFP and were cultured in the presence of G418 ( 500 μg/mL ) . NOK and HEK293T were from ATCC; oral cancer cell lines , gastric cancer cell lines , NPE and NPC cell lines were provided by Prof . Mu-Sheng Zeng ( Sun Yat-sen University Cancer Center ) ; the BL cell lines were provided by Prof . Wenqi Jiang ( Sun Yat-sen University Cancer Center ) ; HEK293-M81 cell line carrying the EBV M81 strain was kindly provided by Prof . Dong-Yan Jin ( The University of Hong Kong ) . Two primary NPC cell lines were isolated from nasopharynx biopsy samples from two male Chinese NPC patients . Briefly , tumor samples were placed in HBSS buffer and minced with sterile scissors . The minced tumor samples were then digested with 1 mg/mL Collagenase I for 15 min at 37°C and cultured in keratinocyte serum-free growth medium supplemented with EGF and Bovine Pituitary Extract . To generate stable gB-expressing cell lines , gB was cloned into the lentiviral vector pDEST-C-SFB and packaged into lentiviruses by co-transfection with the packaging plasmids pMD2G and pSPAX2 in HEK293T cells . Forty-eight hours after transfection , the supernatant was collected and used for infection . Stable pools were selected with medium containing 2 μg/mL puromycin . All cDNAs encoding EBV proteins were amplified from the M81 bacmid . cDNA fragments were subcloned into pDONR201 ( Invitrogen ) entry vectors and subsequently transferred to gateway-compatible destination vectors . pDEST-C-SFB was used for C-terminal tagging with SFB ( S- , Flag and streptavidin-binding tag; gift from Prof . Junjie Chen ) , and pDEST-Myc was used for N-terminal tagging with Myc . N-to-Q point mutants were generated by site-directed mutagenesis PCR with Primerstar GXL ( Takara ) . FBXO2 was cloned into pGEX-4T1 for prokaryotic expression and the in vitro binding assay . All constructs were verified by sequencing . HEK293T , CNE2 and HNE1 cells stably expressing gB-SFB were amplified and subjected to TAP according to our previous methods[31] . MS analyses were performed by Beijing Proteome Research Center and Wininnovate Bio . ( Shenzhen , China ) . For immunoprecipitation , cells were lysed in NETN buffer ( 20 mM Tris-HCl at pH 8 . 0 , 100 mM NaCl , 1 mM EDTA , 0 . 5% NP-40 ) containing 50 mM β-glycerophosphate , 1 μg/mL pepstatin A and 10 μM leupeptin , and the bait proteins were precipitated by S-protein beads ( Navagen ) , Streptavidin beads ( Amersham ) or anti-c-Myc Tag affinity gel ( Biolegend ) as indicated . For the GST pull-down assay , GST or GST-FBXO2 recombinant protein immobilized onto glutathione Sepharose 4B beads ( GE Healthcare ) was incubated with gB protein purified from stably infected HEK293T cells in NETN buffer for 4 h at 4°C . For Con A pull-down , cell lysates were incubated with Concanavalin A ( Con A ) agarose ( Vector Labs ) for 4 h at 4°C . The bound proteins were eluted with 1X SDS loading buffer . HEK293T cells stably expressing SFB-tagged gB were used to purify the gB protein by streptavidin agarose pull-down followed by extensive washing with high-salt NETN buffer ( containing 400 mM NaCl ) ; gB was then eluted with 2 mg/mL biotin ( Sigma ) . The eluted protein was dialyzed in PBS and concentrated by ultrafiltration ( Amicon Ultra 10 MWCO ) to 0 . 5 μg/μL . A total of 2 . 5 μg of gB protein was sent for MS/MS analysis by Shanghai Applied Protein Technology Co . Ltd . Briefly , the protein was reduced with 100 mM DTT , denatured in 8 M urea buffer and alkylated with iodoacetamide ( IAA ) to prevent disulfide bond formation . After a 30 min incubation in the dark at room temperature , the sample solution was diluted in 50 mM NH4HCO3 buffer containing 2 μg of trypsin , 2 μg of chymotrypsin and 2 μg of Glu C and digested at 37°C overnight . Tryptic peptides were digested with 1 μL of PNGase and dried by vacuum centrifugation . Nanoscale liquid chromatography coupled to tandem MS ( nano LC-MS/MS ) was performed by separating the peptide mixture using an Easy-nLC liquid chromatograph system with a Thermo Scientific EASY column ( Thermo Fisher ) , followed by ESI MS identification using a Q-Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer ( Thermo Finnigan ) . Raw data from the Q-Exactive were converted into MGF files via Proteome Discoverer 1 . 3 ( PD1 . 3 , Thermo ) . The subsequent searches were carried out using Mascot Daemon ( version 2 . 2 , Matrix Science ) and filtered based on a peptide false discovery rate ( FDR ) ≤ 0 . 01% . The following antibodies were used in this study: FBXO2 ( sc-398111 , Santa Cruz ) , EBNA1 ( ab81581 , Abcam ) , Zta ( sc-53904 , Santa Cruz ) , β-tubulin ( sc-23948 , Santa Cruz ) , Myc ( sc-40 , Santa Cruz ) , GAPDH ( 60004-1-1g , Protein-tech ) and FLAG ( F3165 , Sigma ) . MG132 , bafilomycin A1 and tunicamycin were purchased from Selleckchem , LLC . siRNAs were synthesized by Guangzhou RiboBio Co . , Ltd . The siRNAs were 21 base pairs long , and the sequences were as follows: control siRNA: UUCAAUAAAUUCUUGAGGUdTdT; FBXO2 siRNAs: #1: GAUGAGAGCGUCAAGAAGUdTdT; #2: CAGUUCUACUUCCUGAGCAdTdT; #3: AAGGUAGAUAGGCCUUAACdTdT . Lipofectamine RNAiMAX ( Invitrogen ) was used for siRNA transfection . CRISPR/Cas9-mediated EBV clearance in C666-1 cells was performed as previously reported [39] . Two gRNAs targeting EBNA1 , 5’-GTGTGAATCATGTCTGACGA-3’ and 5’-GGCCCTGATCCTGAGCCGCC-3’ were cloned into the lentiviral transfer vector Lenti-CRISPRv2 and were used to package lentiviral particles . Stable EBNA1-deleted C666-1 cells were selected with 2 μg/mL puromycin , and the cells were used in experiments within one week after complete puromycin selection . Total RNA was extracted with Trizol reagent ( Invitrogen ) . The EBV copy number was determined by real-time PCR analysis of the BALF5 gene; the primer sequences were as follows: forward , 5’-GGTCACAATCTCCACGCTGA-3’; reverse , 5’-CAACGAGGCTGACCTGATCC-3’ . The primers for FBXO2 were as follows: forward , 5’- CCACGATGAGAGCGTCAAGA -3’; reverse , 5’- GAGCTCGTAGAGGCAACCAG-3’ . The cell fusion assay was performed as previously described [42 , 50] . Briefly , HEK293T cells transfected with plasmids encoding T7 polymerase , gB , gH and gL were used as effector cells; another group of HEK293T cells transfected with the reporter plasmid pT7EMCLuc encoding the luciferase gene driven by the T7 polymerase and an internal control plasmid pRL-SV40 encoding the Renilla luciferase gene driven by the SV40 promoter served as target cells . Twenty-four hours after transfection , the effector and target cells were trypsinized and co-cultured in a 24-well plate for an additional 24 h . Firefly and Renilla luciferase activities were assayed by using the dual-luciferase reporter assay system ( Promega ) with a Veritas luminometer ( Promega ) . The ratio of firefly luciferase activity to Renilla luciferase activity was regarded as the relative fusion activity . The mean value of the control group was normalized to 100% relative fusion activity . For EBV production , Akata-EBV-GFP cells were treated with 0 . 8% ( v/v ) goat anti-human IgG for 6 h , CNE2-EBV-GFP cells were treated with 20 ng/mL 12-o-tetradecanoylphorbol-13-acetate ( TPA ) and 2 . 5 mM sodium butyrate for 24 h to induce EBV-infected cells to transition from the latent phase into the lytic cycle . After culture in fresh medium for 3 days , the supernatants were collected for infection . To infect B cells , 1 mL of 10 mL of supernatant collected from a 10 cm dish of CNE2-EBV-GFP cells was added to 1 × 105 Raji cells or EBV-negative Akata cells for 2 h at 37°C . The cells were then washed with PBS and incubated for a further 36 h in fresh medium . The EBV infection rate was determined by calculating the percentage of GFP-positive cells using flow cytometry ( Beckman ) results analyzed by FlowJo software . Immunofluorescence , cycloheximide chase and in vivo ubiquitination assay have been described previously [51] . Xenograft and immunohistochemistry ( IHC ) Xenograft and IHC were carried out as described previously [52] . For xenograft studies , female BALB/c nude mice ( 6 weeks old ) were purchased from Shanghai Laboratory Animal Center . All mouse experiments were performed in strict accordance with all provisions of the Animal Welfare Act , the Guide for the Care and Use of Laboratory Animals , and the PHS Policy on Humane Care and Use of Laboratory Animals . The protocol was approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University ( Protocol no . GZR2016-105 ) . Primary NPC cell lines were collected from two adult patients with newly diagnosed NPC at the Sun Yat-sen University Cancer Center in 2017 . Written informed consent was provided by all patients before the tumor biopsies were obtained . This study was conducted under the provisions of the Declaration of Helsinki and approved by Ethics Committee of Sun Yat-sen University Cancer Center ( Protocol No . YB2013-04 ) . Recombinant gB protein was purified from HEK293T cells stably expressing gB-SFB by streptavidin bead pull-down followed by biotin ( 2 mg/mL ) elution . Deglycosylation of gB with Endo H , PNGase F or Protein Deglycosylation Mix ( all from NEB ) was performed according to the manufacturer’s recommendations . Enzyme digestion was performed for 3 h at 37°C . Statistical analyses were performed with GraphPad PRISM software ( GraphPad Software Inc . , San Diego , CA ) . The IHC values for FBXO2 in tissues from the mouse tumorigenesis model were calculated using Image-Pro Plus 6 . 0 . | EBV envelope glycoprotein B ( gB ) is a core component of the fusion machinery and plays an essential role in viral entry . EBV gB has been characterized as a high-mannose-containing glycoprotein , but the significance of this sugar type is unknown . In this work , we report the identification of FBXO2 , a glycosylation-dependent E3 ubiquitin ligase component recognizing N-linked high-mannose oligosaccharides , as a novel gB-interacting host protein that controls the levels of gB glycosylation and its membrane transport . The homogenous high-mannose type N-glycan structure as a “script” that directs the degradation of the glycosylated gB by SCFFBXO2 E3 ubiquitin ligase . Our results propose a novel host defense mechanism by which host cells sense the penetrated EBV and attenuate the infectivity of progeny virus . | [
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| 2018 | Epstein-Barr virus activates F-box protein FBXO2 to limit viral infectivity by targeting glycoprotein B for degradation |
Transposable elements in crop plants are the powerful drivers of phenotypic variation that has been selected during domestication and breeding programs . In tomato , transpositions of the LTR ( long terminal repeat ) retrotransposon family Rider have contributed to various phenotypes of agronomical interest , such as fruit shape and colour . However , the mechanisms regulating Rider activity are largely unknown . We have developed a bioinformatics pipeline for the functional annotation of retrotransposons containing LTRs and defined all full-length Rider elements in the tomato genome . Subsequently , we showed that accumulation of Rider transcripts and transposition intermediates in the form of extrachromosomal DNA is triggered by drought stress and relies on abscisic acid signalling . We provide evidence that residual activity of Rider is controlled by epigenetic mechanisms involving siRNAs and the RNA-dependent DNA methylation pathway . Finally , we demonstrate the broad distribution of Rider-like elements in other plant species , including crops . Our work identifies Rider as an environment-responsive element and a potential source of genetic and epigenetic variation in plants .
Transposable elements ( TEs ) replicate and move within host genomes . Based on their mechanisms of transposition , TEs are either DNA transposons that use a cut-and-paste mechanism or retrotransposons that transpose through an RNA intermediate via a copy-and-paste mechanism [1] . TEs make up a significant part of eukaryotic chromosomes and are a major source of genetic instability that , when active , can induce deleterious mutations . Various mechanisms have evolved that protect plant genomes , including the suppression of TE transcription by epigenetic silencing that restricts TE movement and accumulation [2–5] . Chromosomal copies of transcriptionally silenced TEs are typically hypermethylated at cytosine residues and are associated with nucleosomes containing histone H3 di-methylated at lysine 9 ( H3K9me2 ) . In addition , they are targeted by 24-nt small interfering RNAs ( 24-nt siRNAs ) that guide RNA-dependent DNA methylation ( RdDM ) , forming a self-reinforcing silencing loop [6–8] . Interference with these mechanisms can result in the activation of transposons . For example , loss of DNA METHYLTRANSFERASE 1 ( MET1 ) , the main methyltransferase maintaining methylation of cytosines preceding guanines ( CGs ) , results in the activation of various TE families in Arabidopsis [9–11] and in rice [12] . Mutation of CHROMOMETHYLASE 3 ( CMT3 ) , mediating DNA methylation outside CGs , triggers the mobilization of several TE families , including CACTA elements in Arabidopsis [10] and Tos17 and Tos19 in rice [13] . Interference with the activity of the chromatin remodelling factor DECREASE IN DNA METHYLATION 1 ( DDM1 ) , as well as various components of the RdDM pathway , leads to the activation of specific subsets of TEs in Arabidopsis . These include DNA elements CACTA and MULE , as well as retrotransposons ATGP3 , COPIA13 , COPIA21 , VANDAL21 , EVADÉ and DODGER [14–17] . Similarly , loss of OsDDM1 genes in rice results in the transcriptional activation of TE-derived sequences [18] . In addition to interference with epigenetic silencing , TE activation can also be triggered by environmental stresses . In her pioneering studies , Barbara McClintock denoted TEs as “controlling elements” , thus suggesting that they are activated by genomic stresses and are able to regulate the activities of genes [19 , 20] . In the meantime , a plethora of stress-induced TEs have been described , including retrotransposons . For example , the biotic stress-responsive Tnt1 and Tto1 families in tobacco [21 , 22] , the cold-responsive Tcs family in citrus [23] , the virus-induced Bs1 retrotransposon in maize [24] , the heat-responsive retrotransposons Go-on in rice [25] , and ONSEN in Arabidopsis [26 , 27] . While heat-stress is sufficient to trigger ONSEN transcription and the formation of extrachromosomal DNA ( ecDNA ) , transposition was observed only after the loss of siRNAs , suggesting that the combination of impaired epigenetic control and environmental stress is a prerequisite for ONSEN transposition [28] . Studies have further shown that stress-responsive TEs can affect the expression of surrounding genes , by providing novel regulatory elements and , in some cases , conferring stress-responsiveness [28–30] . The availability of high-quality genomic sequences revealed that LTR ( Long Terminal Repeat ) retrotransposons make up a significant proportion of plant chromosomes , from approximately 10% in Arabidopsis , 25% in rice , 42% in soybean , and up to 75% in maize [31] . In tomato ( Solanum lycopersicum ) , a model crop plant for research on fruit development , LTR retrotransposons make up about 60% of the genome [32] . Despite the abundance of retrotransposons in the tomato genome , only a limited number of studies have linked TE activities causally to phenotypic alterations . Remarkably , the most striking examples described so far involve the retrotransposon family Rider . For example , fruit shape variation is based on copy number variation of the SUN gene , which underwent Rider-mediated trans-duplication from chromosome 10 to chromosome 7 . The new insertion of the SUN gene into chromosome 7 in the variety “Sun1642” results in its overexpression and consequently in the elongated tomato fruits that were subsequently selected by breeders [33 , 34] . The Rider element generated an additional SUN locus on chromosome 7 that encompassed more than 20 kb of the ancestral SUN locus present on chromosome 10 [33] . This large “hybrid” retroelement landed in the fruit-expressed gene DEFL1 , resulting in high and fruit-specific expression of the SUN gene containing the retroelement [34] . The transposition event was estimated to have occurred within the last 200–500 years , suggesting that duplication of the SUN gene occurred after tomato domestication [35] . Jointless pedicel is a further example of a Rider-induced tomato phenotype that has been selected during tomato breeding . This phenotypic alteration reduces fruit dropping and thus facilitates mechanical harvesting . Several independent jointless alleles were identified around 1960 [36–38] . One of them involves a new insertion of Rider into the first intron of the SEPALLATA MADS-Box gene , Solyc12g038510 , that provides an alternative transcription start site and results in an early nonsense mutation [39] . Also , the ancestral yellow flesh mutation in tomato is due to Rider-mediated disruption of the PSY1 gene , which encodes a fruit-specific phytoene synthase involved in carotenoid biosynthesis [40 , 41] . Similarly , the “potato leaf” mutation is due to a Rider insertion in the C locus controlling leaf complexity [42] . Rider retrotransposition is also the cause of the chlorotic tomato mutant fer , identified in the 1960s [43] . This phenotype has been linked to Rider-mediated disruption of the FER gene encoding a bHLH-transcription factor . Rider landed in the first exon of the gene [44 , 45] . Sequence analysis of the element revealed that the causative copy of Rider is identical to that involved in the SUN gene duplication [45] . The Rider family belongs to the Copia superfamily and is ubiquitous in the tomato genome [34 , 45] . Based on partial tomato genome sequences , the number of Rider copies was estimated to be approximately 2000 [34] . Previous DNA blots indicated that Rider is also present in wild tomato relatives but is absent from the genomes of potato , tobacco , and coffee , suggesting that amplification of Rider happened after the divergence of potato and tomato approximately 6 . 2 mya [45 , 46] . The presence of Rider in unrelated plant species has also been suggested [47] . However , incomplete sub-optimal sampling and the low quality of genomic sequence assemblies has hindered a comprehensive survey of Rider elements within the plant kingdom . Considering that the Rider family is a major source of phenotypic variation in tomato , it is surprising that its members and their basic activities , as well as their responsiveness and the possible triggers of environmental super-activation , which explain the evolutionary success of this family , remain largely unknown . Contrary to the majority of TEs characterized to date , previous analyses revealed that Rider is constitutively transcribed and produces full-length transcripts in tomato [34] , but the stimulatory conditions promoting reverse transcription of Rider transcripts that results in accumulation as extrachromosomal DNA are unknown . To fill these gaps , we provide here a refined annotation of full-length Rider elements in tomato using the most recent genome release ( SL3 . 0 ) . We reveal environmental conditions facilitating Rider activation and show that Rider transcription is enhanced by dehydration stress mediated by abscisic acid ( ABA ) signalling , which also triggers accumulation of extrachromosomal DNA . Moreover , we provide evidence that RdDM controls Rider activity through siRNA production and partially through DNA methylation . Finally , we have performed a comprehensive cross-species comparison of full-length Rider elements in 110 plant genomes , including diverse tomato relatives and major crop plants , in order to characterise species-specific Rider features in the plant kingdom . Together , our findings suggest that Rider is a drought stress-induced retrotransposon ubiquitous in diverse plant species that may have contributed to phenotypic variation through the generation of genetic and epigenetic alterations induced by historical drought periods .
We used the most recent SL3 . 0 tomato genome release for de novo annotation of Rider elements . First , we retrieved full-length , potentially autonomous retrotransposons using our functional annotation pipeline ( LTRpred , see Materials and Methods ) . We detected a set of 5844 potentially intact LTR retrotransposons ( S1 Table ) . Homology search among these elements identified 71 elements that share >85% sequence similarity over the entire element with the reference Rider sequence [45] and thus belong to the Rider family . We then determined the distribution of these Rider elements along the tomato chromosomes ( Fig 1A ) and also estimated their age based on sequence divergence between 5’ and 3’ LTRs ( Fig 1A ) . We classified these elements into three categories according to their LTR similarity: 80–95% , 95–98% and 98–100% ( S1A Fig ) . While the first category contains relatively old copies ( last transposition between 10 . 5 and 3 . 5 mya ) , the 95–98% class represents Rider elements that moved between 3 . 5 and 1 . 4 mya , and the 98–100% category includes the youngest Rider copies that transposed within the last 1 . 4 my ( S1A Fig ) . Out of 71 Rider family members , 14 were found in euchromatic chromosome arms ( 14/71 or 19 . 7% ) and 57 in heterochromatic regions ( 80 . 3% ) ( Table 1 ) . In accordance with previous observations based on partial genomic sequences [34] , young Rider elements of the 98–100% class are more likely to reside in the proximity of genes , with 50% within 2 kb of a gene . This was the case for only 37 . 5% of old Rider members ( 85–95% class ) ( Table 2 ) . Such a distribution is consistent with the preferential presence of young elements within euchromatic chromosome arms ( 50% , 5/10 ) compared to old Rider elements ( 9 . 4% , 3/32 ) ( Table 2 and S1B Fig ) . In addition , the phylogenetic distance between individual elements is moderately correlated to the age of each element ( Fig 1B ) ( S2 Table ) . To better understand the activation triggers and , thus , the mechanisms involved in the accumulation of Rider elements in the tomato genome , we examined possible environmental stresses and host regulatory mechanisms influencing their activity . Transcription of an LTR retroelement initiates in its 5’ LTR and is regulated by an adjacent promoter region that usually contains cis-regulatory elements ( CREs ) ( reviewed in [48] ) . Therefore , we aligned the sequence of the Rider promoter region against sequences stored in the PLACE database ( www . dna . affrc . go . jp/PLACE/ ) containing known CREs and identified several dehydration-responsive elements ( DREs ) and sequence motifs linked to ABA signalling ( Fig 2A ) . First , we tested whether these CREs were present in the LTR promoter sequences of the 71 de novo annotated Rider elements ( S3 Table ) . Comparison of Rider LTRs to a set of gene promoter sequences of the same length revealed significant enrichment of several CREs in Rider LTRs ( Fisher’s exact test P<0 . 001 ) ( S4 Table ) . It is known , for example , that the CGCG sequence motif at position 89–94 ( Fig 2A ) is recognized by transcriptional regulators binding calmodulin . These are products of signal-responsive genes activated by various environmental stresses and phytohormones such as ABA [49] . We also detected two MYB recognition sequence motifs ( CTGTTG at position 176–181 bp , and CTGTTA at position 204–209 bp ) ( Fig 2A ) . MYB recognition sequences are usually enriched in the promoters of genes with transcriptional activation during water stress , elevated salinity , and ABA treatments [50 , 51] . In addition , an ABA-responsive element-like ( ABRE-like ) was found at position 332–337 bp in the R region of Rider’s LTR , along with a coupling element ( CE3 ) located at position 357–372 bp ( Fig 2A ) . The co-occurrence of ABRE-like and CE3 has often been found in ABA-responsive genes [52 , 53] . The simultaneous presence of these five CREs in promoters of Rider elements suggests that Rider transcription may be induced by environmental stresses such as dehydration and salinity that involves ABA mediated signalling . To test whether Rider transcription is stimulated by drought stress , glasshouse-grown tomato plants were subjected to water deprivation and levels of Rider transcripts quantified by RT-qPCR ( Fig 2B ) . When compared to control plants , we observed a 4 . 4-fold increase in Rider transcript abundance in plants subjected to drought stress . Thus , Rider transcription appears to be stimulated by drought . To further test this finding , we re-measured levels of Rider transcripts in different experimental setups . In vitro culture conditions with increasing levels of osmotic stress were used to mimic increasing drought severity ( Fig 2C ) . Transcript levels of Rider increased in a dose-dependent fashion with increasing mannitol concentration , corroborating results obtained during direct drought stress in greenhouse conditions . Interestingly , tomato seedlings treated with NaCl also exhibited increased levels of Rider transcripts ( Fig 2C ) . ABA is a versatile phytohormone involved in plant development and abiotic stress responses , including drought stress [54] . Therefore , we asked whether Rider transcriptional drought-responsiveness is mediated by ABA and whether increased ABA can directly stimulate Rider transcript accumulation . To answer the first question , we exploited tomato mutants defective in ABA biosynthesis . The lines flacca ( flc ) , notabilis ( not ) and sitiens ( sit ) have mutations in genes encoding a sulphurylase [55] , a 9-cis-epoxy-carotenoid dioxygenase ( SlNCED1 ) [56 , 57] , and an aldehyde oxidase [58] , respectively . Both flc and sit are impaired in the conversion of ABA-aldehyde to ABA [55 , 58] , while not is unable to catalyse the cleavage of 9-cis-violaxanthin and/or 9-cis-neoxanthin to xanthoxin , an ABA precursor [57] . Glasshouse-grown flc , not and sit mutants and control wild-type plants were subjected to water deprivation treatment and Rider transcript levels quantified by RT-qPCR ( Fig 2D ) . Rider transcript levels were reduced in flc , not and sit by 43% , 26% and 56% , respectively . To examine whether ABA stimulates accumulation of Rider transcripts , tomato seedlings were transferred to media supplemented with increasing concentrations of ABA ( Fig 2E ) . The levels of Rider transcripts increased in a dose-dependent manner with increasing ABA concentrations . This suggests that ABA is not only involved in signalling that results in hyper-activation of Rider transcription during drought , but it also directly promotes the accumulation of Rider transcripts . The effectiveness of the treatments was verified by assaying expression of the stress- and ABA-responsive gene SlASR1 ( S2A–S2F Fig ) . Identification in the U3 region of Rider LTRs of a binding domain for C-repeat binding factors ( CBF ) , which are regulators of the cold-induced transcriptional cascade [52 , 59] , led us to test Rider activation by cold stress . However , Rider transcription was not affected by cold treatment , leaving drought and salinity as the predominant environmental stresses identified so far that stimulate accumulation of Rider transcripts ( S2G Fig ) . The suppression of transposon-derived transcription by epigenetic mechanisms , which typically include DNA methylation , maintains genome integrity [2 , 3 , 5] . We asked whether Rider transcription is also restricted by DNA methylation . Tomato seedlings were grown on media supplemented with 5-azacytidine , an inhibitor of DNA methyltransferases . Rider transcript steady-state levels increased in plants treated with 5-azacytidine compared to controls ( Fig 3A ) . Comparison of Rider transcript accumulation in 5-azacytidine-treated and ABA-treated plants revealed similar levels of transcripts and the levels were similar when the treatments were applied together ( P <0 . 05; Fig 3A ) . To further examine the role of DNA methylation in controlling Rider transcription , we took advantage of tomato mutants defective in crucial components of the RdDM pathway , namely SlNRPD1 and SlNRPE1 , the major subunits of RNA Pol IV and Pol V , respectively . These mutants exhibit reduced cytosine methylation at CHG and CHH sites ( in which H is any base other than G ) residing mostly at the chromosome arms , with slnrpd1 showing a dramatic , genome-wide loss of 24-nt siRNAs [60] . To evaluate the role of RdDM in Rider transcript accumulation , we first assessed the consequences of impaired RdDM on siRNA populations at full-length Rider elements . Deficiency in SlNRPD1 resulted in a complete loss of 24-nt siRNAs that target Rider elements ( Fig 3B ) . This loss was accompanied by a dramatic increase ( approximately 80-fold ) in 21-22-nt siRNAs at Rider loci ( Fig 3B ) . In contrast , the mutation in SlNRPE1 triggered increases in both 21-22-nt and 24-nt siRNAs targeting Rider elements ( Fig 3B ) . We then asked whether altered distribution of these siRNA classes is related to the age of the Rider elements and/or their chromosomal position , and thus local chromatin properties . Compilation of the genomic positions and siRNA data in RdDM mutants didn’t reveal preferential accumulation of 21-22-nt siRNAs ( S3A Fig ) or 24-nt siRNAs ( S3B Fig ) over specific Rider classes . Subsequently , we examined whether loss of SlNRPD1 or SlNRPE1 was sufficient to increase levels of Rider transcripts and observed increased accumulation of Rider transcripts in both slnrpd1 and slnrpe1 compared to WT ( Fig 3C ) . We assessed whether this increase in Rider transcript levels is linked to changes in DNA methylation levels in Rider elements of RdDM mutants . There was no significant change in global DNA methylation in the three sequence contexts in the 71 de novo annotated Rider elements ( S3C Fig ) , despite a tendency for young Rider elements to lose CHH in slnrpd1 and slnrpe1 ( S3D Fig ) . Thus , the RdDM pathway affects the levels of Rider transcripts . Also , features of Rider copies such as age and chromatin location alone cannot predict potential for activation based on DNA methylation levels . The life cycle of LTR retrotransposons starts with transcription of the element , then the synthesis and maturation of accessory proteins including reverse transcriptase and integrase , reverse transcription , and the production of extrachromosomal linear ( ecl ) DNA that integrates into a new genomic location [61] . In addition , eclDNA can be a target of DNA repair and can be circularised by a non-homologous end-joining mechanism or homologous recombination between LTRs , resulting in extrachromosomal circular DNA ( eccDNA ) [62–65] . We searched for eccDNA to evaluate the consequences of increased Rider transcript accumulation due to drought stress or an impaired RdDM pathway on subsequent steps of the transposition cycle . After exonuclease-mediated elimination of linear dsDNA and circular ssDNA , Rider eccDNA was amplified by sequence-specific inverse PCR ( Fig 4A ) . Rider eccDNA was absent in plants grown in control conditions but was detected in plants subjected to drought stress ( Fig 4A ) . Sanger sequencing of the inverse PCR products showed that the amplified eccDNA probably originates from the Rider_08_3 copy , which has 98 . 2% sequence homology of the 5’ and 3’ LTR sequences ( S4A Fig ) . Residual sequence divergence may be due to genotypic differences between the reference genomic sequence and the genome of our experimental material . Analysis of CREs in the LTR of the eccDNA revealed the presence of all elements identified previously with the exception of a single nucleotide mutation located in the CGCGBOXAT box ( S4A Fig ) . Examination by quantitative PCR of the accumulation of Rider DNA , which included extrachromosomal and genomic copies , in drought-stressed plants also revealed an increase in Rider copy number due to eccDNA ( Fig 4B ) . Importantly , Rider eccDNA was not detected in sit mutants subjected to drought stress ( Fig 4A ) , suggesting that induced transcription of Rider by drought stress triggers production of extrachromosomal DNA and this response requires ABA biosynthesis . We also examined the accumulation of Rider eccDNA in plants impaired in RdDM . Interestingly , Rider eccDNA was detected in slnrpd1 and slnrpe1 ( Fig 4C ) and increase in Rider DNA copy number due to eccDNA accumulation was confirmed by qPCR ( Fig 4D ) . Absence of newly integrated genomic copies has been further validated by genome sequencing . The eccDNA forms differed between the mutants ( Fig 4C ) . Sequencing of Rider eccDNA in slnrpd1 showed a sequence identical to the Rider eccDNA of wild-type plants subjected to drought stress . Thus , the Rider_08_3 copy is probably the main contributor to eccDNA in drought and in slnrpd1 . In contrast , eccDNA recovered from slnrpe1 had a shorter LTR ( 287 bp ) and the highest sequence similarity with Rider_07_2 ( 89 . 2% ) ( S4B Fig ) . Shortening of the LTR in this particular element results in the loss of the upstream MYBCORE as well as the CGCGBOXAT elements ( S4B Fig ) . We then asked whether DNA methylation and siRNA distribution at these particular Rider copies had changed in the mutants . DNA methylation at CHH sites , but not CG nor CHG , was drastically reduced at Rider_08_3 in slnrpd1 ( Fig 4E , S4C–S4E Fig and S5A Fig ) together with a complete loss of 24-nt siRNAs at this locus ( Fig 4F and S4F Fig ) but DNA methylation at Rider_07_2 was not affected , despite the deficiency of SlNRPD1 or SlNRPE1 ( Fig 4E , S4C–S4E Fig and S5B Fig ) . Levels of 21-22-nt siRNAs in both mutants and 24-nt siRNA in slnrpe1 were increased ( Fig 4F and S4F and S4G Fig ) . Altogether , this suggests that RdDM activity on Rider is highly copy-specific and that different components of the RdDM pathway differ in their effects on Rider silencing . To examine the distribution of Rider retrotransposons in other plant species , we searched for Rider-related sequences across the genomes of further Solanaceae species , including wild tomatoes , potato ( Solanum tuberosum ) , and pepper ( Capsicum annuum ) . We used the Rider reference sequence [45] as the query against genome sequences of Solanum arcanum , S . habrochaites , S . lycopersicum , S . pennellii , S . pimpinellifolium , S . tuberosum , and Capsicum annuum ( genome versions are listed in Materials and Methods ) . Two BLAST searches were performed , one using the entire Rider sequence as the query and the other using only the Rider LTR . Consistent with previous reports , Rider-like elements are present in wild relatives of tomato such as S . arcanum , S . pennellii and S . habrochaites; however , the homology levels and their lengths vary significantly between species ( Fig 5A ) . While S . arcanum and S . habrochaites exhibit high peak densities at 55% and 61% homology , respectively , S . pennellii show a high peak density at 98% over the entire Rider reference sequence ( Fig 5A ) . This suggests that the S . arcanum and S . habrochaites genomes harbour mostly Rider-like elements with relatively low sequence similarity , while S . pennellii retains full-length Rider elements . To better visualize this situation , we aligned the BLAST hits to the reference Rider copy ( Fig 5B ) . This confirmed that Rider elements in S . pennellii are indeed mostly full-length Rider homologs showing high density of hits throughout their lengths , while BLAST hits in the S . arcanum and S . habrochaites genomes showed only partial matches over the 4867 bp of the reference Rider sequence ( Fig 5B ) . Unexpectedly , this approach failed to detect either full-length or truncated Rider homologs in the close relative of tomato , S . pimpinellifolium . Extension of the same approaches to the genomes of the evolutionary more distant S . tuberosum and Capsicum annuum failed to detect substantial Rider homologs ( Fig 5A and 5B ) , confirming the absence of Rider in the potato and pepper genomes [45] . As a control , we also analysed Arabidopsis thaliana , since previous studies reported the presence of Rider homologs in this model plant [45] . Using the BLAST approach above , we repeated the results provided in [45] and found BLAST hits of high sequence homology to internal sequences of Rider in the Arabidopsis thaliana genome . However , we did not detect sequence homologies to Rider LTRs ( Fig 5C and 5D ) . Motivated by this finding and the possibility that Rider homologs in other species may have highly divergent LTRs , we screened for Rider LTRs that would have been missed in the analysis shown in Fig 5A and 5B due to the use of the full-length sequence of Rider as the query . Using the Rider LTR as a query revealed that S . pennellii , S . arcanum and S . habrochaites retain intact Rider LTR homologs , but S . pimpinellifolium exhibits a high BLAST hit density exclusively at approximately 60% homology . This suggests strong divergence of Rider LTRs in this species ( Fig 5C and 5D ) . Overall , the results indicate intact Rider homologs in some Solanaceae species , whereas sequence similarities to Rider occur only within the coding area of the retrotransposons in more distant plants such as Arabidopsis thaliana . Therefore , LTRs , which include the cis-regulatory elements conferring stress-responsiveness , diverge markedly between species . Finally , we performed a reciprocal BLAST against tomato using Rider-like hits from all other species having sequence similarity over the entire element between 50% - 84% and confirmed that all Rider loci in tomato were among the top reciprocal BLAST hits . To address the specificity of this divergence in Solanaceae species , we examined whether the CREs enriched in S . lycopersicum ( Fig 2A ) are present in LTR sequences of the Rider elements in S . pennellii , S . arcanum , S . habrochaites and S . pimpinellifolium ( Fig 5C ) . While the LTRs identified in S . pennellii , S . arcanum and S . habrochaites retained all five previously identified CREs , more distant LTRs showed shortening of the U3 region associated with loss of the CGCG box ( S6 Fig and S5 Table ) . This was observed already in S . pimpinellifolium , where all identified Rider LTRs lacked part of the U3 region containing the CGCG box ( S6 Fig ) . Thus , Rider distribution and associated features differ even between closely related Solanaceae species , correlated with the occurrence of a truncated U3 region and family-wide loss of CREs . Finally , to test the evolutionary conservation of Rider elements across the plant kingdom , we performed Rider BLAST searches against all 110 plant genomes available at the NCBI Reference Sequence ( RefSeq ) database ( www . ncbi . nlm . nih . gov/refseq ) . Using the entire Rider sequence as the query to measure the abundance of Rider homologs throughout these genomes , we found Rider homologs in 14 diverse plant species ( S7 Fig ) . The limited conservation of Rider LTR sequences in the same 14 species , revealed using the LTR sequence as the query , suggests that Rider LTRs are highly polymorphic and that drought-responsive CREs may nevertheless be restricted to Solanaceae ( S8 Fig ) .
Comprehensive analysis of individual LTR retrotransposon families in complex plant genomes has been facilitated and become more accurate with the increasing availability of high-quality genome assemblies . Here , we took advantage of the most recent tomato genome release ( SL3 . 0 ) to characterize with improved resolution the high-copy-number Rider retrotransposon family . Although Rider activity has been causally linked to the emergence of important agronomic phenotypes in tomato , the triggers of Rider have remained elusive . Despite the relatively low proportion ( approximately 20% ) of euchromatic chromosomal regions in the tomato genome [32] ) , our de novo functional annotation of full-length Rider elements revealed preferential compartmentalization of recent Rider insertions within euchromatin compared to aged insertions . Mapping analyses further revealed that recent rather than aged Rider transposition events are more likely to modify the close vicinity of genes . However , Rider copies inserted into heterochromatin have been passively maintained for longer periods . This differs significantly from other retrotransposon families in tomato such as Tnt1 , ToRTL1 and T135 , which show initial , preferential insertions into heterochromatic regions [66] . TARE1 , a high-copy-number Copia-like element , is present predominantly in pericentromeric heterochromatin [67] . Another high-copy-number retrotransposon , Jinling , is also enriched in heterochromatic regions , making up about 2 . 5% of the tomato nuclear genome [68] . The Rider propensity to insert into gene-rich areas mirrors the insertional preferences of the ONSEN family in Arabidopsis . Since new ONSEN insertions confer heat-responsiveness to neighbouring genes [28 , 69] , it is tempting to speculate that genes in the vicinity of new Rider insertions may acquire , at least transiently , drought-responsiveness . We found that Rider transcript levels are elevated during dehydration stress mediated by ABA-dependent signalling . The activation of retrotransposons upon environmental cues has been shown extensively to rely on the presence of cis-regulatory elements within the retrotransposon LTRs [48] . The heat-responsiveness of ONSEN in Arabidopsis [26 , 27 , 70] , Go-on in rice [25] , and Copia in Drosophila [71] is conferred by the presence in their LTRs of consensus sequences found in the promoters of heat-shock responsive genes . Thus , the host’s heat-stress signalling appears to induce transcriptional activation of the transposon and promote transposition [70] . While ONSEN and Go-on are transcriptionally inert in the absence of a triggering stress , transcripts of Drosophila Copia are found in control conditions , resembling the regulatory situation in Rider . Due to relatively high constitutive expression , increase in transcript levels of Drosophila Copia following stress appears modest compared to ONSEN or Go-on , which are virtually silent in control conditions [25–27 , 70] . Regulation of Drosophila Copia mirrors that of Rider , where transcript levels during dehydration stress are very high but the relative increase compared to control conditions is rather modest . The presence of MYB recognition sequences within Rider LTRs suggests that MYB transcription factors participate in transcriptional activation of Rider during dehydration . Multiple MYB subfamilies are involved in ABA-dependent stress responses in tomato , but strong enrichment of the MYB core element CTGTTA within Rider LTRs suggests involvement of R2R3-MYB transcription factors , which are markedly amplified in Solanaceae [72] . Members of this MYB subfamily are involved in the ABA signalling-mediated drought-stress response [73] and salt-stress signalling [74] . This possible involvement of R2R3-MYBs in Rider is reminiscent of the transcriptional activation of the tobacco retrotransposon Tto1 by the R2R3-MYB , member NtMYB2 [75] . Drought-responsiveness has been observed for Rider_08_3 only , despite other individual Rider copies displaying intact MYB core element ( S3 Table ) . This suggests that presence of this CRE is not the only feature required for drought-responsiveness , and other factors , such as genomic location , influence Rider activity . Indeed , Rider_08_3 is located within a gene-rich area , with low TE content that might facilitate its activation . This is strikingly different from Rider_07_2 that is nested in a TE-rich area and isolated from genes ( S6 Table ) . In addition to environmental triggers , Rider transcript levels are regulated by the RdDM pathway . Depletion of SlNRPD1 and SlNRPE1 increases Rider transcript abundance , resulting in production of extrachromosomal circular DNA . Analysis of Rider-specific siRNA populations revealed that siRNA targeting of Rider elements is mostly independent of their chromatin context . This is somewhat unexpected since RdDM activity in tomato seems to be restricted to gene-rich euchromatin and it was postulated that accessibility of RNA Pol IV to heterochromatin is hindered by the compact chromatin structure [60 , 76 , 77] . We identified Rider copies targeted by RdDM , which potentially influences local epigenetic features . Loss of SlNRPD1 and SlNRPE1 leads to over-accumulation of 21-22-nt siRNAs at Rider copies , suggesting that inactivation of canonical RdDM pathway-dependent transcriptional gene silencing triggers the activity of the non-canonical RDR6 RdDM pathway at Rider [78–80] . It is noteworthy that , despite clear effects on Rider transcript accumulation and siRNA accumulation , loss of SlNRPD1 and SlNRPE1 is not manifested by drastic changes in total DNA methylation levels of Rider at the family level . This is in accordance with the modest decrease in genome-wide CHH and CHG methylation described in tomato RdDM mutants , with most of the changes happening on the euchromatic arms while the pericentromeric heterochromatin is unaffected [60] . Distribution of the 71 intact Rider elements in both euchromatic and heterochromatic compartments thus likely hampers detection of major changes DNA methylation over the Rider family . Only young euchromatic Rider elements marginally lose CHH methylation in the slnrpd1 mutant , but this is modest compared to the general decrease in mCHH observed throughout the chromosome arms [60] . As expected , CHH methylation at heterochromatic Rider is not affected . This suggests that SlCMT2 is involved in maintenance of mCHH at heterochromatic Rider copies in the absence of SlNRPD1 , as observed previously for pericentromeric heterochromatin [60] . In general , our observations suggest that epigenetic silencing of Rider retrotransposons is particularly robust and involves compensatory pathways . We identified extrachromosomal circular DNA originating from the Rider copies Rider_08_3 and Rider_07_2 in slnrpd1 and slnrpe1 , respectively . In terms of DNA methylation and siRNA distribution at these two specific copies , loss of SlNRPD1 and SlNRPE1 brought different copy-specific outcomes . Rider_08_3 , the main contributor to eccDNA in slnrpd1 , displayed a reduction in CHH methylation that may contribute to increased transcription and the accumulation of eccDNA . In Rider_07_2 , that provides a template for eccDNA in slnrpe1 , there was no change in DNA methylation levels . Therefore , transcription and the production of eccDNA from this Rider copy is not regulated by DNA methylation . Consequently , eccDNA from Rider_07_2 was not detected in slnrpd1 despite drastic loss of CHH methylation . Despite our efforts , we were unable to apply either drought or ABA treatment to the slnrpd1 and slnrpe1 mutants . In contrast to Arabidopsis [81 , 82] , RdDM mutants in tomato are showing severe developmental defects and are sterile [60] . They are particularly difficult to maintain even in optimal growth conditions , precluding the application of stress treatments . Altogether , it appears that transcriptional control and reverse transcription of Rider copies occurs via multiple layers of regulation , possibly specific for individual Rider elements according to age , sequence and genomic location , that are targeted by parallel silencing pathways , including non-canonical RdDM [83 , 84] . The presence of Rider in tomato relatives as well as in more distantly related plant species has been described previously [34 , 45 , 47] . However , the de novo identification of Rider elements in the sampling provided here shows the distribution of the Rider family within plant species to be more complex than initially suggested . Surprisingly , mining for sequences with high similarity , overlapping more than 85% of the entire reference sequence of Rider , detected no full-length Rider elements in Solanum pimpinellifolium but in all other wild tomato species tested . Furthermore , the significant accumulation of only partial Rider copies in Solanum pimpinellifolium , the closest relative of tomato , does not match the established phylogeny of the Solanaceae . The cause of these patterns is unresolved but two scenarios can be envisaged . First , the absence of full-length Rider elements may be due to the suboptimal quality of genome assembly that may exclude a significant proportion of highly repetitive sequences such as Rider . This is supported by the N50 values within the Solanaceae , where the quality of genome assemblies varies significantly between species , with S . pimpinellifolium showing the lowest ( S7 Table ) . An improved genome assembly would allow a refined analysis of Rider in this species . Alternatively , active Rider copies may have been lost in S . pimpinellifolium since the separation from the last common ancestor but not in the S . lycopersicum and S . pennellii lineages . The high-density of solo-LTRs and truncated elements in S . pimpinellifolium is in agreement with this hypothesis . Comparing the sequences of Rider LTRs in the five tomato species , the unique occurrence of LTRs lacking most of the U3 region in S . pimpinellifolium suggests that loss of important regulatory sequences has impeded maintenance of intact Rider elements . Interestingly , part of the U3 region missing in S . pimpinellifolium contains the CGCG box , which is involved in response to environmental signals [49] , as well as a short CpG-island-like structure ( position 52–155 bp on reference Rider ) . CpG islands are usually enriched 5’ of transcriptionally active genes in vertebrates [85] and plants [86] . Despite the presence of truncated Rider LTRs , the occurrence of intact , full-length LTRs in other wild tomato species indicates that Rider is still potentially active in these genomes . Altogether , our findings suggest that inter- and intra-species TE distribution can be uncoupled and that the evolution of TE families in present crop plants was more complex than initially anticipated . We have further opened interesting perspectives for harnessing transposon activities in crop breeding . Potentially active TE families that react to environmental stimuli , such as Rider , provide an unprecedented opportunity to generate genetic and epigenetic variation from which desirable agronomical traits may emerge . Notably , rewiring of gene expression networks regulating the drought-stress responses of new Rider insertions is an interesting strategy to engineer drought-resilient crops .
Tomato plants were grown under standard greenhouse conditions ( 16 h at 25°C with supplemental lighting of 88 w/m2 and 8 h at 15°C without ) . flacca ( flc ) , notabilis ( not ) , and sitiens ( sit ) seeds ( cv . Ailsa Craig ) were obtained from Andrew Thompson , Cranfield University; the slnrpd1 and slnrpe1 plants ( cv . M82 ) were described before [60] . For aseptic growth , seeds of Solanum lycopersicum were surface-sterilized in 20% bleach for 10 min , rinsed three times with sterile H2O , germinated and grown on half-strength MS media ( 16 h light and 8 h dark at 24°C ) . For dehydration stress , two-week-old greenhouse-grown plants were subjected to water deprivation for two weeks . For NaCl and mannitol treatments , tomato seedlings were grown aseptically for two weeks prior to transfer into half-strength MS solution containing 100 , 200 or 300 nM NaCl or mannitol ( Sigma ) for 24 h . For abscisic acid ( ABA ) treatments , tomato seedlings were grown aseptically for two weeks prior to transfer into half-strength MS solution containing 0 . 5 , 5 , 10 or 100 μM ABA ( Sigma ) for 24 h . For 5-azacytidine treatments , tomato seedlings were germinated and grown aseptically on half-strength MS media containing 50 nM 5-azacytidine ( Sigma ) for two weeks . For cold stress experiments , two-week-old aseptically grown plants were transferred to 4°C for 24 h prior to sampling . Total RNA was extracted from 200 mg quick-frozen tissue using the TRI Reagent ( Sigma ) according to the manufacturer’s instructions and resuspended in 50 μL H2O . The RNA concentration was estimated using the Qubit Fluorometric Quantitation system ( Thermo Fisher ) . cDNAs were synthesized using a SuperScript VILO cDNA Synthesis Kit ( Invitrogen ) . Real-time quantitative PCR was performed in the LightCycler 480 system ( Roche ) using primers listed in S8 Table . Selected Rider primers amplify 64 out of the 71 copies , with 3 mismatches allowed . Localization of Rider primers is shown in S9 Fig . LightCycler 480 SYBR Green I Master premix ( Roche ) was used to prepare the reaction mixture in a volume of 10 μL . Transcript levels were normalized to SlACTIN ( Solyc03g078400 ) . The results were analysed by the ΔΔCt method . Tomato DNA was extracted using the Qiagen DNeasy Plant Mini Kit ( Qiagen ) following the manufacturer’s instructions and resuspended in 30 μL H2O . DNA concentration was estimated using the Qubit Fluorometric Quantitation system ( Thermo Fisher ) . Quantitative PCR was performed in the LightCycler 480 system ( Roche ) using primers listed in S8 Table . Selected Rider primers amplify 64 out of the 71 copies , with 3 mismatches allowed . Localization of Rider primers is shown in S9 Fig . LightCycler 480 SYBR Green I Master premix ( Roche ) was used to prepare the reaction in a volume of 10 μL . DNA copy number was normalized to SlACTIN ( Solyc03g078400 ) . Results were analysed by the ΔΔCt method . Extrachromosomal circular DNA amplification was derived from the previously published mobilome analysis [11] . In brief , extrachromosomal circular DNA was separated from chromosomal DNA using PlasmidSafe ATP-dependent DNase ( EpiCentre ) according to the manufacturer’s instructions with the incubation at 37°C extended to 17 h . The PlasmidSafe exonuclease degrades linear DNA and thus safeguards circular DNA molecules . Circular DNA was precipitated overnight at -20°C in 0 . 1 v/v 3 M sodium acetate ( pH 5 . 2 ) , 2 . 5 v/v EtOH and 1 μL glycogen ( Sigma ) . The pellet was resuspended in 20 μL H2O . Inverse PCR reactions were carried out with 2 μL of DNA solution in a final volume of 20 μL using the GoTaq enzyme ( Promega ) . The PCR conditions were as follows: denaturation at 95°C for 5 min , followed by 30 cycles at 95°C for 30 s , an annealing step for 30 s , an elongation step at 72°C for 60 s , and a final extension step at 72°C for 5 min . Selected primers amplify 68 out of the 71 Rider copies , with 3 mismatches allowed . Primer localization is shown on Fig 4A and 4C , left panel ( grey bar: Rider element , black box: LTR , arrowheads: PCR primers ) and sequences are listed in S8 Table . PCR products were separated in 1% agarose gels and developed by NuGenius ( Syngene ) . Bands were extracted using the Qiagen Gel Extraction Kit and eluted in 30 μL H2O . Purified amplicons were subjected to Sanger sequencing . Five amplicons , obtained from two independent experiments , were sequenced for each eccDNA form . A phylogenetic tree was constructed from the nucleotide sequences of the 71 Rider elements using Geneious 9 . 1 . 8 ( www . geneious . com ) and built with the Tamura-Nei neighbor joining method . Pairwise alignment for the building distance matrix was obtained using a global alignment with free end gaps and a cost matrix of 51% similarity . Genomic coordinates of each of the 71 Rider elements identified by de novo annotation using LTRpred ( https://github . com/HajkD/LTRpred ) have been used to establish their chromosomal locations . Coordinates for centromeres were provided before [32] and pericentromeric regions were defined by high levels of DNA methylation and H3K9me2 ( [60] and David Baulcombe , personal communication ) . The Genbank accession number of the reference Rider nucleotide sequence identified in [45] is EU195798 . 2 . We used Solanum lycopersicum bisulfite and small RNA sequencing data ( SRP081115 ) generated in [60] . Insertion times of Rider elements were estimated using the method described in [45] . Degrees of divergence between LTRs of each individual element were determined using LTRpred . LTR divergence rates were then converted into dates using the average substitution rate of 6 . 96 x 10−9 substitutions per synonymous site per year for tomato [87] . We collected data from previously published BS-seq libraries of tomato mutants of RNA polymerase IV and V and controls [60]: slnrpe1 ( SRR4013319 ) , slnrpd1 ( SRR4013316 ) , wild type CAS9 ( SRR4013314 ) and not transformed wild type ( SRR4013312 ) . The raw reads were analysed using our previously established pipeline [88] and aligned to the Solanum lycopersicum reference version SL3 . 0 ( www . solgenomics . net/organism/Solanum_lycopersicum/genome ) . The chloroplast sequence ( NC_007898 ) was used to estimate the bisulfite conversion ( on average above 99% ) . The R package DMRcaller [89] was used to summarize the level of DNA methylation in the three cytosine contexts for each Rider copy . Tomato siRNA libraries were obtained from [60] and analysed using the same analysis pipeline to align reads to the tomato genome version SL3 . 0 . Briefly , the reads were trimmed with Trim Galore ! ( www . bioinformatics . babraham . ac . uk/projects/trim_galore ) and mapped using the ShortStack software v3 . 6 [90] . The siRNA counts on the loci overlapping Rider copies were calculated with R and the package GenomicRanges . Computationally reproducible analysis and annotation scripts for the following sections can be found at http://github . com/HajkD/RIDER . We retrieved genome assemblies for 110 plant species ( S9 Table ) from NCBI RefSeq [91] using the meta . retrieval function from the R package biomartr [92] . For Solanum lycopersicum , we retrieved the most recent genome assembly version SL3 . 0 from the Sol Genomics Network ftp://ftp . solgenomics . net/tomato_genome/assembly/build_3 . 00/S_lycopersicum_chromosomes . 3 . 00 . fa [93] . Functional de novo annotations of LTR retrotransposons for seventeen genomes from the Asterids , Rosids , and monocot clades ( Asterids: Capsicum annuum , C . baccatum MLFT02_5 , C . chinense MCIT02_5 , Coffea canephora , Petunia axillaris , Phytophthora inflata , Solanum arcanum , S . habrochaites , S . lycopersicum , S . melongena , S . pennellii , S . pimpinellifolium , S . tuberosum; Rosids: Arabidopsis thaliana , Vitis vinifera , and Cucumis melo; Monocots: Oryza sativa ) were generated using the LTRpred . meta function from the LTRpred annotation pipeline ( https://github . com/HajkD/LTRpred; also used in [25] ) . To retrieve a consistent and comparable set of functional annotations for all genomes , we consistently applied the following LTRpred parameter configurations to all Solanaceae genomes: minlenltr = 100 , maxlenltr = 5000 , mindistltr = 4000 , maxdisltr = 30000 , mintsd = 3 , maxtsd = 20 , vic = 80 , overlaps = “no” , xdrop = 7 , motifmis = 1 , pbsradius = 60 , pbsalilen = c ( 8 , 40 ) , pbsoffset = c ( 0 , 10 ) , quality . filter = TRUE , n . orf = 0 . The plant-specific tRNAs used to screen for primer binding sites ( PBS ) were retrieved from GtRNAdb [94] and plant RNA [95] and combined in a custom fasta file . The hidden Markov model files for gag and pol protein conservation screening were retrieved from Pfam [96] using the protein domains RdRP_1 ( PF00680 ) , RdRP_2 ( PF00978 ) , RdRP_3 ( PF00998 ) , RdRP_4 ( PF02123 ) , RVT_1 ( PF00078 ) , RVT_2 ( PF07727 ) , Integrase DNA binding domain ( PF00552 ) , Integrase zinc binding domain ( PF02022 ) , Retrotrans_gag ( PF03732 ) , RNase H ( PF00075 ) , and Integrase core domain ( PF00665 ) . We combined the de novo annotated LTR retrotransposons of the 17 species mentioned in the previous section in a large fasta file and used the cluster program VSEARCH [97] with parameter configurations: vsearch—cluster_fast—qmask none–id 0 . 85—clusterout_sort—clusterout_id—strand both—blast6out—sizeout to cluster LTR retrotransposons by nucleotide sequence homology ( global sequence alignments ) . Next , we retrieved the 85% sequence homology clusters from the VSEARCH output and screened for clusters containing Rider sequences . This procedure enabled us to detect high sequence homology ( >85% ) sequences of Rider across diverse species . To determine the distribution of Rider related sequences across the plant kingdom , we performed BLASTN [98] searches of Rider ( = query sequence ) using the function blast_genomes from the R package metablastr ( https://github . com/HajkD/metablastr ) against 110 plant genomes ( S9 Table ) and the parameter configuration: blastn -eval 1E-5 -max_target_seqs 5000 . As a result , we retrieved a BLAST hit table containing 11 , 748 , 202 BLAST hits . Next , we filtered for hits that contained at least 50% sequence coverage ( = sequence homology ) and throughout at least 50% sequence length homology to the reference Rider sequence . This procedure reduced the initial 11 , 748 , 202 BLAST hits to 57 , 845 hits , which we further refer to as Rider-like elements . These 57 , 845 Rider-like elements are distributed across 21 species with various abundance frequencies . In a second step , we performed an analogous BLASTN search using only the 5’ LTR sequence of Rider to determine the distribution of Rider-like LTR across the plant kingdom . Using the same BLASTN search strategy described above , we retrieved 9 , 431 hits . After filtering for hits that contained at least 50% percent sequence coverage ( = sequence homology ) and at least 50% sequence length homology to the reference Rider LTR sequence , we obtained 2 , 342 BLAST hits distributed across five species . We tested the enrichment of cis-regulatory elements ( CREs ) in Rider using two approaches . In the first approach , we compared Rider CREs to promoter sequences of all 35 , 092 protein coding genes from the tomato reference genome . We retrieved promoter sequences 400 bp upstream of the TSS of the respective genes . We constructed a 2x2 contingency table containing the respective motif count data of CRE observations in true Rider sequences versus counts in promoter sequences . We performed a Fisher’s exact test for count data to assess the statistical significance of enrichment between the motif count data retrieved from Rider sequences and the motif count data retrieved from promoter sequences . In the second approach , due to the unavailability of gene annotation for Solanum arcanum , Solanum habrochaites and Solanum pimpinellifolium we compared Rider CREs to randomly sampled sequence loci from the same genome using the following two step procedure: in step one , we sampled 1000 DNA sequences with the same length as the reference Rider sequence from 1000 randomly sampled loci in the tomato reference genome . When sampling , we also considered the strand direction of the reference Rider sequence . Whenever a Rider sequence was annotated in the plus direction , we also sampled the corresponding set of random sequences in the plus direction of the respective randomly drawn locus . In contrast , when a Rider sequence was annotated in the minus direction , we also sampled the corresponding set of random sequences in the minus direction . In step two , we counted CRE occurrences for each Rider sequence independently and for a set of different CREs . Next , we counted the number of the same CRE occurrences for each random sequence independently to assess how often these CREs were found in random sequences . We then , analogous to the first approach , constructed a 2x2 contingency table containing the respective motif count data of CRE observations in true Rider sequences versus counts in random sequences . We performed a Fisher’s exact test for count data to assess the statistical significance of enrichment between the motif count data retrieved from Rider sequences and the motif count data retrieved from random sequences . The resulting P-values are shown in S4 Table for the first approach and in S5 Table for the second approach . Computationally reproducible scripts to perform the motif count analysis can be found at https://github . com/HajkD/RIDER . To assess the genome quality of Solanaceae species , we calculated the N50 metric for the genome assemblies of Solanum lycopersicum , S . pimpinellifolium , S . arcanum , S . pennellii , S . habrochaites , and S . tuberosum using the following procedure . First , we imported the scaffolds or chromosomes of each respective genome assembly using the R function read_genome ( ) from the biomartr package . Next , for each species individually we determined the sequence length for each scaffold or chromosome and sorted them according to length in descending order . The N50 value in Mbp was then calculated in R as follows: N50 <- len . sorted[cumsum ( len . sorted ) > = sum ( len . sorted ) *0 . 5][1] / 1000000 , where the variable len . sorted denotes the vector storing the ordered scaffold or chromosome lengths of a genome assembly . SRAtoolkit , v2 . 8 . 0 ( https://github . com/ncbi/sra-tools ) and Biomartr 0 . 9 . 9000 ( https://ropensci . github . io/biomartr/index . html ) were used for data collection . Phylogenetic trees were constructed using Geneious 9 . 1 . 8 ( www . geneious . com ) . The de novo retrotransposon annotation pipeline LTRpred is available in the GitHub repository ( https://github . com/HajkD/LTRpred ) . Rider annotation and analysis pipeline is available in the GitHub repository ( https://github . com/HajkD/RIDER ) . Distribution of Rider elements was done using the R package metablastr ( https://github . com/HajkD/metablastr ) . DNA methylation levels were assessed using the R package DMRcaller ( http://bioconductor . org/packages/release/bioc/html/DMRcaller . html ) . Small RNA analysis was done using Trim Galore ! ( www . bioinformatics . babraham . ac . uk/projects/trim_galore ) , ShortStack v3 . 6 ( https://github . com/MikeAxtell/ShortStack ) and GenomicRanges v3 . 8 ( https://bioconductor . org/packages/release/bioc/html/GenomicRanges . html ) . Reference Rider nucleotide sequence ( accession number EU195798 ) is available here ( https://www . ncbi . nlm . nih . gov/nuccore/EU195798 ) . The datasets supporting the conclusions of this article are available at Sequence Read Archive ( SRA ) ( https://www . ncbi . nlm . nih . gov/sra/ ) under accession numbers "SRP081115" , "SRR4013319" , "SRR4013316" , "SRR4013314" and "SRR4013312" . | Transposons are major constituents of plant genomes and represent a powerful source of internal genetic and epigenetic variation . For example , domestication of maize has been facilitated by a dramatic change in plant architecture , the consequence of a transposition event . Insertion of transposons near genes often confers quantitative phenotypic variation linked to changes in transcriptional patterns , as documented for blood oranges and grapes . In tomato , the most widely grown fruit crop and model for fleshy fruit biology , occurrences of several beneficial traits related to fruit shape and plant architecture are due to the activity of the transposon family Rider . While Rider represents a unique endogenous source of genetic and epigenetic variation , mechanisms regulating Rider activity remain unexplored . By achieving experimentally-controlled activation of the Rider family , we shed light on the regulation of these transposons by drought stress , signalling by phytohormones , as well as epigenetic pathways . Furthermore , we reveal the presence of Rider-like elements in other economically important crops such as rapeseed , beetroot and quinoa . This suggests that drought-inducible Rider activation could be further harnessed to generate genetic and epigenetic variation for crop breeding , and highlights the potential of transposon-directed mutagenesis for crop improvement . | [
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| 2019 | Environmental and epigenetic regulation of Rider retrotransposons in tomato |
Malaria transmission requires that Anopheles mosquitoes ingest Plasmodium gametocyte stages circulating in the human bloodstream . In the context of malaria elimination , understanding the epidemiology of gametocytes relative to all Plasmodium infections and the contribution of asymptomatic and sub-microscopic parasite carriers to the gametocyte reservoir is necessary , especially in low endemic settings with predominance of P . vivax . A 13-month longitudinal study was conducted in two communities ( n = 1935 individuals ) of Loreto Department , Peru , with five active screenings for Plasmodium infections and gametocyte stages by quantitative real-time PCR ( qPCR ) and reverse transcription ( RT ) -qPCR , respectively . Parasite prevalence by qPCR was 7 . 2% for P . vivax ( n = 520/7235; range by survey 6 . 0%-8 . 1% ) and 3 . 2% for P . falciparum ( n = 235/7235; range by survey 0 . 4%-7 . 7% ) . Sub-microscopic infections accounted for 73 . 5% of P . vivax ( range by survey 60%-89% ) and almost the totality of P . falciparum cases . Gametocytes were found in 28 . 4% P . vivax infections ( range by survey 18 . 7%-34 . 1% ) , with a peak of 61 . 5% in one community at the start of the transmission season . About 59 . 8% of all P . vivax gametocyte carriers were asymptomatic and 31 . 9% were sub-microscopic . Age patterns for gametocyte prevalence paralleled asexual stage infections and peaked among >15–25 year old individuals . Asexual parasite density was found to be the strongest predictor for P . vivax gametocyte presence in longitudinal multivariate analysis ( odds ratio 2 . 33 [95% confidence interval 1 . 96 , 2 . 78]; P<0 . 001 ) . Despite significant differences in seasonality patterns and P . vivax prevalence found at the local scale , sub-microscopic and asymptomatic infections predominate and contribute significantly to the gametocyte reservoir in different communities of the Peruvian Amazon . Control and elimination campaigns need sensitive tools to detect all infections that escape routine malaria surveillance , which may contribute to maintain transmission in the region .
As countries in the Americas develop plans for malaria pre-elimination , a better understanding of Plasmodium transmission epidemiology is necessary to implement effective interventions , in particular for Plasmodium vivax [1 , 2] . In Peru , similar to other regions in Amazonia , P . vivax is responsible for about 80% of all malaria infections [3] , with asymptomatic low-grade parasitemia reported for ≈75% of them [4 , 5] . Between 2005 and 2011 , malaria was reduced by over 50% thanks to intensification of control efforts and the PAMAFRO campaign ( Malaria Control Program in Border Areas of the Andean Region ) , which included active screening and treatment and distribution of insecticide-treated nets [6] . Unfortunately , and coinciding with a reduction of control measures , a steady increase in cases has been reported in the country since 2012 characterized by high levels of transmission heterogeneity at the local scale [6 , 7] . In this context , accurate description of the human malaria reservoir and the contribution of asymptomatic and sub-microscopic infections to transmission is lacking . Malaria transmission from the human host to the mosquito vector requires that Anopheles species ingest mature sexual forms of Plasmodium parasites , named gametocytes , during a blood meal [8] . Therefore , gametocyte carriage can be used as indirect estimator of the infectiousness potential of individuals in molecular epidemiology studies ( taking into account that mosquito infections may be modulated by inter-individual variation in yet poorly understood vector , parasite and host factors [8 , 9] ) . The characterization of the gametocyte reservoir includes the detection of sexual stage infections , their quantification relative to asexual parasites , the identification of factors that determine gametocyte emergence , determining how gametocyte carriage changes over time , or what is the spatial clustering of sexual stage infections . Most of the current knowledge on Plasmodium gametocyte epidemiology comes from studies on Plasmodium falciparum species . Sexual P . falciparum stages are normally observed in peripheral blood ≈7–12 days after initial asexual infection is established . A recent clinical trials meta-analysis showed that , on average , 12 . 1% of infections carry gametocytes at enrolment by light microscopy ( LM ) , and that these infections are associated with anaemia , absence of fever and low asexual parasite densities; after treatment , gametocyte rate is higher among individuals taking sulphadoxine pyrimethamine or chloroquine as compared to artemisinin combination therapies ( ACT ) [10] . Community-based cross-sectional and cohort studies , which take into account asymptomatic infections , have set the proportion of P . falciparum gametocyte carriers in a wide range from ≈10% by LM in Senegal to as high as 61% in Papua New Guinea ( PNG ) by reverse-transcription quantitative PCR ( RT-qPCR ) or 70 . 1% in Burkina Faso by QT-NASBA [4 , 11–16] . These studies showed that gametocyte carriage is higher in young age groups [11–15] , and correlates with asexual parasite densities -when measured by molecular methods [11 , 15]- , presence of fever or clinical malaria [13–15] , haemoglobin C and S variants [16 , 17] , and blood groups O and B [14] . On the other hand , P . vivax transmission epidemiology and the characteristics of its infectious reservoir have been less studied [8] . P . vivax gametocytes are produced shortly after initial asexual wave of parasitemia in the bloodstream , have a shorter lifetime , and are suggested to infect mosquitoes more efficiently than P . falciparum [8 , 18] . In recent community based surveys , the proportion of infections with P . vivax sexual stages was found to reach >50% by LM [4 , 19] , whereas by RT-qPCR 23 . 5%-96% of infections carried gametocytes in studies in Oceania and Brazil [15 , 20 , 21] . Like for P . falciparum , young age [15 , 20 , 22] and high asexual parasite density were described as the main predictors [15 , 19 , 21–25] . Low haemoglobin levels and fever have also been linked with P . vivax gametocyte presence [15 , 23] , although equally high rates of gametocytes were reported in symptomatic and asymptomatic infections in Brazil [21 , 26] . Here , we aim to improve our understanding of Plasmodium gametocyte epidemiology and generate region specific data that can inform health authorities to tailor transmission-blocking strategies to the local context of transmission heterogeneity . A longitudinal cohort study was conducted in two separate communities , where all individuals were regularly screened for asexual and sexual stage infections using molecular tools for one year . The factors that contribute to gametocyte carriage , with a focus on asymptomatic and sub-microscopic carriers , are discussed .
The study was conducted in the communities of Cahuide and Lupuna , Maynas Province , Loreto Department , Peru ( Fig 1 ) . The region is characterized by a tropical climate with a mean temperature of 27 . 5°C . Despite significant transmission heterogeneity , overall malaria is perennial with marked seasonality and a peak in April-June . In 2013 , Loreto Department accounted for 89 . 5% ( 43284 ) of all malaria cases in Peru , being 81 . 9% ( 35458 ) caused by P . vivax [27] . Cahuide is a rural community 57 km away from Iquitos city . Houses are scattered along 9 km of the Nauta road , at the intersection with Itaya river . The area is characterized by road-driven deforestation and palm-roof production . Monthly entomological inoculation rate ( EIR ) range was 0–2 . 52 infective bites/person/month in 2012 [28] . On the other hand , Lupuna community includes three villages located 500–1000m away from the left bank of river Nanay ( Fig 1 ) . Although closer to Iquitos city than Cahuide , the area is only accessible by boat and mainly forested . Population is relatively stable and works in agricultural activities . Monthly EIR range estimates for Lupuna were 0–1 . 98 in 2012 . In both sites Anopheles darlingi is the dominant vector species [28] . Malaria control in the area relies mainly on passive case detection and LM diagnosis performed at health posts . Active case detection campaigns by LM are performed only when outbreaks or unusual rises in case numbers are reported in a community . As part of a 3-year prospective population-based cohort study with longitudinal follow-up , we analyzed gametocyte carriage every three months from December 2013 to December 2014 ( five surveys ) . A total of 1935 individuals from 442 households aged >3 months were included . At each survey , all individuals were screened for malaria symptoms and completed a questionnaire with demographic data . Blood smears , filter paper blood spots , and three blood drops ( ≈50 μl ±10 μl ) into 250 μl of RNAprotect stabilizer reagent ( Qiagen ) , were collected from finger pricks . RNAprotect-blood mixture was kept cold and transferred to Iquitos for storage at -20°C . Blood smears were stained with Giemsa and examined for malaria parasites under 700x magnification . Parasite counts per 200 leukocytes were used to estimate parasite density , assuming 8000 leukocytes in 1 μl of blood . All infections diagnosed by LM were treated according to National Guidelines from Peruvian Ministry of Health ( MINSA ) , regardless of symptoms . DNA was extracted from one punch of dried blood spots on filter paper ( ≈25mm2 , ≈7 . 5 μl ) using QIAamp DNA Mini kit ( Qiagen ) following manufacturer’s instructions , and eluted in 150 μl of AE buffer . Identification and quantification of Plasmodium species was done by real-time qPCR protocol targeting 18S ribosomal genes , adapted from Mangold et al [29] . Briefly , 5 μl of DNA were added to a final reaction volume of 25 μl including 12 . 5 μl of PerfeCTa SYBR Green FastMix ( Quantabio ) and 300nM primers PL1473F18 and PL1679R18 ( IDT ) , and run in a CFX Connect thermocycler ( Bio-Rad ) . Species were identified by melting temperature ( Tm ) curve analysis using CFX Manager software ( Bio-Rad ) , with a Tm of 77°C ( ±1°C ) for P . vivax and 73°C ( ±1°C ) for P . falciparum . Plasmids with species-specific 18S gene inserts were used as controls . The limit of detection was set at the dilution where at least 60% of the replicates were positive , and corresponded to 1 copy/reaction for both species ( i . e . 4 copies/μl of blood in study samples ) . Sample quantification was done using standard curves built from 1:10 plasmid dilution series . Only samples with both valid Ct and Tm values were considered positive . The presence of mature gametocytes in qPCR positive samples was determined by one-step reverse transcription qPCR ( RTqPCR ) , targeting Pfs25 ( P . falciparum , PF3D7_1031000 ) and Pvs25 ( P . vivax , PVX_111175 ) mature gametocyte specific gene transcripts . RNA was extracted from RNAprotect samples using RNeasy Mini Kit columns ( Qiagen ) and eluted in 50 μl of THE-RNA Storage Solution ( Ambion ) . Contaminant genomic DNA was removed by treatment with TURBO DNAse ( Ambion ) for 1h at 37°C . Parasite RNA presence was confirmed in a random set of 10% of the samples using RT ( Maxima First Strand cDNA Kit , Thermo ) and 18S qPCR . Gametocytes RTqPCR was performed in a LightCycler 480 using LightCycler Multiplex RNA Virus Master kit ( Roche ) , with primers and HEX ( P . falciparum ) - and FAM ( P . vivax ) -labelled hydrolysis probes from Wampfler et al [30] ( IDT ) . All samples were tested in duplicate reaction and controls without RT enzyme were added to exclude false positives due to the presence of genomic DNA . Analysis was done in LightCycler480 software version 1 . 5 . 0 . Replicates with Ct difference >1 were repeated . P . falciparum gametocyte densities were quantified using a standard curve generated from in vitro cultured gametocytes . Briefly , P . falciparum 3D7-E5 strain ( kindly provided by Dr . Alfred Cortés , ISGlobal , Barcelona ) was synchronized with 5% sorbitol and induced for gametocytogenesis by stress with partially spent medium for 2 consecutive days . Asexual stages were removed by 50 mM N-acetyl-glucosamine treatment until day ≈12–14 , when mature stage V gametocytes were harvested and concentrated using MACS magnetic separation ( LD columns , Milteny Biotec ) . A 7-point 1:10 dilution series ranging from 100 . 000 to 0 . 1 gametocytes/μl was prepared in whole blood and resuspended in RNAprotect as described above for gametocyte density quantification . Due to the lack of P . vivax in vitro culture , P . vivax densities were first estimated from a P . falciparum standard curve quantified using a FAM-labelled Pfs25 probe , and a correction factor for the differential expression of Pvs25 vs Pfs25 was then applied using recently published P . falciparum and P . vivax gametocyte trend lines [15] . Clinical malaria was defined as confirmed Plasmodium infection by microscopy and/or qPCR presenting with fever , chills and/or headache at the time of visit , or reporting one of these symptoms during the previous 7 days . Asymptomatic individuals were defined as those with confirmed infection not presenting any of these symptoms at the time of visit or in the past 7 days . Prevalence was defined as the number of parasite carriers out of the total population , whereas the term 'rate' was used to define the proportion of asymptomatic/sub-microcopic/gametocytemic individuals out of the total number of infections . Because only malaria positive samples were processed for RNA-based detection of gametocytes , gametocyte population prevalence is considered an estimate . Incidence of gametocyte carriage was calculated as the number of new sexual stage infections per 1000 person/year . Time-at-risk was calculated as 90 days per each survey in which an individual participated after study initiation . When treatment was reported , 15 days were subtracted as a risk-free time [31] . For individuals positive for gametocytes in two consecutive surveys without reported treatment , only first observation was counted , whereas those positive in two non-consecutive surveys were considered as independent infections . For parasite densities , only results by molecular methods are reported . Comparisons between demographic categorical variables were done using chi-square or Fisher’s exact test , and age means were compared by t-test . Non-parametric Kruskal-Wallis test was used to compare parasite densities . Multilevel regression models were used to determine risk factors for Plasmodium infection during longitudinal follow-up in Stata software ( version 11 . 0; StataCorp ) , with each observation nested by individual and individuals grouped by household . Data were set as panel ordered by time of screening . Logistic regression was used for association analysis with parasite prevalence and gametocyte rate , while log transformed parasite densities were analyzed by linear regression . Univariate models were first run including as independent variables demographic data ( age , gender , pregnancy status , community of residence , bed net coverage ) , work status and occupation , house construction materials , clinical data ( malaria symptoms , history of malaria in previous year ) , and 18S copy numbers–the latter for gametocyte associations only- . Multivariate models were built with stepwise forward selection of all co-variables with 5% significance level in univariate analysis plus age . Predictors added in decreasing order of significance were kept if its addition led to a decrease in Akaike Information Criterion value . Overall significance for variables with multiple categories was estimated using Wald test . P-values <0 . 05 were considered statistically significant . Data were plotted using Prism 7 ( GraphPad ) . Spatial scan analysis was conducted to identify purely spatial ( by survey ) and spatio-temporal ( all surveys ) clusters of gametocyte carriers at households or village level . Separate analysis were run for each community among all qPCR-positive individuals in SaTScan 9 . 4 . 2 [32] . Briefly , circular windows of multiple sizes containing a maximum of 30% of the population were applied , in which the probability that the observed prevalence is higher than the expected under the hypothesis of no clustering was tested using a Bernoulli distribution model . P-values were computed across 999 Monte-Carlo replications . Because spatial scan statistics may have limitations to identify hotspots in areas where data is distributed linearly like Cahuide [33] , autocorrelation analysis was performed in ArcMap 10 . 4 ( ArcGIS , ESRI , [34] ) using Getis-Ord Gi* statistic and False Discovery Rate correction for multiple testing . Distance bands were calculated using Incremental Spatial Autocorrelation tool in windows of 25 m and the distance corresponding to first Z-score peak was selected ( 200m ) . Results were mapped in QGIS 2 . 12 . Written informed consent was obtained from all individuals -or their parents or guardians in the case of minors- before conducting any study activity . The study received ethical approval from the Comité Institucional de Ética , Universidad Peruana Cayetano Heredia ( Lima , Peru; code SIDISI 57395 ) , and the Institutional Review Board , Institute of Tropical Medicine ( Antwerp , Belgium; reference 1080/16 ) .
Out of 1935 censed individuals , 1369 ( 71% ) participated in ≥4 surveys , 290 ( 15% ) participated in 3 surveys and 276 ( 14% ) in ≤2 surveys ( Table 1 and S1 Table ) . 7265 samples were collected over the study period , with full clinical data records available for 5746 visits . Individuals from Cahuide were younger and reported a significantly higher number of malaria cases in the year prior to study initiation than individuals from Lupuna ( Table 1 , P<0 . 001 ) . However , symptomatic malaria by LM was more frequent in Lupuna during the time of study ( P<0 . 001 ) . About 96% of households ( 425/442 ) reported having bed nets , either long-lasting insecticide-treated or locally produced using tocuyo textile . Recruitment was significantly lower in March compared to other months ( S1 Table ) . Overall , individuals sampled in this particular survey did not differ in age , gender , occupation or type of housing , but had significantly higher malaria history in the previous year compared to other surveys ( P = 0 . 001 ) . The difference in malaria history between surveys was not observed after stratifying by community ( P>0 . 852 ) . P . vivax was the most common malaria species both by LM ( 156/7265 , 2 . 1% [range by survey 0 . 7%-3 . 7%] ) and qPCR ( 520/7265 , 7 . 2% [6 . 0%-8 . 1%] ) . A total of 432 individuals ( 22 . 3% of the population ) had a P . vivax infection at some point during follow-up , and 71 ( 3 . 7% ) were positive in more than one survey . By qPCR , prevalence was higher in Lupuna ( mean 9 . 9% [range by survey 8%-12 . 4%] ) than Cahuide ( 5 . 2% [3 . 8%-7 . 8%] , P<0 . 001 , Fig 2A ) , with a marked seasonal pattern peaking in June after major rainfall . On the contrary , Cahuide showed an overall decrease in infections despite a peak in September , with no positive blood smears by the end of the follow-up period in December 2014 . P . vivax infections increased with age until 25 years old ( Fig 3A ) , and peaked among >15–25 year old in Lupuna ( 82/545 by qPCR , 15% [range by survey 10 . 5%-22%] , P<0 . 001; Fig 3A ) . Overall median P . vivax density was 112 copies of 18S/μl ( inter-quartile range [IQR] 44 , 536 ) , with higher parasite densities in Lupuna ( 140 copies/μl [IQR 56 , 1016] ) than Cahuide ( 80 copies/μl [IQR 32 , 296] , P<0 . 001; S1 Fig ) . P . falciparum parasites were detected in 5/7265 blood smears and 235/7265 qPCR samples ( 3 . 2% [range by survey 0 . 4–7 . 7%] ) , originating from 222 different individuals ( 11 . 5% of the population ) . P . falciparum qPCR infections were more frequent in Cahuide ( 3 . 6% [range by survey 0 . 1%-8%] ) than Lupuna ( 2 . 8% [0 . 7–4 . 7%] , P<0 . 001; Fig 2A ) , and decreased with time down to an overall prevalence of 0 . 4% ( 6/1657 ) in the last survey . No differences in P . falciparum prevalence were found by age groups in any community ( P>0 . 174 , Fig 3A ) . Median P . falciparum density ( 28 copies/μl [IQR 12 , 56]; S1 Fig ) was lower than for P . vivax ( P<0 . 001 ) . High rates of sub-microscopic and asymptomatic infections were found throughout the study period for both species ( Table 2 ) . Sub-microscopic infections accounted for 73% ( range by survey 60%-89% ) of all P . vivax and 97 . 8% ( 88%-100% ) of all P . falciparum positive samples . Using the qPCR result for clinical malaria case definition , the number of P . vivax clinical cases increased from 56 ( by LM at screening ) to 101 , and the number of P . falciparum clinical cases from 2 to 30 . Still , the large majority of infections detected after qPCR were asymptomatic ( 77 . 2% [range by survey 72%-82%] for P . vivax and 82 . 7% [60%-90%] for P . falciparum ) . Parasite densities in P . vivax asymptomatic carriers were lower ( 92 copies/μl [IQR 40 , 292] ) than those in clinical cases ( 544 copies/μl [IQR 88 , 5712] , P<0 . 001 ) . No difference in parasite densities was found between P . falciparum asymptomatic and symptomatic infections ( 7 copies/μl [IQR 3 , 14] ) vs 5 copies/μl [IQR 2 , 13] , P = 0 . 364 ) . By community , Cahuide showed significantly higher rates of sub-microscopic infections for both species ( P<0 . 005 ) , as well as higher asymptomatic rates for P . falciparum ( P = 0 . 015; Table 2 ) . P . vivax gametocytes were observed in 72 blood smears ( 1% population prevalence; Fig 2B ) . By molecular methods , gametocytes were detected in 143 samples ( 2% estimated population prevalence ) originating from 135 different individuals ( 7% ) ; only 8/135 ( 6% ) individuals carried gametocytes in more than one time-point . The estimated annual incidence for P . vivax gametocyte carriage was 72 sexual-stage infections/1000 person-year ( 136 . 2 for Lupuna and 72 for Cahuide ) . Seasonal trends in gametocyte prevalence differed by community: in Lupuna , gametocytes by RTqPCR peaked in March ( 8/127 , 6 . 3% est . population prevalence; Fig 2B ) and decreased afterwards ( 21/74 , 2 . 8% est . population prevalence in December ) , whereas in Cahuide gametocyte prevalence decreased during the whole study period , as did asexual parasitemia ( Fig 2B ) . Age patterns for gametocyte prevalence were similar than those for asexual stage infections , and highest among the >15–25 year old group ( Fig 3B ) . P . vivax gametocyte densities by RTqPCR did not vary by survey ( P = 0 . 261 ) or community ( P = 0 . 319; S1 Fig ) . P . falciparum gametocytes were detected in 18 samples by RTqPCR ( 0 . 3% population prevalence ) . Annual incidence was 10 . 7 cases/1000person-year ( 11 . 1 in Cahuide and 10 . 1 in Lupuna ) . Among positive samples , the proportion of P . vivax infections carrying gametocytes was 46 . 2% ( 72/156 [range by survey 42 . 6%-72 . 7%] ) for blood smears and 28 . 4% ( 143/520; 18 . 7%-34 . 1% ) by RTqPCR ( Fig 4A ) . P . vivax gametocyte rates were higher in Lupuna ( 35 . 9% [range by survey 19%-61 . 5%] ) than Cahuide ( 22 . 4% [3%-50%] , P = 0 . 001; Fig 4A ) and did not vary significantly by age category ( P = 0 . 191 ) . The gametocyte rate for P . falciparum was 7 . 6% ( 18/235 [range by survey 3%-50%] ) and was similar in both communities and in all age groups ( P>0 . 234 ) . Sub-microscopic and/or asymptomatic infections with gametocytes were found to constitute a significant proportion of the total P . vivax reservoir , with rates ranging from 9% to 21% overall , and up to 42% for in Lupuna community ( Table 2 ) . If only gametocyte positive infections with full clinical data records are taken into account , sub-microscopic and/or asymptomatic infections represent 67 . 2% ( 82/122 ) of the gametocyte reservoir detected by molecular methods ( Fig 4B ) . P . vivax gametocyte density in asymptomatic individuals ( 8 . 6 gametocytes/μl [IQR 1 . 9 , 85] ) did not differ from that in clinical cases ( 5 . 7 gametocytes/μl [IQR 2 . 0 , 133 . 7] , P = 0 . 696 ) . On the other hand , the P . falciparum gametocyte reservoir was entirely constituted by infections that were either sub-microscopic and/or asymptomatic , although sample size was small ( n = 18 ) . Results for all co-variables associated with P . vivax infection in univariate analysis are provided as S2 Table , and were used to build multivariate models with stepwise forward selection . Final models showed that risk of P . vivax infection was highest in the >15–25 year old group , when compared to children under five ( odds ratio , OR 3 . 1 [95% confidence interval ( CI ) 1 . 75 , 5 . 49] , P<0 . 001; Table 3 ) . Other independent risk factors for P . vivax infection included residence in Lupuna , and living in houses built with palm or other materials -a category that includes mainly plastics and corrugated iron- , as compared to concrete or brick houses . Among infected individuals , total parasite density was the main independent predictor for the presence of gametocytes , with a 10% increase in 18S copy numbers associated to double risk of carrying gametocytes ( Table 3 ) . In addition , a borderline association towards higher risk of gametocyte carriage was found for Lupuna . Age was not found to be a significant predictor for gametocyte risk in multivariate analysis ( P>0 . 411 ) . Models for P . vivax parasite densities showed that 18S copies were positively associated with clinical symptoms ( OR 5 . 93 [95% CI 5 . 73 , 9 . 17] , P<0 . 001 ) as well as residence in Lupuna community ( OR 1 . 59 [95% CI 1 . 05 , 2 . 42] , P = 0 . 028 ) ; S3 Table ) . Parasite density by 18S was the only predictor of gametocyte density ( OR 1 . 16 [95% CI 1 . 01 , 1 . 32] , P = 0 . 034; S3 Table ) , which did not differ between children and adults ( P>0 . 188 ) . Association analysis was not attempted for P . falciparum sexual stage infections due to the low number of cases . Prevalence of P . falciparum asexual infection was associated with the absence of clinical symptoms , whereas no variables were associated with parasite density ( S4 Table ) . SaTScan analysis could not identify spatial clusters with statistical significance ( P<0 . 05 ) , which may have been limited by the low number of observations per time-point together with uneven household geographical distribution . Four areas showed a higher-than-expected risk with significance thresholds below 20% , including three spatial clusters in Cahuide and Lupuna , and a small spatio-temporal cluster in Cahuide ( Fig 5; relative risk range 3 . 8–21 , P-value range 0 . 068–0 . 200 ) . Individuals residing inside these areas did not differ in age , gender , type of household , occupation nor clinical characteristics compared to those outside the cluster ( P>0 . 113 ) . No hotspots at household level were identified by Getis-Ord Gi* statistic .
The data presented in this study constitutes the most accurate quantification of the Plasmodium gametocyte reservoir in Peru to date , and the largest conducted in the Amazon region . A previous study in Peru from 2003 using LM alone reported a gametocyte rate of 22% for P . vivax and 53% for P . falciparum [4] , as compared to the 46% for P . vivax reported here by LM ( almost all P . falciparum infections were sub-microscopic ) . This increase is in line with the overall reduction in endemicity in the country over the past decade , which may translate into a higher investment in sexual stages to sustain transmission [8] . On the other hand , the average gametocyte rate of 28 . 4% for P . vivax by RTqPCR compares to community-level data from a similar epidemiological context in Solomon islands ( 23 . 5% gametocyte rate in 2012 ) [20] , but is lower than the 49% found in PNG in 2010 or a remarkably high 96% in Brazilian Amazon in 2011–12 [15 , 21] . Different factors may explain this variability . First , factors attributable to seasonality and study-designs . While the study in PNG was a cross-sectional conducted during the rainy-season , the study from Brazil estimated gametocyte rates from only a subset of surveys and samples ( n = 55 ) . Our data covers one full year and , in fact , rates reached as high as 61% when taking into account only the transmission peak in Lupuna community . Second , differences in age distribution of malaria infections between the different locations . The proportion of gametocyte carriers is known to be higher among younger age groups in areas of high malaria transmission , where children have higher asexual densities [8] . Thus it is reasonable to expect an increase in gametocyte rates in a high transmission setting like PNG , where P . vivax infections concentrate in children <12 years old , as opposed to our study[15] . Third , factors attributable to different blood collection and parasite detection methods . On one hand , Barbosa et al processed 200μl of blood in Brazil , as compared to 50 μl blood collections both in the present ( estimated volume ) and in other studies [15 , 20 , 21] . Using high blood volumes can increase the chance to detect low-density gametocytemic samples; moreover , the few negative samples reported in that study had asexual parasite densities below 11 parasites/μl , levels that are highly frequent in our surveys . On the other hand , the performance of the all-stage parasite detection qPCR protocol may affect gametocyte rates by diminishing estimates when a highly sensitive asexual qPCR is used . Because gametocytes often represent only a small proportion of the total parasite biomass , the high rate of sub-microscopic infections found in this area compromises technical sensitivity for gametocyte detection , and may explain why gametocyte rates by RTqPCR were lower than those detected by LM . This effect was particularly relevant for P . falciparum ( 98% sub-microscopic rate ) , as the few sexual stage parasites detected were in either submicroscopic or asymptomatic infections . In fact , when only patent infections were taken into account , gametocyte rates by RTqPCR were close to 90% . Sub-microscopic infections predominated in both communities independently of the observed differences in local transmission patterns . Overall , these results highlight the need of using highly sensitive molecular methods for malaria surveillance in pre-elimination settings . In this context , the relevance of sub-microscopic and asymptomatic infections ( i . e . those that would escape routine LM-based diagnostics by field teams ) resides largely on whether they contribute to effective mosquito infections and disease transmission . Although mosquito infectivity is known to increase with parasite density [37] , P . falciparum infections at sub-microscopic levels have been suggested to account for as much as ≈30% of human-mosquito transmissions in countries like Burkina Faso [12 , 37 , 38] . For P . vivax , our understanding of the infectivity of sub-microscopic infections is much more limited , and the few data available has shown some variability , probably due in part to modest sample sizes [18 , 39] . A study in Brazil in which Anopheles darlingi fed directly on 11 P . vivax sub-microscopic patients found only 2 positive midguts [39] , while Vallejo et al showed that 56% of naturally infected P . vivax sub-microscopic carriers from Colombia could infect Anopheles Albimanus , an infectivity rate similar to that of symptomatic carriers albeit with lower number of infected mosquitoes and oocyst counts [18] . Remarkably , the asymptomatic infections in the present study showed median gametocyte densities similar to those in symptomatic individuals . Moreover , a third of all gametocytemic patients were asymptomatic with patent parasitemia . Taken together , the data suggests that a lower success in infectivity of sub-microscopic infections might be compensated by the high frequency of both sub-microscopic and/or asymptomatic carriers ( 67% of P . vivax and all P . falciparum gametocyte carriers ) , as well as the relatively high P . vivax gametocyte densities in part of the asymptomatic population . In addition , asymptomatic parasite carriers will contribute to infectiousness for longer periods of time , as these individuals are not seeking treatment [40 , 41] . In terms of age groups , all those above 5 years of age were found to be more susceptible to both sexual and asexual P . vivax infections in this study , with a peak in >15–25 year old . This parallelism is in agreement with the strong association found between total parasite densities and presence of gametocytes . Conversely , other studies on P . vivax epidemiology have reported that prevalence of sexual stage infections decreases with age [10 , 15 , 19 , 20 , 23] . On one hand , individuals aged >15 years included here worked mainly in agriculture , forestry and fishing activities , what can increase their exposure to Anopheles darlingi bites during daytime . On the other hand , it is also reasonable to think that the sustained period of low transmission in Peru between 2005–2011 might have impacted immune patterns and shifted the acquisition of immunity towards older ages . This finding is in apparent contradiction with the observed high rates of sub-microscopic infections across all age groups , especially for P . falciparum . However , it has been suggested that after a period of reduction in malaria transmission , hosts might have better control of parasitemia and clinical disease provided that some immunity persists , time before reinfection is extended , and new infections are likely to be monoclonal [42] . Indeed , the study area experienced a reduction of the effective parasite population size due to a bottleneck event as a result of 2005–2011 control programs [43] . Whether age patterns will shift again after the increase in transmission observed since 2012 remains to be determined . Significant differences in malaria indicators were found at community level , with residence in Lupuna being independently associated with higher infection rates in multivariate models . Differences between Cahuide and Lupuna communities are not surprising given their demographic and geographical characteristics . Whereas Lupuna is formed by forested riverine villages , Cahuide has scattered households , road-driven deforestation and a much more unstable population . Intense malaria control measures were applied in Cahuide in mid-2012 after a malaria outbreak , thus contributing to explain the lower infection prevalence . Furthermore , population genetic studies in the region showed that P . vivax population is highly structured suggesting different interactions with the host at the local scale [43] . The higher transmission in Lupuna was also accompanied by marked seasonality in P . vivax gametocyte carriage , which peaked one trimester before total infections did . However , since the addition of survey variable did not have a significant effect on the fit of multivariate model , differences in gametocyte rates between surveys are likely due to differences in total parasite densities rather than to a true seasonal effect . Overall , the detailed data obtained for two contrasting communities provides clues on what can be expected in other areas sharing similar characteristics and on how targeted interventions could be adapted . This study has some limitations . On one hand , despite gametocyte carriage is a better indicator of the infectious reservoir than total parasite rates , it still remains indirect as compared to mosquito feedings , which have more control on factors like host and vector immunity . On the other hand , the trimestral sampling strategy allows for epidemiological characterization of gametocyte carriage , but does not allow for accurate calculation of the duration of gametocytemia; future studies on gametocyte dynamics in the area will contribute to answer these questions . In conclusion , asymptomatic and sub-microscopic infections are significant contributors to the gametocyte reservoir in the Peruvian Amazon , despite the high degree of heterogeneity of transmission at the local scale and throughout the transmission season . Gametocyte prevalence peaks in young adults , but rates relative to asexual stage infections are similar across all age groups . Control and elimination campaigns need sensitive tools to detect infections that would otherwise escape routine malaria surveillance and may contribute to the maintenance of transmission in the Amazon region . | Malaria elimination , i . e . the complete interruption of parasite transmission in a region , is in the agenda of health authorities in countries that achieved substantial reduction of the disease burden in the past decade . However , our understanding of transmission epidemiology for low transmission areas where Plasmodium vivax is endemic , like the Amazon basin , is still limited . In this study , we describe the prevalence and risk factors for carrying the parasite stages that are transmitted to the mosquito vectors , named gametocytes , in 1935 individuals from two communities of the Peruvian Amazon that were regularly screened during 1 year . We report that malaria infections with no clinical symptoms and those with parasite levels below microscopy detection threshold , account for two thirds of all P . vivax infections with gametocytes , and that the highest infection rate is found among young adults . In addition , almost the totality of P . falciparum infections detected was sub-microscopic . Because all these infections escape current malaria surveillance systems -based on passive case detection and/or microscopy diagnosis- , new approaches are necessary to target all infections in order to eliminate the malaria transmission reservoir in Peru . | [
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| 2017 | Predominance of asymptomatic and sub-microscopic infections characterizes the Plasmodium gametocyte reservoir in the Peruvian Amazon |
Replication of many RNA viruses is accompanied by extensive remodeling of intracellular membranes . In poliovirus-infected cells , ER and Golgi stacks disappear , while new clusters of vesicle-like structures form sites for viral RNA synthesis . Virus replication is inhibited by brefeldin A ( BFA ) , implicating some components ( s ) of the cellular secretory pathway in virus growth . Formation of characteristic vesicles induced by expression of viral proteins was not inhibited by BFA , but they were functionally deficient . GBF1 , a guanine nucleotide exchange factor for the small cellular GTPases , Arf , is responsible for the sensitivity of virus infection to BFA , and is required for virus replication . Knockdown of GBF1 expression inhibited virus replication , which was rescued by catalytically active protein with an intact N-terminal sequence . We identified a mutation in GBF1 that allows growth of poliovirus in the presence of BFA . Interaction between GBF1 and viral protein 3A determined the outcome of infection in the presence of BFA .
All known positive strand RNA viruses replicate their genomes in association with remodeled cellular membranes . Assembly of replication complexes on membranes is believed to have several advantages . Membranes provide a scaffold that increases the local concentration of proteins involved in replication and facilitates the proper topological orientation of replication complex components . The association with membranes protects replicating RNA from cellular nucleases , and may also prevent induction of cellular innate immune responses by confining dsRNA or other signaling intermediates [1] . Poliovirus is a member of the Picornaviridae family , which consists of small , non-enveloped positive strand RNA viruses that include numerous human and veterinary pathogens , such as polio , rhinovirus ( common cold virus ) , hepatitis A virus , and foot and mouth disease virus . The poliovirus genome is a single RNA molecule of about 7500 nt in length which is directly translated in an infected cell into a single polyprotein that undergoes immediate processing in cis and in trans by three virus-encoded proteases into a cascade of intermediates and mature polypeptides . Non-structural proteins , necessary for RNA replication , are encoded in the P2-P3 region of the genome , while coding sequences for structural proteins , necessary for packaging of progeny RNA but dispensable for replication , are located in the P1 region ( Fig . 1A ) . Infection of cells with poliovirus results in rapid and massive reorganization of virtually all intracellular membranes except for mitochondria , into clusters of tightly-associated vesicles of heterogeneous size which harbor viral replication complexes on their surfaces [2] , [3] , [4] . These replication complexes have been shown to be associated with all of the non-structural viral proteins from the P2 and P3 coding region [5] , [6] . Such massive rearrangements in cellular membrane organization likely require major rewiring of normal membrane metabolism , but the molecular mechanisms underlying induction , formation and functioning of poliovirus membranous replication complexes remain largely unknown . It has been shown that at the early stages of poliovirus infection non-structural virus protein 2B co-localizes with COPII-coated vesicles , budding from ER exit sites [7] . These data together with the observations that poliovirus-induced vesicles are often found in electron micrographs close to the remnants of ER [8] suggest that the COPII-dependent mechanism of vesicle formation may contribute to the development of viral replication complexes . However , Shlegel et al . have identified markers not only from the ER , but also from Golgi and lysosomes , present on polio-induced vesicles . It was proposed that autophagy-like processes may be involved in membrane remodeling in polio infected cells , which would also explain the large proportion of double membrane vesicles observed by electron microscopy of infected cells [9] . Another important clue towards possible cellular pathways involved in formation of polio replication complexes comes from the sensitivity of poliovirus infection to brefeldin A ( BFA ) [10] , [11] . Interestingly , neither formation of COPII-coated vesicles nor autophagy are sensitive to BFA [12] , [13] , [14] , suggesting that these two processes do not fully account for all cellular pathways exploited by poliovirus for its replication . BFA is a fungal metabolite , specifically targeting the activation of small cellular GTPases , Arfs , which are key regulators of the cellular secretory pathway . The inactive , cytoplasmic GDP-bound form of Arf , upon nucleotide exchange to GTP , undergoes conformational changes that allow Arf-GTP to bind membranes . Arf-GTP is referred to as “activated”; it initiates formation of secretory vesicles and regulates cytoskeleton functions and lipid metabolism [15] . Conversion of Arf-GDP into Arf-GTP requires the activity of guanine nucleotide exchange factors ( GEFs ) . In human cells BFA inhibits the function of three GEFs – GBF1 , BIG1 and BIG2 – by stabilizing transient complexes formed between the GEF and Arf-GDP . The specificity of BFA action on these GEFs is provided by the sequence of their Sec7 domains , directly involved in Arf activation [16] . We have shown previously that Arf-GTP accumulates on membranes in poliovirus- infected cells . Surprisingly , in an in vitro reaction , expression of two distinct viral proteins – 3A , a small membrane-binding protein , and 3CD , a soluble protein with protease activity – was sufficient to induce Arf translocation to membranes . We demonstrated that these two proteins specifically engage different cellular GEFs: 3A induces translocation of GBF1 to membranes while 3CD results in association with membranes of BIG1 and BIG2 [17] , [18] , [19] . Thus , all three mammalian BFA-sensitive GEFs could be involved in poliovirus replication . Wessels et al . have demonstrated that direct interaction between GBF1 and 3A from poliovirus or coxsackie virus B3 , a close relative of poliovirus , is responsible for association of GBF1 with membranes in 3A-expressing cells . When 3A was expressed individually , binding of 3A to GBF1 resulted in inhibition of the Arf-activating function of GBF1 [20] , [21] . This mechanism was proposed to explain an established phenomenon of inhibition of cellular protein secretion in polio- and coxsackievirus-infected cells , which had been shown previously to be caused by 3A expression [22] , [23] , [24] . In this paper we show that in the context of normal virus replication , functional GBF1 is required for successful virus propagation , and GBF1-3A interactions determine the outcome of infection in the presence of BFA . Surprisingly , the BFA-sensitive step in poliovirus replication was not the morphological remodeling of cellular membranes , but the functioning of replication complexes , suggesting strong dependence of poliovirus RNA replication on components of the host membrane traffic machinery .
To determine whether host cell factors contribute to BFA sensitivity of poliovirus replication , we took advantage of an available BFA-resistant cell line , BER-40 , which was isolated from parental Vero cells after mutagenesis and subsequent passaging in the presence of BFA [25] . Vero cells are routinely used in large-scale viral vaccine production , and they support robust replication of poliovirus . Figure 1B shows that while replication of poliovirus in Vero cells was severely inhibited by BFA , the BFA-resistant derivative of Vero cells , BER-40 , was able to support replication of the virus in the presence of the inhibitor . These results demonstrate that some cellular BFA-sensitive process is required for successful propagation of poliovirus in Vero cells . To identify the BFA-sensitive host factor involved in poliovirus replication , we decided to investigate the mechanism of resistance of BER40 cells to the inhibitor . Previous attempts to identify the determinants of BFA resistance in BER40 cells were unsuccessful [25] , [26] , [27] . We examined the gene sequences coding for all three high molecular weight Arf GEFs – GBF1 , BIG1 and BIG2 – that are known to be targets of BFA , to determine whether mutations were present in BER-40 cells compared with the parental Vero cell line . Formation of a stable complex between BFA , a sensitive ArfGEF and Arf-GDP is determined by specific amino acids in the Sec7 domains of the ArfGEFs; the rest of the protein does not participate in this interaction [28] . We amplified the Sec7 domain coding sequences of BIG1 , BIG2 and GBF1 from mRNA isolated from Vero and BER-40 cells . Sequence analysis showed that while BIG1 and BIG2 Sec7 domains were identical in both cell lines , BER-40 cells contained two species of GBF1 Sec7 sequences ( Fig . 2A ) . One corresponded to the same sequence found in the parental Vero cells , while the other coded for a substitution of the A in GBF1 residue 795 to E ( A795E ) . The two gene sequences likely arise from genetic heterozygosity in the BER-40 cells rather than a mixture of two cell populations , since a sensitive population would be rapidly selected against in the presence of BFA . To confirm that this mutation was responsible for the BFA-resistant phenotype of BER40 , we introduced it into an expression plasmid coding for YFP-GBF1 fusion protein , shown previously to be indistinguishable from the wild type GBF1 in intracellular localization and functional activities [29] . The mutated plasmid was used to transfect HeLa cells , a human cell line commonly used as a laboratory host for poliovirus and known to be highly sensitive to BFA . Transfection with the mutated plasmid conferred a greatly increased resistance to BFA , compared with cells transfected with an empty vector or cells transfected with a plasmid encoding the wild type GBF1 sequence ( Fig . 2B ) . In addition to increased cell survival in the presence of BFA , the BFA-resistant form of GBF1 also rescued the functional properties of the cells' secretory pathway , which are known to be sensitive to BFA treatment [30] . HeLa cells were co-transfected with pGLUC plasmid , expressing Gaussia luciferase with a natural secretion signal , and with plasmids expressing wild type GBF1 , A795E BFA resistant mutant GBF1 , or an empty vector . As seen in Fig . 2C , expression of the A795E GBF1 mutant almost completely restored secretion in the presence of BFA . Thus , the mutation in the GBF1 Sec7 domain is the major determinant of BFA resistance in the BER-40 cell line and can transfer BFA resistance to HeLa cells . The GBF1 mutation in Ber40 cells conferred resistance of cell growth to BFA , and we predicted that this same mutation also was responsible for poliovirus growth in Ber-40 cells ( see Fig . 1B ) . We have previously shown that ectopic expression of GBF1 can partially rescue poliovirus replication in the presence of BFA in HeLa cells [31] . To determine whether the growth of poliovirus in BER-40 cells in the presence of BFA was due to the mutated GBF1 , we compared the replication of a poliovirus replicon in HeLa cells transfected with plasmids coding for either the wild type or the A795E form of GBF1 . Figure 2D shows that replicon replication in the presence of BFA occurred with much greater efficiency in cells expressing GBF1 A795E . Equal amounts of wild type and mutant GBF1 proteins were synthesized , as measured by western blot analysis ( Fig . 2D , right panel ) . Thus , the substitution of A795E found in the GBF1 sequence from BER-40 cells is responsible for resistance of both cell secretion and viability and poliovirus replication to BFA . We also tested whether the rescue of polio replication from BFA inhibition in HeLa cells was dependent on GBF1's ability to functionally activate Arf . To this end , we exploited another GBF1 mutation that encoded a protein with a single amino acid substitution in the Sec7 domain ( E794K ) , which was shown previously to be inactive in Arf activation [32] . HeLa cells were transfected with expression plasmids for wild type GBF1 and for the E794K GBF1 mutant . While a polio replicon was able to replicate in the presence of BFA in cells expressing active GBF1 , cells expressing the inactive GBF1 mutant were unable to support polio replication in the presence of the same concentration of inhibitor ( Fig . 3A ) , demonstrating that the ability of GBF1 to activate Arf is required for polio replication . As a second approach to evaluate the requirement of GBF1 for poliovirus replication even in cells not treated with BFA , we depleted GBF1 levels in untreated HeLa cells with siRNA . The depletion of GBF1 was very effective after three days of siRNA treatment , and replication of the polio replicon was severely inhibited compared with cells treated with control siRNA ( Fig . 3C ) . Upon completion of the replication experiments the cells treated with GBF1 siRNA showed some signs of cytotoxicity and lysis [33] which resulted in reduction in the amount of BIG2 , another high molecular GEF , as well as other bands ( not shown ) on blots of lysates obtained from GBF1 siRNA-treated cells ( Fig . 3B ) . However , expression levels of control proteins from plasmids introduced at the same time as the polio replicon were identical in control cells and cells treated with GBF1 siRNA ( not shown ) , confirming the specificity of inhibition of polio replication by GBF1 knock-down . We also performed the same experiment in the presence of a broad range caspase inhibitor zVAD-fmk , to prevent possible depletion of cells transfected with polio replicon due to apoptosis , known to be triggered when polio replication is suppressed [34] . Treatment of cells with zVAD-fmk did not change the reduction of polio replication in cells with knocked down GBF1 expression ( not shown ) . Together , these results show that polio RNA replication strongly depends on the activity of GBF1 . Since Arf activation by GBF1 appeared to be essential for polio replication , we tested whether expression of Arf1 Q71L , a mutant Arf1 protein that manifests increased affinity for GTP and therefore does not require GEF-mediated nucleotide exchange to be activated [35] , can rescue polio replication under conditions of suppressed GBF1 activity . Figure 3C shows that expression of this Arf mutant did not support polio replicon replication in the presence of BFA , nor did expression of this mutant restore polio replication in cells treated with anti-GBF1 siRNA ( not shown ) . These data are consistent with previous analyses of the Arf Q71L protein . Expression of this mutant prevented BFA-induced Golgi breakdown and loss of COPI from the membranes; however , because this Arf is unable to cycle , COPI became irreversibly locked on membranes and was not functional [35] , [36] , [37] , [38] . Thus , viral RNA replication depends on the precise temporal and spatial regulation of GBF1-dependent Arf activation and cycling that is characteristic of this group of G proteins . The possibility that there is an Arf-independent GBF1 function that is required for virus replication also cannot be excluded . To confirm that 3A-induced recruitment of GBF1 to membranes results in Arf activation , we utilized an in vitro system that has been extensively exploited to reveal the biochemical machinery of poliovirus RNA replication [39] , [40] , [41] , [42] , [43] . RNA coding for poliovirus 3A was translated in HeLa cell extracts , and membranes were collected by centrifugation and analyzed by immunoblot to asses the proteins that were membrane-associated . As we showed previously [31] synthesis of 3A resulted in increased association of GBF1 with membranes ( Fig . 4 , GBF1 row; compare lanes 1 and 3 ) . Interestingly , a significant accumulation of GBF1 on membranes was also observed in samples treated with BFA , independent of the synthesis of polio 3A protein ( Fig . 4 , GBF1 row; compare lanes 1 , 2 and 4 ) . However , only 3A synthesis resulted in increased amounts of Arf on the membranes . As expected , this Arf was activated , as evidenced by the recruitment of components of the COPI coatomer complex , a downstream effector of Arf activated through the GBF1-dependent pathway [44] . No Arf or COPI accumulation was observed in samples treated with BFA , regardless of the GBF1 association with membranes ( Fig . 4 compare lanes 1 , 2 and 4 ) . These results clearly distinguish between the functional and abortive recruitment of GBF1 to membranes induced by 3A vs . BFA . Recruitment of GBF1 to membranes induced by poliovirus protein 3A leads to a productive cascade of Arf activation and COPI coatomer recruitment , consistent with our previous report [31] that increased levels of Arf-GTP steadily accumulate on membranes with time during poliovirus infection of HeLa cells . The 3A protein of coxsackievirus B3 ( CVB3 ) was shown previously to interact with the N-terminus of GBF1 [21] . CVB3 and poliovirus are closely related enteroviruses , so we speculated that a similar interaction between poliovirus 3A protein and GBF1 might account for the observed requirement for GBF1 in poliovirus replication . First we examined the BFA sensitivity of a wild type poliovirus replicon and a mutant replicon containing an insertion of a Ser residue at position 15 in the 3A sequence . This mutant , called 3A-2 , was shown previously to be defective in inhibiting the cellular secretory pathway [22] , [45] . Wessels et al . showed that when the mutation corresponding to polio 3A-2 was introduced into CVB3 , the resulting 3A protein manifested a severely decreased binding to GBF1 [46] . Replication of the 3A-2 replicon showed an approximately one-hour delay compared with wild type before the rapid phase of viral RNA synthesis begins . Although the final replication level was only slightly less than wild type in the absence of inhibitor ( see insets , Fig . 5A and B ) , replication of the mutant was completely abrogated even at the lowest concentration of the inhibitor tested . The wild type replicon displayed intermediate levels of sensitivity at low BFA concentrations ( Fig . 5A ) . We also tested the level of rescue of replication of the 3A-2 mutant replicon in the presence of BFA by ectopic expression of GBF1 . In this experiment we used our BFA-resistant A795E GBF1 , to obtain the maximum level of rescue . As shown in Fig . 5B , the response of the 3A-2 mutant was significantly lower than that of the wild type replicon . Finally , we showed that GBF1 knockdown had a significantly greater inhibitory effect on the replication of the 3A-2 polio replicon than on the wild-type replicon . In this experiment we treated cells with GBF1 siRNA for a shorter period – 2 days instead of 3- to minimize cytotoxicity of the siRNA and therefore to be able to detect weaker possible replication of the 3A-2 replicon . Under these conditions the reduction in replication of the wild type replicon was clearly visible but , as expected , was less than 90% observed in the cells treated with GBF1 siRNA for 3 days , while replication of the 3A-2 replicon was inhibited to a much greater extent ( Fig . 5C ) . We have shown previously that synthesis of polio 3A protein in HeLa cell extracts results in accumulation of GBF1 on membranes ( [31] and Fig . 4 ) . To see if the pattern of BFA inhibition of replication of polio variants correlated with the ability of the corresponding 3A proteins to engage GBF1 on the membranes , we translated RNAs coding for wild type 3A and 3A-2 mutant in HeLa cell extracts , collected membranes with their associated proteins by centrifugation and assessed the amount of GBF1 by Western blot . Figure 5D shows that the amount of GBF1 bound to membranes after translation of 3A-2 mutant RNA was significantly less than after translation of wild type 3A RNA , correlating with the strong sensitivity of the 3A-2 replicon to BFA . Although the amount of GBF1 found associated with membranes upon translation of 3A-2 coding RNA varied in different batches of HeLa cell extracts , usually synthesis of 3A-2 mutant protein did induce some association of GBF1 with membranes compared with the background levels . Taken together , the data show that the 3A-2 mutant is more sensitive to BFA , more sensitive to depletion of GBF1 by siRNA kockdown , and is harder to rescue from BFA inhibition by providing ectopic GBF1 . These properties are all consistent with low-affinity binding between 3A-2 and its target GBF1 , whose activity is required for virus growth . This analysis does not discriminate between a direct interaction of 3A with GBF1 and indirect activation of GBF1 translocation to membranes by a pathway triggered by 3A . To compare the strength of direct interaction between the two proteins we performed yeast two-hybrid studies with the soluble interacting domains of the wild type and 3A-2 mutant 3A polypeptides ( amino acid residues 1–60 ) and GBF1 . Our results ( not shown ) confirmed previous studies performed in a mammalian two-hybrid system , showing a strong interaction between wild type poliovirus 3A and GBF1 and the severe inhibition of such interaction for the 3A-2 mutation in the corresponding Coxsackie virus protein [20] , [46] . Thus our data show that the sensitivity of poliovirus replication to inhibition by BFA correlates inversely with the strength of interaction of viral protein 3A and GBF1 . The domain attributed with binding to viral protein 3A was shown to reside in the N-terminal region of GBF1 . A deletion of 37 N-terminal amino acids of GBF1 resulted in the loss of interaction with 3A protein from CVB3 , as detected by co-immunoprecipitation experiments [46] . We tested this Δ37 mutation in GBF1 for its ability to rescue polio replication in the presence of BFA . To ensure as great a potential rescue level as possible , we combined the Δ37 mutation with the BFA-resistance mutation A795E , identified in GBF1 from BER-40 cells . Although expression of the YFP fusions of the full length A795E GBF1 was very efficient in rescuing replication of polio replicon RNA in the presence of the inhibitor , the truncated variant of GBF1 was completely ineffective ( Fig . 6A ) . We also tested whether this truncated variant of GBF1 combined with the A795E mutation could confer BFA resistance to HeLa cells . The survival experiments did not show any protection of cells expressing this GBF1 variant from BFA as opposed to cells expressing full length A795E GBF1 ( not shown ) . Therefore the N-terminal region of GBF1 provides some important function ( s ) required for BFA resistance for both virus replication and cell survival . We monitored the distribution of Venus fusions of wild type GBF1 and Δ37 mutant GBF1 in cells infected with poliovirus . The localization of both proteins in mock-infected cells was virtually identical: they were associated with cytoplasmic ER-like structures with some slight concentration in the perinuclear area ( Fig . 6B , panels D and H ) . In infected cells the two proteins behaved very differently . As we reported previously , wild type GBF1 relocalized to sites of poliovirus replication , visualized by staining of 3A protein ( Fig . 6B panels A–C ) [31]; the Δ37 mutant GBF1 showed no apparent association with membrane structures , and displayed the diffuse fluorescence typical of a soluble protein ( Fig . 6B panels E–G ) . A characteristic feature of infected cells expressing the truncated GBF1 mutant was the positive staining of the nucleus , which was always spared in cells expressing wild type GBF1 ( Fig . 6B panels A , E ) and in mock-infected cells expressing either protein ( Fig . 6B panels D , H ) . The difference between localization of wt GBF1 and the Δ37 mutant in infected cells is likely due to the inability of the deletion mutant to be tethered to membranes because of loss of a domain responsible for interaction with protein binding partners , including polio protein 3A . GBF1-induced activation of Arf is required for formation of COPI-coated vesicles in the traffic between ER and cis-Golgi of the cellular secretory pathway [44] . The association of activated Arf with membranes also recruits numerous effector proteins and changes membrane properties due to activation of lipid-modifying enzymes such as phospholipase D [15] , [47] . Although polio RNA replication clearly requires active GBF1 , the Arf-GTP generated by this GEF may participate in two not necessarily mutually exclusive processes: the remodeling of cellular membranes into characteristic polio-induced vesicles , and/or the generation of a favorable membrane microenvironment for RNA replication . Since virus replication is inhibited by drugs that prevent Arf activation , such as BFA , we attempted to determine whether BFA affected either of these two potential inputs of Arf into poliovirus replication . To this end , we utilized a non-replicating poliovirus RNA where the cognate regulatory sequences in the 5′ non-translated region were substituted with an IRES from encephalomyocarditis virus ( EMCV ) to express polioviral proteins . This non-replicating RNA was expressed from a plasmid in HeLa cells by T7 RNA polymerase produced from a recombinant vaccinia virus . In this case accumulation of poliovirus-specific RNA is not dependent on replication , and the synthesis of polio proteins is not affected by the presence of brefeldin A . This construct was used previously to show that synthesis of poliovirus proteins , not RNA replication , is sufficient to form the vesicular structures morphologically indistinguishable from those found in poliovirus-infected cells [48] . When the transfected cells were examined by electron microscopy , specific polio-induced vesicles were observed to have formed , both in the presence and the absence of BFA , thus arguing that Arf activation is not necessary for the morphological development of these structures ( Fig . 7A ) . To monitor the distribution of Arf in cells expressing poliovirus proteins in the presence of BFA we performed a similar experiment in cells expressing an Arf1-EGFP fusion protein . As seen in Fig . 7B , staining of poliovirus protein 3A revealed that regardless of the presence of BFA , the viral protein was localized in the characteristic perinuclear ring of vesicle-like structures ( compare panels B and F ) , while Arf translocated to this region only in the absence of the inhibitor ( compare A and E; C and G ) . As expected , Arf translocation to the virus-induced vesicular structures did occur in the presence of BFA in cells transfected to express the BFA-resistant A795E GBF1 mutant ( not shown ) . The results of this experiment show that in the presence of BFA , when GBF1-dependent Arf activation could not occur , it is possible to form polio-induced membranous structures morphologically indistinguishable from those developed in the absence of the inhibitor . To determine whether these structures were capable of supporting polio RNA replication , we allowed them to form in the presence of absence of BFA with a replication-competent RNA , and then measured their subsequent ability to synthesize RNA . Viral proteins were synthesized from polio RNAs generated by T7 RNA polymerase supplied by recombinant vaccinia virus . To ensure that equal amounts of proteins were produced during the stage of membrane remodeling with and without BFA , both samples were incubated in the presence of 2 mM guanidine-HCl , a specific and reversible inhibitor of polio RNA replication that prevented amplification of viral RNA template . The cells were incubated for 4 . 5 hours , the time that we found in the previously-described electron microscopy studies to be sufficient for vesicular structures to form . The presence of guanidine-HCl during this time blocked RNA replication from starting even if competent replication complexes had formed . After 4 . 5 hours , the guanidine-HCl was removed to allow viral RNA synthesis to proceed from the pre-formed protein-membrane complexes . Figure 8A shows that polio RNA replication was detected only when the initial incubation was performed without BFA , showing that the vesicular structures associated with viral replication proteins formed when Arf activation was inhibited were unable to support viral RNA synthesis despite their similar morphological appearance . To confirm that the observed increase in Renilla signal from the cells whose replication complexes were formed in the absence of BFA was not simply due to healthier cells incubated without BFA , we performed a similar experiment with a plasmid coding for a replication-defective polio RNA containing a deletion in the polymerase gene . In this case no differences were seen between the samples regardless of their BFA treatment ( Fig . 8B ) . These data demonstrate that the BFA-sensitive ( and Arf activation-dependent ) step in polio replication is not the remodeling of host membrane structures , but the ability of those structures to function in viral RNA replication .
Viruses ultimately must depend on cellular structures and factors for their replication . Often host proteins in infected cells are diverted to perform their normal function ( s ) in a new microenvironment or with modified specificity . In this paper we show that replication of poliovirus strongly depends on the activity of a cellular protein , GBF1 . GBF1 activates the small cellular GTPase , Arf1 , by exchanging Arf-bound GDP for GTP to regenerate the active form of Arf . In uninfected cells , GBF1 is required for specific steps during the transfer of proteins and membranes through the secretory pathway , from ER through the Golgi to plasma membrane or endosomes . Activation of Arf by GBF1 occurs during formation of COPI-coated vesicles from the ER-Golgi intermediate compartment ( ERGIC ) . This activity of GBF1 is inhibited by BFA which binds and stabilizes the Arf1-GDP-GBF1 intermediate complex and thus prevents GBF1 from performing multiple rounds of Arf activation [28] , [29] . Replication of poliovirus is also sensitive to this inhibitor [10] , [11] . We demonstrate that GBF1 can rescue poliovirus infection from inhibition by BFA , and that reduced interaction between GBF1 and viral protein 3A increases the sensitivity of poliovirus infection to BFA . Inhibition of polio replication by BFA is specific for the particular host cell , and this specificity is determined by the sequence of the catalytic Sec7 domain of GBF1 in the cell . We identified a new mutation in GBF1 , A795E , that renders the factor resistant to BFA . Expression of this BFA-resistant GBF1 conferred BFA resistance to HeLa cells and allowed efficient rescue of poliovirus replication in the presence of BFA . This mutation generates a single amino acid substitution at a residue that is very close to the BFA binding site identified on the crystal structure of the complex between the GEF Sec7 domain , BFA and Arf-GDP [49] , [50] . The crystal structure of a modified Arno Sec7 domain complexed with Arf1 and BFA shows that the corresponding residue ( A157 ) participates in direct van der Waals interaction with BFA . Interestingly , another amino acid in the Sec7 domain of GBF1 , M832 , whose substitution to L also makes GBF1 resistant to BFA , is located very close to A795 in the crystal structure and also participates in van der Waals contact with BFA [29] , [49] . Previous work from this laboratory demonstrated that two other high molecular weight GEFs that activate Arf , BIG1/BIG2 , are recruited to polio replication complex membranes , both in vitro and in infected cells [31] . Recruitment of BIG1/BIG2 was mediated by viral protein 3CD , independent of 3A's recruitment of GBF1 . Since the activities of these two GEFs are also sensitive to BFA , the data described in this report suggest that BIG1/BIG2 also are not involved in the morphological development of replication vesicles . It is not yet clear whether or what role these GEFs play in this complex process [17] , since any or all of them might perform BFA-insensitive functions that could affect membrane remodeling or other aspects of polio replication . Wessels et al . have performed elegant studies on the fate and consequences of expressing just the 3A protein from polio or Coxsackie virus B3 in mammalian cells . They found loss of COPI coatomer complex on membranes and reduction of activated Arf [21] . They also showed that 3A interacts directly with GBF1 , and concluded that this interaction resulted in inhibition of the Arf-activating property of GBF1 when 3A was expressed by itself in mammalian cells . The single amino acid insertion in the 3A-2 mutant caused the viral protein to lose detectable binding to GBF1 and therefore did not inhibit GBF1's GEF activity [20] , [46] . It was proposed that this 3A-induced inhibition of GBF1 GEF activity was responsible for shutting down the secretory pathway in infected cells . In the course of virus infection , 3A protein is synthesized together with other viral proteins and is likely involved in interactions with other viral products that may significantly modify the outcome of its interactions with cellular proteins [51] , [52] . Our previous data showed that the amount of Arf-GTP steadily increased during the course of infection [31]; thus at least overall GEF activity in infected cells is not inhibited . Moreover Gazina et al . demonstrated that components of COPI coats , whose association with membranes is directly dependent on GBF1-induced Arf activation , are associated with replication complexes of echovirus 11 , a related picornavirus that is sensitive to BFA [53] . A genetic screen of Drosophila cells identified the COPI coatomer as a host factor essential for growth of another picornavirus-like virus , Drosophila C virus [54] . These data do not support the notion that GBF1 activity is inhibited in infected cells . Our data presented here directly show that GBF1 is necessary for poliovirus replication and that only expression of catalytically active GBF1 can rescue poliovirus replication in the presence of BFA . Moreover we showed that synthesis of 3A results in stimulation of GBF1-dependent activation of Arf in vitro . These latter experiments are the most difficult to reconcile with the results from the van Kuppeveld laboratory [21] since they demonstrate stimulation rather than inhibition of Arf activation even in the absence of other viral proteins . Collaborative studies in both laboratories are currently in progress to attempt to understand these apparently conflicting results . Our data also suggest that although the 3A-2 mutant may manifest a much weaker interaction with GBF1 , it still retains some residual ability to induce association of this protein with membranes . We propose that interaction of 3A with GBF1 diverts its activity from its normal function in the secretory pathway to sites of polio replication where it functions in poliovirus ( and likely other BFA-sensitive picornaviruses ) replication . This diversion to viral replication complexes would likely result in inhibition of cellular secretion . Inhibition of cellular secretion leads to reduced presentation of antigens on the surface of infected cells as well as reduced release of cytokines [22] , [55] . Thus , effective subversion of a cellular pathway may provide a double benefit for the virus by sustaining genome replication in the cell as well as inhibiting a pathway important for the organism's defense against infection . Inhibition of the cellular secretory pathway was suggested to provide an advantage for replication of the virus in an animal host , but was believed to be dispensable for replication in cells culture . Although the 3A-2 mutation was found to strongly reduce poliovirus's ability to inhibit cellular secretion [23] , it was reported not to interfere significantly with propagation of the virus in cell culture . On the other hand , when the corresponding mutation was introduced into CVB3 , the mutant was less pathogenic in mice [21] . The mechanism of the apparent attenuation remains to be elucidated . What roles do GBF1-dependent reactions play in poliovirus replication ? GBF1 normally participates in the formation of COPI-coated vesicles on ERGIC structures , and poliovirus replication complexes form on membranous structures that resemble clusters of heterogeneously sized vesicles . Thus , GBF1 may possibly be involved in remodeling host membranes into those structures . In poliovirus-infected cells , however , development of infection very rapidly results in complete reorganization of cellular organelles into specific membranous vesicles , so that ER , ERGIC and other structures are no longer detectable [2] , although some early secretory pathway steps could still be observed in infected cells [56] . Therefore infected cells very rapidly lose the normal morphological substrate for formation of COPI-like vesicles . Supporting this idea is our observation that the Δ37 mutant of GBF1 is distributed like a soluble protein in infected cells while it is indistinguishable from the wt GBF1 in mock-infected cells . Apparently GBF1 in polio-infected cells is no longer retained on membranes through its normal interactions , such as binding to Rab1b and/or p115 [57] , [58] , but wt protein is tethered on remodeled membrane structures through binding to polio protein 3A , while the Δ37 GBF1 mutant is unable to interact with 3A and therefore behaves like a soluble protein . Poliovirus infection is known to induce rapid degradation of nuclear pores and consequent leakiness of the nuclear envelope that allow even high molecular weight soluble proteins to freely penetrate the nuclear envelope in both directions [59] , [60] . Another set of data also suggests that the BFA-sensitive ( and therefore the GBF1-dependent ) step in poliovirus replication is not remodeling of membranes but rather proper functioning of the replication complexes . Poliovirus infection is sensitive to addition of BFA at every stage of the replication cycle ( [11]; our unpublished observations ) , which would be difficult to reconcile with the requirement of such activity only for the morphogenesis of replication structures . Our experiments presented here directly show that BFA does not preclude formation of characteristic vesicle-like structures . The similar appearance by low-power electron microscopy of the membrane structures formed by viral proteins in the presence and absence of BFA was surprising; however , the apparent similarity does not imply that these structures are biochemically and functionally similar , as evidenced by the differences in Arf1 localization and very likely numerous other markers including the effectors normally recruited by Arf . Those structures are unable to support polio RNA replication when the inhibitor is removed . Association of activated Arf with membranes is known to induce binding of many effector proteins and coat complexes and to activate membrane-modifying enzymes [15] , [47] . It is likely that in the polio-induced vesicles , Arf's role could be to bring to the membranes other host proteins that participate in replication of the viral genome or in regulating the lipid composition of these structures to make them suitable for assembly of functional replication complexes . Interestingly , expression of the Arf1Q71L mutant that has increased affinity for GTP and therefore is always in an “activated state” was not able to restore polio replication from either inhibition of GBF1 activity by BFA or from knock-down of GBF1 expression by siRNA . This result may indicate that GBF1 performs some other function in polio replication , unrelated to Arf activation , but the most likely explanation of this inability of the Arf1 Q71L mutant to rescue polio replication is that the replication process requires Arf that can perform normal cycling between its GTP- and GDP-bound form , while Arf1 Q71L is constantly activated and bound to membranes [35] , [36] , [37] , [38] . All positive strand RNA viruses remodel host membranes into novel structures where replication complexes are assembled , and RNA replication of at least some of them was shown to be sensitive to BFA [53] , [61] . It is likely that BFA-sensitive Arf activation is the cellular pathway exploited by diverse groups of RNA viruses .
HeLa cells were maintained in Dulbecco's Minimum Essential Medium , high glucose modification , supplemented with 1 mM sodium pyruvate and 10% heat-inactivated fetal bovine serum . BFA-resistant BER-40 cells and their parental Vero cell line were kindly provided by T . Oda , University of Nagasaki , Japan . They were grown in Eagle's Minimum Essential Medium supplemented with 10% heat-inactivated fetal bovine serum . Plasmids pXpA-3A and pXpA-3A-2 , coding for poliovirus wild type 3A and 3A-2 mutant , respectively , have been described [19] . The pXpA-RenR plasmid , encoding a poliovirus replicon with the Renilla luciferase gene substituting for the capsid coding sequence was previously described [31] . Plasmid pXpA-RenR 3A-2 was obtained by point mutagenesis , and the mutagenized fragment was verified by sequencing . Plasmid pTM-PV-2A-3′ , used for expression of poliovirus non-structural proteins under translational control of the EMCV IRES was generously provided by N . Teterina in our laboratory . Plasmid pXpA-RenR Δ3D is a derivative of pXpA-RenR with a deletion of 190 nt in the polio polymerase sequence . Plasmid pYFP-GBF1 , pYFP-GBF1 E794K and pVenus-GBF1 for expression of GBF1 derivatives have been previously described [29]; pVenus-GBF1 Δ37 , derived from pVenus-GBF1 , was constructed by T . Niu . Plasmid pArf1-EGFP for expression of Arf1-EGFP fusion was described elsewhere [36] . Plasmid pArf1Q71L-CFP was a gift from N . Altan-Bonnet ( Rutgers University , New Jersey ) . Plasmid pCMV-Gluc used for the secretion rescue experiment was purchased from New England Biolabs . Rabbit polyclonal anti-GBF1 antibodies were a gift from N . Altan-Bonnet , Rutgers University , New Jersey . Anti-BIG2 rabbit antibodies were generously provided by M . Vaughan , NHLBI , NIH . Anti-polio 3A mouse monoclonal antibody was a gift from K . Bienz , University of Basel , Switzerland . Rabbit polyclonal anti-COP α and γ were a gift from F . van Kuppeveld , Radbout University , the Netherlands . Mouse monoclonal anti-Arf antibodies recognizing all species of mammalian Arf except Arf4 were from Affinity Bioreagents . Mouse monoclonal anti-actin antibodies conjugated with horse radish peroxidase were from Sigma . Mouse monoclonal anti-GFP antibodies were form Clontech . Secondary antibody Alexa Fluor 594 conjugates used in immunofluorescence were from Molecular Probes . Secondary antibody horse radish peroxidase conjugates used in Western blots were from Amersham . Vero or BER40 cells grown on 6 cm plates were infected with poliovirus at multiplicity of 10 PFU/cell and incubated in the presence of 2 µg/ml of BFA for 6 hours . The cells were subjected to three freeze-thaw cycles to release intracellular virus , and virus yield was determined by standard plaque assay on HeLa cell monolayers . Poly ( A ) -containing RNA from Vero and BER-40 cells was isolated with Oligotex mRNA Mini Kit ( Qiagen ) according to the manual . Reverse transcriptase reaction with oligo-dT primer was performed with MonsterScript 1st-Strand cDNA Synthesis kit ( Epicentre Biotechnologies ) . Sec7 –containing fragments were amplified by PCR using Phusion High Fidelity PCR kit ( Finnzymes ) . Big1 Sec7 PCR primers: GATCGGTCGACACTAGTAAATGATCTATC ( forward ) GATCGAAGCTTCTTAAGAAATCCTTCTGG ( reverse ) ; Big2 Sec7 PCR primers: AGTCAGCATGCATTTAAATGCTGCTAAC ( forward ) , GACTGAAGCTTACCGGTTCCTATCAG ( reverse ) ; GBF1 Sec7 PCR primers: TCAGAAAGCTTATGGAGATCATCACTGTGG ( forward ) CAGAGAATTCCTTAAGCAGAGACTTAGTGTC ( reverse ) . Sequencing primers are available upon request . Polio replicon RNA was transfected into HeLa cells grown in 96 well white plates with a clear flat bottom ( Costar ) at 10 ng/well with Trans-it mRNA transfection reagent ( Mirus ) according to the manufacturer's recommendations . Incubation media contained 60 µM live cell Renilla substrate Endu-Ren ( Promega ) and BFA where indicated . Control samples contained an equivalent amount of DMSO , used as solvent for BFA . Renilla light signal was recorded with SpectraMax M5 multi-well plate reader ( Molecular Devices ) . Each point on a graph is an average of measurements obtained from at least 16 wells . GBF1 siRNA CAACACACCUACUAUCUCU was obtained from Dharmacon . Silencer Negative control #1 siRNA was purchased from Ambion . HeLa cells were seeded at 10 000/well in a 96-well plate , transfected the next daywith Dharmafect1 transfection reagent ( Dharmacon ) according to the manufacturer's recommendations and incubated for 3 more days before the polio replication experiments . HeLa cells were plated at 20 000 per well in a 96-well plate and transfected with plasmids with Lipofectamine LTX ( Invitrogen ) according to the manufacturer's recommendations . The next day the medium was supplemented with 100 ng/ml of BFA and the cells were incubated for two more days with medium change approximately every 8 hours to ensure constant presence of the inhibitor . Cell viability was assessed with CellTiter-Glo luminescent cell viability assay kit ( Promega ) . HeLa cells in a 96 well plate were transfected with pGEM-3Z ( control ) , pYFP-GBF1 wt , or pYFP-GBF1 A795E and pCMV-Gluc vector ( 4∶1 mass ratio ) . The next day the cells were washed with serum-free medium and incubated with BFA ( 1 µg/ml ) or DMSO for 5 h in normal growth medium ( 75 µl/well ) . A portion ( 20 µl ) of the medium from each well was assayed with 20 µl of Gaussia Luciferase assay solution ( New England Biolabs ) . HeLa S10 extracts for translation reactions were prepared as described [43] but were not treated with micrococcal nuclease . Translation reaction mixtures of 50 µl included 2 . 5 µg of RNA transcripts . An aliquot of 9 µl from each reaction mixture was mixed with 1 µl of Redivue VIral methionine ( Amersham ) and incubated for 3 . 5 h at 34°C , after which one-fourth of the material was resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) mini-gel for visualization of translation products . The remaining 40 µl were also incubated for 3 . 5 h at 34°C and then centrifuged for 20 min at 16 , 000×g at 4°C . Pellets were assayed by Western blot with the ECL Advance system ( Amersham ) according to the manufacturer's recommendations . Cells grown on glass coverslips were fixed with 4% paraformaldehyde-phosphate-buffered saline ( PBS ) for 20 min , washed with PBS 3 times and stored in PBS at 4°C . Before staining the cells were permeabilized with 0 . 2% Triton X-100 in PBS for 5 min and washed 3 times with PBS . The cells were then incubated in 3% nonfat dry milk solution for 1 h to block nonspecific binding sites . This solution also was used for dilution of primary and secondary antibodies in which cells were sequentially incubated for 1 h . 10 ng/ml of Hoechst 33342 was added to the first blocking solution to stain nuclear chromatin . Images were taken with Leica DMIRE microscope . Digital images were processed with Adobe Photoshop software . | All positive strand RNA viruses replicate their genomes in association with membranous structures that are formed after infection by remodeling pre-existing cellular organelles . The role of membranes and the mechanisms exploited by viral proteins to orchestrate the formation and functioning of viral membranous replication complexes are largely unknown . Poliovirus replication is severely suppressed by brefeldin A ( BFA ) , a well-known inhibitor of the cellular secretory pathway . Three cellular proteins ( GBF1 , BIG1 and BIG2 ) that activate small GTPases called Arfs , whose activity is necessary for normal functioning of the secretory pathway , are known targets of BFA . Here we demonstrate that poliovirus utilizes the GBF1-dependent Arf activation pathway for its replication . Our data explain the mechanism of BFA inhibition of poliovirus replication by demonstrating that viral protein 3A binds and recruits GBF1 to membranes that support viral RNA synthesis . Inactivation of GBF1 by BFA prevents Arf activation and recruitment , and prevents formation of functional replication complexes . Surprisingly , formation of membranous structures morphologically similar to viral replication complexes occurs in the presence of BFA , although these structures do not function in the synthesis of viral RNA . Other plus strand RNA viruses are known to exhibit sensitivity to BFA and our data suggest that hijacking of the Arf activation pathway may be a common feature shared by diverse groups of viruses . | [
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| 2008 | A Critical Role of a Cellular Membrane Traffic Protein in Poliovirus RNA Replication |
Human defensins are at the forefront of the host responses to HIV and other pathogens in mucosal tissues . However , their ability to inactivate HIV in the bloodstream has been questioned due to the antagonistic effect of serum . In this study , we have examined the effect of sub-inhibitory concentrations of human α-defensin HNP-1 on the kinetics of early steps of fusion between HIV-1 and target cells in the presence of serum . Direct measurements of HIV-cell fusion using an enzymatic assay revealed that , in spite of the modest effect on the extent of fusion , HNP-1 prolonged the exposure of functionally important transitional epitopes of HIV-1 gp41 on the cell surface . The increased lifetime of gp41 intermediates in the presence of defensin was caused by a delay in the post-coreceptor binding steps of HIV-1 entry that correlated with the marked enhancement of the virus' sensitivity to neutralizing anti-gp41 antibodies . By contrast , the activity of antibodies to gp120 was not affected . HNP-1 appeared to specifically potentiate antibodies and peptides targeting the first heptad repeat domain of gp41 , while its effect on inhibitors and antibodies to other gp41 domains was less prominent . Sub-inhibitory concentrations of HNP-1 also promoted inhibition of HIV-1 entry into peripheral blood mononuclear cells by antibodies and , more importantly , by HIV-1 immune serum . Our findings demonstrate that: ( i ) sub-inhibitory doses of HNP-1 potently enhance the activity of a number of anti-gp41 antibodies and peptide inhibitors , apparently by prolonging the lifetime of gp41 intermediates; and ( ii ) the efficiency of HIV-1 fusion inhibitors and neutralizing antibodies is kinetically restricted . This study thus reveals an important role of α-defensin in enhancing adaptive immune responses to HIV-1 infection and suggests future strategies to augment these responses .
HIV envelope glycoprotein ( Env ) is a trimer each monomer of which consists of non-covalently associated transmembrane ( gp41 ) and surface ( gp120 ) subunits [1] . The gp120 binding to CD4 induces the formation of the gp120 coreceptor binding site and enables recruitment of HIV coreceptors ( CCR5 or CXCR4 ) [2] , [3] . The formation of ternary Env-CD4-coreceptor complexes triggers the gp41 refolding into the final 6-helix bundle ( 6HB ) structure [4] , [5] . In the 6HB structure , the three C-terminal heptad repeat ( C-HR ) domains bind in an antiparallel orientation to the hydrophobic grooves of the central N-terminal heptad repeat domain ( N-HR ) . The refolding of gp41 from a native conformation to the 6HB is a multi-step process that proceeds through several intermediate conformations which expose N-HR and C-HR domains [5] , [6] and are collectively referred to as pre-bundle or pre-hairpin intermediates ( PHIs ) . Synthetic peptides derived from the N-HR and C-HR regions inhibit HIV-1 fusion by binding to complementary domains on the gp41 PHIs and blocking the 6HB formation [4] . The gp41 N-HR and C-HR domains are exposed/formed after binding to CD4 or coreceptors , but are not available on the native Env or on the final 6HB structure [7]–[9] . Hence , the inhibitory peptides have a limited window of opportunity to bind to gp41 and block HIV-1 fusion . Functional evidence implies that the time of PHI exposure is a major determinant of the potency of C-HR-derived peptides [10]–[13] . Specifically , the rate of fusion/infection has been shown to correlate with the HIV-1 resistance to neutralizing antibodies and C-HR-derived peptides [10] , [14] . The lifetime of PHIs on the cell surface is a function of the relative rates of their formation upon CD4 binding and disappearance , which could be due to: ( i ) conversion to 6HBs upon virus fusion with the plasma membrane and/or ( ii ) virus clearance from the cell surface followed by fusion with endosomes [15] . The former pathway is operational in a cell-cell fusion model ( e . g . , [8] , [16] ) , whereas the second mechanism appears to be responsible for the HIV-1 escape from peptide inhibitors [10] , [15] ( see below ) . In addition to the relatively short lifetime of PHIs in the course of fusion , HIV-1 entry via endocytosis reveals a novel escape pathway from the peptide inhibitors [15] . Quick HIV-1 uptake following the interactions with CD4 and coreceptors would limit the cell surface exposure of PHIs and thus increase the virus resistance to inhibitors targeting intermediate conformations of Env . Indeed , the inhibitory potency of C-HR-derived peptides is enhanced upon imposing a transient block on HIV-1 endocytosis [15] , [17] . We therefore hypothesized that the sensitivity of HIV-1 to neutralizing antibodies against transiently exposed Env epitopes is modulated by the lifetime of surface-accessible PHIs [10] . This notion is supported by the synergy between a gp41-derived peptide that appears to stabilize PHI and anti-gp41 antibodies [14] . Thus , in addition to steric restrictions on antibody binding [18]–[23] , kinetic factors ( such as the lifetime of PHIs and the on/off rates of antibody binding ) may contribute to the ability of antibodies to engage transiently exposed epitopes [12] , [24] , [25] . For example , antibodies against CD4-induced epitopes neutralize HIV-1 more potently in cells expressing low levels of coreceptors or in the presence of coreceptor antagonists [13] , [26]–[28]; these conditions are known to slow down HIV-1 fusion [10] , [13] . The above considerations suggest that the rate of HIV-1 uptake/fusion can modulate the virus' resistance to entry inhibitors . Our recent study revealed that human α-defensin HNP-1 interferes with several steps of HIV-1 fusion and also selectively inhibits productive uptake of this virus [29] . We therefore asked whether low concentrations of HNP-1 can enhance the activity of antibodies and fusion inhibitors targeting intermediate conformations of Env by delaying the HIV-1 uptake and/or fusion . By examining the effect of HNP-1 on the kinetics of virus fusion , we confirmed that even marginally inhibitory doses of defensin in the presence of human serum increased the lifetime of PHIs on the cell surface . The longer exposure of gp41 intermediates correlated with dramatic potentiation of the inhibitory activity of antibodies and peptides targeting the N-HR domain . By comparison , a less marked enhancement of antiviral activities by defensin was observed for peptides and antibodies targeting other gp41 domains , while HIV-1 neutralization by anti-gp120 antibodies was not affected under our conditions . Importantly , the strong enhancing effect on HIV-1 neutralization was observed in the presence of serum , which antagonizes the ability of HNP-1 to directly inhibit fusion [29]–[32] . Our results thus demonstrate a remarkable synergy between innate and adaptive immune responses in blocking HIV-1 entry and fusion .
We and others have shown that the ability of HNP-1 to inhibit HIV-1 fusion/infection is markedly attenuated in the presence of serum [29] , [30] . Surprisingly , however , serum does not interfere with the HNP-1 binding to cellular and viral targets [29] , implying that the binding itself does not confer anti-viral activity . Here , we asked whether , in spite of its poor inhibitory activity in the presence of serum , the bound HNP-1 can modulate the kinetics of HIV-1 fusion and thus enhance the potency of inhibitors and neutralizing antibodies . The kinetics of HIV-1 fusion and the longevity of PHIs have been measured by adding specific inhibitors of HIV-1 fusion at varied times of virus-cell incubation [10] , [15] , [17] , [29] . From these data , the average lifetime of PHIs can be estimated using a simple three-step kinetic model ( Fig . 1 and [10] ) . This approach revealed correlation between the longevity of PHIs and the inhibitory activity of C-HR-derived peptides . According to our kinetic model , HIV-1 , which is initially attached to a target cell in the cold through non-specific interactions , proceeds through the following three surface-accessible steps of fusion ( Fig . 1 ) : binding to CD4 ( denoted V/CD4 ) and coreceptors ( V/CD4/CoR ) followed by productive endocytosis ( VE ) . The virus progression through sequential steps of fusion is assessed by adding high concentrations of specific fusion inhibitors at different time points . This approach is insensitive to reversible engagement of receptor/coreceptor and thus measures the effective rate constants of progression beyond the fusion steps that are dependent on respective cellular proteins . The internalization of ternary HIV-CD4-CoR complexes culminates in fusion with endosomes ( VF ) [15] , but this step is not kinetically resolved through addition of membrane-impermeant inhibitors and therefore does not contribute to the measured rate constants . We have obtained evidence for the existence of this post-endocytosis fusion step through arresting it at low temperature [15] . The acquisition of resistance to gp41-derived peptide inhibitors has been traditionally interpreted as HIV-1 fusion ( e . g . , [8] , [33] ) . However , the finding that HIV-1 enters through endocytosis [15] suggests that kinetics of escape from these peptides reflects the rate of productive endocytosis which protects the virus from inhibitors and culminates in subsequent fusion with endosomes ( Fig . 1 ) . Importantly , the longevity of surface-accessible PHIs measured by our approach is independent of whether HIV-1 escapes from the C-HR-derived peptides by forming the gp41 6HBs , which implies direct fusion with the plasma membrane ( Fig . 1 , gray dashed arrow ) , or through productive endocytosis [10] . To synchronize the HIV-1 fusion reaction , viruses were pre-bound to target cells in the cold , and virus entry was initiated by shifting to 37°C . Fully inhibitory concentrations of BMS-806 ( blocks CD4-induced conformational changes [34] , [35] ) , AMD3100 or TAK-779 ( block CXCR4 and CCR5 binding , respectively [36] , [37] ) or the gp41-derived peptide C52L ( prevents gp41 refolding [38] ) were added at varied times of incubation . In agreement with our previous kinetic studies [10] , HXB2 acquired resistance to the CD4 binding inhibitor ∼3-fold faster than BaL ( Fig . 1A , C ) . Subsequent steps of CXCR4 binding and acquisition of resistance to C52L by HXB2 occurred after significant delays , whereas BaL engaged CCR5 and escaped the C52L inhibition shortly after binding CD4 . Accordingly , the rate constants calculated based on the three-step kinetic model of HIV-1 fusion ( Fig . 1 and [10] ) reflected the slow progression of the post-CD4 binding steps of HXB2 fusion compared to BaL fusion ( Table 1 ) . From the derived rate constants , we estimated the average lifetime of PHIs defined as the combined time the Env exists in CD4- and CD4/CoR-bound states ( for details , please see [10] ) . Our estimate showed that the HXB2 and BaL PHIs were exposed to inhibitory peptides for ∼6 min and ∼0 . 8 min , respectively . The much longer lifetime of HXB2 intermediates is consistent with the higher sensitivity of this virus to gp41-derived inhibitory peptides ( Table 2 and [10] ) . We next examined the effect of HNP-1 on the kinetics of pseudovirus fusion . Experiments were carried out in medium containing 7 . 3 µM HNP-1 and 10% human serum . This concentration of defensin caused modest ( 15–20% ) reduction of the HXB2 fusion ( Fig . 1B , Inset ) , whereas the BaL fusion was not significantly diminished ( P>0 . 35 ) . We found that under these conditions defensin slowed down the HXB2 Env binding to CXCR4 , but not to CD4 . In contrast , defensin did not significantly alter the rates of CD4 or CCR5 binding by BaL ( Fig . 1 and Table 1 ) . Importantly , both HXB2 and BaL acquired resistance to C52L at much slower rates than in control experiments . These results show that defensin delays the HIV-1 entry process in the presence of serum while marginally affecting the extent of fusion . As discussed above , the slower post-CD4 binding steps of fusion must extend the lifetime of PHIs on the cell surface . Using our kinetic model , we calculated that HNP-1 prolonged the PHI exposure on HXB2 and BaL pseudoviruses by 2- and 5-fold , respectively . Delayed CXCR4 binding and slower endocytosis/fusion equally contributed to the increased PHI lifetime for HXB2 pseudoviruses , whereas delayed steps downstream of coreceptor binding were solely responsible for the prolonged exposure of BaL intermediates in defensin-treated samples ( Fig . 1 and Table 1 ) . Delayed HIV-1 fusion was also observed when experiments were carried out in the absence of serum using a lower concentration of defensin to achieve partial inhibition of fusion ( Fig . S2A , B ) . Also , in control experiments , the linearized HNP-1 analogue , Abu-HNP , did not affect the kinetics of HIV-1 fusion ( Fig . S2C ) . To verify the conclusion that HNP-1 interferes with pseudovirus endocytosis , we measured the HIV-1 uptake using a ratiometric assay developed by our group [29] , [39] , [40] . Internalization of pseudoviruses co-labeled with the pH-sensitive membrane marker , EcpH-ICAM , and the pH-resistant marker for the viral core , Gag-mCherry ( Fig . 2A ) , permitted the visualization of HIV-1 delivery into acidic endosomes . Acidification of endosomal pH resulted in quenching of the EcpH signal and thus reduction of the ratio of EcpH ( green ) and mCherry ( red ) signals ( Fig . 2B , C ) compared to the ratio immediately after the low temperature binding step ( 0 min ) . Incubation at 37°C resulted in virtual disappearance of green fluorescence , whereas the red signal corresponding to the viral core accumulated at the perinuclear regions ( Fig . 2B , second panel ) . By comparison , although HIV-1 endocytosis ( quenching of the EcpH signal ) was not blocked by HNP-1 in the presence of serum , the mCherry signal from viral cores was rather evenly distributed throughout the cytoplasm ( Fig . 2B , third panel ) . This phenotype , along with a somewhat greater EcpH/mCherry ratio than in control experiments ( Fig . 2C ) , indicates that the virus uptake and/or trafficking were delayed by HNP-1 , consistent with our kinetic data ( Fig . 1 ) . In agreement with our previous study [29] , [39] , [40] , defensin strongly inhibited pseudovirus endocytosis in the absence of serum , as evidenced by a marginal reduction in the green/red fluorescence ratio ( Fig . 2B and C ) . To conclude , the fusion kinetics ( Fig . 1 ) and virus imaging ( Fig . 2 ) data are consistent with the notion that defensin prolongs the exposure of PHIs on the cell surface by slowing down HIV-1 endocytosis . However , as we have alluded above and in [10] , the estimated PHI lifetime is independent of whether these intermediates are cleared by endocytosis or through fusion with the plasma membrane . In addition , defensin extends the longevity of PHIs on HXB2 pseudoviruses by slowing down the CXCR4 binding step through down regulation of CXCR4 expression and/or by direct competition with Env-CXCR4 binding [29] . Since the lifetime of gp41 intermediates correlates with the HIV-1 sensitivity to peptides derived from the C-HR domain [10] , we sought to determine whether HNP-1 could potentiate anti-HIV activity of fusion inhibitors and neutralizing antibodies . Toward this goal , we compared the sensitivity of HXB2 and BaL pseudoviruses to a panel of conformation-specific antibodies against distinct epitopes on gp120 and gp41 in the presence and in the absence of HNP-1 . The neutralizing activities of several anti-gp120 antibodies were assessed using the synchronized fusion protocol whereupon viruses were pre-bound to cells at 4°C , and their entry/fusion were initiated by raising the temperature in the presence of varied concentrations of antibodies in a serum-containing medium . Since HIV-1 attached to cells in the cold remains sensitive to CD4 binding inhibitors ( Fig . 1 and [8] , [10] , [41] ) , this protocol can be used to evaluate the ability of antibodies to interfere with the post-attachment steps of fusion upstream of receptor binding . First , we examined antibodies against conformational gp120 epitopes exposed in the context of the native Env trimer . Broadly neutralizing PG9 and PG16 antibodies against a quaternary glycan-containing epitope on the trimeric gp120 [42]–[45] inhibited BaL , but not HXB2 fusion at concentrations up to 50 µg/ml ( Fig . 3A , Fig . S3A and Table 2 ) . By comparison , the m18 mAb which recognizes the gp120 CD4 binding site [46] did not interfere with the BaL fusion within the concentration range tested , but partially inhibited HXB2 fusion ( Fig . S3B and Table 2 ) . Significantly , neither of these antibodies was rendered more potent by a sub-inhibitory dose of HNP-1 in the presence of serum ( Table 2 ) . We next determined whether HNP-1 could modulate the activity of mAbs to CD4-induced gp120 epitopes: 17b [47] , [48] , scFv m9 ( single-chain variable fragment ) and m36 [21] , [49] , which recognize the gp120 epitopes overlapping with or adjacent to the coreceptor binding site [49] , [50] . The inhibitory activity of 17b against either HXB2 or BaL was not detectable , while scFvm9 and m36 significantly reduced fusion of both HIV-1 strains ( Fig . 3B , Fig . S3C and Table 2 ) . As observed for other anti-gp120 antibodies , a sub-inhibitory dose of HNP-1 did not enhance the HIV-1 neutralization by antibodies against CD4-induced epitopes ( Table 2 ) . We assessed the effect of HNP-1 on inhibition of HIV-cell fusion by C34 and N36mut ( e , g ) peptides targeting the complementary N-HR ( coiled coil ) domain [4] , [51] . The C-HR-derived C34 peptide blocks HIV-1 fusion by binding to the complementary N-HR region and preventing the formation of 6HB . The HXB2 and BaL fusion was much more potently inhibited by C34 when a sub-inhibitory concentration of HNP-1 was present in a serum-containing medium ( Fig . 4A , B and Table 2 ) . The potentiation of C34 activity by defensin was particularly apparent for BaL pseudoviruses , which were somewhat more resistant to C34 than HXB2 . Since the potency of C-HR-derived peptides correlates with the lifetime of gp41 PHIs [10] , this result supports our conclusion based on the kinetics data that defensin prolongs the N-HR exposure on the cell surface . The original N36 peptide derived from the N-HR is thought to prevent the 6HB formation by binding to the complementary C-HR region of gp41 [4] . However , the mutant N36mut ( e , g ) peptide , in which several non-conservative substitutions of hydrophobic residues at the heptad repeat positions e and g were made , inhibits HIV-1 fusion in spite of its inability to bind the C-HR domain [51] . This result implies that N36mut ( e , g ) interferes with gp41-mediated fusion by forming non-functional heterotrimers with the N-HR segments [51] , [52] . Dose-response experiments showed that N36mut ( e , g ) only marginally reduced the extent of HXB2 fusion and did not affect the BaL fusion at concentrations up to 20 µM ( Fig . 4C , D ) . A sub-inhibitory dose of HNP-1 in the presence of serum dramatically enhanced the inhibitory activity of the mutant peptide . Given the low baseline sensitivity of BaL to N36mut ( e , g ) , the enhancing effect of HNP-1 was particularly striking . Defensin reduced the IC50 for this peptide from extremely large ( undefined ) to 2 . 4 µM ( Table 2 ) . Next , we tested whether HNP-1 can enhance the activity of mAbs to the N-HR domain , D5 , 8K8 and the bivalent Fab 3674 ( bF-3674 ) . D5 binds to the hydrophobic pocket within the gp41 coiled coil and thus interferes with the C-HR binding and 6HB formation [53] , [54] . The binding site of 8K8 partially overlaps with the D5 binding site , but the latter antibody has been reported to exhibit a greater specificity for the unoccupied N-HR domain than D5 [24] , [55] . bF-3674 recognizes the shallow groove on the coiled coil domain , which in the 6HB structure is located between the two C-HR segment bound to the major hydrophobic grooves of the coiled coil [56] . This antibody is therefore expected to bind to both the coiled coil and 6HB structures . The synergy between bF-3674- and C34-mediated block of HIV-1 entry [56] supports the notion that these inhibitors bind to non-overlapping sites on gp41 . While all three antibodies inhibited HXB2 and BaL fusion , 8K8 and bF-3674 were considerably more potent in our assay than D5 ( Fig . 5 ) . Overall , BaL was more resistant to these antibodies than HXB2 , except that the less potent D5 inhibited both viruses with nearly equal efficiency . A low dose of HNP-1 in the presence of serum greatly enhanced the anti-HIV activity of all three antibodies compared to control experiments ( Fig . 5 and Table 2 ) . As with the inhibitory peptides targeting the N-HR domain , the enhancing effect of defensin on BaL neutralization by mAbs was more apparent than for HXB2 . We also found that a sub-inhibitory dose of HNP-1 retains its ability to sensitize HIV-1 to neutralizing antibodies in media with higher ( 25% ) serum content ( Fig . S4 ) . In control experiments , the inactive linear analog of HNP-1 ( Abu-HNP ) lacking the critical disulfide bonds did not have any effect on the inhibitory activity of the 8k8 antibody ( Fig . 5A , B ) . We also tested whether defensin could confer neutralizing activity to a non-neutralizing monoclonal antibody NC-1 , which recognizes both the free N-HR and the N-HR in the context of 6HBs [52] , [57] . Within the concentration range tested , NC-1 did not inhibit HXB2 or BaL fusion , either in the presence or absence of HNP-1 ( Table 2 ) . The marked enhancement of the anti-HIV activity of peptides and neutralizing antibodies targeting the N-HR domain implies that this region is normally not exposed for a sufficiently long time to allow optimal binding of these inhibitors . To test the effect of HNP-1 on inhibitors targeting the gp41 C-HR domain , we used the 5-helix peptide [58] . This peptide consists of a single polypeptide chain in which three N-HR segments are interspersed with two C-HR segments , leaving a vacant groove that avidly binds C-HR-derived peptides [12] . The HIV-1 fusion experiments in the presence of escalating doses of 5-helix revealed that HXB2 was much more sensitive to this peptide than BaL ( Fig . S5 ) . A sub-inhibitory dose of HNP-1 in the presence of serum modestly ( ∼3-fold ) reduced the IC50 of 5-helix against either viral strain ( Fig . S5 and Table 2 ) . We next tested the synergy between HNP-1 and non-neutralizing or weakly neutralizing antibodies against the gp41 regions downstream of the N-HR , using “cluster I” antibodies against the disulfide-linked loop and “cluster II” antibodies against the C-HR domain . Cluster I mAbs , 50-69 [59] and 240-D [60] , bind to the monomeric or oligomeric gp41 loop region , whereas cluster II antibodies , 167-D IV and 98-6 [60] , appear to bind to the post-fusion 6HB structure [61]–[65] . Neither cluster I nor cluster II antibodies exhibited detectable neutralizing activity under our experimental conditions ( Table 2 ) . As was the case with NC-1 antibody against the N-HR domain , defensin did not confer the HIV-neutralizing ability to the gp41 cluster I or cluster II mAbs . Next , we examined the interactions between HNP-1 and broadly neutralizing antibodies against the gp41 membrane-proximal extracellular region ( MPER ) , 2F5 [66] and 4E10 [67] . These antibodies recognize the native Env relatively poorly , but exhibit improved binding to CD4-induced conformations of gp41 [22] , [68]–[71] . In our experimental system , 2F5 and 4E10 inhibited HXB2 fusion but were less efficient against BaL within the concentration range tested ( Fig . 6 ) . Defensin modestly reduced the IC50 for both antibodies against HXB2 pseudoviruses . By contrast , compared to the virtual lack of inhibition of BaL fusion by 2F5 and 4E10 , HNP-1 caused dramatic potentiation of their neutralizing activity ( Fig . 6 and Table 2 ) . To test our conclusion that a sub-inhibitory dose of HNP-1 strongly potentiates the activity of antibodies against gp41 pre-hairpins in a more physiologically-relevant system , we examined HIV-1 fusion with PBMCs . PBMCs were adhered to a poly-lysine coated 96-well plate and allowed to bind pseudoviruses at 4°C , as described in Materials and Methods . Viruses and cells were incubated with or without antibodies in the presence or absence of defensin at 37°C for 90 min to allow fusion . To evaluate the effect of defensin on HIV-1 neutralization by PG9 , D5 , bF-3674 and 2F5 in these target cells , we compared the fusion efficiencies after incubation with fixed concentrations of antibodies close to their respective IC50s ( when possible ) with and without HNP-1 in serum-containing medium . Notably , whereas 24 µM of HNP-1 did not have a detectable effect on HXB2 fusion with PBMCs ( Fig . 7 ) , defensin enhanced the activity of all tested anti-gp41 antibodies . The fusion inhibitory activity of bF-3674 was particularly strongly enhanced by HNP-1 . As was observed with TZM-bl cells ( Fig . 3A ) , the anti-gp120 antibody PG9 marginally attenuated the extent of HXB2 fusion with PBMCs , and this effect was not modulated by HNP-1 . We next asked whether α-defensin could augment the neutralizing activity of human HIV serum . PBMCs were centrifuged with HXB2 pseudoviruses in the cold , washed and incubated at 37°C with or without pooled serum from AIDS patients , either in the presence or in the absence of defensin . HIV serum diminished the HXB2 fusion with PBMCs by ∼50% , whereas the combination of immune serum with a non-inhibitory dose of HNP-1 further decreased the fusion signal to 33% of the control ( Fig . 7 ) . Synergy between HNP-1 and HIV immune serum reveals an important beneficial role of α-defensin in enhancing the neutralizing activity of naturally occurring human antibodies .
We have previously adapted a direct virus-cell fusion assay to dissect the early steps of HIV-1 entry and have developed a kinetic model to evaluate the lifetime of gp41 pre-hairpin intermediates [10] . Here , we applied these methodologies to investigate the effect of sub-inhibitory concentrations of α-defensin on the kinetics of HIV-1 fusion and its sensitivity to inhibition/neutralization by antiviral peptides and antibodies . Defensin extended the exposure of gp41 intermediates by slowing down HIV-1 uptake/fusion . In addition , HNP-1 delayed the CXCR4 binding step of HXB2 entry . The prolonged PHI exposure correlated with enhanced anti-viral activity of fusion inhibitors and antibodies targeting the transiently exposed gp41 domains , with the most profound effect observed with the N-HR-targeting inhibitors . The neutralization-enhancing effect of HNP-1 was confirmed using PBMCs which are the natural targets for HIV infection . The observed relationship between longevity of PHIs and the efficacy of fusion inhibitors and antibodies supports the kinetic restriction on HIV-1 neutralization . Thus , in addition to steric factors blocking the antibody access to respective epitopes , HIV-1 appears to kinetically limit the antibody or peptide binding to gp41 by minimizing the exposure of key transitional epitopes . While the kinetic effect of defensin appears to be the most likely explanation for HIV-1 sensitization , we cannot rule out the possibility that HNP-1 increases the accessibility of conserved epitopes by creating a more “open” Env conformation . However , the fact that the smaller C-HR-derived peptides , whose binding to the gp41 N-HR is not sterically restricted , are also potentiated by defensin , does not support the “open” conformation model . Of note , the kinetic and steric restriction may not be mutually exclusive , since the prolonged PHI lifetimes could augment the antibody binding by allowing more time for transient exposure of poorly accessible epitopes . Although the mechanism by which HNP-1 prolongs the PHI lifetime is not understood , our previous study has demonstrated the ability of this defensin to ( i ) slow down the CXCR4 binding , ( ii ) interfere with the 6HB formation , apparently through interactions with the gp41 HR domains , and ( iii ) inhibit HIV-1 uptake [29] . The longevity of gp41 PHIs , which can form upon CD4 engagement , is determined by the rates of coreceptor binding and productive endocytosis or fusion with the plasma membrane , as depicted in Figure 1 . While delayed folding into 6HBs ( kF ) stabilizes PHIs , the rate of productive endocytosis ( kE ) or , in the alternative model , the rate of fusion ( kF ) ultimately determines the availability of fusion intermediates on the cell surface . It thus appears that defensin prolongs the PHI exposure by delaying the CXCR4 binding ( for HXB2 pseudoviruses ) and , more universally , by slowing down the fusion steps downstream of coreceptor binding . It is possible that the observed lack of HNP-1 effect on anti-gp120 mAbs and the overall modest neutralizing activity of these antibodies resulted from the post-attachment neutralization protocol employed in this study . We chose a post-attachment neutralization assay in order to separate the HIV-1 binding and fusion steps and to minimize the effect of defensin-mediated down regulation of CD4 and CXCR4 expression [29] . Our protocol thus reduces the complexity of the HNP-1 effects on HIV-1 fusion and enables the kinetic measurements of this process . Importantly , HIV-1 Env does not appear to irreversibly engage CD4 after a brief pre-binding step at 4°C , as evidenced by the ability of BMS-806 and anti-CD4 antibodies to fully block HIV-1 fusion ( Fig . 1 and [8] , [10] , [17] ) . These findings show that our experimental protocol is suitable for studies of HIV-1 neutralization by anti-gp120 mAbs . We therefore surmise that the undetectable effect of defensin on anti-gp120 antibodies is due to the lack of kinetic control over their binding to respective epitopes and probably the lack of significant competition for binding to these epitopes . We found that HNP-1 selectively stimulated the anti-viral activity of peptides and antibodies against the N-HR domain , whereas potentiation of inhibitors targeting other gp41 domains was relatively modest ( Table 2 ) . The reason for this selective effect is presently unclear . The C-HR domains , for example , appear to be at least partially occluded on the native Env [72] . Kinetic studies of the 5-helix binding and inhibition of gp41-mediated fusion indicate that C-HR may be exposed for only a few seconds [12] . It is therefore reasonable to expect that stabilization of PHIs would result in a stronger reduction of the IC50 for 5-helix ( Table 2 ) . However , we have previously shown that the activity of C34 , but not of 5-helix , is enhanced upon stabilizing the gp41 fusion intermediates [73] . This result suggests that the 5-helix binding sites may not be exposed throughout the lifetime of PHIs . It is thus possible that the modest decrease in the IC50 for 5-helix in the presence of defensin reflects its weak effect on the C-HR exposure . Our results with 2F5 and 4E10 in the presence of defensin and serum are indicative of different degrees of the MPER occlusion on Env from different isolates . The dramatic enhancement of the 2F5 and 4E10 activity against BaL pseudoviruses , which are otherwise relatively resistant to these antibodies , suggests that HNP-1 markedly prolongs the availability of their respective epitopes . The less pronounced effect of defensin on HXB2 neutralization by these mAbs , on the other hand , is consistent with considerable exposure of MPER of this protein prior to or during fusion . These findings are in agreement with the report that the MPER domains are exposed on the native Env from lab-adapted strains ( HXB2 , ADA ) , but occluded on BaL and on Env glycoproteins from neutralization-resistant primary isolates [74] . Another explanation for the modest enhancing effect of defensin on antibodies and peptides targeting the gp41 domains other than the N-HR is that HNP-1 may inhibit the 6HB formation [29] by binding to the gp41 C-HR domain in a manner similar to retrocyclin ( a θ-defensin ) [75] . This could result in competition between HNP-1 and mAbs and/or peptides for binding to the C-HR domain , as has been reported for antibodies to the gp120 CD4-binding and coreceptor-binding sites [30] . However , it appears unlikely that sub-inhibitory doses of defensin in the presence of serum could significantly reduce the antibody binding to their epitopes . The fact that defensin does not attenuate HIV-1 neutralization by any of the antibodies used in this study ( Table 2 ) supports this notion . While defensin could also bind to N-HR , this possibility appears inconsistent with its strong enhancing effect on peptides and antibodies targeting this region . The enhanced HIV neutralization by immune serum in the presence of HNP-1 implies that the prolonged exposure of PHIs is also beneficial for antibodies circulating in the bloodstream . Of note , the level of human α-defensin in plasma can reach 6 . 5 µM [76] , [77] , a concentration that is close to one used in our study . The enhancing effect of HNP-1 on anti-gp41 antibodies is not without precedent . Sub-inhibitory doses of N36mut ( e , g ) have been reported to synergize with bF-3674 , 2F5 and 4E10 , apparently by sequestering the N-HR domains and thereby extending the temporal window for antibody binding [14] . In conclusion , this study reveals the previously unappreciated role of innate immunity peptides in enhancing adaptive immune responses to HIV-1 infection . This finding suggests new strategies to improve therapeutic regimens and vaccine efforts . Specifically , our data and published results [14] demonstrate the utility of developing small molecule compounds capable of stabilizing intermediate conformations of HIV-1 Env in vivo and thereby potentiating the neutralizing activity of antibodies against this glycoprotein .
HeLa-derived indicator TZM-bl cells expressing CD4 , CXCR4 , and CCR5 were grown in DMEM supplemented with 10% FBS ( HyClone Laboratories , Logan , UT ) and penicillin/streptomycin ( Sigma , St . Louis , MO ) . HEK 293T/17 cells ( ATCC , Manassas , VA ) were grown in the same medium supplemented with 0 . 5 mg/ml geneticin ( Invitrogen , Grand Island , NY ) . Human peripheral blood mononuclear cells ( PBMCs ) were isolated from whole blood and activated with 10 ng/ml IL-2 and 2 . 5 µg/ml phytohemagglutinin ( PHA , Sigma ) , as described previously [17] . All media and buffers were obtained from HyClone ( Thermo Fisher Scientific , Logan , UT ) or Cellgro ( Mediatech Inc . , Manassas , VA ) . Human serum was obtained from Atlanta Biologicals ( Lawrenceville , GA ) . The following cell lines and reagents were obtained from the NIH AIDS Research and Reference Reagent Program: IL-2 ( from Dr . M . Gately , Hoffmann-La Roche ) [78] , indicator TZM-bl cells ( from Drs . J . Kappes and X . Wu ) [79] , HIV-1 immune serum ( Dr . L . Vujcic , FDA ) [80] , HIV monoclonal antibodies ( mAbs ) PG9 and PG16 ( from IAVI , La Jolla , CA ) [42] , 17b ( Dr . J . Robinson , Tulane University Medical Center ) [47] , [48] , 2F5 and 4E10 ( Dr . Hermann Katinger , University of Natural Resources , Vienna , Austria ) [66] , [67] , 50-69 , 98-6 , 240-D and 167-D IV ( Dr . S . Zolla-Pazner , Veterans Administration Medical Center , New York ) [59] , [60] , NC-1 ( Dr . S . Jiang , New York Blood Center , NY ) [57] , TAK-779 ( Division of AIDS , NIAID ) and pcDNA3 . 1 vector expressing HIV-1 BaL Env ( clone BaL . 01 , Dr . J . Mascola , NIH ) [81] . The antibodies scFv m9 , m36 , m18 were a gift from Dr . D . Dimitrov ( NCI , Frederick , MD ) , the 8K8 mAb was provided by Dr . M . Zwick ( Scripps Research Institute , CA ) , the bivalent Fab 3674 ( bF-3674 ) was from Dr . M . Clore ( NIDDK , NIH ) , and the D5 mAb was from Dr . M . Miller ( Merck ) . The pCAGGS plasmid encoding HXB2 Env was provided by Dr . J . Binley ( Torrey Pines Institute ) . The HIV-1-based packaging vector pR8ΔEnv lacking the env gene was from Dr . D . Trono ( Geneva , Switzerland ) . The C52L recombinant peptide was a gift from Dr . Min Lu ( New Jersey Medical School ) [38] . The C34 peptide was a gift from Dr . L . Wang ( Institute of Human Virology , University of Maryland Baltimore ) , and 5-Helix was a gift from Dr . M . Root ( Thomas Jefferson University ) . BMS-806 was purchased from ChemPacific Corp . ( Baltimore , MD ) , and AMD3100 was from Sigma . HNP-1 and its linear analog Abu-HNP-1 , in which the six Cys residues are replaced by the isosteric α-aminobutyric acid , as well as the N36mut ( e , g ) peptide were all prepared via Boc solid phase peptide synthesis using an optimized coupling chemistry developed by Kent and colleagues [82] . Oxidative refolding of HNP-1 was performed as described [83] , and structural validation of synthetic HNP1 was achieved by X-ray crystallography [84] . All peptides were purified to homogeneity by reversed phase HPLC and their molecular masses ascertained by electrospray ionization mass spectrometry . Peptide concentrations were quantified by UV absorbance measurements at 280 nm using molar extinction coefficients calculated by a published algorithm [85] . Pseudoviruses were produced by transfection of 293T cells using PolyFect reagent ( QIAGEN , Valencia , CA ) , as described in [17] . Briefly , for the BlaM assay , cells were transfected with a mixture of the following plasmids: 2 µg of pR8ΔEnv , 2 µg of BlaM-Vpr-expressing pMM310 vector , 1 µg of pcRev plasmid , and 3 µg of vectors encoding HIV-1 BaL or HXB2 Env . Transfection media was replaced with phenol red-free media after an overnight incubation , and cell culture medium was collected at 48 h post-transfection . Virus-containing medium was passed through a 0 . 45 µm filter , aliquoted , and stored at −80°C . For experiments with PBMCs , viruses were concentrated by pelleting onto a 20% sucrose cushion or using Lenti-X concentrator ( Clontech Laboratories , CA ) . The infectious titer of the virus stock was determined using TZM-bl cells , as described previously [15] . Unless otherwise stated , all HIV-cell fusion experiments were done in the presence of 10% human serum . HIV-1 pseudovirus fusion with target cells was measured using the BlaM assay , as described previously [15] , [17] . HXB2 or BaL pseudoviruses with β-lactamase-Vpr ( BlaM-Vpr ) chimera incorporated into the viral core were bound to PBMCs or TZM-bl cells by centrifugation at 4°C for 30 min at 1 , 550×g . For fusion experiments with PBMCs , cells were allowed to adhere to a poly-L-lysine coated 96-well plate ( 2•105 cells/well ) in Hanks' buffer ( HBSS ) for 30 min at room temperature . Excess cells were removed and wells were blocked with 10% FBS-supplemented HBSS for 15 min . HXB2 pseudoviruses ( 4•105 IU/well ) were pre-bound to adhered PBMCs by spinoculation , as described above . After the virus binding step , cells were washed once with HBSS and incubated at 37°C for 90 min to allow virus entry . The fusion reaction was stopped by placing the plates on ice , and the culture medium was replaced with the BlaM substrate , CCF4-AM ( Invitrogen ) . Cells were left at 12°C overnight , and the BlaM activity was determined from the ratio of blue and green fluorescence signals , using the Synergy HT fluorescence plate reader ( Bio-Tek Instruments , Germany ) . The effect of HNP-1 on the kinetics of HIV-1 fusion with TZM-bl cells was assessed by the “time of addition” experiments , as described in [10] . Briefly , viruses were pre-bound to cells by centrifugation in the cold , and the virus entry was initiated by raising the temperature to 37°C . Fusion was stopped at indicated time points by adding fully inhibitory concentrations of inhibitors . The rate of CD4 binding was determined by adding BMS-806 ( 20 µM ) , CXCR4 and CCR5 binding was assessed using AMD3100 ( 7 µM ) and TAK-779 ( 30 µM ) , respectively . The rate of receptor/coreceptor-mediated endocytosis was measured by adding C52L ( 1 µM ) , which blocks the gp41 6-helix bundle formation . At the end of the incubation ( 37°C , 90 min ) , fusion was stopped by chilling the cells , and the BlaM activity was measured . Internalization of pseudoviruses by TZM-bl cells was measured , as previously described [29] , [39] , [40] . Briefly , HXB2 pseudoparticles were co-labeled with the pH-sensitive derivative of GFP , ecliptic pHlourin ( EcpH ) , fused to the N-terminus of the ICAM-1 transmembrane domain ( EcpH-ICAM ) and with the HIV-1 Gag-mCherry chimera , as the viral core marker . Although Gag-mCherry is not cleaved by the HIV-1 protease [29] , [39] , [40] , inclusion of wild-type HIV-1 pR8ΔEnv vector in the transfection mixture yielded pseudoviruses capable of single-round infection . Quenching of EcpH-ICAM upon entry into early endosomes enables the measurements of HIV-1 uptake and delivery into mildly acidic compartments expressed as the ratio of the EcpH-ICAM signal to the pH-resistant mCherry signal . Pseudoviruses were pre-bound to cells by spinoculation in the cold and the excess virus was removed by washing with HBSS . Varied doses of neutralizing antibodies or inhibitory peptides ( C34 , N36mut ( e , g ) or 5-Helix ) were added to cells in HBSS/10% human serum in the absence or in the presence of 7 . 3 µM of HNP-1 . Fusion was immediately initiated by shifting cells to 37°C . After 90 min , cells were placed on ice to stop fusion , loaded with the CCF4-AM substrate , and incubated overnight at 12°C . The resulting BlaM signal was measured , as described above . | Human neutrophil peptide 1 ( HNP-1 ) is a small cationic peptide that can directly block HIV-1 entry in the absence of serum . However , since serum attenuates the anti-HIV activity of this peptide , HNP-1 is unlikely to inhibit infection in the bloodstream . Here , we demonstrate that sub-inhibitory doses of HNP-1 in the presence of serum can strongly enhance the activity of neutralizing antibodies and inhibitors targeting transiently exposed intermediate conformations of HIV-1 gp41 . HNP-1 appears to exert this effect by delaying post-coreceptor binding steps of fusion and thereby prolonging the exposure of gp41 intermediates . These results imply that the HIV-1 fusion kinetics is an important determinant of sensitivity to neutralizing antibodies and peptides against transiently exposed functional domains of gp41 . The surprising synergy between sub-inhibitory concentrations of HNP-1 and anti-gp41 antibodies suggests new strategies to sensitize the virus to circulating antibodies by developing compounds that prolong the exposure of conserved gp41 epitopes on the cell surface . | [
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| 2013 | Sub-Inhibitory Concentrations of Human α-defensin Potentiate Neutralizing Antibodies against HIV-1 gp41 Pre-Hairpin Intermediates in the Presence of Serum |
The primitive face is composed of neural crest cell ( NCC ) derived prominences . The medial nasal processes ( MNP ) give rise to the upper lip and vomeronasal organ , and are essential for normal craniofacial development , but the mechanism of MNP development remains largely unknown . PDGFRα signaling is known to be critical for NCC development and craniofacial morphogenesis . In this study , we show that PDGFRα is required for MNP development by maintaining the migration of progenitor neural crest cells ( NCCs ) and the proliferation of MNP cells . Further investigations reveal that PI3K/Akt and Rac1 signaling mediate PDGFRα function during MNP development . We thus establish PDGFRα as a novel regulator of MNP development and elucidate the roles of its downstream signaling pathways at cellular and molecular levels .
Neural crest cells ( NCCs ) are a transient and multipotent cell population unique to vertebrates . During development , NCCs give rise to a broad variety of cell types , which contribute to the formation of the peripheral nervous system , cardiac outflow tract , pigment cells , and the majority of craniofacial bones and cartilages [1]–[4] . Alterations of cranial NCC ( cNCC ) development often lead to craniofacial malformations , one of the most prevalent birth defects [5] . These facts underscore the significance of understanding the mechanisms regulating NCCs during craniofacial morphogenesis . At the onset of craniofacial development , the facial primordium is composed of five prominences surrounding the stomodeum: the frontonasal prominence ( FNP ) at the rostral region , two paired maxillary processes in the middle , and a pair of mandibular processes at the caudal end [6] , [7] . These primordia are populated predominantly by NCC derived cells , surrounding a mesodermal core and covered by the overlying ectoderm . The ectoderm then thickens and invaginates to form two bilateral nasal placodes , dividing the FNP into the medial nasal process ( MNP ) and a pair of lateral nasal processes ( LNP ) . The MNP and bilateral maxillary processes contribute together to form the upper lip [8] . In mammals , the MNP further develops into the philtrum and the nasal septum , which later forms the nasal cartilage and bone [9] . Disruption of the MNP usually causes a variety of craniofacial defects , ranging from mild hypoplasia of the nasal bones to complete midfacial clefting . A number of genes regulate maxilla and mandible development , but it remains largely unknown how MNP development is controlled at the molecular and cellular level . Vital dye labeling studies reveal that the NCCs giving rise to different facial prominences share distinct origins along the rostral-caudal axis: NCCs from the diencephalon and anterior mesencephalon give rise to the MNP and LNP , while those originating from the posterior mesencephalon and hindbrain give rise to the maxilla and mandible [10] , [11] . These results suggest that the MNP and other prominences may be regulated through different mechanisms . Multiple genetic factors have been implicated in cranial NCC ( cNCC ) development . Among these , growth factor signaling pathways are essential for induction , proliferation , survival and migration [12]–[14] . BMP , FGF and Wnt signaling together mediate induction of cNCCs from neural ectoderm [13] , [15] . cNCC proliferation and survival are under the control of BMP , FGF and TGFβ signaling , and migration of the cNCCs at the caudal level is regulated by BMP , Wnt , Semaphorin and Ephrin signaling [13] . Growth factors act via binding and activation of their cell surface receptors , which in turn engage multiple intracellular signaling pathways . It remains to be elucidated how these intracellular signaling pathways mediate the receptors' function , especially in developmental contexts . Platelet Derived Growth Factor ( PDGF ) signaling plays essential roles in development and disease [16]–[19] . In mammals , PDGF signaling can be activated by four PDGF ligands ( A , B , C and D ) operating through two receptor tyrosine kinases , PDGFRα and β [17] , [20] . Activation of PDGFRs leads to phosphorylation of intracellular tyrosines and docking of intracellular effectors , which in turn engage downstream signaling cascades including the MAPK , PI3K , PLCγ , STAT and Src pathways . Previous studies from our laboratory and others have shown that PDGFRα and its downstream signaling pathways are crucial for cardiac and cranial NCCs [21]–[24] . PDGFA/PDGFRα signaling has also been implicated in cell migration in zebrafish palatogenesis [25] . However , the precise mechanisms by which PDGFRα regulates cNCCs and MNP development still remain to be elucidated . To this end , we have carried out a detailed study of PDGFRα NCC conditional knockout embryos . Our work reveals novel roles of PDGFRα in regulating NCC migration , and for PDGFRα engaged PI3K signaling in MNP neural crest cell proliferation . Moreover , we show that Rac1 and PI3K signaling mediate these processes under the control of PDGFRα .
To understand how PDGFRα and its downstream signaling pathways regulate NCC development and craniofacial morphogenesis , we generated NCC-specific PDGFRα conditional knockout ( cKO ) embryos by intercrossing PDGFRαfl/fl and Wnt1Cre transgenic mice [21] , [26] . Morphological differences first became visible at E11 . 5 , as the medial nasal processes ( MNP ) of cKO littermates remained separated by an obvious gap relative to the control embryos ( Fig . 1A , B ) . By E13 . 5 , cKO embryos lacked the philtrum and the primary palate ( Fig . 1C , D ) , both of which are derived from the MNP . This observation was confirmed by histological analysis that showed the absence of the primary palate and philtrum in the cKO embryos ( Fig . 1E , F ) . At E18 . 5 , skeletal preparations showed that PDGFRαfl/fl; Wnt1 Cre embryos exhibited a significant cleft ( 6 out of 6 ) and shortening of nasal cartilage ( 88±4 . 8% relative to control ) and premaxilla ( 81 . 2±2 . 0% relative to control ) ( Fig . 1G , H ) . Skeletal analysis of cKO mutants also revealed malformation of the neural crest derived basisphenoid , alisphenoid and pterygoid bones ( Fig . 1G , H ) [1] , [27] , [28] . Other cNCC derived structures , such as the mandible , developed normally in the cKO mutants . No cKO embryos survived past birth . To further understand how PDGFRα signals during MNP development , we analyzed the expression of PDGFRα and its ligands . Using PDGFRαGFP/+ knockin reporter mice [29] , we observed broad GFP expression in the facial structures at E10 . 5 ( Fig . 2A ) . Coronal sections of PDGFRαGFP/+ embryos at different stages revealed that PDGFRα is exclusively expressed in mesenchymal cells of the future facial structures , which are derived predominantly from cNCCs [1] . At E10 . 5 , PDGFRα appears to be expressed equally in both the MNP and the LNP ( Fig . 2B ) ; however by E11 . 5 , PDGFRα expression is reduced in the LNP relative to the MNP mesenchyme ( Fig . 2C ) . PDGFA and PDGFC encode two endogenous ligands that bind specifically to PDGFRα , and inactivation of the two genes together recapitulates the PDGFRα null mutant phenotype [30] . In situ hybridization showed that these genes exhibit overlapping expression in the MNP: PDGFA is expressed in the MNP and LNP epithelium and PDGFC is expressed in both the epithelium and mesenchyme ( Fig . 2D–G ) . The expression patterns of PDGFRα and its ligands indicate paracrine and possibly autocrine PDGFRα signaling during MNP development . To examine the role of PDGFRα in the MNP , we first analyzed cell proliferation and apoptosis in developing embryos . BrdU labeling revealed that in control embryos ( n = 9 ) , 40±3% of MNP mesenchymal cells and 35±2% of LNP mesenchymal cells were proliferating at E11 . 5 . In cKO embryos , MNP mesenchymal cell proliferation was decreased by 30% relative to littermate controls , while LNP mesenchymal cell division was maintained a comparable level ( Fig . 3A , B and E ) . No ectopic apoptosis was identified in cKO MNP cells ( data not shown ) . Since the MNP and LNP are derived from the FNP during embryogenesis , we further traced these defects to the FNP . We found that at E9 . 5 , cKO FNP mesenchymal cell proliferation was significantly downregulated by 19% ( Fig . 3C–E ) , and no ectopic cell apoptosis was identified ( data not shown ) . We also assayed expression of genes critical for MNP development in E10 . 5 embryos . Among them , Six1 and Alx4 expression remained unaltered in cKO and control embryos ( Fig . S1 ) , while Alx3 expression was downregulated in the cKO MNP mesenchyme , but not in the mandible ( Fig . 3F , G ) . Because Alx3 is essential for MNP cell survival and no ectopic apoptosis was found in the cKO MNP ( data not shown ) [31] , the reduction of Alx3 mRNA might be caused by a decrease in cell number in the cKO MNP . A similar change in gene expression has been observed in Pax6 mutant embryos , which showed a migration defect of MNP progenitor cells [32] . Together , these data suggest that PDGFRα regulates cell proliferation in the FNP and specifically in the MNP . PDGFRα regulates cell fates and behaviors through a number of downstream signaling pathways . Previous studies in our laboratory showed that PI3K signaling is the major effector of PDGFRα in craniofacial development . Loss of PDGFRα-mediated PI3K signaling alone caused cleft palate with incomplete penetrance , but inactivation of PDGFRα together with PDGFRβ-mediated PI3K signaling caused facial clefting similar to PDGFRα null mutant embryos [24] . To examine the role of PI3K signaling in MNP development , we first analyzed the phenotype of PDGFRαPI3K/PI3K mice . PDGFRαPI3K/PI3K embryos exhibited a shortened nasal septum at E13 . 5 ( Fig . 4A , B ) and shortened nasal bones at E18 . 5 ( Fig . 4C , D ) . The mutant nasal cartilage was 10% shorter than the heterozygous control and the premaxilla was 14% shorter than the control ( n = 5 , p<0 . 05 ) . As with the cKO MNP , BrdU labeling results revealed that a decrease in cell proliferation in the MNP of PDGFRαPI3K/PI3K embryos at E11 . 5 ( Fig . 4E–G ) . To further substantiate these results , we extended these studies to Mouse Embryonic Palatal Mesenchymal cells ( MEPMs ) . Although MNP cells would be the ideal material at this point , we were not able to maintain MNP cells in culture beyond passage 1 . Similar to MNP mesenchymal cells , MEPMs originate from cNCCs , exhibit stable PDGFRα expression and response to PDGFA stimulation ( Fig . S2 ) and are thus a robust tool to study PDGFRα function . PDGFA treatment significantly increased cell proliferation in WT MEPMs , but failed to do so in PDGFRαPI3K/PI3K MEPMs ( Fig . S3 ) . Conversely , PDGFRαPI3K/PI3K MEPMs exhibited a small decrease in cell proliferation ( data not shown ) . Together these results indicate that PI3K signaling is essential for PDGFRα-regulated neural crest derived cell proliferation . Lineage studies have revealed that MNP cells are predominantly derived from NCCs [1] , [2] . To be able to trace NCCs in the conditional mutants , we introduced the R26R Cre reporter allele [33] into the PDGFRαfl/fl; Wnt1Cre background . CKO and control embryos were age matched by counting the number of somites . Lineage tracing showed that at E8 . 5 LacZ reporter expression was comparable in cKO and control embryos ( Fig . 5A–D , cKO n = 7 , ctrl n = 11 ) , indicating that PDGFRα is not critical for neural crest specification . This observation was confirmed by quantifying the expression of neural crest marker genes Sox10 and Ap2α in E8 . 5 embryos using RT q-PCR ( Fig . 5E , n = 3 ) . By E9 . 5 however , R26R expression was attenuated in the cKO FNP as compared to the control ( Fig . 5F–I , cKO n = 4 , ctrl n = 15 ) . Consistent with the above observations , Sox10 expression was also disrupted in E9 . 5 cKO embryos ( Fig . 5J , K ) . At E10 . 5 , cKO embryos showed fewer NCCs in pharyngeal arches III to VI ( Fig . 5L , M , cKO n = 5 , ctrl n = 7 ) and abnormal bifurcation of the NCCs streams migrating to these pharyngeal arches was observed in some embryos ( Fig . S4A , B ) . Skeletal elements derived from these structures including the hyoid bone , the stapes , and the styloid process were severely deformed or missing in cKO embryos at E18 . 5 ( Fig . S4C–H , n = 6 ) . The cell lineage tracing results , along with the altered gene expression signature and defects in skeletal development indicate that PDGFRα is essential for NCCs to migrate in normal numbers and populate craniofacial regions . To analyze the function of PDGFRα in NCC migration , explant cultures were established from the cranial neural tube ( anterior to the first pharyngeal arch ) of cKO and control embryos . To uncouple potential cell migration defects from alterations in cell proliferation , explants were plated on fibronectin in the presence of mitomycin C . The explant cultures exhibited a significant decrease in emigration of cKO NCC cells ( Fig . 6A–D , n = 3 ) . Moreover morphometric analysis indicated that primary cKO NCC cells were significantly smaller ( 33 . 1% ) than control cells ( n = 50 ) , and exhibited fewer lamellipodia ( Fig . 6E , F ) , which are required for cell migration . The cKO NCCs also exhibited an increased nuclear-cytoplasmic ratio ( 12 . 7% in cKO cells and 4 . 3% in wild type cells ) , as well as fewer focal adhesions ( 34 . 5 per cKO cell and 89 . 1 per wild type cell , n = 50; Fig . 6 G ) . These results suggest that PDGFRα is essential for neural crest cell motility , possibly by regulating cytoskeletal architecture . Alternatively , PDGFA/PDGFRα might also regulate NCC migration by regulating cell guidance [25] , [34] . To distinguish between these possibilities , we carried out further experiments in primary MEPMs at passage 1 . PDGFA acted as a chemo-attractant of primary MEPMs in transwell assays ( Fig . 7A ) . In addition , PDGFA treatment sped up the wound healing rate of MEPMs ( Fig . 7B ) . These data indicate that activation of PDGFRα plays dual roles in neural crest derived cell migration , by stimulating chemotaxis and by regulating cell motility . To investigate if PI3K signaling plays a role in NCC migration , we generated PDGFRPI3K/PI3K; Wnt1Cre; R26R+/− embryos . Lineage tracing showed no obvious cNCC migration defects in PDGFRαPI3K/PI3K; Wnt1Cre; R26R+/− embryos ( Fig . S5 , n = 6 ) . Transwell assays revealed that PDGFRαPI3K/PI3K MEPMs respond and migrate towards a source of PDGFA at a level comparable to wild type cells ( Fig . 7C ) . In addition , the wound healing speed of PDGFRαPI3K/PI3K MEPMs remains comparable to that of heterozygous cells in a scratch assay ( Fig . 7D ) . In summary , these results indicate that PI3K signaling engaged by PDGFRα is not essential for cell migration , in contrast to its role in regulating proliferation of neural crest derived cells during development . The abnormal morphology of cKO NCCs indicates other signaling might be essential to regulate the cytoskeleton downstream of PDGFRα . Rho GTPases constitute a group of major regulators of cell migration that mediate actin reorganization , and lamellipodia and filopodia formation [35] , [36] . PDGF and PI3K/Akt signaling have been shown to phosphorylate guanine nucleotide-exchange factors ( GEFs ) , which in turn prompt the formation of GTP-bound , active small GTPases such as RhoA , Cdc42 and Rac1 [37]–[40] . Inactivation of Rac1 in NCCs caused facial clefting , strikingly resembling the PDGFRα cKO phenotype [41] , [42] , suggesting Rac1 to be a potential mediator of PDGFRα in NCC development . Rac1 activity was attenuated in lysates of E10 . 5 cKO MNP cells ( Fig . 8A ) , and we observed a reduction in the expression of phosphorylated cofilin 1 , an actin depolymerization enzyme required for cNCC development ( Fig . 8 B ) [43] . PDGFA stimulation facilitates phosphorylation of cofilin in MEPMs ( Fig . 8C ) , indicating that PDGFRα can regulate Rac1 activity in neural crest derived cells . Consistent with an important role for Rac1 in mediating PDGF driven functions , treatment of MEPMs with the Rac1 specific inhibitor NSC 23766 blocked PDGFA stimulated proliferation and wound healing ( Fig . 8D , E ) . Further examination revealed that inactivation of Rac1 affected lamellipodia formation at the leading edge of migrating MEPMs , reminiscent of the phenotype of PDGFRα deficient MEPMs ( Fig . 8F–K ) . Inhibition of Rac1 activity in MEPMs also led to smaller size ( 31% of untreated cells , n = 50 ) , fewer focal adhesion complexes ( 22 . 1 per treated cell vs . 83 . 2 per untreated cell , n = 50 ) , and increased nuclear-cytoplasmic ratio ( 9% in treated cells vs . 5% in untreated MEPMs , n = 50 ) ( Fig . 8L ) . Taken together , these results indicate a prominent role for Rac1 in the regulation of PDGF-induced cell migration and proliferation .
Facial clefting is a rare birth defect and its etiology remains poorly understood . In this work , we show that the facial clefting phenotype of PDGFRα mutants is not associated with a defect in NCC specification but rather a subsequent defect in the medial nasal process ( MNP ) , a facial primordium derived from the frontonasal prominence ( FNP ) . We further show that this defect is associated with alterations in both cell proliferation and cell migration , and that PI3K and Rac1 signaling are essential to maintain a normal level of cell proliferation . Last , we provide evidence that Rac1 regulates cell migration at the level of cell motility as well as chemotaxis under the regulation of PDGFRα . A previous study from our laboratory had shown that the facial clefting observed in PDGFRα mutants was of neural crest origin , using chimeric analysis and conditional mutagenesis with the Wnt1Cre driver [21] . Although global defects in cell proliferation and migration were not documented , chimeric analysis identified a role for PDGFRα in development of the pharyngeal arches , which we now show by lineage tracing are deficient in cNCCs by E10 . 5 in cKO embryos . In this work , we have considerably refined the analysis by examining specific rather than overall craniofacial subregions . We were thus able to document disruption of cell migration in the FNP by cell lineage analysis at E9 . 5 , and of cell proliferation in the MNP of cKO embryos by E10 . 5 . Therefore , both the previous study and the present work identify a crucial role for PDGFRα in NCC development . We found that PDGFRα exhibits a strong expression pattern in the MNP mesenchyme at different stages . PDGFA is expressed in the MNP epithelium , and PDGFC is expressed in both the MNP epithelium and the mesenchyme , consistent with paracrine or autocrine PDGFRα signaling during craniofacial development . Prior genetic evidence from our lab , using point mutations in the PI3K binding sites in the PDGFRs , has implicated PI3K as the key signaling pathway regulating craniofacial development [24] . We also found that that PI3K signaling regulates p44/42 MAPK ( data not shown ) . Strikingly , mice carrying a neural crest-specific deletion of Erk1/Erk2 display facial clefting [23] . p44/42 MAPK can also be engaged by other pathways than PI3K , and by other RTKs that are critical for craniofacial development including Fgfrs , Eph receptors , EGFR , Ror or Ryk . Although these RTKs share some similar intracellular domains and engage overlapping signaling pathways , their impact on craniofacial development might reflect tissue-restricted expression patterns of receptors and ligands , as well as engagement of unique combinations of downstream signaling cascades . It will be important to understand if the phenotypic differences associated with different RTKs are due to dosage variation of PI3K signaling , involvement of other unique signaling pathways , or a combination of both . Neural crest cells form through delamination of cells at the lateral plate border of the neural tube that undergo an epithelial to mesenchymal transition . There is extensive evidence that cell-cell contacts through intricate lamellipodial and filipodial extensions play critical roles in regulating how cells exit the neural tube and migrate to their proper destination ( for a review , see [13] ) . Small GTPases , including Rho , Rac and Cdc42 are well known to regulate such cell behaviors , but also cell proliferation and gene transcription under the regulation of multiple RTKs [44] . Recent gene targeting studies showed that inactivation of Rac1 or Cdc42 , or overexpression of a dominant negative Rho kinase in mouse NCCs causes a severe facial clefting phenotype , which strikingly resembles PDGFRα homozygous mutant embryos [21] , [22] , [41] , [42] , [45] . In particular , Rac1 deficient NCCs exhibit decreased proliferation , abnormal cell morphology , as well as disrupted lamellipodia formation [42] , very similar to the defects we have observed in PDGFRα deficient NCCs and MEPMs . The notion that PDGFRα might be a major regulator of Rac1 activity is further supported by our present work and other studies that show that PDGFA stimulates Rac1-GTP levels in a variety of biological settings [46]–[48] . It has been suggested that EGFR could be the major effector of the Rac1 mutant phenotype [41] , as EGF has been used as an agonist of Rac1 activity in a variety of in vitro studies . However EGFR null mutant mice exhibit a much milder craniofacial phenotype with only a cleft palate . In addition , gene expression data showed that EGFR and Rac1 only partially overlap in ectodermal cells during early stages of craniofacial morphogenesis ( Emage , www . emouseatlas . org ) , whereas PDGFRα shows a broad expression pattern similar to Rac1 in cNCCs at E10 . 5 . Functionally , PDGFRα deficient NCCs exhibit abnormal morphology and defective formation of lamellipodia and focal adhesions . These lines of evidence point to a major role for Rac1 in mediating PDGFRα functions in NCC development and craniofacial morphogenesis .
The Mount Sinai School of Medicine Institutional Animal Care and Use Committee ( IACUC ) approved all animal work and procedures used in this study . The Mount Sinai animal facility is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . PDGFRαfl/fl , PDGFRαGFP/+ , PDGFRαPI3K/+ , Wnt1Cre and R26R mice have been described previously [21] , [24] , [26] , [29] , [33] . PDGFRαfl/fl , PDGFRαPI3K/+ and Wnt1Cre mice were maintained on a 129S4 co-isogenic genetic background , and PDGFRαGFP/+ and ROSA26R mice were kept on a C57BL/6J co-isogenic background . Mice and embryos used in lineage tracing studies were maintained on a mixed genetic background . For histology , staged embryos were dissected in ice-cold PBS , fixed in Bouin's fixative , dehydrated through a graded series of ethanol washes , and embedded in paraffin . Sections were cut at 10 µm for hematoxylin and eosin staining . Skeletal analysis was performed on E18 . 5 embryos as described [22] . Craniofacial morphometry was performed as described in Fig . S6 . For immunostaining , embryos were fixed in 4% PFA overnight at 4°C , dehydrated in 30% sucrose/PBS and embedded in OCT . Cryosections were prepared at a thickness of 10 µm . Immunostaining was performed according to standard protocols using antibodies to PDGFRα ( 1∶60; Santa Cruz ) , vinculin ( 1∶200 , Sigma ) , BrdU ( 1∶500 , DSHB ) , Cleaved-Caspase 3 ( 1∶200 , Cell Signaling Technology ) and rhodamine phalloidin ( 0 . 2 µM , Biotium ) . For in situ hybridization , embryos were dissected in ice-cold PBS , fixed in 4% paraformaldehyde ( PFA ) , dehydrated through graded ethanol washes , and embedded in paraffin . Coronal sections were cut at 10 µm and in situ hybridization was performed as described [49] . X-gal staining was performed as described [33] . To measure cell proliferation rates in vivo , BrdU was injected intraperitoneally into pregnant females at a dosage of 50 µg per gram of body weight . Embryos were dissected after 1 hour , fixed in 4% PFA and processed for cryosections and immunostaining using standard protocols . BrdU labeled cells were counted in a random area in the defined mesenchyme at comparable levels in mutant and control samples . Three continuous sections were counted from each of triplicate samples . The result of BrdU labeling was presented as percentage of BrdU-positive cells against total nuclei labeled by DAPI . Student's t-test was used to determine statistical significance . Neural tubes anterior to the first pharyngeal arch were dissected from E8 . 5 embryos . Following two brief washes in ice-cold PBS , heads were sagitally split into two equal halves . The tissue was incubated in 0 . 5% trypsin/2 . 5% pancreatin in PBS for 5 minutes on ice , and then in DMEM with 10% FBS for 10 minutes to stop the reaction . The head mesenchyme was carefully dissected and isolated with fine-tipped Dumont #5 tweezers , and the neural tube was transferred to fibronectin-coated cover slips in a 6 well plate . For NCC emigration assays , neural tubes were cultured in DMEM/F12 with 10% FBS for 6 hours . The explants were then treated with low serum media ( DMEM/F12 containing 0 . 5% FBS ) with 10 µg/ml mitomycin C for two hours . The culture medium was replaced with low serum media and maintained for 24 hours . Results were recorded at 8 hours and 24 hours respectively . The explants were then removed and the emigrating cells were subjected to immunostaining . Primary Mouse Embryonic Palatal Mesenchymal cells ( MEPMs ) were isolated as described [50] . For scratch assays , MEPMs at passage 1 were seeded on fibronectin-coated cover slips in 6 well plates at a density of 100 , 000 cells per well . After reaching 70–80% confluency , cells were starved for 24 hours in DMEM containing 0 . 5% FBS . In some experiments , serum starved cells were pretreated with 30 µM Rac1 inhibitor NSC23766 ( R&D Systems ) for 3 hrs before stimulation with 30 ng/ml PDGFA . The scratch was mechanically created using a sterile P200 pipette tip and washed twice with starving medium to remove cell debris . The wound area was then photographed at marked positions ( 3 different fields per well ) . Cells were allowed to migrate for 12 hours at 37°C before the same fields were recorded . All experiments were performed in triplicate . Scratch results were measured with Image J software ( NIH , Bethesda , USA ) and analyzed using the extension package MiToBo [51] . For transwell assays , cell culture inserts for a 24-well plate ( Fisher ) with a pore size of 8 µm were coated with fibronectin . P1 MEPMs were trypsinized , washed , and suspended in serum free medium at a concentration of 5×106 cells/ml . 300 µl of cell suspension was added to the insert chambers immersed in 500 µl medium with 10% FBS , 0 . 5% FBS , or 0 . 5% FBS with PDGFAA . After incubation at 37°C and 5% CO2 for 3 . 5 hours , the inserts were fixed in 3 . 7% formaldehyde and stained in Mayer's hematoxylin solution . Filters of the inserts were then isolated with a scalpel and mounted . The numbers of cells on the bottom were counted . Data were recorded from 9 high power fields from three independent experiments . Western blot analysis was carried out as described previously [24] , using primary antibodies from Abcam: anti-cofilin ( phospho-S3 ) and from Cell Signaling Technology: anti-cofilin , anti-p44/42 MAPK , anti-phospho p44/42 MAPK , anti-Akt , anti-phospho-Akt ( Ser473 ) . Chemical inhibitors for MAPK ( U0126 , Promega ) , PI3K/Akt ( LY294002 , Stemgent ) and Rac1 ( NSC23766 , R&D systems ) were used in lysate generation for western blot analysis and the Rac1 activity assay , following the manufacturers' instruction . Rac1 activity analysis was performed using the colorimetric based G-LISA Rac1 Activation Assay Biochem Kit ( Cytoskeleton Inc . ) . | Craniofacial anomalies , including cleft lip and palate , are frequent birth defects . Although these are often associated with defects in neural crest development , the more severe phenotypic manifestations of midline defects is facial clefting , which is poorly understood . In this work , we show that the facial clefting phenotype of PDGFRα mutants is not associated with a defect in neural crest cell specification but rather a subsequent defect in the medial nasal process ( MNP ) , a facial primordium derived from the frontonasal prominence . We further show that this defect is associated with alterations in both cell proliferation and cell migration , and that PI3K and Rac1 signaling are essential to maintain a normal level of cell proliferation . Last , we provide evidence that Rac1 regulates cell migration at the level of cell motility as well as chemotaxis under the regulation of PDGFRα . We thus establish PDGFRα as a novel regulator of MNP development and elucidate the roles of its downstream signaling pathways at cellular and molecular levels . | [
"Abstract",
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| 2013 | A Critical Role for PDGFRα Signaling in Medial Nasal Process Development |
Phagocytosis requires locally coordinated cytoskeletal rearrangements driven by actin polymerization and myosin motor activity . How this actomyosin dynamics is dependent upon systems that provide access to ATP at phagosome microdomains has not been determined . We analyzed the role of brain-type creatine kinase ( CK-B ) , an enzyme involved in high-energy phosphoryl transfer . We demonstrate that endogenous CK-B in macrophages is mobilized from the cytosolic pool and coaccumulates with F-actin at nascent phagosomes . Live cell imaging with XFP-tagged CK-B and β-actin revealed the transient and specific nature of this partitioning process . Overexpression of a catalytic dead CK-B or CK-specific cyclocreatine inhibition caused a significant reduction of actin accumulation in the phagocytic cup area , and reduced complement receptor–mediated , but not Fc-γR–mediated , ingestion capacity of macrophages . Finally , we found that inhibition of CK-B affected phagocytosis already at the stage of particle adhesion , most likely via effects on actin polymerization behavior . We propose that CK-B activity in macrophages contributes to complement-induced F-actin assembly events in early phagocytosis by providing local ATP supply .
Dynamic reorganization and stabilization of the actin cytoskeleton and membrane-shape alterations of cells are intimately and reciprocally coupled events that are essential for a variety of distinct cell functions such as adhesion , motility , cytokinesis , and endocytosis [1] . One of the processes that is critically dependent on proper regulation of actin polymerization is phagocytosis , essential for food intake in lower eukaryotes or the elimination of invading microbial pathogens and scavenging of dead cells in higher multicellular eukaryotes [2] . Engulfment of a phagocytic target is a spatially confined process , which is initiated at the cell membrane by recognition of the molecular structure at the surface of the phagocytic targets by dedicated receptors , such as Fc-gamma receptors ( Fc-γRs ) , mannose receptor , or the complement receptor 3 ( CR3 , Mac-1 ) [3–5] . After binding of the target , receptors cluster , become activated , and trigger actin-dependent cytoskeletal changes via the activation of small Rho GTPases and concomitant induction of specific protein kinase signaling cascades [2 , 6] . In Fc-γR–mediated phagocytosis , signals are mediated through Rac and Cdc42 , whereas CR3-regulated phagocytosis of complement-opsonized targets requires only RhoA activation [7] . These pathways ultimately converge and lead to the induction of Arp2/3-mediated actin polymerization , which is considered the main driving force for the formation of circular pseudopod protrusions ( i . e . , a “phagocytic cup” ) around the target [8] . Once the wrapping in cellular membrane protrusions is complete , a contractile force is generated to engulf the particle or dead cell completely and guide the contents of the vesicle into the endocytotic pathway for degradation [9] . All events during early phagocytosis , including ruffle formation , membrane delivery , closure of the phagocytic cup , and short-range movement of newly formed vesicles through the cellular cortex , depend on actin polymerization and myosin motor proteins . In turn , for proper regulation of polymerization of G-actin into F-actin , which involves filament nucleation and extension , a spatially confined supply of ATP for the loading of actin subunits is required [10–12] . Theoretical models predict that ATP primarily promotes an “adjusted fit” of incoming monomers to the end of the actin filaments , and multiple studies agree that the ATP/ADP loading state of actin and related Arp2/3 proteins determine filament assembly or branching behavior [1 , 13 , 14] . Moreover , during filament severing or turnover , energy is used when ATP is hydrolyzed when still bound to F-actin , and Pi is released . Next , ADP-actin dissociates , and free G-actin monomers can be subsequently reloaded with “new” ATP . ATP- or ADP-loaded actin monomers are both competent for polymerization , but the nature of the bound nucleotide differentially modulates the kinetics of the association and dissociation at the pointed or barbed ends of filaments . Also the “storage” of G-actin monomers into thymosin- or profilin-sequestered pools is dependent on nucleotide loading state and hence , the energy state of the cell [12 , 15] . Active recruitment of G-actin and F-actin dynamics thus consumes ATP in several steps , and the active cell-shape remodeling needed for particle ingestion renders phagocytosis a process with a very high local requirement for high-energy phosphoryl ( ∼P ) groups . In fact , the energy dependence of phagocytosis is made even more prominent , because ATP is also necessary to sustain the activity of several nonmuscle myosin ATPases , which help in actin and membrane recruitment , and provide motor activity around the phagocytic cup [16 , 17] . For example , myosin-II activity is implicated in phagocytic cup formation and squeezing [18 , 19] , whereas myosins X and VII may have roles in pseudopod extension and phagosome internalization [17 , 19–21] . By forming an ATP drain for these many actomyosin-based micromechanical events , phagocytosis may thus pose a formidable challenge to cellular energy homeostasis . Indeed , metabolic studies report increased energy turnover during phagocytosis [22 , 23] . Creatine kinase ( CK ) -mediated phosphotransfer plays an important role in local delivery and cellular compartmentation of ATP and transport from glycolytic or mitochondrial production sites [24 , 25] . The CK reaction buffers ATP and ADP levels by the reversible transfer of high-energy phosphoryl onto creatine ( Cr ) to form phosphocreatine ( PCr ) : MgATP2- + Cr ↔ MgADP− + PCr2- + H+ [26] . In muscle , localized delivery of ATP by the muscle-type creatine kinase ( CK-M ) isoform is clearly of importance for sustenance of acto-myosin ATPase activity involved in myofibrillar sliding activity during repeated high-speed contraction [27] . In brain , we [28] have obtained evidence that lack of brain-type CK ( CK-B ) activity affects synaptic coupling efficiency , a process for which active actin remodeling is essential [29] . By analogy , we hypothesized that the functional coupling between actin-based cytoskeletal dynamics and CK-mediated ATP compartmentalization and supply could be more general , and might be of importance for shape changes and dynamics of nonmuscle or neuronal cells as well . Here , we confirm this view and report on the role of the CK-PCr system in the dynamics of phagocytosis . Interestingly , Loike et al . [23] have found that brain-type CK is expressed in macrophages and that PCr levels decrease during phagocytosis . Our data suggest that the metabolic ATP-supply activity of CK-B is of local importance and facilitates specific phagocytosis steps via effects on actin-based events early in the binding-ingestion process .
Phagocytic cup formation is characterized by a localized expansion of the plasmalemmal membrane , coupled to highly active remodeling and myosin-based contraction of the actin cytoskeleton . We studied the possible fate and role of endogenous CK-B in this process , in primary microglia and peritoneal macrophages after induction of phagocytosis with nonopsonized zymosan . Macrophages and microglia [2 , 30] are cells of the immune system that are very active in ruffle extension and uptake of extracellular particles . Although it has been reported that primary macrophages express CK-B [31] , no data are available on the enzyme's behavior under conditions of active phagocytosis . Figure 1A and 1E shows that a fraction of CK-B always remained diffusely distributed throughout the cytosol , as in nonphagocytosing cells , but that a substantial portion of CK-B accumulated around the engulfed zymosan particles at nascent phagosomes . This accumulation did not occur exactly simultaneously in all cells because phagocytosis was not initiated fully synchronously throughout the culture , but at later time points , the concentrated staining dissipated ( unpublished data ) , indicating that CK-B associated only transiently with phagosome structures . Phalloidin staining demonstrated an almost complete overlap with CK-B encircling the zymosan particles in the phagosome ( Figure 1B and 1F ) . To assess whether CK-B is actually locally bound within the cup area , microglia were permeabilized with saponin before fixation to remove most of the unbound cytosolic protein . Strikingly , a fraction of endogenous CK-B remained associated with the actin-rich area ( Figure 1C and 1D ) . These data indicate that part of the CK-B molecules in the endogenous pool partition into sites of active F-actin remodeling . To further verify the general validity of this picture , we analyzed the behavior of endogenous or exogenously transfected CK-B in the murine macrophage cell line , RAW 264 . 7 . As anticipated , we also observed in this cell a uniform cytosolic distribution of endogenous CK-B and coaccumulation with F-actin at nascent phagosomes ( Figure 1G and 1H ) . To compensate for the rather weak endogenous CK-B staining in RAW 264 . 7 cells , we also produced pools of cells with a higher CK-B steady-state level by transduction with retroviral vectors to enhance immunofluorescent detection . Again , prominent accumulation of CK-B together with F-actin appeared in the phagosome ( Figure 1H and 1J ) . Notably , in RAW 264 . 7 cells with an overall high global CK-B level , we noticed CK-B accumulation at the distal tips of filopodia ( Figure 1K and 1L ) . This phenomenon has been observed for a number of other proteins involved in cytoskeletal rearrangement , dynamic adhesion , and phagocytosis , including myosin-X , myosin-VII , and vasodilator-stimulated phosphoprotein ( VASP ) [20 , 21 , 32] . As CK-B's role might involve the delicate interplay between compartmentalized energy supply and local molecular dynamics in the cell cortex area , we monitored the profile and timing of CK-B recruitment at the phagosome in more detail . To obtain dynamic information , we transiently expressed enhanced yellow fluorescent protein ( EYFP ) -tagged CK-B ( via N-terminal fusion ) in RAW 264 . 7 cells , and applied live cell microscopy imaging after induction of phagocytosis . Earlier work showed that N-terminal tagging of CK-B does not affect its enzymatic or structural properties [33]; ( unpublished data ) . In the first ( Figure 2A ) of eight sequential frames of a movie ( see Video S1 ) of typical CK-B behavior in an active macrophage , one particle is already being internalized ( indicated by an asterisk ) , but at that point in time , EYFP-CK-B appears nonpartitioned and is still diffusely distributed throughout the cytoplasm . In subsequent frames ( Figure 2B–2F ) , a clear CK-B accumulation in the phagocytic cup is observed , dissipation of which occurs when the particle is fully internalized ( Figure 2G ) . A second phagocytic event with recruitment is initiated in the same cell at a later time point ( Figure 2G and 2H ) . In control cells expressing nonfused EYFP , no significant accumulation at the site of zymosan ingestion was ever seen . A relatively straightforward interpretation of these observations would be that the spatially confined recruitment of CK-B serves phagocytic cup formation and/or closure , presumably via local delivery of ATP . A hallmark of phagosome formation is the rapid polymerization of F-actin , which drives the membrane extension around the target . Also actomyosin motor sliding is intimately coupled to this process [17 , 19–21] . To study whether these processes are indeed among the ones served by local CK-B activity , we compared zymosan-driven phagocytosis in RAW 264 . 7 cells that were stably coexpressing enhanced green fluorescent protein ( EGFP ) -tagged β-actin and either enhanced cyan fluorescent protein ( ECFP ) alone , ECFP-tagged CK-B , or a mutant CK-B ( C283S ) . This latter CK variant acts as a dominant-negative enzyme , occurring as a normal dimer with only 4% residual kinase activity [34] . Parallel spectral monitoring of fluorescence intensities enabled us to follow simultaneously the dynamic behavior of actin and CK-B variants , or the ECFP control , after induction of zymosan-driven phagocytosis ( Figure 3A ) . Plotting of local signal intensities in relation to the global intensities in the cell body , which remained constant and were comparable for all cells examined ( unpublished data ) , showed that recruitment of ECFP-tagged CK-B and CK-B ( C283S ) occurred in nearly identical spatiotemporal overlap with EGFP-actin recruitment in all cases examined ( Figure 3B and 3C; 10–16 individual events analyzed ) . As anticipated , the average maximal cup/body signal ratio for ECFP-CK-B and ECFP- CK-B ( C283S ) ( 142% ± 47% and 129% ± 21% , respectively ) was significantly higher ( p < 0 . 005 ) than that for ECFP alone ( 100%; Figure 3E ) . Thus , mobilization of CK-B protein to the phagosome area does not appear to be dependent on CK enzymatic activity . All cells examined displayed a clear accumulation of actin in the cup during a typical phagocytic event as defined by an increasing cup-to-body ratio of the EGFP signal ( EGFPCup/EGFPBody > 1 ) ( Figure 3B–3D ) . Strikingly , this accumulation differed significantly between cells expressing ECFP-CK-B or ECFP-CK-BC283S ( Figure 3F ) , and for the ECFP- CK-B ( C283S ) cell line , was markedly decreased ( 61% ± 18% ) compared to ECFP cells ( p < 0 . 005 ) . Cells in an independently generated pool harboring the ECFP-tagged CK-B ( C283S ) exhibited a similar decrement , demonstrating that the observed decrease was not cell line or pool specific ( unpublished data ) . In ECFP-CK-B cells , the green actin signal reached a slightly higher maximal cup/body ratio of 117% ± 51% than in ECFP-control cells ( 100% ) . This difference was not significant , however , and we therefore consider it the result of experimental variation . To establish whether effects of absence/presence of active CK-B affected the temporal profile of actin recruitment , we also compared the timing of actin mobilization between different movies of different cell transfectants . No significant differences were found . These results demonstrate that local presence of metabolically active CK-B alters the magnitude , but not the timing , of actin mobilization at the phagosome . The molecular structure at the surface of the phagocytic target determines which receptor types become ligand bound and activated . Subsequently , receptor-specific downstream signaling events , such as alternative use of small Rho GTPases and kinases , orchestrate the outcome of the phagocytic process [7 , 19 , 35] . To test whether CK-B recruitment is a default response or determined by the surface properties of the target , we repeated our time-lapse experiments with cells that were challenged with native zymosan ( Figure 4A ) , or zymosan opsonized with either complement ( COZ ) ( Figure 4B ) or immunoglobulin G ( IgG ) ( Figure 4C ) . To avoid that effects of other properties of the target , such as rigidity and geometry , would influence the outcome of our study [36 , 37] , we deliberately chose to change only the type of coat , not the particle type ( i . e . , zymosan ) in these experiments . Line-plots of pixel intensities across the phagocytic cup and other areas of the cell body revealed that EYFP-CK-B recruitment occurred independently of the type of opsonization . Montages of control cells with untagged EYFP did not reveal any specific mobilization into or around phagocytic cups ( Figure 4D–4F ) . These data are consistent with the idea that spatially confined presence of CK-B in the cup area is interlinked with general steps in phagocytic cup formation and/or closure . On the basis of this premise , we wondered whether the CK-driven ATP–PCr exchange reaction could be directly or indirectly coupled to the process of particle ingestion . Initially , we chose a pharmacological approach to modulate activity of the entire cellular pool of CK , applying Cr as a stimulating substrate or cyclocreatine ( cCr ) as a reversible inhibitor of the CK reaction . RAW 264 . 7 cells were preincubated with 5 mM cCr or Cr prior to the phagocytosis assay , and cells were then challenged with differentially opsonized and fluorescently labeled phagocytic targets . After 30 min , phagocytic activity was quantified by determining the mean fluorescence intensity of ingested particles in the different cells by fluorescence-activated cell sorter ( FACS ) analysis . In Figure 5 , data are shown that are normalized to values for nontreated cells . Phagocytosis of nonopsonized zymosan was slightly affected by cCr treatment , yielding an efficiency value of 79% ± 9% ( p < 0 . 05 ) , whereas Cr supply had no significant effect ( 102% ± 9% ) ( Figure 5A ) . Interestingly , cCr inhibition decreased phagocytosis of COZ to a much lower level , 37% ± 6% ( p < 0 . 005 ) of that of nontreated cells , whereas Cr addition had no significant effect ( value 84% ± 8% ) ( Figure 5B ) . In contrast , Cr and cCr addition had no significant effect on phagocytosis of IgG-opsonized zymosan , with 97% ± 5% and 88% ± 9% for Cr- and cCr-treated cells , respectively ( Figure 5C ) . In order to verify that this difference was indeed due to differential effects on CR3 and Fc-γR receptor-mediated activities and cannot be attributed to interference with other pathways , we performed receptor-blocking experiments . Capture uptake of the two different types of opsonized zymosan appeared indeed specific for the anticipated receptors ( Figure S1 ) . To study this point further , we also tested phagocytic activity on complement- and IgG-opsonized polystyrene beads , which lack obvious surface ligands such as mannose or β-glucan groups , and therefore form “clean” targets . Interestingly , uptake of complement-opsonized beads was again inhibited by cCr ( 56 ± 4% of control ) , whereas IgG-mediated phagocytosis remained unaffected ( 95 ± 2% of control; Figure S2 ) . Thus , although CK mobilization is seemingly a default event in all types of phagocytosis ( Figure 4 ) , it may only selectively contribute to the efficiency of phagocytic ingestion of nonopsonized or complement-opsonized particles ( Figures 5 and S2 ) . A similar situation was recently reported for the cytoskeletal actin-binding protein talin , whose functional role in phagocytic uptake appeared selectively coupled to CR3 , but which accumulates in phagosomes formed around IgG- and C3-opsonized particles [38] . To address CK-B's specific role in phagocytic activity in another manner , we also compared effects of expression of the CK-B ( C283S ) mutant and that of CK-B . To obtain comparable levels of expression of these proteins across all individual cells in and between cell populations , transduction with retroviral vectors encoding CK-B , the CK-B ( C283S ) mutant , or EYFP was used ( resulting cell pools are hereafter referred to as RAW-CK-B , RAW-CK-B ( C283S ) , and RAW-EYFP cells , respectively ) . Two independent cell pools were established for each construct to rule out potential integration-site–dependent effects and/or effects of overgrowth of specific cell clones . Western blotting was performed to assess expression levels in our stable cell lines ( Figure 6A ) . The levels of the exogenously expressed wild-type ( wt ) or mutant CK-B protein in the RAW-CK-B or RAW-CK-B ( C283S ) cell pools amounted to roughly ten times more than the endogenous CK-B level in these cells . The total enzymatic phosphoryl transfer activity had increased accordingly , and approximated a 10-fold higher steady-state level in both RAW-CK-B cell populations relative to the reference RAW 264 . 7 cell pool ( Figure 6B ) . As anticipated , the total CK activity of the control line expressing EYFP matched that of the endogenous activity in nontransduced control cells . Also , both pools of RAW-CK-B ( C283S ) mutant cells exhibited levels of CK activity that were identical to endogenous levels . Clearly , the residual activity for the mutant CK-B ( C283S ) is very low , and therefore no appreciable increase in CK activity was noticeable despite the almost 10-fold increase in protein level . Assessment of phagocytic capacity with fluorescently labeled zymosan and COZ confirmed that neither the retroviral infection nor the subsequent antibiotic selection procedure had affected phagocytic capacity , since RAW-EYFP cells and noninfected cells behaved identically ( Figure 6C ) . Expression of CK-B ( C283S ) , however , resulted in a considerable drop in phagocytosis efficiency . Uptake of nonopsonized zymosan was at 58 ± 21% and 57 ± 12% ( p < 0 . 005 ) for both RAW-CK-B ( C283S ) #1 and RAW-CK-B ( C283S ) #2 , respectively , relative to nontransduced or EYFP-expressing cells . We observed that overexpression of CK-B had no stimulating effect and did not significantly alter phagocytosis of zymosan ( 108 ± 30% and 113 ± 23% for the two independent cell lines ) . In contrast , in the case of complement-mediated ( COZ ) phagocytosis , both RAW-CK-B cell pools did perform significantly better than controls ( 144 ± 4% and 153 ± 6% , p < 0 . 005 ) ( Figure 6D ) . Conversely , the cell lines expressing CK-BC283S were also significantly impaired in the uptake of COZ ( 79 ± 8% and 66 ± 5% , p < 0 . 05 ) . Thus , expression of CK-B ( C283S ) impaired phagocytosis of both zymosan and COZ , whereas overexpression of wt CK-B stimulated only the phagocytosis of COZ . Importantly , expression of CK-B ( C283S ) did also not influence IgG mediated phagocytosis ( Figure 6E; efficiency of 85 ± 10% and 89 ± 14% for both lines ) , in line with our results with pharmacological inhibition . Unfortunately , our findings of effects of overexpression of wt CK-B on IgG-mediated phagocytosis were inconclusive . One cell line displayed a moderate increase in phagocytotic efficiency ( 118 ± 8%; p < 0 . 05 ) , whereas the other did not differ significantly from the control ( 95 ± 13% ) . Although identical cell pools were used for the experiments shown in Figure 6C–6E , the experiments presented in Figure 6E were performed with cells at a higher passage number . We therefore may have to attribute the borderline stimulation to an effect unrelated to CK-B . Because total cellular CK activity was identical between the parental RAW264 . 7 line and RAW-CK-B ( C283S ) , and in addition , CK-B ( C283S ) is recruited in a similar fashion as wt CK-B ( Figure 3 ) , our findings suggest that the mutant protein competes with endogenous CK-B and thereby lowers the concentration of locally active CK-B molecules at crucial sites in the cell cortical area . Phagocytosis occurs through a series of consecutive steps that ultimately lead to the engulfment of a particle . Probing for adhesion of coat molecules , and the actual binding of the phagocytic target to specific receptors on the protruding cell surface , constitutes one of the first steps in this process . In order to specify which specific phase of the phagocytic process is linked to CK-B , we subjected the wt CK-B or CK-B ( C283S ) cell pools to a particle adhesion assay , using COZ particles as the phagocytic targets with most discriminative effects ( Figure 7 ) . Quantification of the total number of particles per cell ( inside + outside ) in images of the cell lines with adherent and already internalized particles ( Figure 7A–7C; n = 3 experiments ) demonstrated that an average of 1 . 3 ± 0 . 7 of COZ particles associated with RAW-CK-B ( C283S ) cells , significant less than with control cells , which have 2 . 1 ± 0 . 7 particles/cell ( p < 0 . 001 ) . Interestingly , RAW-CK-B cells bound significant higher numbers of particles than control cells ( 2 . 8 ± 0 . 7 particles/cell; p < 0 . 005 ) . Calculation of the percentage of external COZ particles revealed that control cells have 17 ± 10% of particles attached that are not yet ( fully ) internalized . Overexpression of CK-B did not affect this percentage ( 14 ± 9% external ) . With RAW-CK-B ( C283S ) cells , a significantly higher percentage of particles remained external ( 27 ± 18% ; p < 0 . 02 ) . Inhibition of CK-B thus apparently affects both the initial sampling of COZ particles from the added pool as well as well as the process of their internalization . Recently , it has been demonstrated that cells actively probe the extracelullar matrix for adhesion sites by clustering integrins in “sticky fingers” at the leading edge of cells . Actin polymerization has an active role in this process [39] . Since extension–retraction of filopodial tentacles that determine the efficiency of particle uptake in phagocytosis is also based on F-actin in combination with myosin-V , -VII , and -X activities [40] , we decided to study whether the role of F-actin in adhesion of COZ and IgG-opsonized zymosan ( Figures 5B and 5C , and 6D and 6E ) in RAW 264 . 7 cells could be different and correlated with the differential effects of CK-B . Therefore , adhesion experiments with COZ and IgG-opsonized zymosan particles in the presence of low concentrations of the actin polymerization inhibitor cytochalasin D were performed ( Figure 8A and 8B ) . Interestingly , treatment with 50 nM or 100 nM cytochalasin D decreased adhesion of COZ dramatically ( 52 ± 18%; p < 0 . 05; and 47 ± 8%; p < 0 . 001 , respectively; n = 4; Figure 8A ) , whereas adhesion of IgG-opsonized zymosan was not significantly affected ( 108 ± 22% and 91 ± 9% for 50 nM and 100 nM cytochalasin D , respectively; n = 3; Figure 8B ) . We consider this evidence for a different role of F-actin in complement- and IgG-mediated adhesion . To further address whether this discriminative coupling could indeed be linked to CK-B's role in providing adequate ATP supply for F-actin formation [41 , 42] , RAW 264 . 7 cells were treated with cCr , and the F-actin content was determined . Fluorescent phalloidin staining in combination with FACS analysis revealed that in cCr-treated cells , the global F-actin content significantly decreased to 73 ± 9% ( p < 0 . 01 , n = 4 ) of nontreated control cells ( Figure 8C ) . Thus , inhibition of CK-B–mediated activity indeed affects the formation of F-actin in RAW 264 . 7 cells . This is in agreement with our finding that actin recruitment to phagocytic cups is also diminished when CK-B is inhibited ( Figure 3F ) . The question whether there is also a reciprocal relationship , whether the local F-actin state contributes to CK-B recruitment to the phagocytic cup , appeared more difficult to answer . Until now , we were unable to detect a direct binding between actin and CK-B in pull-down experiments ( unpublished data ) . Furthermore , fluorescence recovery after photobleaching ( FRAP ) experiments revealed that the motility of YFP-actin and CFP-CK-B in cup areas differed during the phagocytic process ( Figure S3 ) , arguing against single association between actin and CK-B . Involvement of transient “kiss-and-run” type interactions cannot be excluded , however . Combined , our data suggest that the activities of CK-B that we have described are likely to occur via ATP-supply effects on local F-actin polymerization capacity , which in turn affects CR3-mediated adhesion and internalization .
Phagocytosis requires a rapid and spatially confined reorganization of the actin cytoskeleton . The underlying molecular processes , such as actin polymerization and actomyosin force generation , generate a sudden and localized demand for cellular ATP [22 , 23] . To reciprocate this challenge , sites of ATP production should be coupled tightly to sites of ATP consumption . From studies in cell and animal models , we know that CK isozymes are particularly well equipped for this role , as they provide the cell with a fast ATP regeneration and delivery system that can adequately provide high-energy phosphoryl groups to cellular locales with high energy turnover . Here , we established a tight functional and spatial link between the CK system and the actin-based cytoskeletal machinery in macrophages during phagocytosis . A similar relationship was found for the muscle isoform of CK , CK-M , which associates with the M- and I-bands of skeletal muscle and fuels local ATP-consuming processes , including actomyosin contraction and calcium pumping [26 , 43] . Importantly , CK-M's role in these physiological processes is supportive , not absolutely vital [27 , 43] , just as we report here for CK-B's role in phagocytosis . For CK-M , the molecular nature of events that support its role has been unraveled to some extent . For example , we know that interaction of CK-M with the sarcomeric M-band is mediated via conserved lysine residues . In addition , particular amino acid segments in CK-M enable the protein to bind indirectly to the I-band via the glycolytic enzymes phosphofructokinase ( PFK ) and aldolase , which have actin-binding properties [44] . Interestingly , glycolytic enzymes are also known to be recruited to phagosomes [45 , 46] , so there may be parallel mechanisms . Unfortunately , to explain CK-B dynamics , it is not possible to use simple analogy since the lysine residues involved in CK-M M-band interaction are not conserved in CK-B [47] , and direct sequence comparison is not yielding clear clues for other binding modes—for example to glycolytic enzymes . Several mechanisms could therefore be involved in the recruitment of CK-B . One model would be the transient availability of CK-B binding sites at the nascent phagosome by modification of local proteins or presence of CK-B interacting proteins . Based on its colocalization with CK-B , we tested whether actin could be a candidate for such scaffolding , but pull-down assays and FRAP experiments did not reveal a tight interaction . Also yeast two-hybrid assays did not disclose any CK-B to actin binding opportunities ( unpublished data ) . As another possibility , CK-B binding characteristics could also be transiently modified at the enzyme itself , possibly by ( enzymatic ) events located at the forming cup . Indeed , CK-B is prone to covalent modifications such as phosphorylation [48] , oxidation [49] , methylation [50] , or ubiquination [51] . Simple presence of substrate may also determine binding ability , as recently shown for CK-M [52] . Further studies are necessary to discriminate between all these possibilities . Because phagocytosis is a metabolically demanding process , CK-B recruitment to phagocytic cups could serve to promote or safeguard local events or—reciprocally—shield the rest of the cell from excessive local energy demand . To distinguish between these mechanistic models and elucidate CK-B global and local physiological role ( s ) in macrophages more precisely will be technically challenging because of the confined character of the events . This , therefore , also remains a topic for future study . Of particular importance was our finding that displacement of endogenous CK-B by ECFP-CK-B ( C283S ) during phagocytosis reduced the local accumulation of EGFP-actin in the phagocytic cup area . The observation that inhibition of CK-B activity impairs global F-actin polymerization in RAW 264 . 7 cells was equally revealing . Normally , actin polymerization requires the incorporation of ATP-actin at the barbed end of actin filaments . During filament elongation , ATP is hydrolyzed and ADP-actin is being released at the pointed end . Thus , constant reloading of actin with ATP is required for the continuation of the polymerization cycle [12 , 41 , 42] . We propose here , that CK-B could specifically enhance this process by regenerating ATP at sites of active actin remodeling . In addition to the polymerization reaction , actin nucleation and branching play an important role . The Arp2/3 complex , together with the Wiskott-Aldrich syndrome ( adapter ) protein , WASP [8] , and the motor proteins myosin-I [53 , 54] and myosin-II [19] , helps to guide these processes during shaping of the cup of nascent phagosomes . Recently , the involvement of the formin mDia has also been implicated in phagocytosis of COZ . Inhibition of mDia results in decreased F-actin recruitment at the phagocytic cup and induces a concomitant decrease in efficiency of CR3-mediated , but not Fc-γR–mediated , phagocytosis [55] . Formins promote actin polymerization by increasing polymerization-associated ATP hydrolysis up to 15 times via profilin [56] . Finally , regulation of cell structure via RhoA activation or AMP-activated kinase involvement is also directly energy dependent [57 , 58] . Thus , different types of actin regulatory processes form local and temporal energy drains , which may need compensation by CK-B–mediated ATP regeneration . Various associated myosin motor mechanisms involved in formation of the specialized structures at the phagosome may also be CK-B dependent , because motor activity of myosins is controlled by ATP/ADP ratio . In Dictyostelium , myosin-VII was found to be important for initial adhesion in phagocytosis [21] . In addition , another closely related myosin , myosin-X , was implicated in adhesion and phagocytosis [20 , 59] . However , at this point , we need more mechanistic information about how ATP fuelling separately serves actin and myosin activities in the phagocytic cup before we can analyze CK-B's presumed role ( s ) in detail further . Intriguingly , Olazabal and coworkers [19] have reported that inhibition of myosin-II decreased actin recruitment in phagocytic cups during CR3-mediated phagocytosis , but not Fc-γR–mediated events . Our results regarding differences in ingestion capacity for COZ or IgG-coated zymosan and beads after genetic blocking or pharmacological inhibition of CK activity fit with a model in which ATP-driven actomyosin activities differentially contribute to discrete phagocytic processes . Only phagocytosis of COZ and zymosan was significantly affected under conditions in which CK activity was lowered , Fc-γR–mediated activity was not significantly affected by cyclocreatine or dominant-negative CK inhibition . Of note , phagocytosis of COZ is mainly , but not exclusively , mediated by complement molecules present on COZ . Also , sugar residues that are present on ( non ) opsonized zymosan facilitate recognition by lectin domains in CR3 [60–62] or alternative phagocytic receptors such as dectin-1 [63] . Based on our findings and these background notions regarding differences in pathways involved in cup formation in complement or IgG modes of phagocytosis [7 , 19 , 55 , 64] , it is tempting to speculate that CK-B enhances phagocytosis by modulating specific processes up- or downstream of CR3 . It may be important to note that differences in the actin levels in the phagocytic cup , as seen between active ECFP-CK-B and mutant ECFP-CK-B ( C283S ) –expressing cells , correlated better with the fraction of cells participating in phagocytosis than with the number of fully ingested particles per cell . The observation of the apparent delayed internalization of COZ particles in the RAW-CK-B ( C283S ) line ( Figure 7E ) suggests that after early binding requirements have been fulfilled , CK-B also promotes transition to the next phases of phagocytosis . Therefore , we propose that the CK-B–mediated modulation of actin polymerization is particularly relevant during early CR3-mediated phagocytosis , for example by increasing the number of successful probing attempts for particle binding . Our data also suggest that endogenous levels of resident CK-B molecules are largely sufficient to saturate these requirements for zymosan and COZ phagocytosis . CR3 is also known as CD11b/CD18 or αMβ2 integrin . Because actin polymerization is pivotal in early adhesion events mediated by integrins [39] , a picture emerges in which actin behavior determines the efficiency by which CR3s can bind their target . Actin behavior may be less central for IgG-mediated binding events . Our finding that low concentrations of the actin polymerization inhibitor , cytochalasin D , reduced binding of complement opsonized particles more than IgG-opsonized particles is consistent with such a model . In conclusion , we have demonstrated that CK-B enhances phagocytosis of zymosan and COZ , likely via a specific synergistic role in mechanistic events involved in actin polymerization behavior . Although our data indicate that the enzyme's metabolic role is dominant , we cannot completely rule out a structural role of CK-B in phagocytosis at this point . Taken together with the finding that CK-B ( C283S ) was able to inhibit phagocytosis without decreasing the total CK activity , our data suggest that CK-B acts to steer the delicate local balance in ATP/ADP ratio , during formation of the phagocytic cup , around the time that pseudopod–filopodium extensions or CR3-mediated adhesions are formed . Because we do see CK-B in filopodia and phagocytic cups in our RAW cells ( Figures 1G–1L and 4 ) , but also observe CK-B accumulation in dynamic actin structures of other cell types during adhesion to substratum , spreading , and crawling , ( e . g . , in neurons and astrocytes; unpublished data ) , this raises the exciting possibility that CK-B facilitates rapid cytoskeletal dynamics in a broad range of specialized events that occur during tissue development and disease , including dendritic spine generation in brain [65] , formation of immune-synapses , or protrusion dynamics for cancer cell invasion .
Resident peritoneal macrophages were isolated from adult ( 10–20 wk old ) mice of mixed genetic background ( C57BL/6 × 129Ola ) . After sacrificing the mice by cervical dislocation , cells were harvested by rinsing the peritoneum twice with 5 ml of ice-cold HBSS . Collected macrophages were cultured for 24 h in RPMI supplemented with 10% fetal calf serum ( FCS ) , glutamine ( 0 . 3 g/l ) , sodium pyruvate ( 0 . 11 g/l ) , and gentamycin ( 0 . 05 mg/ml ) . Primary microglia were collected from neocortices of newborn mice as described [66] . RAW 264 . 7 murine macrophages were maintained in RPMI 1640 ( Gibco ) containing 10% FCS , glutamine ( 0 . 3 g/l ) , sodium pyruvate ( 0 . 11 g/l ) , and gentamycin ( 0 . 05 mg/ml ) . Expression plasmids construction and transfection was done according to standard procedures . To generate catalytically inactive CK-B , cysteine-283 was replaced by a serine using the QuikChange Site-Directed Mutagenesis kit ( Stratagene ) . Retroviral expression constructs were created by insertion of the ORFs of CK-B , CK-B ( C283S ) , and EYFP into retroviral vector pLZRS-IRES-zeo [67] , giving rise to pLZRS-CK-B , pLZRS-CK-B ( C283S ) , and pLZRS-EYFP , respectively . Retroviral transduction was performed as described [67] . As targets , unlabeled zymosan particles were used . Complement opsonization was performed as described [68] , and cells were activated with 200 nM phorbol 12-myristate 13-acetate ( PMA ) 15 min prior to phagocytosis of COZ . IgG opsonization of zymosan was done using zymosan A BioParticles opsonizing reagent from Invitrogen . For assay of phagocytic uptake efficiency , fluorescently labeled particles were added to 1 × 105 RAW 264 . 7 cells at a ratio of ten particles per cell . The mean fluorescence of 1 × 104 cells per sample was analyzed on a Becton-Dickinson FACScan flow cytometer . Indirect immunofluorescence was performed according to standard procedures . For live cell imaging , a Zeiss LSM510meta confocal laser scanning microscope was used . For dual imaging of RAW 264 . 7 cells stably expressing GFP-actin and ECFP constructs , spectral recordings were taken and separated using the linear unmixing option . Line plots were generated with ImageJ ( National Institutes of Health ) software . Cell lysates were prepared using standard procedures . Immunoblotting was performed as described using the anti-CK-B antibody 21E10 [69] . CK activity was determined by an enzyme-coupled reaction . Cells grown on glass cover slips were PMA stimulated ( 200 nM , 15 min ) , washed with serum-free RPMI , and incubated with FITC-labeled COZ particles ( 10 per cell ) for 30 min at 37 °C . Cells were washed two times with PBS to remove nonbound particles , fixed and external zymosan was stained with anti-zymosan IgG ( Molecular Probes ) . Cells were then permeabilized ( 3 min , 0 . 1% Triton X-100 in PBS ) and incubated with Alexa660 conjugated goat anti-rabbit IgG and Alexa 568 conjugated phalloidin ( Molecular Probes ) . The total number of particles and the number of external adherent particles per cell were calculated . A detailed description of all procedures can be found as Protocol S1 . | To do work , cells need energy in the form of ATP . High and sudden energy demand is seen during cell-shape change , a process in which ATP fuels the cytoskeletal machinery that drives cell-morphology alteration . How a cell organizes high-energy surges without disrupting global ATP homeostasis remains an important research question . One view proposes that ATP is heterogeneously distributed , but the cytoskeletal proteins actin and myosin receive regional and preferential access to ATP . Yet this model raises another question: how is ATP funneled to these proteins from distant sources ? To address some of these questions , we studied the highly localized molecular events controlling actin dynamics around phagocytic activity of macrophages . We demonstrate that actin and creatine kinase-B ( CK-B ) , a long-known enzyme involved in ATP supply , are simultaneously recruited into the sites of action during the early phases of particle ingestion . Local availability of CK-activity and local generation of ATP promotes on-site actin remodeling and particle capture efficiency , and thus supports successful initiation of the first phases of phagocytosis . Interestingly , this coupling between local CK-activity and actin regulation is only relevant for complement-mediated phagocytosis ( used by immune cells to target specific particles for ingestion ) . We predict that our findings may also shed light on how shape dynamics is energized in other cell types . | [
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| 2008 | Creatine Kinase–Mediated ATP Supply Fuels Actin-Based Events in Phagocytosis |
The Kaposi’s sarcoma associated herpesvirus ( KSHV ) is an oncogenic virus that causes Kaposi’s sarcoma , primary effusion lymphoma ( PEL ) , and some forms of multicentric Castleman’s disease . The KSHV ORF57 protein is a conserved posttranscriptional regulator of gene expression that is essential for virus replication . ORF57 is multifunctional , but most of its activities are directly linked to its ability to bind RNA . We globally identified virus and host RNAs bound by ORF57 during lytic reactivation in PEL cells using high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation ( HITS-CLIP ) . As expected , ORF57-bound RNA fragments mapped throughout the KSHV genome , including the known ORF57 ligand PAN RNA . In agreement with previously published ChIP results , we observed that ORF57 bound RNAs near the oriLyt regions of the genome . Examination of the host RNA fragments revealed that a subset of the ORF57-bound RNAs was derived from transcript 5´ ends . The position of these 5´-bound fragments correlated closely with the 5´-most exon-intron junction of the pre-mRNA . We selected four candidates ( BTG1 , EGR1 , ZFP36 , and TNFSF9 ) and analyzed their pre-mRNA and mRNA levels during lytic phase . Analysis of both steady-state and newly made RNAs revealed that these candidate ORF57-bound pre-mRNAs persisted for longer periods of time throughout infection than control RNAs , consistent with a role for ORF57 in pre-mRNA metabolism . In addition , exogenous expression of ORF57 was sufficient to increase the pre-mRNA levels and , in one case , the mRNA levels of the putative ORF57 targets . These results demonstrate that ORF57 interacts with specific host pre-mRNAs during lytic reactivation and alters their processing , likely by stabilizing pre-mRNAs . These data suggest that ORF57 is involved in modulating host gene expression in addition to KSHV gene expression during lytic reactivation .
Kaposi’s sarcoma-associated herpesvirus ( KSHV; HHV-8 ) is a human gammaherpesvirus and the etiological agent for Kaposi’s sarcoma ( KS ) , primary effusion lymphoma ( PEL ) , KSHV-inflammatory cytokine syndrome ( KICS ) , and some cases of multicentric Castleman’s disease ( MCD ) [1–4] . The KSHV life cycle includes both a latent and a lytic state , which require different viral gene expression programs and distinct interactions with the infected host cell [5–9] . During latency , a small subset of KSHV genes is expressed that allows propagation and maintenance of the KSHV genome in the absence of viral replication . In contrast , during lytic reactivation KSHV orchestrates the ordered synthesis of numerous viral products that enable assembly of viral particles . The timing and amount of expression for each gene product is important for efficient production of infectious virions . During both lytic reactivation and latency , the virus manipulates the cell environment and gene expression machinery to modulate human and viral gene expression . One KSHV factor critical for viral gene expression is the ORF57 protein ( Mta ) [10–12] . While no host homologs are known , every herpesvirus encodes a homolog of ORF57 and each is essential for virus replication [13–15] . ORF57 is multifunctional , but most of its known activities are associated with posttranscriptional regulation of gene expression . For example , ORF57 has been reported to increase the export of intronless viral RNAs by interaction with the cellular REF/Aly protein , or the related UIF protein [16–18] . In this model , ORF57 serves as a bridge between the viral RNAs and the cellular transcription and export ( TREX ) complex , a multisubunit protein complex including the REF/Aly or UIF proteins that promotes cellular mRNA export [19 , 20] . TREX is recruited to the 5´-end of cellular RNAs as a result of splicing [21] , but this mechanism is not feasible for the virus since most KSHV genes are intronless [22] . Therefore , it is a compelling hypothesis that ORF57 compensates for the lack of splicing of intronless genes by recruiting the TREX complex to unspliced viral genes . Indeed , ORF57 enhances the RNA expression of various intronless reporters [18 , 23–34] , but has little effect on analogous intron-containing genes [27 , 29 , 33] . It is important to note that the proposed role of ORF57 in mRNA export remains somewhat controversial . In some cases , little or no effect on mRNA nucleocytoplasmic RNA distribution has been observed with intronless reporters [23 , 25 , 31 , 35] . In addition , point mutations that abrogate the ORF57 interaction with REF/Aly support viral replication [36] . ORF57 stabilizes viral RNAs in the cell nucleus , independent of its reported ability to export intronless RNAs . This function was first suggested by the observation that the levels of the polyadenylated nuclear ( PAN ) RNA is up-regulated by co-expression of ORF57 in transient transfections[31 , 33] . Direct determination of PAN RNA half-lives further showed increases in PAN RNA levels upon co-expression of ORF57 [27 , 37] . Moreover , PAN RNA levels are reduced in cells infected with an ORF57-null bacmid [38 , 39] . In addition to PAN RNA , ORF57 increases the nuclear and cytoplasmic abundance of specific viral mRNAs [23 , 25 , 31 , 35] . Presumably , the protection of these RNAs by ORF57 in the nucleus ultimately leads to more viral intronless RNAs escaping nuclear decay and being transported to the cytoplasm . In fact , intronless transcripts are subject to a polyadenylate ( poly ( A ) ) -tail dependent nuclear RNA decay pathway [40 , 41] . Therefore , the apparent specificity of ORF57 for intronless RNAs may be dictated by their susceptibility to this RNA decay pathway , but this has yet to be formally tested . Of course , functions for ORF57 in nuclear RNA stability and mRNA export are not mutually exclusive . In addition , ORF57 promotes viral RNA splicing [42] , regulation of host gene expression [43] , genome instability [44] , translation [45] , and may be involved in transcription [33 , 34 , 46 , 47] . ORF57 binds directly to RNA , but whether binding is driven by specific RNA sequences , cellular factors , and/or if ORF57 binding is coupled to RNA synthesis or processing remains unclear . In the case of PAN RNA , two groups reported the presence of specific sequences in the 5´ end of PAN RNA , dubbed the ORF57-responsive element ( ORE ) [28] or Mta-responsive element ( MRE ) [26] . The PAN RNA ORE was necessary and sufficient for maximal ORF57-responsiveness of PAN RNA and heterologous reporters . An additional MRE was found in the viral IL6 transcript , further supporting the idea that ORF57 binding is driven by specific sequences [43] . However , ORF57 is capable of robust enhancement of the levels of artificial intronless reporters that did not co-evolve as ORF57 ligands ( e . g . β-globin , CAT , luciferase ) , suggesting that its binding may be nonspecific or driven by binding of general cellular factors [27–29 , 33 , 35 , 48] . Importantly , RNA binding is important for ORF57 function [18 , 24 , 27 , 31] . When the ORE was deleted from PAN RNA , ORF57 had a significantly reduced effect on PAN RNA levels . However , this could be complemented by artificially tethering ORF57 to PAN RNA , formally demonstrating that association of ORF57 to PAN RNA was necessary and sufficient for PAN RNA up-regulation [27] . Because of the importance of ORF57 RNA-binding for function , a comprehensive understanding of the RNAs bound by ORF57 during lytic infection is required to understand ORF57’s essential activities in viral replication . Indeed , an unbiased screen for ORF57-bound RNAs revealed novel targets , including one host RNA ( IL6 mRNA ) , and suggested a new ORF57 mechanism [43] . However , this study was low throughput compared to the next generation sequencing methodologies currently available . Global analysis of RNA-binding proteins is complicated by the propensity for RNA-binding proteins to reassort in cell extract [49 , 50] . That is , RNA-binding proteins will potentially lose or gain interactions with RNAs that are not bound in cells , so this caveat must be accounted for in data collection and interpretation . Our previous studies demonstrated that ORF57 binds targets in extract upon cell lysis that were not bound in vivo [27] , thereby necessitating the use of crosslinking methods to analyze ORF57-RNA interactions in cells . In this study , we have adapted high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation ( HITS-CLIP ) for identification of ORF57 targets during lytic reactivation [51] . As predicted , we identified CLIP tags mapping to the 5´ end of PAN RNA and additionally observed ORF57 interactions with RNAs generated at the KSHV origins of lytic replication ( oriLyt ) . Examination of host targets revealed ORF57 binding sites near the 5´ end of a subset of the transcripts and these often mapped close to the first exon-intron junction . We then monitored the RNA levels of four potential ORF57 targets ( BTG1 , EGR1 , TNFSF9 , and ZFP36 ) at various times following lytic induction . Interestingly , the levels of these ORF57-bound pre-mRNAs persisted longer than controls , suggesting that ORF57 may be stabilizing these cellular pre-mRNAs . Using a metabolic labeling strategy , we selectively monitored transcripts synthesized over a short time window subsequent to viral reactivation . Consistent with the steady-state experiments , we observed that pre-mRNA levels of the ORF57 targets were higher after induction relative to controls that were not bound by ORF57 . Most importantly , we show that ORF57 is sufficient to increase the pre-mRNA levels of BTG1 , EGR1 , and ZFP36 , but its effects on mRNA levels differ . EGR1 mRNA abundance was increased by ORF57 while BTG1 and ZFP36 mRNA levels were largely unaffected . We suggest these transcript-specific distinctions are due to cell-type specific differences in the relative splicing and decay efficiencies of these RNAs in the nucleus . These studies represent the first high-throughput analysis of ORF57-bound RNAs and they provide insights into ORF57 interactions with host and viral RNAs during lytic reactivation .
To identify RNAs bound by ORF57 during lytic reactivation , we performed HITS-CLIP [51 , 52] in lytically reactivated TREx BCBL1-Rta cells [53] . In these cells , the gene encoding the KSHV transcription factor Rta is under the control of a tetracycline/doxycycline-inducible promoter . Rta expression is necessary and sufficient to drive KSHV lytic reactivation . We used both doxycycline and the histone deacetylase inhibitor sodium butyrate to achieve the highest levels of reactivation . HITS-CLIP analysis controls for reassortment of RNAs and proteins in extract by employing ultraviolet ( UV ) irradiation to covalently crosslink proteins to their ligand RNAs . Extracts are then generated , nuclease treated to partially digest RNA , and immunoprecipitations are performed under high stringency conditions . The immunoprecipitated RNA fragments are radiolabeled allowing them to be visualized after they are run on a denaturing PAGE and transferred to a nitrocellulose membrane . Protein-RNA complexes are cut from the nitrocellulose membrane , treated with protease , and libraries made from the resulting immunoprecipitated and gel-purified RNAs are analyzed by high-throughput sequencing . Due to ORF57’s propensity to be lost in insoluble nuclear fractions and to precipitate in lysate , we had to perform extensive optimization of the cross-linking and extract preparation steps . Under these conditions , immunoprecipitation of RNA was undetectable when we immunoprecipitated with non-specific antibodies ( Fig . 1 , lane 1 , top ) . When the cells were not exposed to UV light , no RNA was present ( lane 3 , top ) , but the protein remained efficiently precipitated as determined by western blotting of the immunoprecipitated complexes ( lane 3 , bottom ) . Uninduced samples retain a weak signal at both the RNA and protein levels ( lane 2 ) , presumably due to a small population of cells undergoing spontaneous lytic reactivation [54] . Upon treatment with high concentrations of micrococcal nuclease ( MNase ) , we observed several bands . One protein-RNA complex migrated near 50 kDa ( lanes 4 and 6 ) , consistent with the 51 kDa ORF57 , and this band shifts to a higher molecular weight ( MW ) smear when the MNase concentration was decreased ( lanes 5 and 7 ) . In addition to this 50-kDa complex , we observed signals from a lower MW complex ( ~37kDa , single asterisk ) and fainter bands migrating at ~100 kDa and ~150 kDa ( double and triple asterisks , respectively ) . Western blotting confirmed that the 50-kDa , 100-kDa , and 150-kDA bands correspond to ORF57 , whereas the 37-kDa band did not react with ORF57 antibodies ( lanes 4 and 5 , bottom ) . The 100-kDa and 150-kDa ORF57 protein bands are only present upon treatment with UV light . We interpret these data to represent complexes in which two ( 100 kDa ) or three ( 150 kDa ) ORF57 protein molecules were cross-linked to the same RNA . We think this is a reasonable interpretation given the sizes of the complexes , the previous demonstration of ORF57 homomultimerization [31 , 55] , and a similar phenomenon was observed in HITS-CLIP studies of the TDP-43 RNA binding protein [56] . We further characterized the cross-linked immunoprecipitated complexes by analyzing the RNA in the complexes . First , we examined the length of the RNAs in each protein-RNA complex and found that RNA purified from the higher molecular weight complexes had a longer average size than the lower molecular weight complexes ( S1A Fig . ) . This observation further supports our interpretation that the slower migrating complexes are from ORF57 multimers bound to the same RNA , as this would require a longer RNA platform for multimerization . Second , we rationalized that we could use the presence of PAN RNA in the different MW complexes as a measure of biologically relevant ORF57-RNA complexes . We analyzed RNAs purified from the different MW complexes by northern blots . PAN RNA was immunoprecipitated from the putative ORF57 complexes , but was largely absent from the 37-kDa complex ( S1B Fig . ) . In contrast , when we probed for the 5 . 8S rRNA , a common contaminant due to the high abundance of rRNA , the signal was stronger in RNAs derived from the 37-kDa band ( S1C Fig . ) . Thus , we conclude that both the 50-kDa , 100-kDa , and 150-kDa RNA-protein complexes contain ORF57 and its bound RNAs , whereas the 37-kDa band is a contaminating protein . Therefore , we made libraries of RNA fragments derived from the 50-kDa complex ( Fig . 1A , dashed box , lane 7 ) and sequenced them using strand-specific high throughput sequencing methods . Herein we refer to the reads from this library as the “CLIP tags” or “pellet” samples . As reference samples , we generated libraries from RNA isolated from the induced cells immediately prior to UV treatment; these libraries are referred to as “input” samples . We developed a pipeline for HITS-CLIP analysis to identify continuous regions of the genome where CLIP tags were enriched when compared to the equivalent region in the input samples . We dubbed these regions enriched clusters and they represent RNA fragments bound by ORF57 . The general workflow for enriched cluster identification is given in Fig . 2A and further details are provided in Materials and Methods . After trimming , raw reads were simultaneously aligned to the KSHV and human genomes . Any group of >10 overlapping tags was defined as a cluster and each cluster was divided into 20-bp bins . The covalent crosslinking of proteins to RNA often generates mutations during reverse transcription due to cross-linked protein adducts remaining on the RNA [57] . In addition , protein-RNA crosslinks generate types of mutations that are characteristic to the specific protein being crosslinked . The presence of such mutations within a putative binding site increases confidence that the identified CLIP tags are bona fide binding sites . We determined whether any classes of mutations were overrepresented in the CLIP tag clusters compared to the input clusters . Using various criteria , we defined T→C transitions and nucleotide deletions to be characteristic to ORF57 crosslinking ( see Materials and Methods ) . Further bioinformatic analyses resulted in the assignment of a p-value to the CLIP tag bins and a statistical cutoff of p-value<0 . 001 was applied to each bin; adjacent bins reaching this threshold were combined to defined enriched clusters . The p-value assigned to the enriched cluster is the average p-value of the bins that constitute that enriched cluster . Most importantly , this p-value encompasses three parameters . First , the CLIP tag clusters were referenced to the input clusters to obtain a fold enrichment value . A high fold enrichment value denotes that the number of tags in a specific pellet cluster relative to the corresponding input cluster was higher than this pellet-to-input ratio for the entire dataset . Clusters with larger fold enrichment values were given increased statistical significance . Second , the presence of T→C transitions or nucleotide deletions in the CLIP tag clusters provided additional statistical weight , but not all enriched clusters contain mutations . Third , the reproducibility of the data among the three biological replicates was considered for the clusters . In general , we observed high reproducibility among our three biological replicates across both the input and pellet samples ( S2 Fig . ) . These analyses led to the identification of 2 , 448 enriched clusters mapping to both the KSHV ( 219 ) and human ( 2 , 229 ) genomes ( S1 Table ) . We additionally applied a looser statistical cutoff to our data ( p<0 . 05 ) because at least one known ORF57 target was not recovered in the higher stringency data set ( see below ) . This analysis led to 6 , 933 and 343 enriched clusters mapping to the human and viral genomes , respectively ( S1 Table ) . The analysis detailed here refers to the high stringency data set ( p<0 . 001 ) unless otherwise noted . Our analysis identified 219 enriched clusters in the viral genome . The enriched clusters mapped broadly across the KSHV genome and were observed on plus and minus strand RNAs , consistent with a general role for ORF57 in KSHV RNA biogenesis ( Fig . 2B ) . As expected , we identified enriched clusters in PAN RNA and the identified clusters were located near the 5´ end of the RNA ( Fig . 3A ) [26–28] . Interestingly , induced mutations were observed at several positions across the transcript , suggesting multiple binding sites of ORF57 on the RNA ( Fig . 3A , asterisks ) , which likely result from ORF57 multimerizing along the transcript . Surprisingly , we observed two enriched clusters mapping to the minus strand along PAN RNA ( Fig . 2C , orange arrows ) . To our knowledge , no transcripts have been identified in this orientation from this location [7] and we observed that some of the other enriched clusters were on the opposite strand from known transcripts . We can think of three possible explanations for this observation . First , this could be an artifact in library preparation . Given the excessively high abundance of PAN RNA [7 , 58] , errors in preserving strand specificity in a small fraction of the RNAs could lead to observable peaks . Second , these data could be pointing to novel transcripts . However , we saw no discrete bands that were induced upon virus induction using a PAN RNA sense strand as a probe and deep RNA-seq studies did not identify a transcript at this locus [7] , so we think this is unlikely . Third , given the high transcription rates of KSHV genome during viral replication , we can imagine that some transcriptional noise occurs across the genome . If ORF57 binds to RNAs co-transcriptionally , the resulting transcripts may bind to ORF57 . In any case , the identification of enriched clusters at the 5´ end of PAN RNA demonstrates that our HITS-CLIP analysis successfully identified ORF57-bound RNA fragments . KSHV has two origins of lytic replication called oriLyt-L and oriLyt-R that contain AT-rich palindromes and GC-rich regions [59–61] . The oriLyt-L region is examined more closely in Figs . 2C and 3A ( green ) . The 1 . 4-kb transcript produced from oriLyt-L is essential for replication ( 1 . 4 kb , purple ) . The function of the 1 . 4 kb RNA is not clear , but it contains a small ORF and could serve additional functions as a noncoding RNA [7 , 62 , 63] . Two enriched clusters overlapped with the 1 . 4-kb RNA: one spanned the GC-rich region while the second was 3´ to the GC-rich region ( Fig . 3A ) . The ORF57 interaction with RNA at oriLyt-L extends observations from chromatin immunoprecipitation ( ChIP ) studies that demonstrated an association between ORF57 and oriLyt-L DNA [46] . The observed ChIP peak approximately corresponds to the position of the 1 . 4-kb RNA and the identification of enriched clusters at the oriLyt suggests that the interaction with viral DNA observed in the ChIP assays was due to ORF57 binding to the 1 . 4 kb RNAs . However , based on the HITS-CLIP data alone , we cannot distinguish whether the association between ORF57 and RNA in this region is due to an interaction with the 1 . 4 kb RNA or with the overlapping K5/K6 antisense transcript [7 , 62] ( Fig . 3A ) . Indeed , we found four enriched clusters that unambiguously map to the K5/K6 antisense transcript , so ORF57 could be solely binding this RNA or to both the 1 . 4-kb RNA and the K5/K6 antisense RNA . Based on the ChIP data , we suggest that the latter is more likely . We show other viral enriched clusters mapping to the K10 , ORF18 , and bicistronic ORF21-ORF22 and K14-vGPCR loci ( Fig . 3B ) . We included both the K10 and K9 genes for easy comparison of the relative levels of input and pellet samples between a transcript with enriched clusters and one without enriched clusters . We were surprised that we identified no enriched clusters mapping to the ORF59 mRNA ( Fig . 3C ) , a well-characterized target of ORF57-mediated up-regulation [23–25 , 30 , 31 , 38 , 45] . Visual inspection of the ORF59-ORF58 locus showed a peak near the 5´ end of ORF59 that is more prevalent in the pellet than input samples , consistent with an ORF57-binding site , but the fold enrichment for this peak did not reach the levels of significance used as our cutoff ( p<0 . 001 ) . Of course , statistical cutoffs are necessarily arbitrary and this example emphasizes that the statistical cutoffs we originally used to define enriched clusters were conservative . This prompted us to repeat the analysis with a less stringent statistical threshold ( p<0 . 05 ) . As expected , we see more enriched clusters across the viral genome ( S3 Fig . ) and enriched clusters were identified in ORF59 and in K9 ( Fig . 3B and 3C , blue bars ) . Given the extent of binding observed , our data are consistent with a relatively general mode of binding by ORF57 to KSHV RNAs . However , in this study , we will shift our focus to novel ORF57-bound host ( pre- ) mRNAs . A total of 2 , 229 enriched clusters mapped to the human genome and these ORF57-bound RNA fragments correspond to ~700 unique host genes . The human enriched clusters were derived from clusters with tag counts spanning several orders of magnitude , so we can be confident that our bioinformatics pipeline has a wide dynamic range ( Fig . 4A ) . We determined where the enriched clusters mapped in relationship with specific gene features . Over one third of the clusters mapped to introns , while 27% was found in coding sequences ( Fig . 4B ) . To look for biases in the location of enriched clusters across genes , we calculated where each enriched cluster midpoint maps as a fraction of the length of that specific gene . The results were subsequently compiled to a single model gene ( Fig . 4C ) . While enriched clusters were found throughout the length of target genes , we observed a clear overrepresentation of enriched clusters near the 5´ ends of genes ( Fig . 4C ) . The enriched clusters concentrated at 5´ ends ( Fig . 4C , bracket ) were examined based on where they mapped to gene features ( Fig . 4D ) . As expected for 5´-enriched fragments , we detected increases in the percentage of enriched clusters mapping to the 5´ UTR and upstream 2 kb and decreases in the intergenic regions , downstream 2 kb and 3´ UTR annotations . We further observed a small increase in the percent mapping to intronic regions ( 36% in the total and 43% in the 5´-most clusters ) , but it is unclear whether this increase is significant . Next , we determined the distances between the transcription start sites ( TSS ) and the 5´ enriched clusters and observed that the 5´ enriched clusters do not peak directly at the TSS , but rather ~300–500 bp downstream of the TSS ( Fig . 4E ) . In contrast , when we examined the distances between the 5´ enriched clusters and the first exon-intron boundary , we observed a peak coincident with this boundary ( Fig . 4F ) . Consistent with the observed 43% intronic reads , the peak is not solely on the exonic sequence but spans the exon-intron junction . These characteristics were nearly identical for the low stringency dataset ( S4 Fig . ) , suggesting that this dataset identified additional ORF57-bound RNAs . Taken together , these data show that a subset of the ORF57-bound RNA fragments map to the 5´ end of the transcript and are particularly concentrated near the 5´-most exon-intron boundary . The enriched clusters were found in RNAs encoding proteins involved in a wide variety of functions including RNA processing , DNA metabolic processes , and cell cycle processes ( S2 Table ) . In particular , RNA processing was enriched in the 5´-enriched , high and low stringency , which is interesting given ORF57s functions in posttranscriptional gene regulation . We did not identify enriched clusters in human IL6 mRNA [43] , but the IL6 RNA levels were low in the input samples , so this result is inconclusive . To validate our HITS-CLIP assay , we chose four candidate transcripts from the 5´ enriched clusters data set for further analysis: EGR1 , ZFP36 , BTG1 , and TNFSF9 ( Fig . 4A , colored shapes ) . We selected these RNAs based on their expression levels , fold enrichment values ( S1 Table ) , and their potential biological relevance to KSHV pathogenesis . EGR1 ( ZNF225 , KROX24 , AT225 ) is a highly regulated putative tumor suppressor gene that regulates the transcription of genes involved in multiple cellular processes such as inflammation and apoptosis [64 , 65] . ZFP36 ( TTP/tristetraproline , GOS24 , TIS11A ) regulates cell proliferation and inflammation by binding 3´ UTRs with AU-rich RNA elements and regulating mRNA stability [66] . BTG1 is a transcription factor that has also been implicated in cytoplasmic mRNA decay . BTG1 negatively regulates cell proliferation and BTG1 mutations are associated with leukemias [67 , 68] . TNFSF9 ( CD137L , 4–1BBL ) is involved in T-cell activation , but its expression is associated with B-cell lymphomas [69–71] . These genes all function to modulate cell growth and therefore their regulation has potential relevance to KSHV pathogenesis and/or life cycle . Inspection of the sequence traces confirmed the presence of enriched clusters mapping toward the 5´ ends of these RNAs but not in the GAPDH or β-actin controls ( Fig . 5 ) . Multiple enriched clusters were found in each of these RNAs: three , five , four , and four enriched clusters were identified for EGR1 , ZFP36 , BTG1 , and TNFSF9 , respectively ( Fig . 5 , black bars above pellet reads ) . These data are similar to what was observed with PAN RNA ( Fig . 3A ) and consistent with the proposal that once ORF57 binds an RNA , it subsequently binds several places along the transcript due to its multimerization [28] . In fact , nearly half of the genes identified in the high stringency dataset ( 44 . 7% ) and over half of the genes ( 58 . 7% ) in the low stringency dataset had more than one enriched cluster ( S5 Fig . ) . Moreover , the enriched clusters in introns are readily observable in the sequence traces ( Fig . 5 ) . As expected , the proportion of HITS-CLIP tags relative to the input samples is considerably lower for GAPDH and β-Actin as these RNAs contained no enriched clusters . The MED29 gene contains no enriched clusters , but is located immediately upstream of ZFP36 and is transcribed from the same strand as ZFP36 . We included the sequence traces for the MED29-ZFP36 locus , because visualization of both of these genes provides a convincing internal control for the identification of enriched clusters ( Fig . 5 , bottom ) . In this case , the input levels of MED29 RNA were similar to or even slightly higher than those for ZFP36 . In contrast , the number of tags found in the pellet samples was overrepresented in the ZFP36 samples when compared to MED29 , consistent with our identification scheme for enriched clusters . Overall , this visual inspection supports the conclusion that our bioinformatic pipeline is reliable and identified ORF57-bound RNA fragments within introns and 5´ ends of host genes . Moreover , these results suggest that the four transcripts are reasonable candidates to further investigate as potential functional targets of ORF57 activity . Interestingly , BTG1 , EGR1 , and ZFP36 genes all contain only a single intron and TNFSF9 has two introns ( Fig . 5 ) whereas the average intron number in humans is ~7–8 [72] . Moreover , ORF57 upregulates intronless transcripts [11–13 , 73] , so we asked whether genes with few introns were overrepresented among the host RNAs we identified . However , this was not the case ( S5 Fig . ) . The average number of exons in the annotated genes represented by all of our enriched clusters , was not significantly lower than the exon number in the entire genome . Similarly , the subset of genes with 5´-end enriched clusters does not deviate considerably from the average number of exons compared to the entire genome . There is , however , a shift in the average exon number of clusters enriched at the 3´ ends of transcripts towards a larger number of exons , but it is not clear whether this is biologically relevant . We next wanted to see if the candidate ORF57-bound host RNAs behaved differently when compared to unbound RNAs during lytic induction . To do so , we monitored transcript steady-state levels at various time points following lytic induction . Upon examination of the mRNA levels of GAPDH and β-actin mRNAs , we saw a marked decrease in transcript levels over time after induction , consistent with these transcripts being subject to RNA decay by KSHV host shutoff ( Fig . 6A ) [74] . The mRNAs of the ORF57-bound candidates ZFP36 , BTG1 , EGR1 , and TNFSF9 were largely similar to the controls in that each degrades over time after induction . Interestingly , both BTG1 and TNFSF9 were induced ~2 . 5 and 4-fold , respectively , upon reactivation . This induction was likely due to viral reactivation rather than a nonspecific consequence of butyrate as it was also observed with dox only , albeit at lower levels ( S6 Fig . ) . After this brief induction , the steady-state levels of these mRNAs also decrease . For comparison , we examined two stable nuclear RNAs , the host nuclear noncoding RNA 7SK was unaffected by lytic reactivation and the stable KSHV PAN RNA was robustly induced early in infection but was not lost over time ( Fig . 6C ) . Enriched clusters were identified in the introns of these RNAs ( Fig . 5 ) , so we monitored the pre-mRNA abundance of candidate ORF57-regulated transcripts . Steady-state pre-mRNA levels of transcripts bound by ORF57 followed a different pattern than GAPDH or β-Actin control pre-mRNAs ( Fig . 6B ) . Control pre-mRNAs rapidly disappear: at 8 hours post-induction ( hpi ) GAPDH and β-actin pre-mRNA levels were reduced by ~4-fold . This rapid disappearance is likely due to splicing of the pre-mRNA , but could also be a result of pre-mRNA decay or even transcriptional shutdown . In contrast , the steady-state levels of pre-mRNAs bound by ORF57 peak at ~12 hpi . In addition , these pre-mRNA peaks occur after the corresponding mRNA begins to decrease ( Fig . 6B compared to Fig . 6A ) . These data demonstrate that the pre-mRNAs bound by ORF57 are subject to different processing and/or decay kinetics during lytic reactivation than unbound controls . The presence of cellular transcripts made prior to lytic induction , and therefore prior to the presence of ORF57 , potentially confounds the interpretation of steady-state analyses . To be sure we are comparing RNA products made prior to ORF57 expression with those generated after ORF57 expression , we employed a metabolic labeling strategy [75 , 76] . We performed a 2-hr transcription pulse with 4-thiouridine ( 4SU ) , a nucleoside analogue readily incorporated into nascent RNAs by all three human RNA polymerases . The presence of 4SU allows us to biotinylate those transcripts generated during the two-hour pulse and these RNAs can then be selected by streptavidin bead purification . Thus , in this assay we specifically monitor the levels of newly made transcripts synthesized during a defined 2-hr period prior to or after KSHV reactivation . We compared newly made RNAs collected from uninduced cells ( 0 hpi ) with those from cells at 12 hpi . For the 0 hpi samples , we added 4SU and harvested RNA from uninduced cells 2 hrs later; for 12 hpi samples we added 4SU at 10 hpi and we collected RNA at 12 hpi ( Fig . 7A ) . For GAPDH and β-actin , the levels of newly made mRNA and pre-mRNA decreased after induction of virus ( Fig . 7B , top panels ) . The effect of induction on newly made GAPDH and β-actin RNAs was pronounced: the amount of ( pre- ) mRNA generated during the two-hour window between 10–12 hpi was only ~2 . 5–4% of that made prior to lytic reactivation ( Fig . 7C ) . In contrast , none of the pre-mRNAs or mRNAs for the four ORF57 target candidates decreased in such a dramatic fashion . In fact , the pre-mRNAs for BTG1 and TNFSF9 both increased compared to the pre-induction levels ( Fig . 7B and 7C ) , consistent with the apparent induction observed in the steady-state levels ( Fig . 6 ) . Newly made EGR1 and ZFP36 pre-mRNA levels were 77% and 64% of the pre-induction levels contrasting with the GAPDH and β-actin controls ( Fig . 7B and 7C ) . Interestingly , the mRNA levels of the candidates were all lower than pre-induction levels . However , they were all significantly higher than the GAPDH and β-actin controls ( Fig . 7C ) . Possible models describing the relationship between ORF57 function , pre-mRNA and mRNA production is explored in further detail below . As an additional control , we showed that newly made PAN RNA is detected only subsequent to induction , as expected ( Fig . 7B ) . Perhaps surprisingly , the levels of newly made ribosomal RNAs also decreased upon lytic induction , but whether this is a virus-induced reduction or a host response to virus has not been determined ( Fig . 7B ) . As an important control , the recovery of RNAs from cells that were not treated with 4SU ( Fig . 7B , -4SU ) was negligible , thereby demonstrating that our assay is specific for 4SU-containing RNAs . Alongside the steady-state and binding analyses , these data suggest that the metabolism of candidate RNAs ZFP36 , BTG1 , TNFSF9 , and EGR1 is affected by ORF57 and further suggest that ORF57 primarily functions at the pre-mRNA level for these targets . In addition , these observations validate HITS-CLIP as an approach to uncover potential novel functions and targets of ORF57 . Because steady-state levels of the selected candidates were monitored in the context of viral infection , it is possible that changes in their RNA levels were due to the action of viral proteins other than ORF57 . Clearly , the contributions of host shut-off to the steady-state and newly made RNA levels ( Figs . 6 and 7 ) cannot be overlooked . To test whether ORF57 is responsible for changes in pre-mRNA metabolism , we monitored the steady-state levels of the BTG1 , ZFP36 , and EGR1 pre-mRNAs in HEK293 cells 48 hr after transfection with varying amounts of a Flag-tagged ORF57 expression construct ( pcFl-ORF57II , Fig . 8A ) . Unfortunately , TNFSF9 was undetectable in our HEK293 cells , so it was excluded from the analysis . We observed a dose-dependent increase in BTG1 and ZFP36 pre-mRNA levels in the presence of ORF57 , but no such increase was observed for the β-actin or GAPDH control pre-mRNAs ( Fig . 8A , left ) . The effects of ORF57 were considerably more dramatic on EGR1 pre-mRNA . At the highest levels tested , we observed an ~150-fold increase in EGR1 pre-mRNA over the vector alone control ( Fig . 8B , right ) . These data show that ORF57 is sufficient to increase the steady-state pre-mRNA levels of BTG1 , EGR1 , and ZFP36 . So far , we have shown that ORF57 binds ( Figs . 4 and 5 ) and enhances the pre-mRNA levels ( Figs . 6–8 ) of the candidate host genes examined here . We suggest two primary models by which ORF57 could increase pre-mRNA levels that make different predictions regarding the effects of ORF57 on mRNA levels . The first model proposes that ORF57 binds to the pre-mRNA and inhibits splicing , thereby resulting in increased pre-mRNA levels . Supporting this model , the ORF57 homolog ICP27 inhibits pre-mRNA splicing [77–79] . Furthermore , it is easy to imagine that ORF57 binding near exon-intron boundaries ( Fig . 4F and 5 ) would sterically inhibit splicing . In the second model , ORF57 binds pre-mRNAs and inhibits their decay , consistent with its previously ascribed role as a nuclear stability factor [23 , 24 , 26 , 27 , 31 , 33] . These models make distinct predictions regarding the effects of ORF57 on mRNA production . Inhibition of splicing would result in a decrease in mRNA levels because the production of mature mRNAs is diminished . In contrast , the second model predicts that mRNA levels will be unaffected or increase upon stabilization of the pre-mRNAs by ORF57 . For example , if pre-mRNAs are subject to competition in the nucleus between decay and splicing , stabilization will favor the splicing machinery and increase mRNA production . On the other hand , some pre-mRNAs may simply be “dead-end” products that are no longer substrates for splicing ( see Discussion ) . In this case , ORF57-mediated pre-mRNA stabilization would have little effect on mRNA production . We therefore examined the effects of ORF57 on BTG1 , ZFP36 and EGR1 mRNA levels in transfected HEK293 cells ( Fig . 8B ) . While ZFP36 mRNA levels were unaffected by ORF57 expression , we observed an ~30% decrease in BTG1 mRNA . However , it is important to note that the controls GAPDH and β-actin levels dropped by ~20% and 13% , respectively , so the BTG1 mRNA decreases seem unlikely to be specific . In contrast , EGR1 mRNA levels increased in a dose-dependent fashion upon ORF57 expression , but not to the same extent as the pre-mRNA ( 8 . 5-fold compared with 148-fold ) . A reasonable explanation for these data is that a significant fraction of EGR1 pre-mRNA is inefficiently spliced and degraded under normal conditions . However , ORF57-mediated stabilization of the EGR1 pre-mRNA permits splicing of some of the stabilized pre-mRNAs resulting in increased mRNA production . This model proposes that under normal cell culture conditions EGR1 pre-mRNAs are inefficiently spliced and degraded . Importantly , cells tightly regulate EGR1 expression by rapidly inducing EGR1 upon re-addition of serum to serum-starved cells [80] . We reasoned that cells would increase EGR1 pre-mRNA splicing efficiency to maximize EGR1 mRNA production under inducing conditions . If this assumption is correct , then ORF57 is predicted to have less of an effect on induced EGR1 pre-mRNA and mRNA levels after serum induction because the cells shift the competitive balance between pre-mRNA splicing and decay to favor pre-mRNA splicing . To test this idea , we transfected HEK293 cells with pcFL-ORF57II or an empty vector control ( pcDNA ) . Approximately 36 hours after transfection , we replaced the media with serum free media to serum starve the cells overnight . The next morning , we added serum back to the media and harvested RNA from cells 0 , 15 , 30 , and 60 minutes following serum induction . We then compared the levels of EGR1 mRNA and pre-mRNA ( Fig . 8C ) . The serum-starved EGR1 levels ( 0 min ) were nearly identical to the non-serum starved samples ( basal ) . As expected , ORF57 had a robust , statistically significant effects of ~140-fold and ~10-fold for the pre-mRNA and RNA , respectively . In contrast , 15 minutes following induction the presence of ORF57 increases the pre-mRNA and mRNA levels by 2 . 4 and 1 . 5-fold , respectively and these changes are not statistically significant ( p-value>0 . 05 ) . In the control samples , the pre-mRNA levels begin to diminish after the 15-minute time point , but the ORF57 expressing cells maintain a high level of pre-mRNA . The mRNA levels remain comparable between the samples for up to 60 minutes , likely due to persistence of the induced mRNA in the cytoplasm . These observations further support the model that ORF57 binds and stabilizes a subset of host pre-mRNAs . In addition , these data validate the results of our HITS-CLIP analysis and strongly suggest that ORF57 modulates the processing of host ( pre- ) mRNAs in addition to its roles in viral gene regulation . Furthermore , they suggest that the consequences of ORF57-mediated stabilization depend on the nuclear processing and decay rates of the specific bound transcript .
In this study , we performed HITS-CLIP of the KSHV ORF57 protein in a lytically reactivated PEL cell line to globally identify ORF57-bound RNA fragments . We recovered enriched clusters that map to both human and viral genomes ( Figs . 2–5 ) . For the viral RNAs , our results were validated by the identification of the known ORF57 binding site at the 5´ end of PAN RNA [26–28] . In addition , our previous ChIP study showed that ORF57 interacts with the KSHV genome near oriLyt-L [46] , but ChIP assays do not distinguish between direct interactions with DNA , indirect interactions mediated through other DNA-bound proteins , or indirect interactions bridged via nascent RNA . Our discovery here of enriched clusters over the RNAs overlapping with oriLyt-L ( Figs . 2 and 3 ) strongly supports the hypothesis that the ChIP signals were due to interactions with RNA . Interestingly , the ORF57 ChIP signal across the oriLyt-L was not lost upon RNase treatment [46] . In addition , we observed putative ORF57 multimers bound to the same RNA that were resistant to high levels of MNase ( Fig . 1 ) . Further supporting multimerization , ~50% of the human genes had more than one enriched cluster per transcript S5 Fig . ) . This represents a minimum estimation because a single enriched cluster could possibly represent more than one binding site , whereas two enriched clusters are unlikely to arise from one ORF57 binding event . Together , these data suggest that ORF57 binds to nascent transcripts , multimerizes , and forms a complex containing relatively inaccessible RNA . We also observed enriched clusters at the oriLyt-R region ( S1 Table ) supporting a general function for ORF57 at these sites . How and whether this binding relates to KSHV DNA replication is unknown , but oriLyt-associated transcription is essential for DNA replication [81] and this function is conserved in EBV [82] . The identification of known ORF57 targets and the identification of novel host RNAs affected by ORF57 validate our approach . Admittedly , our analysis is not without its limitations . HITS-CLIP data presents bioinformatic challenges and various methods have been applied to filter bona fide bound fragments from background ( discussed in reference [83] ) . The condensed viral genome presents potential additional challenges , which could obscure the identification of viral enriched clusters . For example , if ORF57 binds to a majority of viral transcripts , the density of clusters in the pellets would be higher overall and equivalent among transcripts , so specific binding events may not be identified as enriched . In fact , we needed to decrease the stringency to identify at least one known ORF57 target ( ORF59 , Fig . 3 ) . We chose to define ORF57 binding sites by examination of the pellet tag counts relative to the input samples . The advantage of this approach is that it provides a robust control for expression levels so that highly expressed genes are not overrepresented in the data set . As described in more detail below , this approach may bias against the identification of stable efficiently processed RNAs if ORF57 is removed from transcripts at later stages of RNA metabolism . We do emphasize that these caveats would lead to a potential underrepresentation of the number and scope of RNAs bound by ORF57 and highlight that the definition of enriched clusters employed herein is conservative . Importantly , the raw data are publically available ( NIH GEO database , GSE64413 ) , so independent researchers can apply bioinformatic pipelines using our data . In summary , the validation of our novel targets and identification of previously known targets support the conclusion that we have generated a robust data set , but we expect that further data mining will yield additional information regarding ORF57 targets . We identified a subset of enriched clusters that showed enhanced ORF57 binding at the 5´ ends of host transcripts , near the first exon-intron boundary ( Fig . 4 ) . Examination of the levels of four of these RNAs during lytic reactivation demonstrated altered kinetics of these pre-mRNAs compared to control pre-mRNAs ( Figs . 6 and 7 ) . Importantly , ORF57 expression was sufficient to increase the pre-mRNA levels of BTG1 , ZFP36 , EGR1 , as well as EGR1 mRNA , but had little effect on BTG1 or ZFP36 mRNA abundance . We can imagine several models of ORF57 activity on these RNAs that are not mutually exclusive . Our preferred model proposes that some cellular pre-mRNAs are not constitutively spliced , but rather are subject to either splicing or decay in the nucleus ( Fig . 9A ) . ORF57 binds the inefficiently spliced pre-mRNAs and stabilizes them thereby increasing pre-mRNA , and in some cases , mRNA levels ( Fig . 9B ) . This model proposes no new mechanisms for ORF57 , but extends its previously described activities in nuclear viral RNA stability to host pre-mRNAs . We further suggest that the distinct effects on mRNA levels ( Fig . 8B ) derive from the cellular metabolism of the specific pre-mRNAs and are not due to an activity of ORF57 directly . In the case of EGR1 , the splicing and decay machineries are in kinetic competition with each other so increases in pre-mRNA stability shift the competitive balance such that more pre-mRNA splicing occurs ( Fig . 9B , “Precursor” ) . In contrast , BTG1 and ZFP36 unspliced pre-mRNAs are fated to be discarded by a pre-mRNA decay pathway and are not subject to further pre-mRNA splicing upon stabilization; thus , their pre-mRNA levels increase with no concomitant increase in mRNA ( Fig . 9B , “Dead-end” ) . As a result , the biological relevance of ORF57-mediated stabilization of BTG1 and ZFP36 pre-mRNA is not immediately obvious because ORF57 appears stabilize dead-end products . Perhaps in other cell types stabilized BTG1 and ZFP36 pre-mRNAs are mRNA precursors similar to EGR1 . In fact , newly made BTG1 and ZPF36 mRNA levels remain higher than GAPDH or β-actin at 12 hpi ( Fig . 7C ) , consistent with the idea that the stabilized pre-mRNAs are converted to mRNAs in lytically reactivated TREx BCBL1-Rta cells . Overall , should this model prove true , these studies lay the foundation for a new role for ORF57-mediated RNA stabilization , and they provide additional insight into the relationship between processing and nuclear decay of cellular pre-mRNAs . If ORF57 protects both viral and cellular RNAs from host nuclear RNA decay factors , then it is of interest to identify the cellular RNA decay pathway involved . We have recently identified a nuclear RNA decay pathway that is dependent on the nuclear poly ( A ) -binding protein , PABPN1 , and the poly ( A ) polymerases PAPα and PAPγ [27 , 28 , 41 , 84] . Several lines of evidence suggest that ORF57 inhibits this pathway . First , PABPN1-dependent decay is responsible for the degradation of an unstable allele of PAN RNA and ORF57 is sufficient to stabilize this same allele of PAN RNA [27 , 28 , 41 , 84] . Second , substrates for PABPN1-dependent decay must be polyadenylated and display a longer nuclear dwell-time , as may be expected for intronless viral RNAs and inefficiently spliced pre-mRNAs [41 , 85] . Third , our unpublished studies suggest that EGR1 levels increase when this pathway is inhibited ( S . Bresson and NKC ) . Therefore , we speculate that ORF57 protects inefficiently processed pre-mRNAs from PABPN1-mediated nuclear RNA decay and our ongoing studies seek to directly test this hypothesis . In an alternative model , increases in pre-mRNA levels result from inhibition of splicing by ORF57 . Consistent with this idea , the herpes simplex virus ORF57 homolog ICP27 inhibits splicing [77–79] . In addition , it is easy to imagine that the presence of ORF57 at the 5´-most splice site ( Fig . 4 ) would occlude the splicing machinery . In fact , we observed a slight decrease in BTG1 mRNA levels in ORF57 expressing cells , but GAPDH and β-actin were also reduced so the specificity of this effect is questionable ( Fig . 8B ) . We think this model less likely than the stability model for several reasons . First , it is difficult to reconcile the splicing inhibition model with the observed increases in EGR1 mRNA levels . Second , a previous report showed that , unlike ICP27 , ORF57 enhances the splicing of some transcripts [42 , 86] and the Epstein-Barr virus ( EBV ) homolog of ORF57 , SM , also modulates pre-mRNA splicing [87 , 88] . Third , we consider the stability model to be the simplest mechanism as it involves a previously documented function for ORF57 . While we favor the stability model , we are cautious in our interpretations of the mechanisms driving the changes in steady-state levels of pre-mRNA and mRNA . The pre-mRNA-to-mRNA ratio is often taken as a measurement of splicing efficiency . However , when one considers nuclear pre-mRNA decay as a contributing factor , interpretation of this ratio becomes muddled . For example , in our preferred model ( Fig . 9 ) , the increases in pre-mRNA-to-mRNA ratios for dead-end transcripts are attributed to stabilization rather than inhibition of splicing . For “precursor” RNAs the situation is even more complex . If the mRNA is not particularly stable , one will detect increases in the pre-mRNA-to-mRNA ratio due to the longer half-life of the pre-mRNA . In contrast , if the resulting mRNA is very stable in the cytoplasm , then the steady-state pre-mRNA-to-mRNA ratio will decrease because the mRNA accumulates . Of course , this complexity does not mean that changes in pre-mRNA-to-mRNA ratios observed upon ORF57 expression are not due to alterations in splicing [42 , 86] , but we emphasize that distinction between modulation of splicing versus nuclear RNA decay represents an empirical challenge that can not be discerned solely from pre-mRNA-to-mRNA ratios . Thus , while splicing inhibition is not our preferred explanation for ORF57-mediated up-regulation of host pre-mRNAs , we cannot exclude this hypothesis without further experimentation . In principle , our data are also consistent with a role for ORF57 in inducing transcription of these targets , particularly in the case of EGR1 . The EGR1 response to ORF57 in HEK293 cells ( Fig . 8C ) does not perfectly mimic EGR1 induction by serum , but it is similar at the pre-mRNA level . Because EGR1 induction is likely at the level of RNA synthesis [64 , 65 , 89 , 90] , it remains possible that ORF57 is initiating this transcriptional response . If ORF57 induces EGR1 , it is not fully induced as there is still an ~10-fold induction of EGR1 mRNA in the presence of ORF57 ( compare mRNA levels at 0 with 30 min , Fig . 8C ) . While this model seems unlikely given ORF57’s known roles in posttranscriptional gene regulation and its binding to EGR1 ( Fig . 5 ) , ORF57 has been implicated in transcription of some viral genes [34 , 46 , 47] . Moreover , the documented cross-talk between RNA-binding proteins , the spliceosome and the transcription machineries suggest that this model must not be overlooked [91–97] . Further dissection of ORF57 mechanisms is necessary to distinguish among these models . ORF57 is sufficient to increase the abundance of intronless RNAs including viral mRNAs , PAN RNA , and intronless reporters . In the case of PAN RNA and vIL6 , this up-regulation is enhanced by the presence of the specific cis-acting sequences that appear to serve as specific ORF57 binding sites [26 , 28 , 43] . However , ORF57 binding is not restricted to the ORE or ORE-like elements . In fact , given its ability to up-regulate a wide variety of nonviral reporters of no biological relevance for ORF57 , ORF57 must be recruited to RNAs in a general fashion . When we analyzed the enriched cluster sequences using MEME , a de novo motif discovery algorithm [98] , we were unable to retrieve significant conserved elements . Therefore , it remains possible that its specific binding to PAN RNA and vIL6 are exceptional and that ORF57 is recruited to RNAs in a largely sequence non-specific fashion . Interestingly , a similar balance between general and specific RNA-binding has been proposed for the EBV SM protein [99] supporting a conserved mode of RNA binding between the gammaherpesvirus posttranscriptional regulators of gene expression . How could ORF57 achieve apparent specificity in binding and function if it has little or no sequence specificity ? ORF57 has been reported to interact with a number of host RNA-binding proteins , so it is possible that these proteins dictate the apparent specificity of binding [16–18 , 25 , 37 , 55] or regulation of local concentrations of ORF57 could contribute to binding preference as well . Alternatively , ORF57 may transiently bind nearly all newly made RNAs . In the case of efficiently processed RNAs ( e . g . GAPDH , β-actin ) , the binding has little consequence because the pre-mRNAs are quickly spliced , exported and translated . If ORF57 is removed from the RNA during splicing , export , or translation , we would not have identified an enriched clusters because the ratio of ORF57-bound immature RNA to the unbound fully processed cytoplasmic mRNA would be relatively low . In contrast , an inefficiently processed cellular ( pre- ) mRNA would remain engaged with ORF57 in the nucleus and therefore be more likely to be identified as an enriched cluster due to its higher ratio of bound to unbound RNAs in the cell . In addition , transcripts with a longer nuclear dwell time will display an apparent ( but indirect ) functional specificity due to ORF57-mediated protection of those transcripts from nuclear decay . That is , efficiently exported RNAs are not subject to nuclear decay and are thus unaffected by ORF57 binding in the nucleus . In contrast , those transcripts that have a longer nuclear dwell time would normally be subject to degradation , but ORF57 protects them and increases their abundance . Because of its critical role in the viral life cycle , a mechanistic understanding of ORF57 functions is essential to the understanding of KSHV replication and pathogenesis . Our high throughput screening of ORF57-bound RNAs begins to address interactions of ORF57 with viral and host RNAs . This work extends existing data supporting a general role for ORF57 in the stabilization of a wide variety of viral RNAs . In addition , these data suggest that ORF57 nuclear RNA stabilization function is not restricted to viral RNAs , but further modulates the processing and decay of host transcripts during lytic reactivation . Ongoing studies seek to identify the precise molecular mechanisms of ORF57 interactions with the host cell RNA decay machinery that promote the stabilization of viral and host transcripts in the nucleus . In addition , it is of great interest to determine how changes in gene expression induced by ORF57 binding of host RNAs affect viral replication and/or pathogenesis .
TREx BCBL1-Rta cells [53] were carried in RPMI-1640 media ( Sigma ) supplemented with 10% tetracycline-free fetal bovine serum ( FBS , Clontech ) , penicillin-streptomycin ( Sigma ) , 2 mM L-glutamate , and 100 μg/ml hygromycin ( Sigma ) . Lytic reactivation was induced by addition of 1μg/ml doxycycline and 3 mM sodium butyrate . HEK293 cells were grown in DMEM media ( Sigma ) supplemented with 10% FBS ( Sigma ) , penicillin-streptomycin , and 2 mM L-glutamate . HEK293 cells were transfected using TransIT-293 ( Mirus ) according to the manufacturer’s protocol . The flag-tagged ORF57 expression vector was previously described ( pcFl-ORF57II , [27] ) . For the ORF57 dose-dependency experiments ( Fig . 8A and 8B ) , HEK293 cells were transfected in a 12-well plate with a combination of pcDNA3 and pcFl-ORF57II totaling 800 ng . Twenty-four hours after transfection the cells were ~100% confluent and transferred to 6-well plates . The next day ( 48 hrs post-transfection ) , cells were ~70–80% confluent and RNA was harvested in TRI-Reagent ( Molecular Research Center ) . For serum starvation and induction experiments ( Fig . 8C ) , we transfected 6 μg of pcDNA3 or pcFl-ORF57II in a 60-mm plate . The following morning , we split these cells evenly among five wells of a 6-well plate . Approximately 10 hrs later , cells were ~70–80% confluent and we replaced the media with serum free media . The next morning ( 48 hrs post-transfection ) , we added serum to 20% and harvested as described . We stress that cell confluency and freshness of the media were crucial for reproducibility in the HEK293 transfection experiments . RT-qPCR assays were performed with standard techniques using iTAQ Universal SYBR Green Supermix ( Bio-Rad ) with a final primer concentration of 100 nM . The conditions were 40 cycles of 95°C for 3 sec and 60°C for 30 sec with a 7500 Fast real-time PCR system ( Applied Biosystems ) . Random hexamers were used for first-strand synthesis and gene-specific primers are given in S3 Table . HITS-CLIP was performed essentially as previously described [51 , 52] , but changes were made to maximize the solubility and recovery of ORF57 . For each immunoprecipitation a total of 2x107 cells were reactivated at 5x105 cells/ml and then collected at 20 hpi . Cells were washed in phosphate buffered saline ( PBS , Sigma ) , and resuspended in 3 mL of ice-cold PBS . Crosslinking was performed on ice in a Spectrolinker ( Spectronics Corporation ) at 125mJ/cm2 ~2 cm from the 254nm UV bulb . Five separate immunoprecipitations were performed for each biological replicate for a total of 108 cells per pellet sample . Cells were pelleted , frozen on dry ice and stored at -80°C . Lysates were generated by previously described procedures [49] with a few changes . The composition of the SDS lysis buffer was 0 . 5% SDS , 50mM Tris pH 6 . 8 , 1mM EDTA , 0 . 125 mg/ml Heparin , 1mM DTT , 1mM PMSF and 1X protease inhibitor ( Calbiochem ) , while RIPA correction buffer was 1 . 25% NP40 , 0 . 625% sodium deoxycholate , 62 . 5 mM Tris pH 8 . 0 , 2 . 25 mM EDTA , 187 . 5 mM NaCl , 0 . 125 mg/ml Heparin , 1mM DTT , 1mM PMSF , with 1X protease inhibitors . Initial steps of extract preparation were as described [49] up to and including initial shearing of DNA with a QIAShredder ( Qiagen ) . Next , CaCl2 was added to 5 mM with 30 U of RQ1 DNase ( Promega ) and incubated for 15 min at 25°C . For RNA digestion , MNase ( New England Biolabs ) was diluted to 10 gel units/μL in RIPA buffer and 5 μL of this freshly diluted 1:200 stock was added to the extract . No dilution was performed for the “high MNase” samples ( Fig . 1 ) . RNA digestion proceeded at 25°C for precisely 10 min , after which 47 μL of 300mM EGTA was added to stop the reaction . After clarification of the lysate by three successive centrifugation steps at 21K x g for 10 min , the lysate was pre-cleared and the ORF57-RNA complexes were immunoprecipitated with protein A Dynabeads ( Invitrogen ) . The ORF57 antibodies [28] were affinity-purified from rabbit serum using the AminoLink Plus Immobilization kit ( Pierce ) as per the manufacturer’s instructions . We used 48 μg of purified antibody per immunoprecipitation . The immunoprecipitated complexes were washed twice with RIPA Buffer , twice with high salt buffer ( 5X PBS , 0 . 1% SDS , 0 . 5% NP40 ) , and twice with 1X PNK Buffer . Gel purification of complexes was performed essentially as described in [51 , 52] . Briefly , immunoprecipitated cross-linked RNAs were 5´-end-labeled with PNK and γ32P-ATP . The protein-RNA complexes were resolved in a 4–12% Bis-Tris NuPAGE Novex gel and transferred to a nitrocellulose membrane . Covalently bound , ORF57-RNA complexes were cut from the membrane , treated with proteinase K and the recovered RNAs were size selected in a denaturing urea gel . For each biological replicate , the RNAs from five immunoprecipitation reactions were combined prior to library preparation . For input samples , total RNA was recovered with TRI-Reagent at 20 hpi and rRNAs were depleted using the Ribo-Zero Magnetic Kit ( Epicentre ) . Input RNAs were treated with MNase , end-labeled , and size selected prior to library preparation to mirror the pellet samples . Libraries were made according to Illumina’s TruSeq Stranded mRNA Sample Preparation Guide , but the fragmentation step was omitted . Sequencing was performed on an Illumina HiSeq 2500 sequencer at the Eugene McDermott Center for Human Genetics . A detailed step-by-step lab protocol for HITS-CLIP with ORF57 can be obtained by contacting the corresponding author ( NKC ) . Raw sequencing reads for all biological samples in this study are available , along with processed data files , under GEO accession number GSE64413 . Adaptors marked as N at the 3’ends of the paired-end sequencing data were first trimmed . Then Gsnap was used to align the sequencing data with parameters “-A sam—maxsearch 1-N 1-t 4-n 1” [100 , 101] . The reads were aligned to the concatenated hg19 and U75698 . 1 genomes . Reads that mapped across splice-junctions were discarded . Mapping statistics are provided in S4 Table . Unaligned reads by Gsnap were subsequently mapped to the hg19 transcriptome by Novoalign ( Novocraft ) . Paired-end read pairs were merged into single-end format if they overlap by at least 1 bp . Merged reads that have the same chromosome , strand , left-most mapping coordinate and right-most mapping coordinate were defined as PCR amplification duplicates and collapsed to unique tags , keeping only the tag with the highest sequencing quality . Then tags from all three pellet and three input experiments were simultaneously overlapped to identify CLIP clusters with at least 10 tags from any one or more conditions . CLIP clusters were continuous regions with non-zero binding tag count on each base within the clusters . Then the CLIP clusters were binned by 20bp and the tag counts and mutation counts on each base were summed for each bin within each experimental condition separately . Deletions and T→C mutations were chosen as characteristic RT-induced mutations of ORF57 binding according to comparative analysis of all the 12 types of substitutions , deletions and insertions ( see below ) . The summed total tag count and summed characteristic mutation count within each bin were multiplied by 0 . 8 and multiplied by 0 . 2 , respectively , and further summed to yield a value we termed the overall binding intensity for that bin . As a result , each bin will have 6 overall binding intensity numbers corresponding to the three pellet and three input samples . We used DESeq to analyze the binding intensity data of the three pellet vs . three input samples [102] . For DESeq analysis , we used the median intensity value of each condition for normalization and employed a negative binomial test for identifying differentially bound regions between the pellets and input samples . The analysis was conducted separately for human binding sites and virus binding sties and a p-value was assigned for each bin . Bins that were more enriched ( p-value<0 . 001 ) in the pellets compared to the inputs were extracted and neighboring enriched bins were concatenated into continuous regions indicating the highly reliable ORF57 binding sites . The binding sites were screened to retain only those sites that span at least 3 bins and have on average at least four tags in the pellet samples . In addition , the ORF57 binding sites that overlap repeating sequences including rRNAs , tRNAs , low complexity regions , LINEs , SINEs and simple repeats were discarded [103] . The remaining binding sites are referred to as enriched clusters . In order to identify reverse transcription mutations that are characteristic of ORF57 crosslinking sites [57] , we performed several analyses ( S7 Fig . ) . First , the mutations on all the sequencing tags for all biological replicates were summed for both the pellet and input samples , and a ratio of pellet/input mutations was calculated for each mutation type . The higher this ratio , the more mutation counts of the given type there are in the pellet samples compared to the input samples ( S7A Fig . ) . In this case , the deletion mutations dominated , followed by insertions and T→C mutations . However , it is important to note that this analysis does not consider the absolute number of mutations , which differed considerably among the mutation types , particularly in the input samples ( S7B Fig . ) . More importantly , T→C and A→G mutations , rather than deletion mutations , are the most abundant mutation in the HITS-CLIP pellets ( S7B Fig . ) . The sequencing depths of HITS-CLIP and input samples were further considered to yield a per-tag rate for each mutation type ( S7C Fig . ) . In this case , deletion mutations showed the largest increase when we compared pellet to input , but T→C and A→G mutations have the larger absolute mutation rates in HITS-CLIP samples . Finally , ORF57 crosslinking should lead to both sequencing tag and characteristic mutation enrichment around binding sites , so the total tag count in the pellets should display a positive correlation with the characteristic mutation count . For our analysis , the sequencing tags were clustered and binned into 20-bp units . Total tag counts as well as mutant tag counts were profiled on the bin-level for all clusters . The correlation was calculated between the bin-level total tag count and each type of mutant tag count data ( S7D Fig . ) . Deletion , T→C and C→T mutations have the largest increases in Pearson correlation in pellet samples compared with the input controls . Based on all of these analyses , we chose deletion and T→C mutations as characteristic mutations for the ORF57 protein binding sites . Mutant bases with deletions and T→C mutations were extracted from within the enriched clusters . T-tests with p-value cutoff 0 . 05 are applied to test whether the mutant bases have significantly higher rates of mutant tag vs . total tag ratio in the pellets . The genome coordinates for enriched cluster mutations with p-value<0 . 05 are given in S5 Table . Enriched clusters mapping to the human genome were annotated by genomic features including coding sequence ( CDS ) , 3´ UTR , 5´ UTR , intron , 2kb upstream region , 2kb downstream region , miRNA/snoRNA and intergenic region . 2kb upstream region represents 2kb sequences before all genes’ transcription start sites . 2kb downstream region represents 2kb sequences after all genes’ transcription stop sites . Intergenic regions were defined as those parts of the genome that do not belong to any of the other categories listed here . If a cluster or a tag overlaps more than one feature , its count were divided proportionally and added towards each of the corresponding feature categories . Metabolic labeling and isolation of newly made RNAs were modified from previous protocols [75 , 76] Twenty micrograms of DNase-treated RNA was biotinylated in a 50 μl reaction containing 10mM TrisHCl ( pH 7 . 5 ) , 1mM EDTA , 0 . 1% SDS , and 0 . 2mg/ml EZ-Link Biotin-HPDP ( ThermoScientific ) . The reaction was incubated for 3 hr at room temperature . RNA was extracted twice with chloroform and ethanol precipitated in 1M ammonium acetate with 15 μg of glycoblue ( Ambion ) as a carrier . Streptavidin selection was carried out with 20 μl of the bead slurry of Dynabeads MyOne Streptavidin T1 ( Invitrogen ) . The beads were pre-washed in MPG1:10-I buffer ( 100mM NaCl , 1mM EDTA , 10mM Tris pH 7 . 5 and 0 . 1% Igepal ) . After the last wash , the beads were resuspended in 180 μL of MPG1:10-I and pre-blocked with 0 . 1 μg/μl poly ( A ) RNA , 0 . 1 μg/μl salmon sperm DNA , 0 . 1 μg/μl of Torula yeast RNA ( Sigma ) . After precipitation , the biotinylated RNA was resuspended in 30 μl of water . RNA was heated at 65°C for 5 min and subsequently nutated with 170 μl of the blocked beads for 30 min at room temperature . The binding reaction was then washed with 300 μl of: MPG1:10-I , MPG1:10 ( 100mM NaCl , 1mM EDTA , and 10mM TrisHCl ( pH 7 . 5 ) ) at 55°C , MPG1:10-I , MPG-I ( 1M NaCl , 10mM EDTA , 100mM Tris pH 7 . 5 and 0 . 1% Igepal ) , MPG-I , MPG1:10-I , MPG-I no salt ( 10mM EDTA , 100mM Tris pH 7 . 5 and 0 . 1% Igepal ) and MPG1:10-I . The bound RNAs were then eluted by incubating for 5 min in 200 μl of MPG1:10-I with 5% β-mercaptoethanol first at room temperature then a second elution was performed in the same buffer at 65°C for 5 min . Eluted fractions were combined , phenol:chloroform:isoamyl alcohol ( 25:24:1 ) extracted , chloroform extracted , and then ethanol precipitated with sodium acetate and 15 μg of glycoblue . RT-qPCR was performed to detect specific RNAs . | During viral replication , the oncogenic Kaposi’s sarcoma-associated herpesvirus ( KSHV ) modulates both host and viral gene expression . KSHV ORF57 is a multifunctional posttranscriptional regulator that is essential for viral replication and stabilizes viral RNAs . Previous studies demonstrated that ORF57 RNA-binding is essential for its activity , but the full spectrum of ORF57 targets are unknown . Here we employed a high-throughput analysis to identify RNA fragments bound by ORF57 during lytic reactivation . As expected , we found targets that mapped to the viral genome , and we further uncovered novel host targets , a subset of which had ORF57 bound near their 5´ ends . Further examination of this subset demonstrated that ORF57 bound preferentially at the 5´-most exon-intron boundary . ORF57 affected the pre-mRNA abundance from these genes , most likely by stabilizing otherwise unstable inefficiently spliced pre-mRNAs . In at least one case , this stabilization led to increases in mRNA expression of the host gene . We suggest that KSHV employs the same mechanism to stabilize intronless viral RNAs and cellular unspliced pre-mRNAs to modulate viral and host gene expression during lytic reactivation . | [
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| 2015 | HITS-CLIP Analysis Uncovers a Link between the Kaposi’s Sarcoma-Associated Herpesvirus ORF57 Protein and Host Pre-mRNA Metabolism |
The control and elimination of Plasmodium vivax will require a better understanding of its transmission dynamics , through the application of genotyping and population genetics analyses . This paper describes VivaxGEN ( http://vivaxgen . menzies . edu . au ) , a web-based platform that has been developed to support P . vivax short tandem repeat data sharing and comparative analyses . The VivaxGEN platform provides a repository for raw data generated by capillary electrophoresis ( FSA files ) , with fragment analysis and standardized allele calling tools . The query system of the platform enables users to filter , select and differentiate samples and alleles based on their specified criteria . Key population genetic analyses are supported including measures of population differentiation ( FST ) , expected heterozygosity ( HE ) , linkage disequilibrium ( IAS ) , neighbor-joining analysis and Principal Coordinate Analysis . Datasets can also be formatted and exported for application in commonly used population genetic software including GENEPOP , Arlequin and STRUCTURE . To date , data from 10 countries , including 5 publicly available data sets have been shared with VivaxGEN . VivaxGEN is well placed to facilitate regional overviews of P . vivax transmission dynamics in different endemic settings and capable to be adapted for similar genetic studies of P . falciparum and other organisms .
In the Asia-Pacific region , Plasmodium vivax is responsible for between 20 and 280 million malaria cases per year , inflicting a significant burden of morbidity and mortality . Over the last decade , the prevalence of P . falciparum has declined in many endemic countries as a result of intensified malaria control interventions , but outside Africa this has been associated with a rise in the proportion of P . vivax cases , reflecting the limited efficacy of interventions against this species [1] . This trend emphasizes the need for innovative new strategies to reduce P . vivax transmission . A critical weakness of conventional malaria surveillance is the lack of information on the genetic dynamics of the parasite population—an important reflection of underlying transmission potential . Previous studies have demonstrated the utility of genotyping parasite population samples at highly polymorphic short tandem repeat ( STR ) markers such as microsatellites to inform on P . vivax diversity , population structure and underlying transmission patterns [2–19] . These simple molecular approaches complement the more traditional measures of transmission intensity as well as providing a surrogate marker for transmission intensity , informing on outbreak dynamics , reservoirs of infection , and the spread of infection spread within and across borders [20 , 21] . However , individual projects have limited potential to address regional questions . The challenges of imported and border malaria associated with highly mobile human populations emphasizes the need for a framework to support integrated , multinational comparative analyses . Effective comparison between studies and sites has been confounded by heterogeneity of methodologies such as the number and location of markers used , size standards , allele calling/binning , and specifications for calling minor alleles reflecting minor clones in polyclonal infections [22] . To address some of these challenges , the Vivax Working Group ( VxWG ) of the Asia Pacific Malaria Elimination Network ( APMEN ) has worked with research partners in 15 Asia Pacific countries to develop a consensus panel of STR markers ( MS1 , MS5 , MS8 , MS10 , MS12 , MS16 , MS20 , pv3 . 27 and msp1F3 ) and genotyping methods [23] . The web-based VivaxGEN platform was developed to facilitate standardized allele calling , data analysis and sharing across P . vivax studies using consensus STR marker sets such as the APMEN panel . The VivaxGEN platform provides a repository for FSA files ( the primary data files containing the raw fragment analysis data generated during capillary electrophoresis runs ) . To date , no such repository exists for P . vivax STR data . The capacity to derive allelic data directly from the FSA files ensures high accuracy and standardization in allele-calling between different sample batches produced at different time points and/or on different machines from possibly different studies . This feature also supports flexibility in defining allele-calling thresholds , enabling user-defined settings that may be applied to one or more sample batches . The VivaxGEN platform also provides tools for standard population genetic analyses that can be applied to multiple sample batches to evaluate local and regional trends in the prevalence of polyclonal infections , population diversity , structure and differentiation both spatially and temporally . Data export tools are available to allow users to conduct more bespoke analyses not provided within the platform framework .
All genotyping data described in the manuscript has been published [4 , 9 , 12 , 14 , 34] . As described in the original publications , all samples were collected with written informed consent from the patient , parent or legal guardian ( individuals < 18 years of age ) . Approval was provided by the Institutional Review Board of Jiangsu Institute of Parasitic Diseases ( IRB00004221 ) , Wuxi , China; the Research Ethics Board of Health , Ministry of Health Bhutan ( REBH 2012/031 ) ; the Korea Centers for Disease Control and Prevention Institutional Review Board , Republic of Korea ( Protocol No . 2011-02CON-14-P ) ; the Eijkman Institute Research Ethics Commission , Indonesia ( EIREC 45/2011 ) ; the Ethics Review Board of Addis Ababa University College of Natural Sciences , Ethiopia ( RERC/002/05/2013 ) ; the Ethics Review Board of Armauer Hansen Research Institute , Addis Ababa , Ethiopia ( AHRI-ALERT P011/10 ) ; the National Research Ethics Review Committee of Ethiopia ( Ref . no . 3 . 10/580/06 ) ; and the Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research , Darwin , Australia ( HREC 2012–1871 , HREC-2012-1895 and HREC-13-1942 ) . The VivaxGEN platform was developed as a multi-tier web application system , utilizing PostgreSQL as its backend Relational Database Management System ( RDBMS ) and leveraging on several common external tools for genotype data analysis . PostgreSQL was chosen as the RDBMS as it provided ACID operations and complex SQL query optimization in an open-source package . The backend is programmed in Python , while the web interface uses JavaScript and jQuery library for interactivity . YAML was chosen as the format for platform configuration and data exchange/interoperability . Sample and assay data uploading process can be performed using either batch processing with tab or comma-delimited files in conjunction with a zip file containing raw FSA files , or interactively using sample and assay editing interface . Detailed instructions on data upload , and an accompanying tutorial dataset can be found in Tutorial 1 ( Uploading your metadata and FSA files ) provided on the VivaxGEN website and in S1 File . VivaxGEN provides a framework to store and process raw FSA files with standardized allele calling tools . This framework reduces the heterogeneity that may be introduced from different fragment analysis methods . A Python based library called FATOOLS , which can also be used as a stand-alone command line utility , was developed to provide the raw FSA processing capabilities in VivaxGEN . This library utilizes numpy ( www . numpy . org ) and scipy ( https://www . scipy . org ) scientific libraries for its numerical processing . The library provides methods for base normalization of traces , peak scanning and classification , standard size determination , peak calling and allele annotation , as well as FSA assay quality controls . A detailed guide on the FSA fragment analysis process in VivaxGEN can be found in the Guide on Fragment Analysis manual provided on the website and in S2 File . Briefly , base normalization is undertaken using a top-hat morphological transform algorithm implemented in scipy . A simple peak finding algorithm and a CWT-based peak scanning algorithm implemented in scipy are also included in the library [24] . A combination of greedy algorithm and dynamic programming is employed for standard size alignment and size determination . Results of each step of the FSA and fragment analysis processing are stored in the system for aiding manual inspection and assay verification . The source code for FATOOLS is available for stand-alone usage and further development ( http://github . com/trmznt/fatools ) . To aid the manual inspection of traces , a trace viewer is included in the web interface , as shown in Fig 1 . Detailed instructions on the manual data editing tools can be found in Tutorial 2 ( Inspecting FSA files and data cleaning ) provided on the VivaxGEN website and in S1 File . The trace viewer is coded in JavaScript and enables users to identify and edit incorrectly annotated alleles . The form-based web interface also provides a number of allele and sample filtering options . Details on the allele and sample filtering tools can be found in Tutorial 3 ( Data analysis ) provided on the VivaxGEN website and in S1 File . Alleles can be filtered according to marker name ( Marker ) , marker failure rate in the given sample set ( Marker quality threshold ) , absolute minimum relative fluorescence unit ( RFU ) ( Allele absolute threshold ) and relative RFU of minor peaks compared to the highest intensity peak ( Allele relative threshold ) . Suspected stutter peaks can also be filtered according to a user-defined stutter range in base pairs ( Stutter range ) and ratio ( Stutter ratio ) based on the RFU relative to the highest intensity peak in the given range . Samples can also be filtered according to genotyping success rate across the given marker set ( Sample quality threshold ) , to exclude polyclonal infections or multi-locus genotypes that are presented more than once in the given sample set ( Sample filtering ) , or by passive versus active case detection ( Detection differentiation ) . Sample querying and grouping can be performed using a query syntax modeled on the NCBI Entrez system with some modification . Detailed instructions on how to perform data analysis using custom queries is provided in Tutorial 4 ( Data analysis with custom query ) provided on the VivaxGEN website and in S1 File . Boolean operations can be applied to classify sample groups based on spatial ( by country level or by 1st , 2nd , 3rd or 4th administrative division level ) or temporal ( by year or quartile of sample collection ) definitions . The query from the form-based web interface is converted into a YAML-based query internally , which can then be run in the database . An interface that accepts YAML-based query is also provided , enabling the user to apply bespoke sample grouping operations not supported by the form-based web interface . Instructions on how to perform data analysis in VivaxGEN using YAML queries is provided in Tutorial 5 ( Data analysis with YAML query ) provided on the website and in S1 File . A suite of population genetic measures and associated statistical tests that are commonly used in STR-based P . vivax studies to gauge underlying patterns of transmission intensity , stability and boundaries , including rates of polyclonality , population diversity , genetic relatedness , population structure and out-crossing/inbreeding rates , can be applied to the genotyping data from one or more sample batches . Population genetic measures currently supported within VivaxGEN include ( i ) expected heterozygosity ( HE ) , an index of population-level diversity , ( ii ) individual infection and population average measures of the Multiplicity of Infection ( MOI ) , a measure of the genetic complexity within infections , ( iii ) proportion of polyclonal infections , and ( iv ) Principal Coordinate Analysis ( PCoA ) with plots illustrating the population structure and genetic relatedness between infections based on a genetic distance matrix . External software employed by the platform include ( i ) LIAN for measuring linkage disequilibrium ( LD ) using the index of association ( IAS ) [25] as a gauge of out-crossing/inbreeding rates , ( ii ) Arlequin for measures of genetic differentiation between populations using the fixation index ( FST ) [26] , ( iii ) the APE ( Analysis of Phylogenetics and Evolution ) package in R for building neighbor-joining trees for assessment of genetic relatedness between infections [27] , ( iv ) the FactoMineR package in R for generating Multiple Correspondence Analysis ( MCA ) plots to assess population structure and genetic relatedness based on the nominal categorical data [28] , and ( v ) the DEMEtics package in R for calculating the genetic differentiation index D [29 , 30] . A standardized measure of genetic differentiation , F'ST , adjusted for marker diversity to support greater comparability between studies using different marker sets is calculated internally in VivaxGEN using the output from Arlequin and following the method described by Hedrick [31] . Further details on the population genetic tools can be found in the Guide on Data Analysis manual provided on the VivaxGEN website and in S2 File . The VivaxGEN platform has tools for exporting genotype data in several formats supported by other commonly used population genetics softwares including LIAN [25] , Arlequin [26] , Genepop [32] and STRUCTURE [33] . Tab-delimited formats suitable for R’s data frame or Python’s pandas data frame are also provided . VivaxGEN users may choose to keep their data private , accessible to all or only specified researchers or they may allow their data to be open access . The repository currently holds data obtained from published studies on P . vivax samples from China [12] , Ethiopia [4] , Indonesia [14] , South Korea [9] and Bhutan [34] . Private accounts have been generated for users with data sets on P . vivax samples from Iran , Malaysia , Myanmar , and Vanuatu . The platform can be accessed at http://vivaxgen . menzies . edu . au . The source code for the platform , licensed under GNU GPL version 3 , can be obtained from https://github . com/trmznt/plasmogen .
The VivaxGEN platform was developed as a framework to support standardized allele calling and greater ease of data sharing for comparative analyses between different STR-based studies in P . vivax . Relative to Single Nucleotide Polymorphisms ( SNPs ) , where a maximum of 4 alleles arising from the 4 different nucleotides are possible at a given position , STRs may exhibit dozens of alleles , measured as different repeat lengths . Although STRs offer high discriminatory potential between independent infections , comparison of STR alleles ( fragment size variants ) between different sample batches produced at different time points and/or in different laboratories is considerably more challenging than comparison of the discrete allele forms generated from the analysis of SNPs . Despite the application of a size standard , replicates of the same sample may exhibit slight variation ( usually less than 1 bp difference ) in fragment size . In order to address this variation , alleles can be assigned to bins encompassing a range of fragment sizes usually reflecting the size of the repeat unit . However , whilst one researcher might assign fragment sizes of 254 . 4 bp and 255 . 7 bp to two different allele bins such as “254” and “256” respectively , another researcher might assign both alleles to bin “255” , and yet another might assign these fragment sizes to allele bin “256” , creating artificial differentiation between datasets . As illustrated in Fig 2 , the VivaxGEN platform provides a common interface for fragment size allele calling using the raw FSA files and applying a standardized binning system , which facilitates comparability between different datasets . By virtue of this feature , using the VivaxGEN platform , it was possible to identify a distinct , population-specific allele profile at the MS20 locus in South Korea versus Bhutan , Ethiopia and Indonesia ( Fig 3 ) . The distinct MS20 allele profile observed in South Korea is postulated to reflect a single major reservoir of P . vivax infections , most likely from North Korea [9] . Future data entries to VivaxGEN on MS20 genotypes from other vivax-endemic regions are likely to provide further important insights on this phenomenon and other transmission patterns . One of the greatest challenges in genotyping Plasmodium samples ( and other microorganisms ) is the identification and characterization of polyclonal infections [22] . Owing to artefacts such as background noise , stutter peaks , and overlapping peaks ( also known as pull-up peaks or bleed ) in multiplex reactions where amplicons are labelled with different fluoresceins . Some of these artefacts may not be automatically detected and excluded from the peak binning during the fragment scanning process . To address this challenge , the VivaxGEN platform provides utilities enabling visual inspection of individual electropherogram traces and editing of allele annotations . The platform also enables user-defined relative minimum RFU thresholds for calling minor alleles: an approach that is commonly applied in STR-based Plasmodium studies to reduce the prevalence of artefact peaks , and enhance comparability in the sensitivity to detect minor peaks in samples of differing quality such as DNA derived from dried blood spots versus blood tubes [35] . Different studies may however apply different thresholds . A benefit of the integrated database and analytical framework in VivaxGEN is that population genetic measures such as the average MOI or proportion of polyclonal infections can be compared between different sample batches at the same user-defined threshold–and indeed multiple different thresholds can be explored . Capitalizing on the feature to incorporate samples from multiple studies ( batches ) within an analytical procedure , we used the platform to compare multi-locus genotypes ( MLGs ) between different published datasets stored in the database . As illustrated in Fig 4A , Multiple Correspondence Analysis ( MCA ) demonstrated clear distinction of the MLGs at the 9 APMEN standard markers between Ethiopia , Indonesia and South Korea , whilst the Bhutanese isolates displayed a broad range of MLGs with overlap in both Ethiopia and Indonesia . It is widely acknowledged that different STR markers have different strengths in their ability to detect polyclonal infections and/or to define population structure [36] . Amongst the APMEN panel , 5 markers ( MS1 , MS5 , MS10 , MS12 and MS20 ) have been defined as “stable” , with optimal utility for analysis of population differentiation [36] . Therefore , the effect of repeating the analysis using the 5 stable markers was assessed ( Fig 4B ) . A similar pattern was observed to the full marker panel , adding assurance that the clustering patterns had not been affected by the high diversity markers . The integrated data repository , allele calling and data analysis tools in VivaxGEN promote exploratory and semi-interactive analysis in a common web interface . Compared to other popular softwares for processing microsatellite data , VivaxGEN is unique in providing both the capability to process and store raw electropherogram data ( FSA files ) and to perform statistical and population genetic analysis commonly applied in studies of Plasmodium ( Table 1 ) . A data export utility enables population genetic analysis outputs for a given parameter set to be downloaded from VivaxGEN to facilitate data reporting . These features greatly simplify data processing and exploration , and should enable malaria researchers who are new to the field of population genetics to conduct robust data analysis with greater autonomy . The integrated data repository should also foster collaborations between different research institutions and allow analyses on regional trends as well as population differences between countries . The outcomes will inform national malaria control and elimination programs on malaria transmission dynamics , may help distinguish local from imported parasite populations and facilitate malaria surveillance .
The VivaxGEN platform is well placed to facilitate regional overviews of P . vivax population genetic patterns in different endemic settings , informing on the underlying transmission dynamics of this highly adaptive parasite . The system is amenable to being adapted for STR-based analyses in P . falciparum and other microorganisms or other forms of genetic data such as SNP-based genotypes . The open access source code is provided to facilitate developments for such applications . | The Plasmodium vivax malaria parasite inflicts significant morbidity in endemic populations across the globe , but has been overshadowed by the more fatal P . falciparum parasite . In malaria-endemic regions outside of Africa , the declining prevalence of P . falciparum is coupled with a proportionate rise in P . vivax , reflecting the greater refractoriness of P . vivax to transmission interventions . This worrying trend emphasizes the need for a better understanding of the patterns of P . vivax transmission and spread within and across borders . Genotyping parasite population samples at short tandem repeat ( STR ) markers such as microsatellites informs on diversity , population structure and underlying transmission patterns . We have established vivaxGEN , an online platform providing a repository for P . vivax STR genotyping data , and tools for standard population genetic analyses . The platform currently holds publicly available data from 5 vivax-endemic countries that can be browsed on the website ( http://vivaxgen . menzies . edu . au ) . VivaxGEN will support researchers to conduct local STR-based P . vivax studies with greater autonomy and foster collaborative studies enabling regional overviews of P . vivax diversity in different endemic settings and across borders . The system can be adapted for STR-based analyses in other microorganisms and the open access source code is provided to facilitate these developments . | [
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| 2017 | VivaxGEN: An open access platform for comparative analysis of short tandem repeat genotyping data in Plasmodium vivax populations |
Mechanical coherence of cell layers is essential for epithelia to function as tissue barriers and to control active tissue dynamics during morphogenesis . RhoA signaling at adherens junctions plays a key role in this process by coupling cadherin-based cell-cell adhesion together with actomyosin contractility . Here we propose and analyze a mathematical model representing core interactions involved in the spatial localization of junctional RhoA signaling . We demonstrate how the interplay between biochemical signaling through positive feedback , combined with diffusion on the cell membrane and mechanical forces generated in the cortex , can determine the spatial distribution of RhoA signaling at cell-cell junctions . This dynamical mechanism relies on the balance between a propagating bistable signal that is opposed by an advective flow generated by an actomyosin stress gradient . Experimental observations on the behavior of the system when contractility is inhibited are in qualitative agreement with the predictions of the model .
Spatial and temporal patterns of intracellular signaling are thought to play important roles in determining their functional outcomes . This is exemplified by the RhoA GTPase , a major regulator of actomyosin-based contractility in eukaryotic cells [1 , 2] . Characteristically , localized RhoA activity defines where contractility is generated and , accordingly , contractile events are distinguished by distinctive subcellular patterns of RhoA signaling . For example , RhoA signaling concentrates at the contractile ring during eukaryotic cell division , co-localizing with the contractile ring that mediates cytokinesis [3] . Another distinctive example occurs in confluent epithelia during interphase: here a prominent zone of active RhoA is found at the apical zonula adherens ( ZA ) where E-cadherin adhesion couples to actomyosin to generate a zone of high junctional tension [4–6] . As RhoA is necessary for the biogenesis of contractile actomyosin at the ZA [4] , this further supports the concept that control of the subcellular expression of RhoA signaling plays a fundamental role in determining where contractility is established within cells . In the present study , we therefore chose the ZA as a model to understand how the spatial expression of RhoA signaling is determined within cells . The activity of RhoA is controlled by upstream regulators , notably guanine nucleotide exchange factors ( GEFs ) that activate RhoA by GTP-loading and GTPase-activating proteins ( GAPs ) that facilitate its inactivation [7–9] . The location of active GEFs is commonly thought to play a key role in defining where RhoA signaling is initiated [2] . For epithelial junctions , we earlier identified the Ect2 GEF as responsible for activating junctional RhoA [4] . As Ect2 itself localized to the ZA , it could be interpreted as a point source for RhoA activation , which ultimately promoted junctional contractility by recruiting and activating non-muscle myosin IIA ( NMIIA ) [4 , 10] , an actin-dependent motor protein that is the major contractile force generator in eukaryotic cells . More recently , we also described a feedback network that allows junctional NMIIA to support RhoA signaling , once it has been activated [6] . This feedback involves the scaffolding of Rho kinase ( ROCK ) by stabilized NMIIA at the ZA , which then antagonizes the junctional recruitment of the RhoA inactivator , p190B RhoGAP , to thereby sustain active RhoA . By combining computational modeling with experimental analysis we found that this biochemical feedback network displayed properties of a bistable system [11] , which could account for the stable intensity of signaling that is observed within the RhoA zone of the ZA [6] . However , RhoA is a lipid-anchored molecule , which can potentially diffuse in the membrane away from its source of activation [12 , 13] . Furthermore , mathematical models have revealed that reaction-diffusion systems of membrane-bound proteins can generate dynamic zones that exhibit travelling wave fronts that are not static or confined . In particular , these occur when diffusion is combined with bistability in the underlying dynamical system of non-linear interactions [14 , 15] , akin to what we identified for the NMIIA-RhoA feedback network of the ZA [6] . Despite this , we observed that the morphology of the RhoA zone at the ZA was stable ( Fig 1 , S1 Movie ) , both in its width and the definition of its borders , over time scales ( 10s min ) that are much longer than that of its constituent molecules ( RhoA T1/2 ~ 0 . 5 sec ) [16] . This observed stability therefore implies that other mechanisms must exist to control the spatial distribution of active RhoA ( GTP-RhoA ) at the ZA . Importantly , the NMIIA that influences RhoA signaling at the ZA is part of a contractile cortex that can generate gradients of stress at cell-cell junctions [5 , 10] . Recently developed models based on a mesoscopic representation of the actomyosin cortex as an active fluid [17–20] have shown that local differences in contractility can pattern the cortex by generating advective flows that follow the gradients of contractility . We therefore considered whether embedding a bistable signaling network within a contractile medium might influence the patterns of RhoA signaling that can be generated . To test this , we combined the mesoscopic fluid model of the junctional cortex with a simplified scheme where RhoA and NMIIA undergo mutual activation to generate bistability upon dynamic exchange with the cortex . We then compared our model predictions with analysis of the temporal changes of GTP-RhoA in response to perturbations that affect the contractile properties of NMIIA . We conclude that advection and bistability-driven travelling waves exert opposing effects to stably delimit the RhoA zone at the adherens junctions .
In seeking to understand how meso-scale stability of an NMIIA-RhoA system is achieved at adherens junctions ( Fig 1 ) we considered a minimal model of a RhoA-NMIIA positive feedback loop that can exhibit bi-stability . This corresponds to the case in which RhoA and NMIIA mutually recruit each other to the cell cortex following a Hill type process of cortical adsorption , and dissociate from the cortex with the dissociation rates , kRhoA and kNMIIA , respectively [6]: d[RhoA]dt=sRhoA[NMIIA]nκNMIIAn+[NMIIA]n−kRhoA[RhoA] d[NMIIA]dt=sNMIIA[RhoA]nκRhoAn+[RhoA]n−kNMIIA[NMIIA] ( 1 ) Here , sRhoA and sNMIIA are the saturation rates for cortical binding of RhoA and NMIIA , respectively , [NMIIA] and [RhoA] are the cortical concentration of NMIIA and RhoA , respectively . κNMIIA and κRhoA are the half-saturation concentrations of NMIIA and RhoA and n is the Hill coefficient ( n = 4 for our calculations ) . Unless otherwise stated we assume κRhoA = κNMIIA . Note also that experimentally , the cortical recruitment of both RhoA and NMIIA is accompanied by their activation [4 , 6]; accordingly , we use either RhoA and GTP-RhoA to refer to the same active form of RhoA that is present in the cell cortex . Then , for simplicity we study the system formed by the cortex as a one-dimensional element , with proteins that are not bound to the cortex located away in the cytoplasm . On the cortex , the two components can diffuse and can also be transported by cortical flow generated when a gradient of active cortical stress is present . The dynamics of the species concentration can be now described in the x spatial dimension by the reaction-advection-diffusion equations: ∂[RhoA]∂t+∂∂x ( v ( x ) [RhoA] ) =sRhoA[NMIIA]nκNMIIAn+[NMIIA]n−kRhoA[RhoA]+DRhoA∂2[RhoA]∂x2 ∂[NMIIA]∂t+∂∂x ( v ( x ) [NMIIA] ) =sNMIIA[RhoA]nκRhoAn+[RhoAn]−kNMIIA[NMIIA]+DNMIIA∂2[NMIIA]∂x2 where v ( x ) is the distribution of the flow velocity , and DRhoA and DNMIIA are the cortical diffusion coefficients of RhoA and NMIIA , respectively . We then consider NMIIA as the mechanically active component of the system , which generates active contractile stress that monotonically increases with its concentration . Assuming that the forces are in quasi-equilibrium , then the drag force is balanced by the stress ( σ ) divergence γv=∂σ∂x where γ is the friction coefficient . In the above equation , the stress σ ( x ) is the sum of the viscous and active stresses [18 , 20] σ ( x ) =η∂v ( x ) ∂x+ς[NMIIA]Δ+[NMIIA] The viscous stress is proportional to the velocity gradient where η is the viscosity coefficient . The active stress is an increasing function of the myosin concentration at the cortex that is linear at small concentrations and when the concentration is much larger than the half-saturation constant Δ it reaches the maximal active stress ς . Without this saturation the positive feedback of myosin on itself can lead to runaway self-contraction and singular solutions in certain regimes [18 , 20] . We previously found that this description of actomyosin generated stresses reproduced well the dynamics of actomyosin networks at the lateral adherens junctions of epithelial cells [20] . Introducing non-dimensional variables using the unit length l = ( η/γ ) ½ , time unit τ = l2/D and unit concentrations s/k we obtain ∂[RhoA]∂t+Pe∂∂x ( v[RhoA] ) =α ( [NMIIA]nκn+[NMIIA]n−RhoA ) +∂2[RhoA]∂x2 , ∂[NMIIA]∂t+Pe∂∂x ( v[NMIIA] ) =α ( [RhoA]nκn+[RhoA]n−[NMIIA] ) +∂2[NMIIA]∂x2 v=∂2v∂x2+∂∂x ( [NMIIA]K+[NMIIA] ) The most important non-dimensional parameter of the system is the Peclet number Pe = ς / ( γD ) that represents the strength of advection by the contractile flow relative to diffusion ( Bois et al , 2011 ) , and α = η/ ( kγD ) is the ratio of diffusion time vs . the timescale of reaction kinetics . For simplicity we assumed that the parameters of the reaction kinetics and diffusion for RhoA and NMIIA are the same . Although the real values of these parameters are likely to be somewhat different for the two components [16] , this assumption is not essential and does not influence the general qualitative behavior of the system . To understand the system’s behavior , we first consider the RhoA-NMIIA system with the two interacting components uniformly distributed in the cortex without diffusion and advection . An example of the typical phase-portrait and nullclines of this system is shown in Fig 2 . The uniform bistable system has two stable steady states . One corresponds to low contractility , where both species are uniformly and poorly recruited to the cortex ( i . e . [RhoA] = [NMIIA] = 0 ) . The second state represents a contractile state , similar to the one found at steady state at the epithelial ZA [6] , distinguished by effective recruitment to , and a high uniform concentration of NMIIA and RhoA at , the cortex ( [RhoA]high , [NMIIA]high ) . The system also has an intermediate unstable steady state . The trajectory leading to this saddle point on the phase diagram ( stable manifold , green curve in Fig 2 ) forms the boundary that divides the basins of attraction of the two stable steady states . We note that the bistability is parameter dependent . As an approximate condition , it requires that the half-saturation constants for the activation of each component should not be larger then the corresponding maximum concentrations of the other component . Otherwise there is only one intersection of the nullclines and the stable contractile state does not exist . In the case of the spatially distributed system in a static medium with diffusion but without contractile stress and flow ( v = 0 ) , the solution of the reaction-diffusion system produces co-moving cortical travelling fronts for both NMIIA and RhoA ( similar to the one shown in the top-left panel of Fig 3 ) . These correspond to asymptotic solutions of the general form C ( x , t ) = f ( x-st ) that connect the two stable steady states , and the constant s is the speed of the propagating front [21 , 22] . If in a certain part of the domain the system is initially in the “high” state ( [RhoA]high , [NMIIA]high ) and elsewhere in the low state ( [RhoA]low = [NMIIA]low = 0 ) , then the boundary between the two regions moves with speed s , so that the dominant steady state , which is determined by the parameters of the bistable dynamics , expands in space while the other shrinks . We choose the parameters of the system such that the stable steady state with high concentration of NMIIA and RhoA is dominant ( [RhoA]high , [NMIIA]high ) over the low concentration steady state . We then model the case when a uniform state with ( [RhoA]low = [NMIIA]low = 0 is perturbed by a spatially localized pulse of RhoA activation , whose amplitude is larger than the threshold determined by the saddle point . This leads to a local increase in [RhoA] and [NMIIA] followed by the propagation of the “high” state ( ( [RhoA]high , [NMIIA]high ) with constant speed in both directions until a uniformly “high” state is reached in the whole domain . Therefore , this model implies that in the absence of advection the locally recruited , active RhoA would tend to spread away from the source ( i . e . the ZA ) as a consequence of diffusion driven by the action of the bistable signaling . Of note , this spreading would be limited by other conditions: for example , if the total amount of either RhoA or NMIIA is limiting in the system , then the total amount of protein may not be sufficient to propagate the wave beyond a certain distance [23 , 24] . The decay rates of RhoA or NMIIA may also vary in different cortical regions , for example having higher values in regions outside the ZA [6] . Next we investigate the dynamics of the system in the presence of cortical advection generated by myosin activity where both NMIIA and RhoA are advected by the flow . In our model advection is driven by a mechanical stress gradient that is induced by the local recruitment of NMIIA . When the medium is contractile the numerical simulations show two qualitatively different cases ( Fig 3 ) . When the contractility is relatively weak , i . e . the Peclet number is lower than a certain threshold , we find again travelling front solutions as in the case of a medium with diffusion but without advection , however the propagation speed is reduced ( Figs 3 and 4 ) . The gradient of the active stress generates actomyosin flow regions that are localized around the edge of the moving fronts directed towards the RhoA zone . Increasing the Peclet number causes the propagation speed to decrease gradually , and when Pe is above a certain threshold a stationary localized zone of RhoA and NMIIA forms that is sustained by a convergent flow ( Figs 3 and 4 ) . Moreover , we found that stationary results are independent of whether we start simulations using either small or big active zones as initial conditions . Thus , the diffusive spread of the molecular components balanced by a steady contractility-driven directed transport maintains a signaling zone that is stable for time scales much longer than the time scales of cortical dissociation and diffusion . We then analyzed the extent to which interaction between diffusion , bistable signaling and NMIIA-dependent advection could influence the stabilization and size ( i . e . width ) of the RhoA zone . Of note , the RhoA zone of the ZA constitutes a small proportion of the height of the lateral surface of epithelial cells ( e . g . <5% ) . For this we first analyzed the effect of varying the Peclet number with a fixed value of α ( Fig 5A and 5B ) . We observed that increasing contractility has a pronounced effect on narrowing the RhoA zone , decreasing it to 1 . 5 units length for a biologically relevant lower limit of Pe~30 for the lateral junctions where junctional contractility is ~5 times lower than at the ZA [20] . When contractility is further decreased , then the predicted RhoA zone width first increases up to about 3 length units , after which it tends to infinity , which implies that the system cannot generate a stable RhoA zone within the spatial length scales of the lateral cell-cell interface ( Fig 5B and 5C ) . In contrast to the case when contractility was varied , having faster reactions ( large α ) led to wave fronts that propagate faster and therefore require stronger contractile flow ( i . e . larger Pe ) in order to create a stationary active zone ( Fig 5A and 5C ) . We also tested the prediction of the one-dimensional model in two-dimensional simulations . ( For details of the implementation of the two-dimensional simulations see [20] ) We found qualitatively similar behavior to the 1D case . Stripe-like initial conditions generate a travelling front that becomes stationary when the Peclet number is sufficiently high . An example of the distribution of the stationary active zone is shown in Fig 5D . In the above model both RhoA and NMIIA are advected by flow . However , for the contractile cortex , NMIIA is likely to be more directly affected by the flow than RhoA . NMIIA interacts directly with the cortical F-actin network that is advected by contractile flow [25] . In contrast , RhoA is thought to principally interact with the lipid bilayer of the plasma membrane through its farnesyl anchor [2] . Accordingly , we extended our numerical analysis to consider the case where only NMIIA was advected by flow . We found that even in this case the fronts of RhoA and NMIIA remain co-localised ( Fig 6 ) . This is due to the biochemical coupling between the two species , as they mutually contribute to each other’s recruitment from the cytosol mediated by the NMIIA-RhoA feedback loop . Therefore it is sufficient for contractile flow to act on one of the species in order to produce qualitatively similar behavior as described above . However , there is , of course , change in the quantitative details of the effect of contractility ( Fig 6 ) . When only one species is advected by the flow , in general there is a weaker reduction of the front speed and the transition to the stationary front regime is also shifted to higher Peclet numbers , i . e . the formation of a stable RhoA zone requires stronger contraction . In addition , when a stationary active zone is generated the distribution of the mechanically active component NMIIA is narrower in comparison to the zone formed by RhoA , which is not transported by the flow ( Fig 6 ) . We also expect that a similar behavior would be observed for the case where the species exhibit different reaction kinetics of binding to the cell cortex , as RhoA has a turnover kinetics of binding to the cortex that is 10 times greater that the observed for NMIIA . We then sought to test the predictions of this model for the RhoA zone of the ZA . We imaged active , GTP-loaded RhoA ( GTP-RhoA ) using a location biosensor derived from the C-terminus of anillin ( GFP-AHPH , Fig 7 ) . This reporter binds specifically to GTP-RhoA , and thus its localization identifies the location of GTP-RhoA [6] . As previously described [6] , GTP-RhoA localized in a prominent ring-like zone at the apical poles of confluent MCF7 mammary epithelial cells ( Figs 1 and 7A ) . Kymographic analysis of live-cell movies further confirmed that both the spatial definition of the zone ( its width and definition of boundaries ) and its signal intensity were stable over the 2 hr duration of the movies ( Fig 7A and 7D–7F , S1 Movie ) . Based on our computational analysis we now predicted that diffusion driven by bistable signaling between NMIIA and RhoA would tend to cause an outward-travelling front of GTP-RhoA , unless this was counteracted by local mechanical stress ( advective flow ) at the ZA . We tested this prediction qualitatively by monitoring the spatiotemporal response of the GTP-RhoA zone when NMII was inhibited . For technical reasons , we blocked contractility using the ROCK inhibitor , Y-27632 , as this drug is compatible with live-cell imaging . Moreover , due to the geometry of cell in the monolayer and the position of the Rho zone ( Fig 1 ) , measurements could be only done in the XY plane of the cortex parallel to the plane of the microscope stage , which corresponds to the apical surface of the epithelial monolayer . Whereas control cells ( Fig 7A , 7D and 7E ) , retained GTP-RhoA as a tightly defined band at apical junctions , addition of Y-27632 caused cortical GTP-RhoA to diffuse progressively outwards from the junction , leading to a broader zone within ~50 min of adding the drug ( Fig 7B and 7D–7F , S2 Movie ) . This broadened zone then faded after 2 hrs treatment , likely due to inactivation of GTP-RhoA by the cortical recruitment of p190B RhoGAP [6] . Indeed , when this experiment was performed in p190B RhoGAP RNAi cells , we found that the GTP-RhoA zone persisted and displayed even more pronounced outward flux from the junction after treatment with Y-27632 ( S3 Movie ) . We then also compared the effect of increasing contractility in cells with the results from simulations . To mimic the effect of increasing contractility we first treated cells with Y-27632 ( 1 hr ) to inhibit NMII , and then washed out the drug to allow contractility to recover . In the experiments shown , imaging began immediately upon drug wash-out ( Fig 7C–7F , see also S4 Movie ) . Kymographs showed that the GTP-RhoA zones were initially broad and comparable to what we observed after adding Y-27632 ( Compare Fig 7C , 0 min with Fig 7B , 40 min ) . After wash-out of the drug , the GTP-RhoA zone became progressively narrower ( Fig 7C–7E ) , associated with a slight increase in the mean fluorescence intensity of GTP-RhoA ( Fig 7F ) . We then compared these results with numerical simulations of similar experiments . An example of a simulation that mimics the effect of the inhibitor is shown in Fig 8 . Here , after reaching the steady state with a stationary active zone , an external perturbation eliminates contractility and bistability in the system by increasing the decay rate of NMIIA , as is predicted to occur experimentally upon addition of Y-27632 . We further assume that molecular turnover ( degradation of GTP-RhoA ) is somewhat slower than diffusion ( i . e . small α ) . Numerically , we found that this caused a biphasic change in the RhoA zone: a transient broadening that is followed by overall complete inactivation , that matches qualitatively with what we observed in our experiments .
The stable RhoA zone of the epithelial ZA provides a challenging problem of biological organization . In particular , the RhoA zone in established epithelial monolayers displays clearly defined borders whose width is stably maintained , despite being comprised of molecules that can potentially diffuse in the plasma membrane away from their source of activation . Our current analysis leads us to propose that this paradox is resolved by the opposed action of two processes: bistability-driven spreading and contractile advection . Importantly , both processes derive from the functional interplay between active RhoA and cortical NMIIA . The key lies in the fact that the higher concentration of NMIIA found at the ZA can contribute both to bistable feedback and drive contractile flows . Then , the tendency for bistable feedback to promote the outward propagation of RhoA and NMIIA by travelling wave fronts is countered by inward advection promoted by the gradient of contractility . Travelling front solutions of reaction-diffusion systems are well known in mathematical models of diverse biological systems [14 , 15 , 21] . Examples include neuronal action potentials [26] , calcium waves ( e . g . in cardiac muscle ) , actin polymerisation waves in motile cells [27] , wound healing [28–31] , cancer cell invasion [32–34] , and invasive species in ecology [35] . This kind of behavior typically arises from the combination of diffusion and bistability of the underlying dynamical system of nonlinear interactions between the components [22] . The bistability identified in the NMIIA-RhoA feedback network is capable of generating such travelling waves in simulations and this was supported experimentally by the observation that the RhoA zone broadened progressively when contractility was inhibited . This is consistent with GTP-RhoA diffusing away from its source at the ZA until it was inactivated by p190B RhoGAP . It further implies that when NMIIA activated by RhoA is capable of diffusion , then the system will tend to generate travelling waves . However , we further found that when the bistable system occurred in a contractile medium , then the propensity for travelling wave fronts to propagate away from the ZA could be counteracted by advection . Depending on the strength of contractility , this resulted in fronts that propagate slower than in a passive medium or , when the contractility is sufficiently strong , the front may be transformed into a stationary , localized active zone . This is consistent with previous modeling studies that showed that advection by fluid flows can have nontrivial effects on propagation of reaction-diffusion fronts [36 , 37] In the present case of a contractile RhoA-NMIIA cortex , directional transport due to the flow produced by the stress gradient at the border of the active zone can counteract the propagation of the front . We found that this phenomenon is robust and persists even when only one component of the coupled dynamical system ( NMIIA ) is directly affected by the flow . Our analysis implies that the balance between the stress gradient and bistable wave-front propagation can suffice to determine the size and stability of the RhoA zone . This is consistent with experimental evidence that a contractile gradient exists at cell-cell junctions , where stress is greatest at the ZA , which is predicted to be capable of counteracting the direction of wave front propagation [5] . The ability of cells to maximize stress at the site of the ZA is likely to reflect multiple mechanisms . These include both the localization of the RhoA activator Ect2 [4] that initiates RhoA signaling at the ZA and the ability of bistability to potentiate the magnitude of the RhoA signal and spatially constrain it [6] . However , additional factors can also contribute to support the contractile gradient , including F-actin stabilization by N-WASP at the ZA [5 , 38] as well as other signaling pathways , like Akt , which have been shown modulate the width and organization of the zonula adherens [39] . How these additional factors may influence the behaviour of the system that we have characterized will be an interesting issue for future research . This general model of bistability within a contractile medium has the capacity to generate diverse spatial patterns , depending on the parameters that are set . For example , the front propagation may coexist with the clustering instability characteristic of active contractile systems without biochemical coupling [18 , 20] Indeed , we found computationally that in the propagating front regime the uniform active zone may be subject to clustering , where the contractile component forms a periodic pattern of high concentration clusters behind the front , while the non-advected component remains uniform ( Fig 9 ) . Such behavior is restricted to intermediate Pe values , since at small Pe the contractility is below the clustering instability threshold , while at large Pe the front propagation is blocked and the active zone is restricted to a single cluster . Alternatively , there are biological circumstances when junctional RhoA signaling is downregulated to facilitate morphogenesis [40] . Here , maneuvers that broaden the RhoA zone may provide a pathway for junctional mechanics to be altered without altogether abolishing the apical ZA . Ultimately , the capacity for the NMIIA-RhoA to act as a shape-generator reflects the fact that this system couples bistability to a contractile medium . Moreover , the combination of RhoA with NMIIA occurs in many other cellular circumstances , from cell division to locomotion . The basic principles of the system that we have characterized at the ZA are readily applicable to these other situations and , potentially , to other mechanically active contractile reaction-diffusion systems .
Y-27632 ( no . Y0503 ) was from Sigma . The GFP–AHPH location RhoA biosensor was a kind gift from M . Glotzer ( University of Chicago , USA , [41 , 42] ) . MCF-7 cells were from ATCC and cultured in DMEM; supplemented with 10% foetal bovine serum ( FBS ) , 1% non-essential amino acids , 1% L-glutamine , 100 U/ml penicillin and 100 U/ml streptomycin . For the expression of the GTP-RhoA reporter ( GFP-AHPH , [6] ) and live cell imaging , cells were grown till 80–90% confluent on glass bottom dishes ( Shengyou Biotechnology ) , transfected using Lipofectamine 3000 and analyzed by live cell spinning disk microscopy . Cells were imaged in clear Hank’s balanced salt solution supplemented with 5% FBS , 10 mM HEPES ( pH 7 . 4 ) and 5 mM CaCl2 . Only cells expressing low levels of GFP–AHPH were used for analysis . p190B RhoGAP was depleted using a combination of two siRNAs designed against the 3’ UTR region of p190B RhoGAP mRNA ( NM_001030055 . 1 ) . Sequences are as follows: siRNA_5007 Sense: GCAUGACUGGAGAGGUUUATT and siRNA_5063 Sense: GCUGCUGCAUGCAACCUUATT ) . For live-cell imaging using Y-27632 , dishes were mounted on the microscope stage , cells were then selected for imaging and time-lapse imaging ( 1 Z-stack/minute ) was initiated . After three minutes of imaging , 1 volume solution of movie media with 120 μM Y-27632 was added to the cells which gave a final concentration of 60 μM . For these experiments , a higher concentration of Y-27632 ( compared to other reports citing this drug usage ) was used , to capture its effects on the organization and distribution of the Rho zone in a relatively short time scale . Although similar phenotypes , but with a slow kinetics , were observed when Y-27632 was used at 30 μM . In Y-27632 washout experiments , cells were pretreated with 30 μM Y-27632 for 1 hour in 2 ml of movie-media . Then 1 . 7 ml of media was removed and cells were placed on the microscope . After 3 minutes of time lapse imaging ( as indicated above ) , 1 . 5 mL of fresh imaging media ( without Y-27632 ) was added to the cells and time lapse was continued . Live-cell imaging of GFP–AHPH was performed on a Zeiss inverted spinning-disc confocal microscope equipped with a 63 × 1 . 3 NA multi-immersion immersion objective ( Zeiss ) , a CSU-X1-A Yokogawa spinning-disc unit and two Roper Evolve EMCCD 512 × 512 cameras . Z-tacks ( ~30 slices , 512x512 pixels ) with a 1 μm z-step where acquired every minute with a spatial resolution of 0 . 211 μm/pixel . For kymograph analysis and measurements of fluorescence intensity and width of the Rho zone at the apical adherens junctions , sum projections in the Z-axis where performed on the movies and kymographs of lines traced orthogonal to apical adherens junctions and measurements of the junctional position in it where obtained as described in [43] using a custom made Image J plugin . From this data , the position of the junction in every frame was set as X = 0 while the limits of the zone was obtained by applying a threshold by above the background on the obtained kymograph and analyzed over time . From this then the average fluorescence intensity of GFP-AHPH and width of the Rho zone was measured using a custom made MatLab script . | Mathematical models play a key role in uncovering mechanisms responsible for the formation of patterns in cells and tissues . The well known Turing mechanism based on nonlinear reaction kinetics and differential diffusion explains the formation of static patterns , while positive feedback interactions can generate dynamical structures such as propagating fronts and excitable pulses . Recent studies have demonstrated the importance of mechanical forces that can lead to novel mechanisms of pattern formation such as clustering and oscillations in contractile systems . Here we investigate how contractile forces in mechanically active media can affect bistable front propagation . We found that contraction regulates the front speed or can fully suppress its propagation in space to create a static localized zone . The proposed model provides a new mechanism for cross-talk between mechanical activity of cells and biochemical signaling . | [
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| 2017 | Bistable front dynamics in a contractile medium: Travelling wave fronts and cortical advection define stable zones of RhoA signaling at epithelial adherens junctions |
Chronic infections are characterized by the inability to eliminate the persisting pathogen and often associated with functional impairment of virus-specific T-cell responses . Costimulation through Glucocorticoid-induced TNFR-related protein ( GITR ) can increase survival and function of effector T cells . Here , we report that constitutive expression of GITR-ligand ( GITRL ) confers protection against chronic lymphocytic choriomeningitis virus ( LCMV ) infection , accelerating recovery without increasing pathology . Rapid viral clearance in GITRL transgenic mice coincided with increased numbers of poly-functional , virus-specific effector CD8+ T cells that expressed more T-bet and reduced levels of the rheostat marker PD-1 . GITR triggering also boosted the helper function of virus-specific CD4 T cells already early in the infection , as was evidenced by increased IL-2 and IFNγ production , and more expression of CD40L and T-bet . Importantly , CD4-depletion experiments revealed that the expanded pool of virus-specific effector CD8 T cells and the ensuing viral clearance in LCMV-infected GITRL tg mice was entirely dependent on CD4 T cells . We found no major differences for NK cell and regulatory T cell responses , whereas the humoral response to the virus was increased in GITRL tg mice , but only in the late phase of the infection when the virus was almost eradicated . Based on these findings , we conclude that enhanced GITR-triggering mediates its protective , anti-viral effect on the CD8 T cell compartment by boosting CD4 T cell help . As such , increasing costimulation through GITR may be an attractive strategy to increase anti-viral CTL responses without exacerbating pathology , in particular to persistent viruses such as HIV and HCV .
The adaptive immune system has evolved to detect and remove virally infected cells . However , multiple viruses , such as human immunodeficiency virus ( HIV ) , hepatitis C virus ( HCV ) or hepatitis B virus ( HBV ) have acquired successful counter-measures to escape from anti-viral immunity , thereby preventing complete clearance and leading to chronic and harmful infections . Cellular immunity against these viruses has been thoroughly investigated , but safe ways to boost immunity to achieve full viral elimination have yet to be developed . Apart from the emergence of viral escape mutants , three important challenges must be tackled to allow for the successful engineering of such anti-viral treatments . Firstly , prolonged exposure to viral antigens leads to functional “exhaustion” of antigen-specific T cells , which is characterized by a progressive loss of effector functions , such as cytotoxicity and the ability to simultaneously produce multiple cytokines ( reviewed in [1] ) . This strongly contributes to decreased protection against the pathogen and is difficult to overcome by subsequent ( immuno ) therapy . Secondly , boosting adaptive immunity may lead to a detrimental inflammatory response and could cause life-threatening immunopathology [2] . Thirdly , this stimulation can also break the delicate immunological threshold for self-tolerance and may thereby lead to autoimmunity [3] . Thus , successful stimulation of protective immune responses against chronic viral infections requires that exhausted T cell responses are boosted without exacerbating pathology or inducing autoimmunity . The LCMV model has proven to be a relevant model to study persistent viral infections . T cell dynamics and T cell exhaustion were initially characterized in this system [4] and later extended to a variety of human persistent infections , including HIV [5] . Chronic LCMV infection is characterized by the inability of host immune components to rapidly control the virus and the development of exhausted T cells [6 , 7] . Nevertheless , CD8 T cell responses and antibody responses are critical in this chronic infection model to eventually reduce LCMV titers below detection levels , and both antiviral responses are dependent on help from CD4+ T cells [8 , 9] . Interestingly , although T cell exhaustion results in impaired viral clearance , it may also be essential to prevent overwhelming damage to host tissues [10–12] . Early after infection , the ensuing T cell response to LCMV infection mediates destruction of splenic architecture that is characterized by depletion of macrophages from the marginal zone and follicular dendritic cells . This in turn leads to loss of integrity of B cell follicles , thereby delaying the induction of protective anti-viral antibody response [13 , 14] . Thus , control and eventual clearance of chronic LCMV is dependent on a fine balance between effective adaptive responses and prevention of immunopathology . Costimulatory molecules are promising candidates for immunotherapy , as they are key modulators of T cell responses . TNFR superfamily members , such as CD27 , OX-40 , 4–1BB and GITR positively regulate the survival , proliferation and function of CD4+ and CD8+ T cells during immune activation ( reviewed in [15–17] ) . In particular , GITR may be a promising candidate for the task of fostering a “balanced” boosting of T cell responses during chronic infections , given its well documented effects on effector and regulatory T cell biology . GITR and GITRL expression are coordinately regulated during immune responses: GITR is expressed at low levels on naïve T cells , up-regulated upon activation and maintained on CD4+ and CD8+ effector T cells , regulatory T cells ( Tregs ) , follicular T helper cells ( Tfh ) and regulatory follicular T helper cells ( Tfr ) ( reviewed in [18 , 19] ) . GITR’s unique ligand , GITRL , is temporarily expressed on activated APCs , such as DCs , B cells and macrophages [20–23] . GITR ligation on T cells in vitro with endogenous or recombinant GITRL , mGITRL transfected cells , or agonist anti-GITR antibodies enhances IL-2Rα ( CD25 ) , IL-2 and IFNγ expression , cell proliferation and survival , especially in the context of a sub-optimal TCR signal [22 , 24–27] . A protective role for GITR-mediated costimulation in T cell immunity was shown in experimental cancer therapy settings , in which GITR triggering enhanced CD8 T cell responses to tumor antigens with no or only limited autoimmunity [28–30] . GITR stimulation in vitro also increases Treg numbers , enhances IL-10 production , and augments their suppressive capacity , which may contribute to immune homeostasis in vivo [31 , 32] . Our previous studies demonstrated that in vivo GITR stimulation through transgenic expression of its natural ligand on B cells increased the cell numbers of both effector and regulatory CD4+ T cells in steady state conditions [33] . GITR triggering regulated the functional balance between these two populations as evidenced by a functional gain in cytokine production in the effector population , with a simultaneous expanded Treg population that retained their suppressive capacity . We tested the functional consequence of increased numbers of both regulatory and effector T cells in the experimental autoimmune encephalomyelitis ( EAE ) model and found a significant delay of disease onset in GITRL transgenic ( tg ) mice [33] . These findings imply that enhanced triggering of GITR through its natural ligand in vivo is protective rather than harmful , as it regulates the functional balance between regulatory and effector T cells . This concept was corroborated in a different mouse model where GITRL was overexpressed on MHCII-expressing cells [34] . Given the ability of GITR to stimulate adaptive immunity without enhancing immunopathology , we examined the impact of increased costimulation through GITR during chronic viral infection with LCMV . We found that B cell-specific GITRL tg mice infected with LCMV Clone 13 recovered from pathology and eliminated the virus faster than their WT counterparts , in a CD4+ T cell-dependent manner . Boosting GITR-signaling resulted in a more “acute-like” infection , with a quantitative and qualitative increase in virus-specific T cells . These studies provide insights into the regulation of a chronic viral infection by the GITR/GITRL axis and it provides a rationale for therapeutic interventions aimed at improving clearance of chronic viral infections .
To investigate the impact of enhanced costimulation through GITR on a chronic viral infection , we infected WT and GITRL tg mice with LCMV Clone 13 . LCMV Cl13 infection induces severe immunopathology that is characterized by extensive weight loss within the first two weeks post infection ( p . i . ) , primarily due to the anti-viral immune response [35] . While infection-induced weight loss was comparable for both mouse strains during the first week , GITRL tg mice rapidly regained their body weight during the second week of the infection , whereas WT littermates did not recover and remained below their initial weight until the end of the experiment at day 30 p . i . ( Fig . 1A ) . This was also reflected by the gradual decline of spleen cellularity in WT mice during the course of the infection , while GITRL tg mice quickly recovered from a significant drop in splenocyte numbers by day 15 p . i . ( Fig . 1B ) . Examination of splenic architecture at this time-point showed , as expected , that LCMV infection in WT mice induced depletion of MOMA-1+ marginal metallophilic macrophages and disintegration of the B cell follicles in the white pulp ( Fig . 1C ) . Interestingly , the integrity of the marginal zone and architecture of the white pulp was also affected in GITRL tg mice , but less severe than in WT mice ( Fig . 1C ) . Finally , at day 30 p . i . GITRL tg mice had undetectable viral loads in peripheral blood , and strongly reduced viral loads in bone marrow and spleen compared to WT mice ( 88-fold , p<0 . 01 , and 233-fold , p<0 . 05 , respectively; Fig . 1D ) . In summary , GITRL tg mice showed accelerated recovery from chronic LCMV infection and a strongly increased viral clearance without increased pathology . Because GITR triggering increases cell numbers and function of effector CD4+ T cells [33 , 34] , we assessed whether the increased protection of GITRL tg mice against chronic LCMV correlated with enhanced CD4+ T cell responses . We first examined the dynamics and phenotype of ensuing CD4+ T cell response in GITRL tg mice . We found similar numbers of GP66-specific CD4+ T cells in WT and GITRL tg mice at different time points during the first three weeks of infection ( Fig . 2A ) , indicating that the overall induction of anti-viral CD4+ T cell responses was unaltered . A recent study suggested that CD4+ T cells progressively differentiate towards Tfh cells during chronic LCMV infection to sustain antibody responses and control the virus [36] . To determine whether the constitutive GITRL expression altered the levels of Tfh cells , we examined the expression of CXCR5 on the CD4+ T cell population in LCMV infected mice . Even though the levels of CXCR5+ CD4+ T cells were increased before and at day 8 p . i . in GITRL tg mice , this difference was absent from day 15 p . i . onwards ( Fig . 2B ) . Of note , the proportion of CXCR5+ GP66-specific CD4+ T cells did not differ between GITRL tg and WT mice ( Fig . 2C ) . Besides CXCR5 , expression of several surface molecules has been used to identify Tfh cells , including high expression levels of ICOS , PD-1 and Bcl6 and low expression of SLAM [37] . However , the expression level of these molecules can also reflect recent activation and have been shown to be modulated during chronic LCMV infection [36 , 38] . Analysis of the phenotype of total and GP66-specific CXCR5+ CD4+ T cells revealed that , irrespective of antigen specificity , CXCR5+ CD4+ T cells expressed higher levels of PD-1 and ICOS and lower levels of SLAM than CXCR5- CD4+ T cells ( Fig . 2D and E ) and these levels were even higher in LCMV-specific CXCR5+ CD4+ T cells . Interestingly , while the expression of PD-1 and ICOS decreased over time , the total and GP66-specific CXCR5+ CD4+ T cells of WT mice retained higher levels of these molecules than those of GITRL tg mice , and expressed higher levels of Bcl6 , suggesting either a reduced Tfh differentiation and/or reduced activation in the transgenic mice ( Fig . 2D , E and F ) . Moreover , the kinetics of Tfh marker expression correlated with that of the B cell response . In WT mice , the numbers of germinal center B cells increased during the course of the infection , while GITRL tg mice gradually lost this cell population ( S1A–S1B Fig . ) . In concert with this finding , GITRL tg mice also had a strongly reduced fraction of B220lo CD138+ plasma cells compared to their WT littermates at day 30 p . i . ( S1C Fig . ) . Yet , analysis of LCMV-specific IgG revealed that the humoral immune response to the virus was enhanced in GITRL tg mice compared to WT mice , albeit late during infection ( S1D Fig . ) . Together , these data support previous observations of sustained Tfh and germinal center B cell response in WT mice , and further show that enhanced GITR costimulation overrides the escalation of Tfh responses , while it enhances the generation of virus-specific antibody responses at late time points after infection . Given that Treg cells express high levels of GITR [39] and that this population is expanded and fully functional in GITRL tg mice in uninfected mice [33] , we followed the proportion of FoxP3+ cells within the CD4+ T cell compartment in LCMV-infected GITRL tg and WT mice . Similar to what we found for CXCR5+ T cell responses , percentages of FoxP3+ cells were higher prior to infection , and at day 8 pi in GITRL tg mice compared to their WT counterparts . However , T reg numbers were equally high in WT and GITR tg mice by day 15 pi ( S2A–S2B Fig . ) . We also examined the levels of CXCR5 expression in FoxP3+ cells , as a measurement of their ability to migrate into B cell follicles and interact with B cells . In both WT and GITRL mice , FoxP3+ CD4+ T cells expressed lower levels of CXCR5 than FoxP3- CD4+ T cells . We found no difference in the percentages of FoxP3+ CXCR5+ CD4+ T cells between the two groups of mice ( S2C Fig . ) . Thus , the initially increased numbers of Treg cells in GITRL tg mice did not lead to an enhanced expansion of this population during chronic LCMV infection and we found no indication of increased interaction between Treg and B cells in the GITRL tg mice . Finally , as GITR-triggering can modulate CD25 expression on T cells [22] , we followed the expression of CD25 on FoxP3+ CD4+ T cells , which peaked at day 8 pi and subsequently declined ( S2D Fig . ) . In line with our previous work [33] , Tregs from GITRL tg mice have lower levels of CD25 expression than WT mice , though there was no difference in the kinetics ( S2D Fig . ) . These data indicate no major differences in the magnitude of regulatory T cell responses between WT and GITRL tg mice during chronic LCMV infection . When we analyzed expression of CD25 in FoxP3- CD4+ T cells from GITRL tg and WT mice , we found that GITRL tg mice had significantly higher levels of CD25+ FoxP3- effector CD4+ T cells early after infection ( day 8 p . i . ) , which then declined , while in WT mice the abundance of these cells peaked a week later ( day 15 p . i . , Fig . 3A ) . These results suggested that LCMV-related activation of CD4+ T cells was faster in the transgenic mice . We then analyzed virus-specific production of cytokines and expression of CD40L by CD4+ T cells at day 8 p . i . in the 2 groups of mice . Interestingly , we found a higher percentage of CD40L+ IFNγ+ CD4+ T cells in response to stimulation with a CD4-restricted viral peptide in GITRL tg mice ( Fig . 3B ) . These virus-specific cells not only were increased in percentage , but they also expressed higher levels of IFNγ on a per cell basis ( Fig . 3C ) and contained a significantly higher proportion of cells simultaneously expressing IL-2 ( Fig . 3D ) . Transcription factors T-bet and Eomes have been related to CD4+ T cell function/exhaustion during LCMV infection , as T-bet is expressed in exhausted CD4+ T cells , though it is higher in functional memory T cells , whereas Eomes is rather increased in ( a subset of ) exhausted CD4+ T cells [38] . We found expression of T-bet and Eomes restricted to a subset of virus-specific CD4+ T cells ( Figs . 3E and S3A ) . Total CD4+ T cells from GITRL tg mice contained a higher percentage of both T-bet+ and Eomes+ cells than those from their WT counterparts , which were clearly separate populations ( S3B Fig . ) . Virus-specific CD4+ T cells also contained separate populations expressing only one of the two transcription factors ( S3C Fig . ) . Interestingly , we found a strong increase in T-bet+ virus-specific T cells in GITRL tg mice , while Eomes+ virus-specific T cells were not significantly different ( Fig . 3E ) . In summary , virus-specific CD4+ T cells from GITRL tg mice are more polyfunctional than those from WT mice , which correlates with an increased expression of the transcription factor T-bet , indicating that GITR-mediated costimulation boosts the rapid induction of functional Th1 cells upon LCMV Cl13 infection . We reasoned that the early activation and polyfunctionality of virus-specific CD4+ T cells could also reflect on an enhanced CD8+ T cell response to the virus . Kinetic analysis of the viral load revealed the GITRL tg mice were able to rapidly control the infection , as at day 8 expression of viral RNA in spleens was already 7-fold lower compared to WT littermates and this difference further increased over the course of the infection , reaching a 176-fold reduction by day 21 p . i . ( Fig . 4A; p<0 . 05 ) . To determine whether the CD8+ T cell response against LCMV correlated with viral clearance in GITRL tg mice , we examined the kinetics of the anti-viral CTL response in GITRL tg mice . An overall CD8+ T cell expansion in GITRL tg mice was already evident at day 8 p . i . ( Fig . 4B ) , and included a strong increase CD8+ T cells directed against the early and intermediate epitopes NP396 and GP33 ( Fig . 4C ) . On day 8 p . i . , expression of the rheostat marker PD-1 on the LCMV-specific CTLs was comparable . Strikingly , on day 15 p . i . the expression of PD-1 was reduced in GITRL tg mice , while it further increased in WT mice ( Fig . 4D ) . At this time point , a larger part of the LCMV-specific CD8+ T cells in GITRL tg mice were KLRG1+CD127- CD8+ T cells compared to those from WT mice ( Fig . 4E ) . Cells with this phenotype were originally identified as short-lived effector CD8+ T cells [40] , but it was recently shown that these cytotoxic cells can also be maintained after acute LCMV infection and that they are highly protective upon re-infection [41] . After restimulation with different CD8-restricted viral peptides , CD8+ T cells from GITRL tg mice contained a significantly higher percentage of IFNγ+ TNFα- and IFNγ+ TNFα+ cells ( Fig . 5A and B ) . Ex vivo measurement of Granzyme B expression showed that , in both groups of mice , almost all GP33+ CD8+ T cells were Granzyme B+ , irrespective of KLRG1 expression , and there was no difference in the expression levels of this molecule between groups ( Fig . 5C ) . However , virus-specific CD8 T cells from GITRL tg mice were more able to degranulate ( as measured by CD107α/β expression ) after restimulation with viral peptides ( Fig . 5D ) . CD107α/β+ cells were also mostly IFNγ+ , further demonstrating that the virus-specific CD8 T cells in GITRL tg mice are more polyfunctional ( Fig . 5D ) . Finally , GP33+ CD8+ T cells from GITRL tg mice had a higher expression of T-bet , but similar expression of Eomes , when compared to their WT counterparts ( Fig . 5E ) . Together , these findings illustrate that GITRL tg mice have a greatly enhanced anti-viral CD8 T cell response , both quantitatively and qualitatively , early after infection and this coincides with faster viral clearance . High antigen levels drive CD8+ T cell exhaustion in chronic LCMV infection [42] . We thus examined virus-specific CD8+ T cell responses during the chronic phase of the LCMV infection . As seen during the acute phase , total CD8+ T cell numbers were significantly increased in GITRL tg mice when compared to WT mice at day 27 p . i . ( Fig . 6A; p<0 . 01 ) We next measured the responses to the three immunodominant CD8-restricted epitopes , and found a trend to elevated responses in GITRL tg mice ( significant for the late epitope GP276 ( Fig . 6B; p<0 . 05 ) . Phenotypic analysis indicated that nearly all LCMV-specific CD8+ T cells were CD44hiCD62L- effector-memory T cells in both GITRL tg and WT mice ( data not shown ) . Again , GITRL tg mice contained many more KLRG1+CD127- effector CD8+ T cells ( Fig . 6C and D ) . The expression levels of PD-1 on virus-specific CD8+ T cells were also decreased in GITRL tg mice ( Fig . 6E ) , suggesting that these cells were more functional than their WT counterparts . Importantly , GITRL tg mice contained not only more IFNγ-producing CD8+ T cells , as expected from the MHC-class I tetramer stainings ( Fig . 6F ) , but also a much higher fraction displayed a polyfunctional phenotype as determined by co-production of IFNγ with TNFα and/or IL-2 ( Fig . 6G ) . These data thus demonstrate that virus-specific CD8+ T responses are protected from exhaustion in GITRL tg mice . Because GITR triggering increases cell numbers and function of effector CD4+ T cells [33 , 34] , and because we found increased virus-specific CD4+ T cell function in LCMV-infected GITRL tg mice ( Fig . 3 ) , we next assessed whether the increased protection of GITRL tg mice against chronic LCMV infection required CD4+ T cells . GITRL tg and WT mice were injected with a depleting antibody against CD4 before and early during the infection ( on days-3 and day 4 p . i . ;[43] ) . This regimen successfully depleted the CD4+ T cells in the first week and prevented the recovery of body weight in GITRL tg mice in the second week of the infection with LCMV ( Fig . 7A ) . The pattern of weight loss observed in CD4-depleted GITRL tg mice was comparable to that found in WT mice and CD4+ T cell depletion in WT mice did not further enhance weight loss ( Fig . 7A ) . Analysis of viral loads revealed that depletion of CD4+ T cells during the initial phase of the infection greatly impaired viral clearance in GITRL tg mice . At day 30 p . i . , CD4-depleted GITRL tg mice had a 1728-fold increase in viral loads , compared to non-depleted GITRL tg mice ( ratio LCMV RNA over HPRT: 33 . 7 ± 13 . 2 vs 0 . 0195 ± 0 . 0334 , respectively; p<0 . 001; n = 3 vs 5 ) . This finding demonstrates that CD4 T cells play a critical role to control chronic LCMV infection in GITRL tg mice . We next examined whether the enhanced anti-viral CD8+ T cell response was also mediated by CD4+ T cells . Indeed , depletion of CD4+ T cells in GITRL tg mice abrogated the early increase in CD8+ T cell expansion during the first week of the infection and induced a crash of both the total CD8+ T cell pool ( Fig . 7B ) and the LCMV-specific CD8+ T cells at day 15 p . i . ( Fig . 7C ) . While the total CD8+ T cell pool was maintained in CD4-depleted WT mice , virus-specific CD8+ T cells greatly contracted in the absence of CD4+ T cells ( Fig . 7B and C ) . CD4-depletion also prevented the observed decrease of PD-1 on the remaining LCMV-specific CD8+ T cells from GITRL tg mice on day 15 p . i . On day 30 p . i . , CD4-depleted WT and GITRL tg mice had even higher levels of PD-1 than non-depleted WT mice ( Fig . 7D and E ) . Maintenance of KLRG1+ GP33+ CD8+ T cells in GITRL tg mice was also dependent on the presence of CD4+ T cells ( Fig . 7F ) . Finally , these effects in peripheral blood could also be seen in the spleen , where the increase in total and virus-specific CD3+ CD8+ T cells at day 35 p . i . was also lost in the absence of CD4+ T cells ( Fig . 7G ) . Together , we conclude that the protective anti-viral CD8+ T cell response in GITRL tg mice is fully dependent on CD4+ T cells , suggesting that although direct GITR triggering on CD8+ T cells might account for some increase in T cell function , it is not sufficient to clear the virus and prevent CD8+ T cell exhaustion in this model of chronic viral infection .
Here we describe how enhanced costimulation through GITR accelerated viral clearance during chronic LCMV infection , reduced pathology and prevented CD8+ T cell exhaustion . Protection from the chronic infection was CD4+ T cell-dependent and coincided with a strong increase in virus-specific effector CD8+ T cells . These data suggest that increased costimulation through GITR functionally boosted an early virus-specific CD8+ T cell response , leading to faster viral clearance and preventing the establishment of chronicity . Robust and functional CD4+ T cell responses are critical in the generation of an effective antiviral response , as they can prevent CD8+ T cell exhaustion during chronic viral infections , including LCMV and HCV [9 , 44] . In chronic LCMV , it has been suggested that persistence of antigen drives differentiation towards a Tfh phenotype , in order to sustain antibody responses [36] , which would compensate for the gradual exhaustion in CD8+ T cell function . Because enhanced GITR-mediated costimulation in the steady state led to increased Tfh cell numbers ( Fig . 2B ) , we expected an increased humoral anti-viral response in GITRL tg mice . However , the number of virus-specific CXCR5+ CD4+ T cells in GITRL tg mice was similar to WT mice and their phenotype was less sustained , which coincided with a reduction in germinal center B cells and plasma cells at later time-points in GITRL tg mice . Surprisingly , this coincided with an increase rather than a decrease in virus-specific IgG levels at day 30 in GITRL tg mice compared to WT mice . As the viral loads were already contained at this time point in GITRL tg mice , this boost in anti-LCMV antibodies cannot explain the observed early protection against the virus . Instead , it is more likely that the late boost in anti-viral antibodies is a consequence rather than a cause of LCMV clearance , which fits with the concept that presence of this virus negatively affects the development of protective antibodies [45] . In conclusion , overexpression of GITRL on B cells protects against viral chronicity , but this could not be attributed to an increased humoral immune response to the virus . In contrast to the Tfh and antibody response , we found that increased viral clearance in GITRL tg mice on day 8 coincided with more virus-specific CD8 T cells and a qualitative increase in T cell help . Although we did not find more GP66-specific CD4 T cells ( Fig . 2A ) , GITRL tg mice did develop a rapid response of CD25+ FoxP3- and T-bet+ CD4+ T cells early after infection and displayed a strong increase in CD4+ T cells expressing CD40L and producing IL-2 and IFNγ upon restimulation with viral peptide ( Fig . 3 ) . As CD4+ T cells play an important role in sustaining virus-specific CD8+ T cells during chronic LCMV infection [9] , it is highly likely that the observed increase in helper function of CD4 T cell from GITRL tg mice boosts the CD8 T cell response in chronic LCMV . Indeed , GITRL tg mice developed more virus-specific CD8 T cells with an effector phenotype ( KLRG-1+ CD127- ) , which also produced and secreted more different cytokines upon peptide restimulation than WT mice ( Fig . 5 ) . These results are in agreement with previous observations that particularly the KLRG1hi effector CD8+ T cells were lost in chronically infected mice [46] . The increased polyfunctional cytokine response and decrease in PD-1 expression in CD8 T cells from GITRL tg mice was maintained till the end of the infection , indicating that these cells were prevented from exhaustion . Decreased PD-1 levels may be the result of increased viral clearance , as sustained PD-1 expression has been linked to persistent antigen exposure [47] . However , it may also be a direct cause of the increase in T-bet expression ( Fig . 5E ) , as T-bet can directly repress transcription of the gene encoding PD-1 in both CD4+ and CD8+ T cells [48] . In conclusion , enhanced GITR-mediated costimulation boosts the development of effective Th1 cells upon LCMV infection and enhances and sustains a pool of highly functional virus-specific effector CD8+ T cells , thereby preventing the establishment of viral chronicity . Importantly , viral persistence , immunopathology and T cell function are intimately linked in the LCMV model . It is therefore likely that the observed weight gain decreased spleen pathology , late boost in antiviral antibodies and possibly also part of the T cell phenotype in GITRL tg mice is the result of lower viral loads during the infection due to the enhanced CTL function early on . GITR-GITRL interactions can occur between different types of T cells and APCs , and it is not yet clear what the impact is of GITR-mediated costimulation on every T cell subset . We reasoned that increased GITRL expression on B cells would target CD4+ T cells rather than CD8 T cells , as the latter do not enter B cell follicles . Indeed , in the steady state , GITRL tg mice showed significant alterations in the CD4+ but not the CD8+ compartment [33] . Interestingly , transgenic overexpression of GITRL on MHC-II-expressing cells , i . e . macrophages , dendritic cells and B cells , also leads to very similar alterations in the CD4+ T cell compartment with no changes observed in the CD8+ T cells [34] . This would argue that , at least in the steady state , GITRL-expressing APCs mainly influences CD4+ T cell numbers and function . In line with this , we observed down-regulation of GITR only on CD4+ T cells but not in CD8+ T cells in the transgenic mice in the steady state ( S5A Fig . ) . In contrast , upon LCMV infection , we found that enhanced GITRL expression on B cells strongly affected CD8+ T cell numbers , phenotype and function , but we found that this was fully dependent on CD4 T cells . This does not imply that GITR triggering on CD8 T cells does not play a role , but may be a reflection of the fact that CD8 T cells interact less with B cells compared to CD4 T cells . In fact , there is ample evidence from in vitro [26] and in vivo [49–51] experiments that GITR triggering has a stimulating role on the function of CD8 T cells ( reviewed in [52] ) . Yet , the observation that protection to LCMV chronicity in GITRL tg mice was completely lost when CD4 T cells were depleted ( Fig . 7 ) , demonstrates that GITR-mediated costimulation on CD8 T cells is at least not sufficient for the protective effect . However , we cannot exclude an additive contribution of GITR triggering on the CD8 T cells in the presence of the CD4 compartment . We found no differences in GITR expression in CD4+ or CD8+ T cells during LCMV infection ( S5B Fig . ) , which may be related to the upregulation of GITR on T cells following LCMV infection [51] . In conclusion , we postulate that GITR-mediated costimulation enhances CD8-mediated viral clearance by boosting the helper function of CD4 T cells . Apart from the well-established role of CD8 T cells in LCMV clearance , it could be that NK cells , which express GITR , are also partly responsible for the early-enhanced viral clearance observed in the GITRL tg mice . However , several lines of evidence indicate that NK cell activation promotes pathology and chronic LCMV infection and limits CD8 T cell function , through impairment of APC function [53 , 54] . Analysis of NK cell numbers , maturation ( DX5 , CD11b and CD27 expression ) and activation ( KLRG1 expression ) both in the steady state and during LCMV infection revealed no differences between groups ( S4 Fig . ) , which makes it not very likely that GITR signaling in NK cells plays an important role in the observed protection against LCMV chronicity in GITRL tg mice . Although we postulate that the protected phenotype of GITRL tg mice to LCMV infection is due to direct GITR-mediated costimulation on the virus-specific T cells , it could be that pre-existing differences between WT and GITRL tg mice prior to the infection also have an effect . As described previously , GITRL tg mice have more effector and regulatory CD4+ T cells in the steady state [33] , though this phenotype is age-dependent and not yet very pronounced in the young mice ( ~5 weeks old ) we use for LCMV infection . Although we did find somewhat increased numbers of Tregs in young GITRL tg mice prior to infection ( S2B Fig . ) , it is unlikely that they play an important role in the early phase of the infection , as Tregs lose their suppressive capacity upon exposure to type I IFNs [55] . Chronic LCMV infection has been shown to expand Tregs due to the expression of endogenous retroviral superantigens , but this only occurs in the late phase of the response [56] . We observed that GITRL tg mice had more Tregs than WT mice throughout the infection , though the kinetics was similar ( S2B Fig . ) . Besides , an increase in Tregs is associated with a decreased anti-viral immune response , which would not be in line with the increased protection we found in GITRL tg mice . Hence , it is most likely that GITR-mediated costimulation on Tregs does not have a major influence on T cell response to LCMV . Whether it does play a role in decreasing immunopathology in GITRL tg mice remains to be addressed . The role of costimulation in immunity against chronic LCMV infection has also been examined for other TNFR family members and is highly diverse . Although similar , each of these molecules also has its own characteristic impact on cell type and effector function . Signaling through CD27 during acute and chronic LCMV infection enhances IFNγ and TNFα production by CD4+ T cells , but this actually contributes to pathology by inducing disruption of splenic architecture early during infection , which interferes with viral clearance by delaying the generation of virus-specific antibodies [14 , 57] . Costimulation through OX40 is required for optimal antiviral cellular and humoral immunity against LCMV Clone 13 , but mice lacking OX40 had a much healthier appearance and lost significantly less weight than WT mice [58] . Interestingly , enhanced triggering through GITR boosted protective immunity to LCMV , and this was not accompanied by an expected increase in pathology , but rather by faster recovery of body weight and spleen cellularity and architecture . This would make GITR a very attractive target for boosting anti-viral immunity , as it would simultaneously prevent from tissue pathology . Several preclinical studies in humans have shown that GITR-targeted therapies are effective in increasing the size and functionality of T cell response against different tumors . Most of these studies have used either the agonist antibody DTA-1 or GITRL-Fc molecules , although novel approaches with DNA and DC vaccines expressing GITRL have also been reported ( reviewed in [18 , 19] ) . Interestingly , it has recently been shown that combined PD-1 blockade and GITR triggering can enhance anti-tumor immunity in murine cancer models , which can be further promoted with chemotherapeutic drugs [59] , thus highlighting the potency of GITR stimulation also in combination therapy . The results presented in our current study open possibilities for targeting GITR in the treatment of chronic viral infections . In mouse models of chronic Friend virus ( FV ) infection , DTA-1 therapy during the acute phase of the infection produced faster Th1 immune responses and reduction in viral loads and pathology [60] . Although less marked , there was also improved CD8+ T cell function and reduction in viral loads after a combined transfer of transgenic CD8+ T cells and DTA-1 therapy in the chronic phase of FV infection [61] . In summary , the observations from this and previous papers suggest that GITR-targeted therapies could be used , in combination with other approaches , to restore function in exhausted CD8+ T cells during chronic viral infection without boosting immunopathology .
GITRL tg mice were maintained on a C57BL/6 background and bred in the animal department of the Academic Medical Center ( Amsterdam , The Netherlands ) under specific pathogen-free conditions . GITRL tg mice or their WT littermates were infected at 4–6 wk of age , age- and sex-matched within experiments , and were handled in accordance with institutional and national guidelines . LCMV clone 13 was grown in BHK-21 cells and tittered on Vero cells ( both cell lines were kindly provided by Dr . E . John Wherry , University of Pennsylvania , USA ) , as previously described [62] . Mice were infected with 2×106 PFU , i . v . All mouse experiments were carried out in accordance with the Dutch Experiment on Animals Act and approved by the Animal Care and Use Committee of the University of Amsterdam ( Permit numbers: DSK100401 , DSK100044 , DSK39 and DSK101745 ) . Spleens were formalin fixed , dehydrated in 30% sucrose solution and frozen in tissue tek embedding compound ( Sakura Finetek , Torrance , CA ) . Sections of 5 μm were cut and stored at -20°C . Before staining sections were subjected to antigen retrieval with proteinase K ( Roche , IN , USA ) ( 3 min . 20 μg/ml in TE buffer pH 8 . 0 at RT ) and blocked with 5% BSA/PBS . Sections were stained with rat-anti-MOMA-1 hybridoma supernatant ( kind gift from Dr . Reina Mebius , VUMC , Amsterdam ) 1:10 O/N at 4°C . As secondary antibody Alexa Fluor 647 conjugated donkey-anti-rat IgG was used ( Jackson immunoresearch ) . Slides were blocked with 5% normal rat serum , 10 min RT and subsequently incubated with B220 Alexa Fluor 488 conjugated antibody ( eBioscience ) and Ter-119-biotinylated antibody ( eBioscience ) , 1h RT . Finally sections were incubated with streptavidin Alexa Fluor 555 ( Invitrogen ) . Hoechst was used as nuclear couterstain . Sections were mounted with Mowiol . Fluorescent images were obtained using a Zeiss Axio Examiner Z1 microscope . RNA was extracted using Trizol ( Invitrogen ) and complementary DNA was made with random hexamers and Superscript II reverse transcriptase ( Roche ) . Quantitative real-time polymerase chain reaction ( PCR ) was performed in duplicate with Express SYBR GreenER reagents ( Invitrogen ) on the StepOnePlus RT-PCR system ( Applied Biosystems ) , and data were normalized using HPRT as a reference gene . Primer sequences are available on request . To deplete CD4+ T cells before LCMV infection , mice were injected i . p . with 500 μg anti-CD4 antibody ( clone GK1 . 5 ) on days-3 and 4 post-infection . In all cases , CD4 T cell depletion was confirmed via flow cytometry . To quantify LCMV-specific antibodies , LCMV Clone 13 was used to coat 96-well Maxisorp ELISA plates ( Nunc ) overnight . Plates were blocked with 2% milk/PBS . Subsequently , serum isolated from the indicated mice was diluted 1/10 and then 3-fold serial dilutions were made . These dilutions were incubated on the LCMV-coated plates . Plates were subsequently incubated with a biotinylated donkey anti—mouse IgG antibody ( Jackson immunoresearch ) , followed by a streptavidin-alkaline phosphatase conjugate ( Jackson immunoresearch ) . p-Nitrophenyl phosphate ( Sigma ) was used as substrate . Optical density values were read using an ELISA plate reader ( GENios Plus , Tecan ) at 405nm and corrected at 550nm . The following mAbs from eBioscience were used: anti-CD44 ( IM7 ) , anti-CD4 ( RM4–5 ) , anti-CD8 ( 53–6 . 7 ) , anti-CD62L ( MEL-14 ) , anti-B220 ( RA3–6B2 ) , anti-PD-1 ( RMP1–30 ) , anti-CD127 ( A7R34 ) , anti-GL-7 ( GL-7 ) , anti-CD40L ( MR1 ) , anti-Eomes ( Dan11mag ) , anti-CD107α ( 1D4B ) and anti-CD107β ( ABL-93 ) . From BioLegend: anti-KLRG1 ( 2F1 ) , anti-CD3 ( 145–2C11 ) , anti-SLAM ( TC15–12F12 . 2 ) ; and from BD Biosciences: anti-Bcl6 ( K112–91 ) , anti-CXCR5 ( RF8B2 ) , anti-ICOS ( 7E . 17G9 ) , anti-FAS ( Jo2 ) , anti-CD138 ( 281–2 ) , anti-CD25 ( 3C7 ) , anti-IFN-γ ( XMG1 . 2 ) , anti-IL-2 ( JES6–5H4 ) , anti-TNF-α ( MP6-XT22 ) , anti-FoxP3 ( MF23 ) and anti-T-bet ( O4–46 ) . Biotin conjugates were visualized by streptavidin-PE-Cy7 or streptavidin-eFluor 450 ( eBioscience ) . Where possible , cells were stained in the presence of anti—CD16/CD32 block ( 2 . 4G2; kind gift from Louis Boon , Bioceros , Utrecht , The Netherlands ) and dead cells were excluded by staining with LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit ( Invitrogen ) . Lymphocytes were isolated from spleen , stained , and analyzed by flow cytometry . Virus-specific CD8+ or CD4+ T cells were examined with MHC class I or class II tetramers . MHC class I peptide tetramers for NP396 and GP33 peptides were made according to standard procedures [63] , while the MHC class I tetramer for GP276 and the MHC class II tetramer for GP66 and its control with CLIP peptide were obtained from the NIH Tetramer Core Facility ( Emory University , Atlanta , GA ) . For ICS , 2×106 splenocytes were cultured in the presence or absence of peptide ( 2 μg/ml ) and brefeldin A for 5 hr at 37C . Staining was carried out with the BD cytofix/cytoperm kit . Samples were collected by an LSR Fortessa or a Canto II flow cytometer ( BD ) and analyzed with FloJo software ( Tree Star ) . Mean values± SD are shown . Statistical analysis was performed using either 2-tailed Student t test , one-way or two-way ANOVA with GraphPad Prism 5 software . | The ability of the immune system to rapidly respond to a viral infection is a prerequisite for the survival of an individual . The immediate reaction of innate immune cells and the subsequent response of antigen-specific lymphocytes is usually effective for rapid neutralization and removal of the invading virus . Yet , such protective immune responses need to be well controlled , as they can cause severe tissue damage that may disable the host more than the infection itself . One way that has evolutionarily been proven effective to deal with this balancing act between protective immunity and prevention of immunopathology is to render virus-specific T cells “exhausted” when the virus cannot be eradicated and the host becomes chronically infected . Exhausted T cells progressively lose their ability to kill other cells and produce different cytokines . The benefit of this exhausted state of anti-viral immunity is that it induces less tissue damage , but the downside is obviously less efficient control over the viral infection . Many immunotherapeutic and vaccination strategies against chronic viral infections are currently dedicated to overcome the exhausted state of the virus-specific T cells and thereby clear the virus . However , the accompanying risk is an exaggerated immune response with overt immunopathology . Here we describe in a mouse model that enhanced triggering through the costimulatory molecule GITR on T cells is able to provide protection upon viral infection and clear an otherwise persistent virus , but importantly without the development of collateral damage due to immunopathology . We show that GITR-mediated costimulation enhances a protective CD8 T cell response , for which CD4 T cell help is required . Our study provides new insights in how a particular costimulatory pathway can be utilized to boost anti-viral immunity , which is highly relevant for the development of safe immunotherapeutic strategies against chronic viral infections in humans . | [
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| 2015 | Enhanced CD8 T Cell Responses through GITR-Mediated Costimulation Resolve Chronic Viral Infection |
Spatial modelling of STH and schistosomiasis epidemiology is now commonplace . Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however , limited attention has been given to propagated uncertainties , their interpretation , and consequences for the mapped values . Using currently published literature on the spatial epidemiology of helminth infections we identified: ( 1 ) the main uncertainty sources , their definition and quantification and ( 2 ) how uncertainty is informative for STH programme managers and scientists working in this domain . We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis ( PRISMA ) protocol . We searched Web of Knowledge and PubMed using a combination of uncertainty , geographic and disease terms . A total of 73 papers fulfilled the inclusion criteria for the systematic review . Only 9% of the studies did not address any element of uncertainty , while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it . In addition , 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable ( morbidity indicator ) . Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it . Uncertainty was mostly defined as precision , and quantified using credible intervals by means of Bayesian approaches . None of the studies considered adequately all sources of uncertainties . We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework . Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified . Finally , more attention should be given to mapping and interpreting uncertainty , since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration .
Helminth infections from as soil-transmitted helminths ( STHs ) and schistosomes are among the most prevalent neglected tropical diseases ( NTDs ) affecting human populations living in countries where clean water , sanitation , and hygiene ( WASH ) are limited . STHs and schistosomes , affect more than 1 . 7 billion and 252 million [1 , 2] people worldwide respectively . The majority of these infections are concentrated in sub-Saharan [3 , 4] and North Africa , Asia , and central and Andean regions of Latin America [1] . STH and schistosome infections influence directly the nutrition status , educational development , individual productivity , physical and mental development in human populations [5] . The World Health Organization ( WHO ) , the World Bank and other agencies defined control and elimination targets in the poorest populations [6] . Although the global burden of NTDs declined by 27% from 1990 to 2010 in upper-middle income countries [6] , low and lower middle income countries still need attention . Besides , according to the Global Burden of Disease Study 2010 [1] , STHs due to intestinal nematode infections , and schistosomiasis , caused the largest number of cases reported in 2010 . In order to improve population health and accomplish WHO targets , the 2012 London declaration for Neglected Tropical Diseases and the 2013 World Health Assembly resolution highlighted the importance of mass drug administration ( MDA ) with benzimidazoles [7 , 8] to communities at risk . To identify communities at risk , indirect indicators of morbidity such as prevalence of infection and intensity of infection can be measured via surveying at-risk populations [9] . Communities at risk can then be categorized into disease prevalence classes ( e . g . low , moderate , high ) based on WHO guidelines [10] . In the absence of empirical data on infection at unsampled communities , one way to identify communities at risk is to study the role of the environment ( physical and biological ) to characterize potential habitats of parasites and intermediate hosts , as well as to understand the ecology and epidemiology of infections . Statistical modelling of the spatial distribution of helminth infections provides empirical relationships between infections and risk factors , which can then be used to predict the level of infection prevalence at unsampled locations [9 , 11–13] . In the statistical model , prevalence or another morbidity indicator , is treated as the response variable . Although statistical modelling of helminth infections is useful to effectively and efficiently manage surveillance , control and prevention of the infection [14] , the mapped outputs should be interpreted with care because these can be weakened by several sources of uncertain information [15] . Sources of uncertainty that need to be accounted for in the modelling process include differences in variable selection criteria , statistical methods used , selected spatial and temporal scales of analysis [16] , sampling design , sensitivity and specificity of diagnostic techniques as well as the quality of the spatial data used . Uncertainty has been the subject of extensive discussion in Geographic Information Science ( GIScience ) [17–32] and related subjects [33–43] . Uncertainty may relate to ( 1 ) a state of mind and our perception of the world or ( 2 ) statements about the world or observations on natural phenomena [17 , 18 , 22 , 32] and is relevant in terms of specifications and representations , measurement and the transformations , processing and modelling performed on raw data to turn them into usable information [17 , 22] . In order to address uncertainty , a more formal approach is required [17 , 18] . Here we conceptualize uncertainty as imperfection , which is further categorized as inaccuracy or imprecision . Imprecision may arise because the phenomenon is vague ( i . e . , the phenomenon is not clearly defined ) , ambiguous ( i . e . , different definitions can be applied to the phenomenon ) [23 , 32] or due to the granularity of the observation [17] . In the spatial setting granularity relates to the resolution or spatial support ( area or volume ) of the observation and affects our ability to discern objects [17 , 44] . Imprecision may also arise due to natural variability , measurement error and model variability and may be described statistically , for example by the variance or standard deviation [32 , 45 , 46] . In this context , model variability may arise due to uncertain data , stochastic processes within the model or variability between competing models . The reader may be familiar with the narrow statistical definition of precision as the inverse of the variance [47] , whereas the imprecision that is applied here encompasses a wider set of concepts [17 , 18] . Put another way , in this conceptualization , variance is not the only measure of precision . Accuracy is a measure of closeness between the observed phenomenon and reference observations , considered representative of the reality [17 , 45 , 48] . Accuracy assessment is often referred to as validation [20 , 49] . Common measures of accuracy include the root mean square error ( RMSE ) for continuous data [45 , 48] , the overall accuracy ( OA ) for categorical data [27 , 28 , 50] and the area under the receiver operator characteristic curve ( AUC ) for binary data [45] . Bias relates to accuracy and refers to systematic differences between the observations and reference data . Accounting for uncertainty in disease mapping is important for the assessment of the applicability and validity of the predicted morbidity indicators [15] . Furthermore , it will allow a complete risk assessment and the identification of potential sources of bias [51] . Ignoring uncertainty can lead to incorrect predictions , thus wrong estimates of disease burden , which can result in misleading public health advocacy and decisions regarding disease control . Consideration of information about uncertainty is critical for control programs , health care workers , populations at risk , and other involved users who attempt to reduce prevalence and incidence of helminth infections across the affected areas [51 , 52] . For example , control programs need accurate information to decide about drug distribution strategies and the frequency of treatment of the target populations . Decision makers can use information about uncertainty to target more resources ( e . g . , data acquisition ) or to focus investigative efforts on low or highly uncertain risk areas [53 , 54] . This paper is a systematic review that aims at the identification of the gaps in knowledge of the different components of uncertainty associated with mapping and modelling helminth infections . It also aims at providing a basis for a complete uncertainty communication , by evaluating the impact of uncertainty on the predicted morbidity indicators . This paper starts by investigating how uncertainty is informative for decision makers , public health scientists and the affected community . It then identifies main sources of uncertainty in helminth infection mapping studies , and how uncertainties have been defined and quantified . Regarding the sources of uncertainty , their definition and quantification , the focus will be put on sources relating to Earth Observation . The significance of this paper is that it contributes to inform control programs and health workers about the importance of uncertainty in mapping and modeling helminth infections , by putting special attention on relevant sources of uncertainty , and analyzing their real influence on the predicted morbidity indicator values used to guide mass drug administration strategies and their cost effectiveness .
An online search was performed using two search engines , the Web of Knowledge ( Core collection and MEDLINE ) and PubMed . Only articles published in English were considered . The date range was 1 January 1980 to 24 October 2016 . The search strategy aimed at the identification of primary research studies that have looked into establishing the geographical limits of STH and schistosomiasis present only in humans; therefore the search strategy combined variations of three terms: spatial , helminth infection , and uncertainty terms . The full list of terms used in the systematic review is shown in Table 1 . Six searches were performed by combining the three terms in each search engine , using the keywords described in Table 2 . After removing duplicates , the abstracts of 139 papers were read . Papers written in languages other than English ( 11 papers ) were automatically excluded . Review papers ( 14 papers ) were also excluded . Further criteria were then applied to select the final papers to read , but also to make the reading process more efficient . The inclusion criteria considered were ( i ) the presence of the three spatial , uncertainty and helminth infection search terms in the abstracts and ( ii ) also articles related to only STH and schistosomiasis helminth infections . The papers were classified into schistosomiasis and soil transmitted helminth studies . The selection of the papers , data acquisition and analysis was undertaken by the first author . The PRISMA flow diagram is given in Fig 1 . Data collection from each paper focused on addressing three main research questions . ( 1 ) How is uncertainty informative for decision making in the public health context ? ( 2 ) What are the different uncertainty sources reported in the reviewed studies ? ( 3 ) How were uncertainty and its sources defined and quantified in the studies ? Papers addressing these questions were enumerated . Fig 2 illustrates the relevant three uncertainty stages that drive the final mapping and modelling of STH and schistosomiasis infections . The first stage ( pink box ) describes the origin of uncertainty coming from data sources , including uncertainties in the response variable and covariates . The second stage ( orange box ) shows how uncertainty from the pink box propagates through the predictive model ( green box ) . The green box incorporates uncertainties derived from the selection of the predictive model , considering that there could be different ways to model the same helminth infection . It also includes uncertainties in model structure , which refers to all possible limitations and assumptions in the selected model , such as: the lack of understanding about the interaction between the environment , helminth infections and human populations , as well as the assumptions of stationarity and spatial isotropy [9] . Finally , the green box includes uncertainties in the methods used to estimate the model parameters . The third stage ( yellow box ) , shows how uncertainty in the predicted morbidity indicator is addressed , firstly in policy and decision making settings and secondly in a scientific setting . This stage aims to understand how information on uncertainty is used practically and how is it defined and quantified . The blue box represents different elements of data quality that relate to the sources of information ( pink box ) , and the predicted morbidity indicators ( yellow box ) , which due to its wide field of study and importance was separated into a different box .
The total number of papers found in each search is shown in Table 5 . Table 6 shows the resulting number of read and discarded papers presented per infection . In total 73 papers were selected , from which 14 were review papers . While the identified review papers were not included in this review we examined their reference lists; this yielded another 14 valuable references that had not been identified by our original search . Finally 73 primary research papers were included in our systematic review . Our results demonstrate that the annual number of publications on mapping and modelling STH and schistosome infections was constant until the year 2007 and steadily increased since then; since 2008 a total of 49 ( 67% of the total ) papers were published ( Fig 3 ) .
Most of the studies used information on uncertainty to guide MDA campaigns and evaluate their cost effectiveness . Information on uncertainty was also used to evaluate the role of risk factors in mapping helminth infections . Nevertheless , prevention strategies , improvements in sampling design , and mapping of uncertainty have not yet been addressed [113–116] . We advise to use information on uncertainty not only to inform about MDA campaigns , but also to inform about prevention strategies such as improving sanitation and hygiene education [117] or delineating potential transmission sites [116] . Transmission control is important for its public health relevance , since potential disease transmission sites could guide direct intervention measures at the place of infection [62 , 116] . Likewise , mapping of uncertainty is also recommended , since it is known to be an important tool for public health decision making , especially to determine the geographical distribution of areas for which information is lacking [112] . Mapping could be used as a tool to improve the sampling strategy and modelling efforts . Maps of uncertainty could also support communication of uncertainty to the affected communities . A complete exploration and judgement of uncertainty information would enhance the assessment of the risk of getting these infections , and would allow to understand potential impacts on human health [51] . While most studies identified and discussed different sources of uncertainty , this was mainly limited to a qualitative discussion , rather than a quantitative one [118] ( Table 11 ) . For instance , 38 ( 52% ) papers highlighted qualitatively the importance of sampling design in mapping helminth infections , but only two studies ( 3% ) have quantified their possible effects on the accuracy of the predicted morbidity indicator . An example is given by Clements et al [119] , where uncertainties in the predictions were used to identify areas requiring further data collection before programme implementation . The lack of a quantitative assessment limits the utility of the findings in both policy/decision making setting and a scientific setting [51 , 118 , 120 , 121] . Communication of uncertainty will never be complete without an extensive quantification of uncertainties in all possible information sources [51 , 120 , 122] , where model assumptions , selection of covariates and acquisition of survey data are clearly explained , either within the publication or as supplementary information . Fig 4 shows the three uncertainty stages previously described in Fig 2 , where these stages encompass specific uncertainty components , which need to be considered for a complete uncertainty communication . Each of these components is analyzed in the next sections . Acknowledging and incorporating uncertainty in mapping and modelling helminth infections is a step-by-step process , which should be considered formally when developing geographical models of helminth infection . Geographical models aim at informing , not only about MDA campaigns and their cost-effectiveness , but also prevention strategies , where it is necessary to define transmission areas and plan and guide hygiene education and infrastructure programs in water sanitation and hygiene . A quantitative and qualitative analysis of uncertainty is necessary for a complete assessment of risk , to understand potential impacts on human health , and to allow a complete uncertainty communication to public health managers . Five components of uncertainty analysis were recognized: ( 1 ) uncertainty in the response variable , ( 2 ) uncertainty in the covariates , ( 3 ) uncertainty in the relationship between them , ( 4 ) uncertainty in the predictive model , and ( 5 ) the propagated uncertainty on the results . Our conclusions are shown diagrammatically in Fig 5 , which aims at providing a framework for a full uncertainty evaluation when undertaking spatial modeling of helminth infections for policy formulation . Uncertainty analysis should start by identifying possible sources of uncertainty in the studies and categorize them such that at least the most important ones can be incorporated into the predictive model . Sampling design and EO data have been acknowledged as the major sources of uncertainty and should be given primary attention in the modelling process . In particular , sampling design , diagnosis , selection of significant risk factors , and selection of an adequate spatial support of analysis . Next , uncertainties in the response variable and covariates should be quantified and incorporated into the model . Methods used to define the relationship between covariates and response variables should also be documented , as well as the selection of the predictive model and its limitations . Finally , uncertainties in the parameters and response variables should be quantified , and uncertainty mapping should be performed as a valuable element for uncertainty communication and policy formulation . | In recent years spatial modelling studies of schistosome and soil-transmitted helminth infections have become commonplace; however there is no standard framework for uncertainty evaluation and reporting . In this study we aim to identify faults in existing studies and propose a framework for evaluation and reporting . We conducted a systematic review of the literature to identify the gaps in knowledge in relation to how uncertainty is dealt with in existing studies addressing the spatial modelling of helminth infections . It was found that none of the studies considered adequately all sources of uncertainty . Uncertainty in the response variables and covariates should be incorporated into the modelling framework . More attention should be given to mapping and interpreting uncertainty , and to quantify the different sources of uncertainty present in the observed covariates ( environmental variables ) , measured response variable ( morbidity indicators ) , used model and uncertainty representation and interpretation of the predicted morbidity indicators . | [
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| 2016 | Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence |
Overexpression of miRNA , miR-24 , in mouse hematopoietic progenitors increases monocytic/ granulocytic differentiation and inhibits B cell development . To determine if endogenous miR-24 is required for hematopoiesis , we antagonized miR-24 in mouse embryonic stem cells ( ESCs ) and performed in vitro differentiations . Suppression of miR-24 resulted in an inability to produce blood and hematopoietic progenitors ( HPCs ) from ESCs . The phenotype is not a general defect in mesoderm production since we observe production of nascent mesoderm as well as mesoderm derived cardiac muscle and endothelial cells . Results from blast colony forming cell ( BL-CFC ) assays demonstrate that miR-24 is not required for generation of the hemangioblast , the mesoderm progenitor that gives rise to blood and endothelial cells . However , expression of the transcription factors Runx1 and Scl is greatly reduced , suggesting an impaired ability of the hemangioblast to differentiate . Lastly , we observed that known miR-24 target , Trib3 , is upregulated in the miR-24 antagonized embryoid bodies ( EBs ) . Overexpression of Trib3 alone in ESCs was able to decrease HPC production , though not as great as seen with miR-24 knockdown . These results demonstrate an essential role for miR-24 in the hematopoietic differentiation of ESCs . Although many miRNAs have been implicated in regulation of hematopoiesis , this is the first miRNA observed to be required for the specification of mammalian blood progenitors from early mesoderm .
MicroRNAs ( miRNAs ) are small ( ~22 nucleotide ) RNA molecules that regulate gene expression post-transcriptionally . They are implicated in important cellular processes such as apoptosis , proliferation , and differentiation [1] . Work from many laboratories , including our own , shows that miRNAs regulate hematopoietic progenitor cell fate decisions and immune cell function [2–4] . However , the role of miRNAs in regulating the earliest hematopoietic stem and progenitor cell development is less characterized . Additionally , a role for miRNAs has not been described for directing the development of the mammalian hemangioblast or hemogenic endothelium , the early mesoderm that gives rise to primitive and definitive hematopoietic cells [5 , 6] . Studies of mouse embryos and embryonic stem cells ( ESCs ) have defined the ontogeny of mammalian embryonic hematopoietic cells [7] . During embryogenesis primitive hematopoietic progenitor cells ( HPCs ) are produced first in the yolk sac , and then in the embryo proper . Definitive hematopoiesis begins in the aorta-gonad-mesenephros ( AGM ) region off the embryo , and later switches to the fetal liver . These tissues arise from a subset of mesoderm , the lateral plate mesoderm . In vertebrates , the initial hematopoietic and endothelial lineages are generated simultaneously in the same region of the embryo [8 , 9] . This suggests that these lineages arise from a common mesodermal derived progenitor termed the hemangioblast . The first direct evidence demonstrating the existence of the hypothesized hemangioblast came from work with ESCs . Keller and colleagues identified a progenitor from ESC derived embryoid bodies ( EBs ) that formed a blast colony in methylcellulose , which contained clonal blood and endothelial cells [10] . They termed this progenitor the blast colony-forming cell ( BL-CFC ) , which was proposed to be the in vitro equivalent of the hemangioblast . These progenitors were enriched in the EB population of cells that coexpresses the mesoderm specific transcription factor T ( Brachyury ) , and the tyrosine kinase receptor Flk1 [11] . This same group later demonstrated the transient existence of BL-CFCs in the gastrulating mouse embryo within the primitive streak [12] . In the aortic-gonad-mesenephros ( AGM ) region of the mouse embryo , blood has been shown to arise from differentiated endothelial cells termed the hemogenic endothelium , which appears independent of a hemangioblast cell [13 , 14] . During differentiation of ES derived BL-CFCs , it has also been observed that BL-CFC cells form a hemogenic endothelium intermediate during the development of HPCs [5] . The ESC differentiation system has been valuable for dissecting the molecular regulation of the development of mesoderm into HPCs . Extracellular signals including BMPs , FGFs , VEGF , Notch ligands , and Wnts [15–18] regulate a complex network of transcription factors to direct embryonic hematopoietic development . The Ets transcription factor Etv2 is essentially for hemangioblast specification in zebrafish [19 , 20] . A direct connection to mammalian hemangioblast development has not been shown , but Etv2 is necessary for development of embryonic blood progenitors and vasculature as demonstrated by mouse gene targeting experiments [21] . Etv2 expression is correlated with markers of hemangioblast and hemogenic endothelium in differentiating ESCs , and genetically acts upstream of the transcription factor Scl in blood development [22 , 23] . Scl is required for the differentiation of BL-CFC cells into hemogenic endothelium [5 , 24] . After formation of hemogenic endothelium , another transcription factor , Runx1 , is required for the production of definitive HPCs [5 , 25] . These signaling pathways , and transcription factors are known to regulate and/or be regulated by miRNAs , so it would be surprising if miRNAs did not have a role in the production of HPCs for mesoderm [26–29] . Additionally , global knockdown of miRNAs in Xenopus embryos results in an ablation of hemangioblast development [30] . We recently identified miR-24 as a miRNA that regulates the development of adult hematopoietic progenitor cells [3] . MiR-24 is produced from two distinct mammalian genes mirn23a , and mirn23b . The mirn23a gene codes for three miRNAs , miR-23a , miR-27a , and miR-24–2 , which is expressed as a pri-miRNA from its own independent transcription unit . Mirn23b codes for miRs-23b , -27b , and 24–1 , and is embedded in an intron of the aminopeptidase O ( AMPO ) gene . The mature miR-24 miRNAs ( miRs-24–1 , -24–2 ) produced from both clusters are identical in sequence whereas the mature miRs-23a/b and-27a/b differ by one nucleotide at the 3 prime ends ( outside of the seed sequence which is responsible for target selection ) . The mirn23a miRNAs are enriched in hematopoietic cells compared to mirn23b miRNAs [3] . When we retrovirally expressed the miRs-23a , 27a , and 24 in hematopoietic progenitors we observed that these miRNAs inhibited B cell development and potentiated the development of monocytes and granulocytes [3] . Additionally , we observed that miR-24 is necessary and sufficient to generate this phenotype . To determine if miR-24 is required for proper monocyte and granulocyte development , we antagonized miR-24 in murine embryonic stem cells ( ESCs ) by infecting them with a lentivirus encoding an shRNA targeting miR-24 . Stable knockdown clones were differentiated into embryoid bodies ( EBs ) . To our surprise , hematopoiesis was dramatically inhibited in miR-24 knockdown ( KD ) EBs . In this report we examine the requirement for miR-24 in blood development from ESCs . In addition , we interrogated Trib3 as a critical target for miR-24 to repress during the development of HPCs . Previously; Trib3 was identified as a direct miR-24 target in vascular smooth muscle cells [31] . Trib3 modulates BMP/ Smad signaling through inhibition of the E3 ubiquitin ligase Smurf1 [32] , that negatively regulates Smad1 and Smad 5 . MiR-24 targeting of Trib3 potentially links it to known pathways regulating embryonic hematopoiesis [33–36] .
Previously , we demonstrated that enforced expression of miR-24 in hematopoietic progenitors promoted myeloid ( monocytes and granulocytes ) development [3] . To determine if miR-24 is required for the development of myeloid cells , we infected mouse ESCs with a lentivirus expressing an shRNA that binds and inhibits miR-24 ( miArrest-24 , Genecopoeia , Rockville , MD ) . For controls , we generated ESCs expressing a scrambled non-targeting shRNA . The miR-24 shRNA clones had reduced expression of miR-24 that persisted for at least 6 days of EB differentiation as determined by quantitative reverse transcriptase PCR ( Q-RT-PCR ) ( S1A , B Fig . ) . Decreased expression of miR-24 may underestimate the reduction in miR-24 as we may detect miR-24 bound inactive to the shRNA . MiR-24 knockdown did not affect the expression of clustered miRNAs , miRs-23a and 27a ( S1C Fig . ) . When ESC lines were differentiated , we observed that , in contrast to wildtype and scrambled control shRNA expressing ESCs ( Fig . 1A ) ; the miR-24 knockdown clones did not generate EBs with hemoglobinized cells ( Fig . 1B ) . This suggested that blood was not being made and this was confirmed by a dramatic reduction in the expression of hematopoietic transcription factors Gata1 ( P<0 . 0005 ) and Sfpi1 ( P<0 . 0008 ) in miR-24 KD d6 EBs compared to wildtype and scrambled control derived EBs ( Fig . 1C ) . We also performed hematopoietic colony assays with cells isolated from d9 EBs as previously described [37] . On average we observed less than 1 hematopoietic colony generated from 50 miR-24 knockdown EBs ( S2 Fig . ) . To support the conclusion that the hematopoietic defect is due specifically to knockdown of miR-24 , we also generated miR-24 knockdown ESCs with an independent shRNA expressing lentiviral vector ( miRZIP , Systems Biosciences , Mountain View , CA ) . A similar defect in blood production in EBs was observed using this alternative method of targeting miR-24 ( S3A , S3B Figs . ) . We observed that miR-24 was necessary for blood development [3] . To determine if this requirement was early or late in embryonic hematopoiesis , we performed flow cytometry on cells derived from d6 EBs to determine if HPCs were being made in the absence of miR-24 . EBs generate HPCs that can be identified by the cell surface expression of CD41 [38] . There were almost no HPCs derived from miR-24 KD clones ( Figs . 2A , S4 ) . A similar defect in HPC production was observed from ESCs with miR-24 targeted by the miRZIP lentiviral vector ( S3C Fig . ) . Consistent with this result , we observed significantly decreased expression of the transcription factors Scl and Runx1 between wildtype/ scrambled controls and miR-24 KD clones in d4 EBs with respective P values of less than 0 . 007 , and 0 . 002 , as well as at d6 with P values of less than 0 . 003 , and 0 . 008 ( Fig . 2B , C ) . These two factors are expressed in HPCs and function downstream of the hemangioblast [24 , 25] . The defect in hematopoiesis could be due to a global defect in development . To examine if early germ layer tissue was being formed from miR-24 KD ESCs , 2 scrambled control , and 2 miR-24 KD clones were differentiated and RNA harvested at d3 and d4 . Q-RT-PCR analyzed expression of Pax6 ( Ectoderm ) , FoxA2 ( Endoderm ) , and T ( Brachyury , mesoderm ) . No significant differences ( P>0 . 05 ) in the expression of these genes were observed when comparing the average values obtained from scrambled and miR-24 KD clones ( Fig . 3A ) . Hematopoietic cells in the developing EB and embryos are derived from mesoderm tissue . Mesoderm gives rise to paraxial and lateral plate mesoderm with the later giving rise to hematopoietic cells . To determine if there was a defect in nascent mesoderm differentiation we analyzed expression of the genes Twist ( lateral plate ) and Tbx6 ( paraxial ) . We did not observe a significant effect of miR-24 knockdown on Tbx6 expression , though expression was variable within control and 24 KD clones . MiR-24 antagonism did lead to a significant decrease in Twist expression at both d3 ( P<0 . 002 ) and d4 ( P< 10-8 ) ( Fig . 3B ) . This suggested a reduction in lateral plate mesoderm , consistent with the observed decrease in hematopoiesis . To further investigate this , we performed flow cytometry analysis of d4 EBs to determine if early mesoderm populations were being made in miR-24 KD clones . Lateral plate mesoderm is identified by the cell surface expression of FLK1 , and the lack of expression of PDGFRα . In contrast , paraxial mesoderm is identified by expression of PDGFRα and lack of expression of FLK1 [39 , 40] . Nascent mesoderm coexpresses both FLK1 , and PDGFRα , and can give rise to either lateral plate or paraxial mesoderm . By flow cytometry , we did not observe a defect in mesoderm production , as lateral plate ( FLK1+PDGFRα- ) and paraxial mesoderm ( FLK1-PDGFRα+ ) were observed in miR-24 KD EBs ( Fig . 3C ) . Besides blood , lateral plate mesoderm is responsible for the production of cardiac muscle and blood vessels ( endothelial cells ) . To examine production of blood vessels we performed vascular sprout assays [41] . Forty d6 miR24 KD EBs and control-scrambled shRNA EBs were plated onto collagen-coated plates in media containing angiogenic cytokines . Sprout formation was assessed 6 days later ( total 12 days of culture ) . EBs were scored in blinded fashion according to 4 standard classes based on vascular sprout formation: I- no sprout formation , II- few sprouts , III- many sprouts but no network , IV- many sprouts with network [41] . In the development of class 3 and 4 sprouts , we saw large variability between the two miR-24 KD clones examined ( Fig . 4A ) . However there was a slight decrease in the generation of class 4 sprouts between miR-24 KD and control ESC clones , which was statistically significant ( P<0 . 002 ) . The sprouts on individual EBs appeared morphological similar between control and MiR-24 KD EBs ( Fig . 4B ) . We also analyzed expression of Pecam1 ( CD31 ) to evaluate blood vessel/ endothelial cell development . Pecam1 expression was evaluated in d4 , d8 , and d12 EBs . Initially Pecam1 expression was decreased between miR-24 KD and control EBs at d4 ( P<0 . 0002 ) ( Fig . 4C ) . However by d8 and d12 , there are no longer any significant differences in Pecam1 expression ( P>0 . 05 ) . We also examined the expression of endothelial gene Tie2 at d8 and d12 of EB differentiation . There was no significant difference in Tie2 at d8 , however there was a small increase in Tie2 detected in the miR-24KD clones at d12 ( P<0 . 0007 ) ( Fig . 4D ) . To assay cardiac development , we generated EBs by hanging drop and then replated individual EBs into single wells of a 96 well plate . After 5d cardiac muscle differentiation was evaluated by determining the percentage of wells positive for contracting cells . We did not observe a significant difference in the frequency of wells positive for beating EBs in the miR-24 KD cultures compared to control cultures ( Fig . 5A ) . We also assayed expression of cardiac muscle genes Nkx2 . 5 , and Mef2c between control EBs ( wildtype and Scrambled ) and miR-24 KD clones . Our analysis did not demonstrate any significant differences in the expression of these two genes ( Fig . 5B , C ) . Mef2c expression does appear to be decreased if we just compare the miR-24 KD clones to the scrambled control clone . Since there was not a general defect in mesoderm production , we further investigated when in HPC development miR-24 is required . Although lateral pate mesoderm is diminished as evaluated by Twist expression , it does develop as observed by flow cytometry ( FLK1/PDGFRα ) and assaying cardiac and vascular sprout development . We performed BL-CFC assays with parental RW4 cells , scrambled shRNA clones and miR-24 shRNA clones . ESCs were differentiated into EBs for either 2 . 75d or 3 . 0d , and dissociated into single cell suspensions . The cells were then cultured in methylcellulose media for 4 days and BL-CFC colonies counted ( Fig . 6 ) [42 , 43] . Compared to RW4 cells we observed that scrambled and miR-24 KD clones had a reduced ability to make BL-CFCs . There was no difference in the production of BL-CFCs from scrambled and miR-24 shRNA expressing clones at d2 . 75 ( Fig . 6A ) . From d3 EBs we observed a slight but significant increase ( P<0 . 0007 ) in BL-CFCs obtained from miR-24 KD clones compared to scrambled control clones ( Fig . 6B ) . However the number of BL-CFCs obtained from the miR-24 KD cells was lower than the numbers observed with wildtype cells . To determine if the BL-CFC colonies were identical between controls and miR-24 KD cultures , we examined gene expression in the BL-CFC colonies . Twenty colonies from each culture were picked for generation of cDNA , and then individual gene expression was analyzed by Q-RT-PCR . Consistent with the EB results ( Figs . 1C , 2B , 2C ) , there is a dramatic decrease in the expression of Gata1 , Runx1 , and Scl in the miR-24 KD BL-CFCs compared to control scrambled shRNA expressing clones ( Fig . 6D ) . However endothelial genes Cdh5 ( Ve-Cadherin ) , and Pecam1 were expressed similarly between control and miR-24 KD cultures . This suggests that there is a defect in the development of hemogenic endothelium , or the differentiation of hemogenic endothelium into blood . We attempted to determine if hemogenic endothelium was being produced by culturing Flk1+ cells , and analyzing by flow cytometry for the markers Tie2 , and cKit according to the methods of Lancrin et al . [5]but were unable to generate sufficient number of differentiated cells to make any conclusions . We also analyzed the expression of the transcription factor Etv2 to evaluate early hematopoietic progenitor generation . RNA was isolated from d3 , d4 , and d5 EBs . As previously observed Etv2 expression initially increases with wildtype EB differentiation and then decreases [21] . We observed a significant decrease in Etv2 expression between control clones and the miR-24KD clones at all time points ( Fig . 6C ) . However Etv2 was reduced approximately 50% which was not as dramatic a reduction as observed with the downstream factor Scl ( Fig . 2B ) . Expression of the gene encoding pre-miR-24–2 enhances hematopoietic development of d3 EB derived cells . The BL-CFC ( hemangioblast ) is present in the T positive FLK1+ fraction of the differentiating EBs [11] . From d4 EBs generated from an ESC line with GFP knocked-in to the T locus we isolated T- ( GFP- ) /Flk1- , T+ ( GFP+ ) /Flk1- , and T+ ( GFP+ ) /Flk1+ cells by FACs ( Figs . 7A , S5 ) . RNA was prepared from the isolated cell population and the validity of the sort was examined by assaying the expression of genes known to be associated with each fraction ( S5 Fig . ) [11] . Q-RT-PCR was performed with RNA extracted from the isolated fractions to examine the relative levels of mature mirn23a/ mirn23b miRNAs: miR-24 , mir-23a , miR23b , miR-27a , and miR-27b ( Fig . 7B , 7C ) . MiR-24 was enriched in the mesoderm T+ ( GFP+ ) /Flk1- fraction . MiR-24 decreased in the T+ ( GFP+ ) /Flk1+ cells , the least expression was observed in the double negative fraction . A similar pattern was seen with miRs-23a , and 27a . However expression of miR-23b and miR-27b was highest in the double negative fraction with lowest levels expressed in the T+ ( GFP+ ) /Flk1+ fraction . Since endogenous mirn23a gene expression increased in mesoderm tissue fated to become hematopoietic tissue , we overexpressed it via retrovirus in differentiating ESCs to determine if exogenous expression could enhance hematopoietic development . We hypothesized that in order for mirn23a to enhance hematopoietic development that it needs to be expressed in the newly formed mesoderm . To express mirn23a after early mesoderm was formed , we first differentiated wildtype ESCs into EBs for 3d without viral infection . EBs were then disaggregated into single cell suspension and infected with control ( MSCV ) retrovirus or the mirn23a-expressing virus . EBs were reformed by hanging drop and allowed to differentiate for additional 5 days . Contribution of the infected cells ( GFP+ ) to the CD41+ HPC population was then assayed by flow cytometry . Expressing the mirn23a cluster at a later stage in development significantly enhanced the development of CD41+ HPCs ( Fig . 7D ) . Three independent transductions/ differentiations were performed demonstrating that mirn23a expression increased HPCs production ( P<0 . 008 ) ( Fig . 7E ) . Trib3 is a validated target of miR-24 in vascular smooth muscle cells [31] . Misregulation of Trib3 has the potential to influence hematopoietic differentiation through the BMP4/ Smad Pathway [32–34 , 36] . To determine if miR-24 targeting of Trib3 plays a role in the early hematopoiesis , we examined expression of Trib3 in d4 miR-24 KD EBs . Consistent with Trib3 being a miR-24 target we observed a 2 to 3 fold increase in Trib3 mRNA in miR-24 KD EBs compared to controls ( Fig . 8A , P<0 . 00002 ) . Additionally , Trib3 has the opposite expression pattern in d4 fractionated EBs compared to miR-24 ( Fig . 8B ) . Trib3 is expressed highest in the non-mesoderm fraction , whereas miR-24 is more highly expressed in the T+ mesoderm fractions . To test whether this overexpression of Trib3 contributes to the block in hematopoietic development observed in miR-24 KD EBs , we expressed Trib3 in wildtype ESCs at the onset of differentiation , and assayed HPC development 6d later . Trib3 was expressed in ESCs via retroviral transduction of cells freshly withdrawn from LIF . Infected cells were identified by co-expression of GFP . HPC generation as evaluated by CD41 expression was reduced almost 2-fold by expression of Trib3 compared to ESCs infected with control virus ( P<0 . 001 , Fig . 8C , 8D ) . A similar inhibition of EB hematopoiesis was observed if we infected d3 EBs with Trib3 retrovirus , and then examined the CD41+ population after 5 additional days of EB culture ( S6 Fig . ) . Additionally if we knocked down Trib3 expression in ESCs with lentiviral delivered shRNA , we observed the opposite effect on hematopoiesis . EBs derived from Trib3 shRNA-expressing ESCs had an increased production of CD41+ cells , compared to EBs derived from ESCs expressing a non-targeting shRNA ( S7 Fig . ) .
Using two different lentiviral vectors delivering distinct shRNAs targeting miR-24 , we observed that miR-24 is required for hematopoietic development from ESCs . The absence of hemoglobinized cells and greatly reduced expression of hematopoietic transcription factors Sfpi1 and Gata1 indicate that blood development is greatly impaired in EBs generated from miR-24 KD ESCs . Flow cytometry revealed that miR-24 is required early in development as we observed a reduction in the number of CD41+ HPCs developing within EBs when miR-24 is antagonized . Consistent with the flow cytometry , we detect reduced expression of transcription factors essential for early hematopoiesis: Scl , and Runx1 . The defect in hematopoiesis is not due to a general inability of the miR-24 ESCs to differentiate . ESCs with antagonized mR-24 express endoderm , ectoderm , and mesoderm genes normally compared to control ESCs . Cell surface expression of FLK1 , and PDGFRα indicates that lateral plate , and paraxial mesoderm is made in knockdown cells . However decreased expression of Twist1 in miR-24 KD EBs suggests that there is a reduction in lateral plate mesoderm . This may be due to the decreased production of the lateral plate derived blood cells . Lateral plate mesoderm also gives rise to vasculature and cardiac tissue . We observed no deficits in the generation of cardiac tissue . Vascular development may be slightly impaired as we observe an early decrease in expression of the endothelial gene Pecam1 and a slight decrease in class 4 sprouts in an EB vascular sprout assay . Due to the decreased expression of Scl , it was surprising that we did not observe enhanced cardiac muscle development . Scl has been demonstrated to be a repressor of early cardiac muscle development [44 , 45] . However , only overexpression of Scl has been reported to affect ESC cardiac development . Loss of Scl resulted in increased cardiac differentiation in vivo in the mouse , but this may differ from reduced expression we see in our system . In addition , our assay may not be sensitive enough to detect changes in cardiac development . However , since we see a dramatic impairment in HPC production , but little or no impairment of other mesoderm derived tissue the data demonstrates that miR-24 is not needed generally for differentiation . The phenotype we observed from miR-24 KD ESCs cells suggest an early requirement for miR-24 in embryonic hematopoiesis , potentially in the development or function of the hemangioblast . Results from the BL-CFC assays suggest that miR-24 is not required for the commitment of mesoderm to the hemangioblast , as we did not observe a requirement for miR-24 in BL-CFC generation . However , the decreased expression of Etv2 , and Runx1 , along with the dramatic reduction in Scl suggests that there is a defect in differentiation downstream of the hemangioblast . Expression of endothelial genes Pecam1 , and Cdh5 was unaffected in miR-24 KD BL-CFC suggesting a potential defect in the development of hemogenic endothelium from the BL-CFC , or a defect in the ability of hemogenic endothelium to produce HPCs . MiR-24 was previously shown to target Trib3 in vascular smooth muscle cells [31] . Consistent with it being a miR-24 target in EBs , Trib3 levels increase in d4 miR-24 KD EBs . Additionally Trib3 expression goes down with mesoderm commitment in wildtype EBs , as miR-24 levels go up . To determine if increased Trib3 contributes to the phenotype we retrovirally transduced differentiating wildtype ESCs with Trib3 retrovirus to mimic the overexpression seen in miR-24 KD ESCs cells . Trib3 expression significantly inhibited production of HPCs from infected ESCs compared to ESCs infected with control virus . The inhibition of hematopoiesis is not as dramatic as what is observed with the antagonism of miR-24 , however it suggests that Trib3 downregulation contributes to miR-24’s ability to promote early hematopoiesis . Consistent with Trib3 being a negative regulator of hematopoiesis , we observed an increase in CD41+ HPC production when Trib3 was antagonized in ESCs . Trib3 is a member of the Tribbles family of proteins , which includes Trib1 , and Trib2 . Trib3 is a negative regulator of the serine threonine kinase Akt [46] . In the zebrafish embryo , PI3K/ Akt signaling is necessary for hematopoiesis and angiogenesis [47] . Additionally , Trib3 negatively regulates Smurf1 , the E3 ubiquitin ligase , which targets Smads 1 and 5 proteins for destruction [31 , 32] . BMP signaling through Smads is critical component of the gene regulatory network that drives early hematopoietic differentiation . Smad1 is required specifically at different stages of early development of mesoderm into hematopoietic progenitors with increased expression of Smad1 in nascent ESC derived mesoderm increasing the development of hemangioblast , whereas increased expression in the hemangioblast and/or hemogenic endothelium blocks hematopoietic differentiation [33 , 36] . The distinct temporal effects of Smad1 on hematopoiesis are similar to what we observe with miR-24 , which will inhibit hematopoiesis when expressed at the beginning of ESC differentiation , but increases hematopoiesis when expressed at d3 of differentiation . Increased Trib3 in miR-24 KD ESCs may disrupt BMP signaling inhibiting development of HPCs . It will be important to determine if disruption of PI3 kinase and/or Smad signaling contributes to the block in hematopoietic development we observe . Identification of other targets of miR-24 that may be upregulated in EBs to block hematopoiesis is also critical . Previous to this study no miRNA had been observed to regulate hematopoiesis at this early point in mammalian ontogeny . However , a requirement for miRNAs in embryonic hematopoiesis was recently shown in Xenopus [30] . Morpholino knockdown of the microprocessor complex member Dgcr1 results in a global knockdown of mature miRNAs in the Xenopus embryo , and ablates production of the hemangioblast and hemogenic endothelium as evaluated by decreased expression of Runx1 and Flk1 . Candidate miRNAs in regulating embryonic hematopoiesis were identified by examining miRNAs enriched in AGM HPCs isolated from the d11 . 5 mouse embryos . As a secondary screen , AGM HPC miRNAs were subjected to bioinformatics analysis to determine if any of these miRNA genes had ChIP-seq peaks for hematopoietic transcription factors binding within 5 kb of the pre-miRNA locus . The top candidate in this screen was miR-142 . Interestingly in their analysis miR-24 was enriched in the AGM HPCs , and was the third top ranked miRNA gene in the ChIP-seq analysis . Expression of a mature miR-142 , but not miR-24 was able to rescue hemangioblast development in Dgcr1 knockdown embryos , however miR-142 was unable to rescue hemogenic endothelium development . These results appear consistent with what we have observed that miR-24 is not required for hemangioblast development , but is needed for subsequent hematopoietic differentiation . Understanding the gene regulation network of stem cells is critical for the development of protocols for directing the differentiation of stem cells into tissues needed for transplant . Unfortunately , it is still difficult to match donors with transplant recipients , with many patients unable to obtain life-saving tissue that is needed . The generation of induced Pluripotent Stem Cells ( iPSCs ) from adult cells holds the promise of generating abundant patient specific tissues for transplant [48–50] . Efficient and robust iPSC differentiation , or possibly other patient-derived adult stem cells could lead to production of hematopoietic stem cells ( HSCs ) for transplantation therapy to treat hematological disorders . MiRNAs are attractive drug targets due to their small size , which may make them amenable to making small molecule mimics as well as making antagonists [51] . MiR-24 is the first miRNA to be involved in this early step of differentiation of mammalian mesoderm to tissue that will give rise to HPCs . Manipulating miR-24 or its downstream targets potentially could be used for directing the differentiation of nascent mesoderm produced from pluripotent stem cells .
ShRNA antagonist of miR-24 ( miArrest miRNA inhibitor ) or scrambled non-targeting shRNA was expressed in a lentiviral vector co-expressing mCherry and puromycin resistance ( GeneCopoeia , Rockville , MD ) . For supplementary experiments miR-ZIP vectors to deliver an independent miR-24 targeting shRNA and scrambled shRNA were used ( System Biosciences , Mountain View , CA ) . pLKO . 1 lentiviral plasmids expressing Trb3 ShRNA antagonists were obtained from Thermo Scientific ( Waltham , MA ) . pVSV-G , pMDL , and pRSV-REV plasmids ( National Gene Vector Biorepository , Indianapolis , IN ) were co-transfected with lentiviral plasmid into 293FT cells with Lipofectamine 2000 Transfection Reagent ( Invitrogen , San Diego , CA ) . For retroviral production MigR1-EGFP , MigR1-Trib3 , MSCV-EGFP , and MSCV-mirn23a retroviral plasmids were co-transfected into 293FT cells together with the retroviral packaging vector pCL-Eco ( Imgenex ) using Lipofectamine 2000 ( Invitrogen ) . For both lentivirus and retrovirus production 48h and 72h post-transfection viral supernatants were harvested and concentrated with Centricon Plus-70 filters ( Millipore ) . MigR1-Trib3 plasmid was kindly provided by Dr . Keyong Du , Tufts University , Boston , MA . RW4 ESCs ( ATCC , Manassas , VA ) were adapted from growth on mitomycin C-treated MEFs to growth on plates coated with 0 . 1% porcine skin gelatin ( Sigma , St . Louis , MO ) . After passaging 3 times on gelatinized plates , cells were frozen or immediately used for viral infections . RW4 ESCs were maintained on gelatin coated plates in DMEM ( Invitrogen , Carlsbad , CA ) , 15% fetal bovine serum ( FBS , Hyclone , Pittsburgh , PA ) , 50 U/ml penicillin , and 50 ug/ml streptomycin , 1 X Non Essential Amino Acids , 2mM Glutamax , 55 μM 2-mercaptoethanol ( BME ) , and 1000U/ml LIF ( ESGRO , Millipore , Billerica , MA ) . Dulbecco’s Modified Eagle Medium ( DMEM ) and media additives unless otherwise indicated were obtained from Invitrogen ( Carlsbad , CA ) . Gelatin adapted GFP-Brachyury ESCs were obtained from Valerie Kouskoff ( Cancer Research UK Manchester Institute ) , and were cultured as the RW4 cells . Embryoid bodies were generated from ESCs by either liquid culture or methylcellulose culture . For liquid culture ESCs were washed out of LIF containing media , and plated onto 10 cm petri plates ( Fisher Scientific , Pittsburgh , PA ) at 10 , 000 cells/ml in ESC media without LIF ( Differentiation media ) . ESCs were harvested at the indicated times . For methylcellulose differentiation , 10 , 000 ESCs were washed out of LIF and subsequently cultured in 1 . 5 ml 0 . 9% methylcellulose-base medium ( Stem Cell Technologies , Vancouver , CA ) supplemented with 10% FBS ( Hyclone ) , 5% protein-free hybridoma medium-II ( Invitrogen , Carlsbad , CA ) , mIL-1 ( 5 ng/ml ) , mIL-3 ( 5 ng/ml ) , SCF ( 10 ng/ml ) , mGM-CSF ( 5 ng/ml ) and hEPO ( 3 U/ml ) at 37°C and 5% CO2 for 14 days . All cytokines were obtained from Invitrogen except for hEPO , which was obtained from Stem Cell Technologies . 2000 ESCs/ 35mm dish were differentiated into EBs for 11 days in 1 . 0% Base Methylcellulose ( M3120 , Stem Cell Technologies , Vancouver , CA ) , 15% FBS ( Hyclone ) , 10ug/ml insulin ( Sigma , St . Louis , MO ) , 100ug/ml mFGF2 ( Invitrogen , Carlsbad , CA ) , 50ug/ml VEGF ( Biolegend , San Diego , CA ) , 10 ng/mL mIL-6 ( Invitrogen , Carlsbad , CA ) , 2 U/mL hEPO ( Stem Cell Technologies , Vancouver , CA ) , 450 μM monothioglycerol ( MTG , Sigma , St . Louis , MO ) . 125 d11 EBs were plated onto 35mm plates in ES-Cult Endothelial Collagen Medium ( Stem Cell Technologies , Vancouver , CA ) containing 50 ng/ml mVEGF , 100 ng/ml mFGF2 , 10 ng/mL mIL-6 , and 2 U/mL hEPO . Sprout formation was assessed 4 days later . EBs were scored in blinded fashion according to 4 standard classes based on vascular sprout formation: I- no sprout formation , II- few sprouts , III- many sprouts but no network , IV- many sprouts with network [41] . EBs were initially generated by hanging drops . ESCs were plated 25 , 000-cells/ ml in ESC media minus LIF in 20ul drops hanging from an inverted 15cm plate lid . The 15cm plate contained 10ml H20 to keep the chamber humidified . EBs were cultured for 5d , and then individual EBs were transferred to an individual well of a 96 well tissue culture plate . After 5d of culture , each well was examined for the presence of beating cells indicating the presence of cardiac tissue . Primary EBs were generated from ESCs by liquid culture onto 10 cm non-tissue culture petri plates ( Fisher Scientific , Pittsburgh , PA ) at 5 , 000 cells/ml in Iscove’s Modified Dulbecco Medium ( Life Technologies , Grand Island , NY ) supplemented with 15% differentiation fetal bovine serum ( Stem Cell Technologies , Vancouver , BC , Canada ) , 5% protein-free hybridoma medium-II ( Life Technologies ) , 200 ug/mL iron-saturated holo-transferrin ( Sigma , St Louis , MO ) , 50 ug/mL ascorbic acid ( Sigma ) , 450 uM monothioglycerol ( Sigma ) , 2mM glutamine ( Life Technologies ) and 100ug/mL penicillin/streptomycin ( Life Technologies ) . Primary EBs was differentiated at 37°C and 5% CO2 for 2–4 days . At d2 . 75 or d3 , the primary EBs from liquid culture were harvested and digested with Accumax ( Millipore , Billerica , MA ) and further dissociated into single cell suspension by passaging with a p200 pipet tip several times . For hemangioblast assays , 12 , 500 cells/mL in 35mm dishes were differentiated into BL-CFC colonies for 3–4 days in 0 . 9% Base Methylcellulose ( M3120 , Stem Cell Technologies , Vancouver , CA ) supplemented with 10% differentiation fetal bovine serum ( Stem Cell Technologies , Vancouver , BC , Canada ) , 200 ug/mL iron-saturated holo-transferrin ( Sigma , St Louis , MO ) , 50 ug/mL ascorbic acid ( Sigma ) , 450 uM monothioglycerol ( Sigma ) , 2mM Glutamax ( Life Technologies , Grand Island , NY ) , 100ug/mL penicillin/streptomycin ( Life Technologies ) , 20% D4T endothelial cell conditioned media ( Gift from Diana Ramirez , Case-Western University , Cleveland , OH ) , 100 ng/mL SCF ( Life Technologies ) and 5ng/mL VEGF ( Biolegend , San Diego , CA ) . BL-CFC colonies were assessed 3–4 days later . For gene expression analysis 20 BL-CFCs per genotype were picked at d4 of differentiation into PBS and pelleted by centrifugation . Cells were lysed , and used for preparation of cDNA with a Cells-to-Ct kit ( Ambion ) according to manufacturers instructions . The cDNA was then subjected to quantitative PCR using gene specific primers and fluorescently labeled probe as described below . ESCs were differentiated into EBs by methylcellulose culture as described above . After 9 days , EBs were harvested and disaggregated with trypsin and mechanical shearing with a 21-gauge needle . Cells were then replated into Methocult GF 3434 methylcellulose media ( Stem Cell Technologies ) . The number of cells plated was equal to the number of cells in 50 EBs derived from wild-type ES cells . Number and colony type was enumerated 7 days later . For miR-24 knockdown studies , RW4 ESCs were plated at 100 , 000 cells per well of a 6 well plate 24h pre-infection . The next day media was replaced with 1 . 5ml ESC media containing lentivirus . Cells were spin infected for 1 . 5h . Media was replaced the next day . 48h post-infection cells were split onto 10cm plates in ESC media containing 5ug/ml puromycin ( Invitrogen , Carlsbad , CA ) . Ten to 12 days later resistant , colonies were isolated . Clones were tested for knockdown of miRNA expression using miR-Taqman assays as described below . For retroviral infection of ESCs , cells were trypsinized and washed out of LIF containing media with PBS ( 2 . 7mM KCl , 1 . 8mM KH2PO4 , 137mM NaCl , 10mM Na2HPO4 , pH 7 . 4 ) . 50 , 000 cells were replated in 1 . 5 ml of differentiation media containing retroviral supernatant and centrifuged at 2125 x g for 90 minutes . 1 . 5 ml of differentiation media was added to the cell . The cell suspensions were then used to form EBs by the hanging drop method . All 3mls of cell suspension were plated as 20ul drops on an inverted 15cm tissue culture plate lid . The lid was then replaced onto a petri plate and incubated in the tissue culture incubator for 2 days to allow for formation of EBs . After 2d the EBs were transferred to a 10 cm petri plate containing 7 ml of differentiation media . EBs were cultured for an additional 4 days , and differentiation assayed by flow cytometry . For infection of EBs cells , single cell suspensions were prepared from d3 EBs prepared in liquid culture as described above . EBs were washed in PBS and disaggregated into single cell suspensions by incubating with 1ml of Accumax ( Millipore , Billerica , MA ) at 37°C for 30 minutes . To further break up the EBs , the cell solution was pipetted up and down in a 2ml pipet with a p200 tip at the end . Cells were spun down and resuspended in 1 . 5 ml of differentiation media . EB cells were then virally infected and reformed into EBs by hanging drop as described above . EBs were examined for hematopoietic differentiation after 5 days . Total RNA was prepared using TRIzol ( Life Technologies , Grand Island , NY ) according to the manufacturer’s protocol . For mRNA gene expression complementary DNA ( cDNA ) was prepared by reverse transcribing 1ug of total RNA using the High Capacity cDNA Reverse Transcriptase Kit according to manufacturer’s protocol ( Life Technologies ) . Gene specific primer sets with fluorescent probes were obtained from IDT ( Coralville , IA ) . ΔΔCT calculations were used to normalize signal versus GAPDH as the control . For miRNA analysis , we used miR-specific reverse transcription primers and Taqman primers obtained from Life Technologies . Expression level of miR-24 was normalized to sno202 snRNA expression . All experiments were performed in triplicate using BioRad CFX96 C1000 System ( BioRad , Hercules , CA ) . EBs were pelleted by centrifugation and washed with PBS . EBs were disaggregated by the addition of 1ml Accumax cell dissociation buffer ( Millipore , Billerica , MA ) and incubated at 37°C for 30 minutes . Single-cell suspensions were collected and washed with PBS and incubated with the indicated antibodies . CD41-PE , FLK1-PE , ckit-APC , PDGFRα-biotin , and avidin-APC-Cy7 were obtained from Biolegend ( San Diego , CA ) . Stained cells were subsequently assessed using Beckman Coulter FC500 Flow Cytometer ( Brea , CA ) and data was analyzed using Flowjo software ( Tree Star , Ashland , OR ) . | Studies of mouse embryos and embryonic stem cells ( ESCs ) have defined the ontogeny of mammalian embryonic hematopoietic cells . The ESC differentiation system has been valuable for dissecting the molecular regulation of the development of mesoderm into HPCs . Extracellular signals regulate a complex network of transcription factors to direct embryonic hematopoietic development . Mammalian miRNAs have previously not been described to regulate this genetic network during embryonic hematopoiesis . However , a role for miRNAs in producing the hemangioblast , and hemogenic endothelium in Xenopus has been described . Our work with ESCs demonstrates a specific requirement for the miRNA , miR-24 , in the development of hematopoietic progenitors cells ( HPCs ) . Antagonizing miR-24 in ESCs does not affect generation of BL-CFCs , the in vitro equivalent of the hemangioblast , but does compromise the ability of those BL-CFCs to produced HPCs . Expression of transcription factors required for HPC production downstream of the hemangioblast , Scl , and Runx1 , is greatly reduced by antagonizing miR-24 . These results identify miR-24 , as a mammalian miRNA required for the development of blood from newly formed mesoderm . | [
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| 2015 | MiR-24 Is Required for Hematopoietic Differentiation of Mouse Embryonic Stem Cells |
Somatic mutations in protein-coding regions can generate ‘neoantigens’ causing developing cancers to be eliminated by the immune system . Quantitative estimates of the strength of this counterselection phenomenon have been lacking . We quantified the extent to which somatic mutations are depleted in peptides that are predicted to be displayed by major histocompatibility complex ( MHC ) class I proteins . The extent of this depletion depended on expression level of the neoantigenic gene , and on whether the patient had one or two MHC-encoding alleles that can display the peptide , suggesting MHC-encoding alleles are incompletely dominant . This study provides an initial quantitative understanding of counter-selection of identifiable subclasses of neoantigenic somatic variation .
In every human cell , proteins are constantly being degraded into component peptides , and a subset of this pool of peptides are displayed on MHC class I receptor proteins ( encoded by human leukocyte antigen or HLA genes ) . As somatic mutations arise , some cause differences in MHC-displayed peptides , producing antigens that can be differentially recognized by T cells and lead to the specific destruction of tumor cells by the immune system [1] . In addition to the production and display of ‘non-self’ peptides that can arise directly from mutation , genetic and epigenetic alterations can cause tumor cells to express many proteins more highly [2] . Together , these changes mean that cancer cells have an altered repertoire of proteins and therefore of tumor antigens . Tumor antigens can be classified into two categories: tumor-associated self-antigens ( which may also be displayed by non-cancer cell types ) and antigens derived from tumor-specific mutant proteins . The latter class of tumor-specific ‘neo-antigenic’ mutations are ideal targets for cancer immunotherapy , because neo-antigens that can potentially be recognized by the mature T-cell repertoire are less likely to be found in healthy cells/tissues [3] . It has been reported that neo-antigens are likely to be more immunogenic , presumably due to the T-cell maturation process in which T-cells capable of high-avidity recognition of self-antigens are eliminated [4] . Immuno-therapy approaches exploiting neo-antigenicity , however , have been hampered by the fact that every tumor possesses a unique set of mutations that must first be identified [5] . Moreover , individual patients can differ dramatically in their immune systems , based on HLA type and other allelic variation in immune genes , as well their unique repertoire of mature immune cells . Thus , personalized immuno-therapy could positively benefit the patient during cancer treatment [6–8] . After recognition , the process of tumor-cell killing by T-cells may release more tumor neo-antigens in a potentially therapeutic virtuous cycle [9] . In principle , any coding mutation has the potential to generate a mutant peptide that can be presented by MHC class I molecules and subsequently recognized by cytotoxic T cells . However , a crucial challenge for the personalized treatment approach is determining the MHC-binding potential of non-self peptides that arise from somatic tumor mutations , and determining which among them are most likely to be potent neo-antigens in a given cancer type , and given the patients repertoire of HLA alleles that encode different MHC class I receptors . To improve our understanding of neo-antigenicity in cancer , we conducted several analyses of somatic mutations and the ability of corresponding mutant peptides to be displayed by MHC class I receptors across different cancer types . More specifically , we quantified the impact of predicted antigenicity on the spectrum of tumor missense somatic mutations . We expected to find that somatic mutations would be less frequent in MHC-displayed peptides , presumably because the immune system is more likely to have eliminated cells bearing these mutations . Other groups have identified depletion of predicted-displayed mutations based on patient HLA-A genotypes [10] , without quantifying the extent of depletion . Other work reported that predicted-MHC-displayed mutations were depleted in colorectal and clear cell renal cancer [11] . However , this phenomenon was not explored in detail , e . g . , it did not consider patient genotypes at all HLA loci or consider expression levels of the displayed peptide . Here , we quantified the extent to which somatic mutations are significantly depleted in peptides that are predicted to be displayed by MHC class I proteins ( without considering patient HLA type ) . We further characterized the dependence of this depletion on the inferred expression level of each peptide . Next , we refined the preceding analyses by considering individual patient HLA alleles . Finally , we extended this analysis by relating depletion of somatic mutations to the number of HLA alleles predicted to display peptides bearing that mutation . Thus , we quantitatively estimated the ‘neoantigenicity’ of different classes of somatic variants in individual patients .
As somatic mutations arise , we should expect that the more immunogenic mutations are more likely to be counter-selected due to clearance of the mutant cell by the immune system , and therefore depleted from observed tumor genomes . To formally test this hypothesis and to begin to quantify the expected depletion effect , we examined somatic cancer mutations in human cancer samples , beginning with data from the Pan-cancer Analysis of Whole Genomes ( PCAWG ) study [12] . The immunogenicity of a protein-coding mutation depends in part on whether or not it yields a mutant peptide that is displayed by a MHC class I protein receptor . MHC class I binding peptides were predicted using the NetMHC server [13 , 14] . In total , we examined 121 , 258 missense somatic mutations from 2 , 834 PCAWG patients for whom HLA type had been assigned [15] . Those mutations were distributed across more than 10 , 700 genes . Missense somatic mutations from PCAWG were separated into two groups: either falling within or outside of predicted MHC binding peptides . For an initial analysis , we modeled all MHC class I alleles with available display predictions as being present in each patient ( we revisit this issue later ) . Because a mutant protein must be expressed in order to yield a displayed peptide , we also examined the dependence of missense variant depletion on gene expression levels . More specifically , we analyzed the relationship between the missense mutation density within MHC-binding peptides and the expression level of the corresponding protein in the appropriate cancer type ( see Methods ) . Then , for mutations both within and outside of MHC binding peptides , we calculated the mutation density for five classes of peptide: those that were undetectably expressed and those in each of four gene expression quantiles ( Methods ) . As expected , we found that mutation density and expression level are negatively correlated , and that the average mutation density within MHC binding peptides is lower than that of MHC binding peptides for expressed peptides ( Fig 1; ratio of mutation density within MHC-displayed peptides to that outside displayed peptides = 0 . 94; Fisher’s exact test , P-value < 2 . 2e -16 ) . As a control , we further compared the mutation density within and out of MHC binding peptides in undetectably-expressed genes . Our results indicated that there was no significant depletion of missense somatic mutations within MHC binding peptides that are not detectably expressed ( Fig 1; odds ratio = 1 . 01 , P-value = 0 . 65 ) . Although the odds ratio was near 1 for non-expressed proteins , as one might naively expect , we note that the sequence specificity of specific MHC class I receptor alleles can lead to HLA-allele-dependent amino acid ( and therefore nucleotide-level ) sequence biases in the peptides displayed , which could in turn yield sequence-dependent differences in mutation density . To account for this , we performed a correction by dividing the mutation density ratio of expressed proteins by that of non-expressed proteins . Although in this case the corrected mutational density ratio was 0 . 93/1 . 01 , which is still 0 . 93 , it did make a difference for other results below . Thus , our analysis of PCAWG data confirmed the expected phenomenon that somatic mutations are depleted within expressed MHC-displayed peptides . Quantifying the MHC-display-dependent depletion effect in non-expressed peptides served as a crucial negative control for sequence biases of peptides displayed by particular HLA alleles . For a mutant protein to yield a peptide that is displayed by a given allele of the MHC class I receptor , that allele must of course be present in the cells of that patient . Because the analyses above were based on a hypothetical ( and unrealistic ) patient who bears all 12 of the common HLA alleles for which display predictions are available , the depletion effect sizes estimated above are likely to be conservatively small . Indeed , individual patients can differ dramatically in their immune systems , in part due to allelic variation in HLA genes . Therefore , we sought to characterize the mutation depletion phenomenon using , for each somatic variant , only peptide display predictions for the subset of HLA alleles carried by the patient in which that somatic variant was detected . Re-examining the PCAWG data , there were 12 , 552 genes in which at least one variant was predicted to be neo-antigenic , e . g . , presented by the MHC class I protein of the patient carrying this mutated gene . For these genes , we again examined the tendency for depletion of mutations within MHC binding peptides relative to non-MHC binding peptides , now taking patient HLA type into account . Within expressed proteins , the ratio of mutation density within predicted-displayed MHC binding peptides to that outside predicted-displayed peptides was 0 . 82 ( Fisher’s exact test , P-value < 2 . 2e -16 ) . Within non-expressed proteins , the corresponding ratio was 0 . 98 ( Fisher’s exact test , P-value = 0 . 19 ) , yielding a corrected mutational density ratio for expressed proteins of 0 . 83 ( 0 . 82/0 . 98 ) . Our analysis showed that missense mutations tend to be counter-selected within MHC binding peptides , both in an idealized patient with unknown HLA type , and when accounting for HLA type in each specific patient sample . In each case , the phenomenon depended on expression level of the gene encoding that peptide ( Fig 2 ) . In all subsequent analyses , we considered only peptides expressed according to RNA-Seq analysis of the appropriately-matched cancer type . In the above analysis , we only considered for each peptide whether or not the patient carried an HLA allele predicted to display that peptide but did not consider how many copies of the displaying allele were present in that patient . However , peptides for which two copies of the displaying HLA alleles were present could be more efficiently displayed . ( This could be due either to increased expression of the displaying allele by increased gene dosage , or a decreased chance that the displaying allele would be silenced where the phenomenon of mono-allelic expression occurs [16] ) . We assessed this hypothesis further by testing , for patient samples where ‘likely-displayed’ mutations were found , whether the number of alleles that can display the MHC binding peptides was associated with the extent of mutation depletion . Missense variants from the 2 , 834 PCAWG patient samples were separated into three types ( Fig 3 ) . “D0 , ” where the patient has zero HLA class I alleles that are predicted to display the mutant peptide; “D1” , where only one HLA class I allele type can display the peptide , i . e . , the patient is heterozygous at the relevant HLA locus and the patient has only one HLA allele that can display the peptide; and “D2” , where two HLA class I alleles are predicted to display the peptide . These two alleles can either be two copies of the same MHC allele ( i . e . , the patient is homozygous for a displaying allele ) or be two different alleles ( i . e . , the patient is heterozygous with alleles that are both predicted to display the peptide ) . For both D1 and D2 mutations , we found that the mutation density within patient-displayed MHC binding peptides is lower than that observed outside of MHC binding peptides of the same protein . For expressed MHC binding peptides of type D1 , the ratio of mutation density within displayed peptides to that outside of displayed peptides was 0 . 91 ( Fisher’s exact test , P-value = 1 . 96e -7 ) . This ratio for non-expressed peptides was 0 . 99 ( Fisher’s exact test , P-value = 0 . 39 ) , yielding a corrected mutational density ratio of 0 . 92 ( 0 . 91/0 . 99 ) for expressed D1 peptides . For expressed displayed peptides of type D2 , the ratio was 0 . 79 ( Fisher’s exact test , P-value = 9 . 73e-9 ) . The corresponding ratio in non-expressed displayed peptides D2 that can be displayed by two distinct HLA alleles is 1 . 02 ( Fisher’s exact test , P-value = 0 . 64 ) . Thus , a corrected mutational density ratio 0 . 77 ( 0 . 79/1 . 02 ) was observed for expressed D2 peptides displayed by two HLA alleles . Thus , we find that the depletion for mutations in MHC-displayed peptides is stronger if the patient has more alleles predicted to display a mutant peptide ( Fig 4 ) , and therefore that HLA alleles are incompletely dominant . We repeated the above analyses using the missense somatic mutations detected from 5 , 213 patient samples provided by the TCGA project [17] , examining the distribution pattern of 676 , 171 missense mutations detected in more than 10 , 800 genes . Analysis of this TCGA data confirmed the tendency of depletion of mutations within MHC binding peptides relative to non-MHC binding peptides , both with and without considering patient HLA types ( S1 Fig ) . Considering only patient-displayed MHC binding peptides , the corrected mutational density ratio was 0 . 54 ( with 95% confidence interval of 0 . 539–0 . 545 estimated by bootstrap resampling; S2 Fig ) . Our analysis of the TCGA data confirmed that mutations displayed by two display-enabling HLA alleles ( mutations of type “D2” ) were more strongly depleted than mutations displayed by a single display-enabling allele ( S2 Fig ) , further supporting the conclusion that HLA alleles are incompletely dominant . To address concerns that the depletion phenomenon stems from a bias in the spectrum or rate of mutation for expressed genes , we also analyzed 1 , 048 , 575 synonymous mutations in 5 , 134 samples . We did not find depletion of synonymous mutations within patient-displayed MHC binding peptides ( S3 Fig ) . Within expressed proteins , the ratio of synonymous mutation density within predicted-displayed MHC binding peptides to that outside predicted-displayed peptides was 1 . 05 ( Fisher’s exact test , P-value = 0 . 99 ) . Within non-expressed proteins , the corresponding ratio was 1 . 03 ( Fisher’s exact test , P-value = 0 . 78 ) , yielding a corrected mutational density ratio for expressed proteins of 1 . 01 ( 1 . 05/1 . 03 ) . The 95% confidence interval of the corrected mutational density ratio for synonymous variants was 1 . 00 to 1 . 01 ( based on bootstrap resampling 500 times; S2 Fig ) . That we observed no depletion of synonymous mutations in patient displayed MHC binding peptides is consistent with the hypothesis that the depletion phenomenon arises from a selection that depends on expression of the mutant protein . We next repeated our analysis by considering different cancer types separately . Here , we chose the six different types for which the most samples were available: breast cancer ( BRCA , 973 samples ) , thyroid cancer ( THCA , 386 samples ) , skin cutaneous melanoma ( SKCM , 341 samples ) , prostate adenocarcinoma ( PRAD , 329 samples ) , gastric adenocarcinoma ( STAD , 275 samples ) and uterine corpus endometrial carcinoma ( UCEC , 240 samples ( see S2 Table ) . Significant depletion of predicted-displayed mutations had ( without considering patient HLA type or peptide expression ) been found previously for BRCA and STAD [11] . We also included adenomatous colorectal cancer ( COAD , 60 samples ) , because Rooney et al . noted significant depletion for this cancer type . Considering patient HLA genotypes and proteins in the 75–100%ile of expression level , we could confirm the trend of depletion of mutations in MHC-binding peptides for BRCA , STAD and COAD . We also found depletion for THCA , which had not been previously reported . Although we could not confirm depletion of mutations in UCEC , SKCM and PRAD for genes at 75%-100% expression percentile , depletion was seen for UCEC and SKCM at other expression percentiles ( S4 Fig ) . As a negative control , we performed the same analysis for synonymous mutation density within predicted-displayed MHC binding peptides relative to that outside predicted-displayed peptides . The depletion ratios , which did not vary greatly from unity for any of the seven cancer types , were as follows: COAD , 0 . 97; BRCA , 0 . 96; THCA , 0 . 94; STAD , 0 . 96; UCEC , 0 . 99; SKCM , 0 . 95; and PRAD , 1 . 00 . For non-expressed genes , the corresponding results were: COAD , 1 . 04; BRCA , 1 . 01; THCA , 1 . 03; STAD , 0 . 98; UCEC , 1 . 02; SKCM , 0 . 97; and PRAD , 1 . 00 . Only for BRCA were there enough samples to separate mutations into the three categories , D0 , D1 and D2 , although even for BRCA only 8–10 mutations fell into the D2 category . Still , the increased depletion that had been seen for D2 vs D1 and D0 when considering all cancer types together could be confirmed for BRCA ( S5 Fig ) .
In this study , we examined signatures of immune selection pressure on the distribution of somatic mutations , quantifying the extent to which somatic mutations are significantly depleted in peptides that are predicted to be displayed by MHC class I proteins , and characterizing the dependence of this depletion on expression level . We also examined whether the extent of immune selection pressure on somatic mutations depends on whether there are one or two HLA alleles that can display the peptide . It is important to note that peptides , whether displayed by class I MHC receptors or not , are subject to other forms of purifying selection . This could be due to essentiality of encoded functions or immunogenicity arising by mechanisms other than MHC class I display ( e . g . , MHC class II display [18] ) . Forms of purifying selection that are independent of class I MHC display should tend to lower mutational density in both displayed and non-displayed peptides . Although this phenomenon is expected to shrink the observed absolute difference in mutational density between displayed and non-displayed peptides , it should not affect the relative difference . Only expressed MHC binding peptides that can be displayed by at least one patient HLA allele are immunogenic in terms of class I MHC display . In our analysis using the PCAWG dataset , we found mutation densities to be similar for mutations within or out of the predicted MHC binding peptides when the gene was not expressed ( Fig 1 ) . That proteins must be expressed to be antigenic is one explanation for the fact that many “likely-displayed” mutations were nevertheless observed in a tumor . We also note that , although expression levels were obtained from tumors of matched type , they were generally not taken from precisely the same tumors for which we had somatic missense variant data . Thus , an explanation for presence of a likely-displayed mutation in an apparently-expressed gene is that this gene is not actually expressed in the specific tumor sample in which it appears . This could be due to differences in environment , germline or somatic genetic background , or epigenetic escape by silencing . More refined estimates of the depletion effect in future studies might come from using expression data from a specific patient tumor sample . We also noticed that at the 75-100th percentile of gene expression level , there are only weak or even no differences between nonsynonymous and synonymous mutation density for several comparisons . It has been reported that dN/dS diminishes in more highly expressed genes , presumably due to a tendency towards heightened purifying selection for the function of highly expressed proteins in cancers [19 , 20] . Our data is consistent with this phenomenon , considering only peptides that are not predicted to be displayed by MHC . Although it stands to reason that MHC display would provide additional purifying selection , and indeed we see this for several comparisons , our statistical power to detect significant differences must necessarily decrease where there is reduced mutational density in non-displayed peptides . We note that the terms “in MHC binding peptides” and “out of MHC binding peptides” were applied based on whether or not peptides were predicted to be displayed by at least one of the 12 common HLA-A or HLA-B allele types . We expect to observe depletion of somatic mutations out of MHC binding peptides if patients do not have a common displaying allele type . This is because failure to display by any of the common alleles increases the chance that there is display for another allele , e . g . , one of the HLA-C alleles or less common HLA-A or HLA-B allele types . We expect that this information will be useful in building a model that predicts the antigenicity of any given missense mutation detected by whole genome or whole exome sequencing . Although scores for observed mutations based on counter-selection of similar mutations may over-estimate neoantigenicity ( if a somatic mutation has been observed , it has obviously not yet been cleared by the immune system ) , such scores could point to ‘cryptic immunogenicity’ of a somatic variant . In cases of cryptic immunogenicity , some therapies might enable immune clearance of cancer cells by revealing this immunogenicity , e . g . by relieving tumor-derived suppression of immune cells . The ability to score each observed somatic mutation in a specific tumor for its potential to stimulate an immune response would therefore be potentially useful in scoring tumors with greatest potential to benefit from immunotherapy . Similarly , improved ability to predict which somatically mutated peptides are more likely to be neo-antigens could potentially help in choosing peptides as personalized cancer vaccines to specifically stimulate immune cells to recognize and specifically clear the patient’s tumor cells . Our results also supported the idea that having two copies of the display-enabling allele is more effective for peptide display than having just one copy . This could result from a gene-dosage effect ( i . e . , incomplete dominance as suggested earlier ) . Alternatively , it could result from monoallelic expression ( MAE ) . MAE , the phenomenon that only one allele of a given gene is expressed , is a frequent genomic event in normal tissues . MAE-derived silencing of one or more HLA-encoded alleles could potentially cause failure to express MHC binding-peptide-encoding genes , which may , in turn , alter the immunogenicity of somatic mutations . A previous study showed that the genome-wide rate of MAE was higher in tumor cells than in normal tissues , and the MAE rate was increased with specific tumor grade . Oncogenes exhibited significantly higher MAE in high-grade compared with low-grade tumors [16 , 21 , 22] . The role of MAE in immunogenicity of cancerous cells is entirely unclear . Because HLA alleles are known to be subject to MAE [16] , it may be interesting in future studies to assess the impact of MAE by comparing the mutation rates between homozygous ( same alleles ) and heterozygous ( two different alleles ) samples at HLA class I loci A and B respectively using the allele-specific expression data . One example of a potential therapy that might emerge from this study is that de-silencing ( either global or targeted ) could lead to the display of otherwise-cryptic neo-antigens and therefore to immune clearance of cancerous cells , especially when used in combination with current immunotherapy strategies . If we can better understand the interplay between individual immune systems and the likelihood that cancer cells bearing specific somatic mutations are cleared , we will gain insight into the therapeutic potential of MAE modulation . For example , if MAE can indeed limit peptide display efficiency , then therapies reducing MAE could potentially increase the efficiency of immune clearance of tumor cells . With the analysis conducted here , we can begin to quantify the efficiency of immune clearance of somatically mutated cells . For example , for somatic mutations in proteins expressed in a given cancer type , the depletion ratios we observed were as low as 0 . 77 in the PCAWG data and as low as 0 . 54 for TCGA data ( in each case this was for expressed peptides predicted to be displayed by an MHC receptor encoded by two copies of the same HLA allele ) . This result allows us to predict that cells bearing somatic mutations falling within DNA segments encoding such peptides are cleared roughly 23–46% of the time by the immune system at tumor stages that are earlier than those examined in PCAWG sequencing studies . Because any inaccuracy in estimating protein expression levels or peptide display would be expected to diminish our ability to detect the depletion phenomenon , this estimate of immune clearance rate is likely conservatively low . Here , we did not consider finer-grained cancer subtypes ( e . g . , triple-negative BRCA ) . Although such an analysis would be very interesting and could help identify immune-isolated tumor types , it would require more samples with the requisite HLA type information to be well-powered .
This study used two different collections of cancer-cell-derived somatic variants . First , we used data from the Pan-cancer Analysis of Whole Genomes ( PCAWG , May 2016 version 1 . 1 ) project [23 , 24] , including 121 , 258 missense somatic cancer mutations in 10 , 745 genes detected from 2 , 834 patient samples . The number of patient samples for each cancer type is shown in S1 Table . Second , we examined data downloaded from The Cancer Genome Atlas ( TCGA ) project , obtaining 676 , 171 missense somatic cancer mutations in 18 , 106 genes detected from 5 , 213 patient samples ( S2 Table ) . For TCGA data , we restricted ourselves to cancer types with more than five samples , a known expression level for each gene in a tumor sample of broadly-matched type , and HLA type information for each patient . We also examined 1 , 048 , 575 synonymous mutations in 5134 samples as a control . Data were downloaded from Broad Institute TCGA Genome Data Analysis Center ( 2016-01-28 ) . Protein sequences were downloaded using BioMart R package [25] based on the Ensemble Protein IDs provided in PCAWG and TCGA datasets . Each missense mutation was mapped to the corresponding protein based on the position of the mutation with respect to a given protein ( Fig 5 ) . Also , we validated that the wild type residue given for the mutation was found at the corresponding position within the downloaded protein sequence . We used the NetMHC server , version 3 . 4 ( 13 , 14 ) to predict MHC binding peptides associated with 12 common HLA class I alleles: HLA-A*0101 , HLA-A*0201 , HLA-A*0301 , HLA-A*2402 , HLA-A*2601 , HLA-B*0702 , HLA-B*0801 , HLA-B*1501 , HLA-B*2705 , HLA-B*3901 , HLA-B*4001 , and HLA-B*5801 . For this study , NetMHC scores were obtained for MHC binding peptides of length nine ( Although it is possible for peptides with 10 or 11 residues to bind , this is less common and such cases are more difficult to predict ) . Also , only strong MHC class I binding peptides with NetMHC affinity score of 50 or less were selected ( smaller NetMHC scores correspond to higher affinity ) . For each class of proteins and variants examined , we determined the total number of mutations falling within and outside of predicted MHC binding peptide regions for each protein . To test for significant differences in proportions of counts in different groups of peptides , we performed Fisher’s exact test using the “stats” package in R . We estimated gene expression levels for TCGA patient samples using TCGA RNA-Seq data [26] . Data were downloaded from Broad Institute TCGA Genome Data Analysis Center ( 2016-01-28 ) . The expression level of each gene for each cancer type was estimated using the median expression level of that gene across all TCGA samples of that cancer type . Genes were classified as detectably expressed ( if the RNA-Seq by Expectation Maximization or RSEM normalized expression value was greater than 0 ) . Detectably expressed genes were grouped into four expression quantiles according to the RSEM normalized expression value . For PCAWG samples , the four-digit HLA type for 2834 patients was determined by a Bayesian method ALPHLARD ( BioRxiv; https://doi . org/10 . 1101/323766 ) and all HLA types are shown in S3 Table . For TCGA samples , the four-digit HLA type of the 5213 TCGA patients was predicted using PolySolver [17] . This study has been approved by the Research Ethics Committee of University of Toronto and the NCBI dbGaP ( the Database of Genotypes and Phenotypes ) Authorized Access system , project # 15046 ) . | Cancer immunotherapy and personalized cancer vaccines depend on clearance of cancer and pre-cancer cells by the immune system . However , little is known about the strength of this phenomenon as it acts on the cell populations which give rise to tumors . Here we provide an initial quantitative estimate of the fraction of neo-antigen-containing cells in this population that are cleared by the MHC class I-dependent immune system . The impacts of both neo-antigenic gene expression and the number of neo-antigen-displaying MHC alleles on this clearance phenomenon were examined . A more complete understanding of immune clearance of neoantigenic cells and how this phenomenon varies between patients and cancers , has the potential to guide immunotherapy and cancer vaccines . | [
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| 2019 | Quantifying immune-based counterselection of somatic mutations |
Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops . The commonly used classification of beta turns ( Type I , I’ , II , II’ , VIa1 , VIa2 , VIb , and VIII ) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution , and represents only two thirds of beta turns observed in proteins ( with a generic class Type IV representing the rest ) . We present a new clustering of beta-turn conformations from a set of 13 , 030 turns from 1074 ultra-high resolution protein structures ( ≤1 . 2 Å ) . Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn . We define 18 turn types compared to the 8 classical turn types in common use . We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues ( e . g . , ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’ ) . We identify 11 new types of beta turn , 5 of which are sub-types of classical beta-turn types . Up-to-date statistics , probability densities of conformations , and sequence profiles of beta turns in loops were collected and analyzed . A library of turn types , BetaTurnLib18 , and cross-platform software , BetaTurnTool18 , which identifies turns in an input protein structure , are freely available and redistributable from dunbrack . fccc . edu/betaturn and github . com/sh-maxim/BetaTurn18 . Given the ubiquitous nature of beta turns , this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination , refinement , and prediction programs .
Ordered protein structures consist of elements of regular secondary structure , such as alpha helices and beta-sheet strands , and irregular elements of structure referred to as loops or coil regions . Loops comprise half of residues in protein structures . Due to the globular nature of folded proteins , the direction of the peptide chain often has to change radically within a few short residues within loops . These changes in direction are often accomplished by turns , which can occur multiple times in loop regions . Turns consist of segments between 2 and 6 amino acids ( delta , gamma , beta , alpha and pi turns respectively ) , and are identified by the distance between the Cα atoms of the first and last residues and sometimes by the existence of specific hydrogen bonds within the segment [1–6] . These turn fragments are often hydrophilic , since loops are usually on the protein surface [7–9] . Four-residue beta turns are by far the most common turn type [5] , constituting 25–30% of all protein residues [10] . The first examination of beta turns was by Venkatachalam in 1968 [1] . He identified a key hydrogen bond formed between the backbone carbonyl oxygen atom of the first residue and the backbone amide hydrogen atom of the fourth residue of a beta turn . Venkatachalam devised a system of three turn types: I , II , and III , and their mirror images I' , II' , and III' . He predicted that the mirror images would be disfavored due to steric clashes . In 1973 , Lewis et al . observed that the backbone 1–4 hydrogen bond is not present in a large number of peptide fragments that might still be considered turns [11] . Instead of the older hydrogen bond criterion , they established the definition of a beta turn requiring a distance between the 1 and 4 alpha-carbon atoms of less than 7 Å , while the central residues 2 and 3 are not part of a helix . This beta-turn definition expansion by Lewis et al . led to the adoption of ten different turn types: I , I’ , II , II’ , III , III’ from Venkatachalam and new types IV , V , VI , and VII . Type V turns had φ , ψ dihedrals of the 2nd and 3rd residues around ( -80° , +80° ) and ( +80° , -80° ) respectively . Type VI turns contained a cis proline at the 3rd residue . Type VII turns had ψ2 of 180° or φ3 of 180° . The turns not fitting the established definitions , because two or more of the dihedrals of residues 2 and 3 were not within 40° of the defined values , were placed in a miscellaneous category , type IV by Lewis et al . Richardson et al . [2] kept the same 7 Å criterion used by Lewis et al . [11] and from analysis of a larger data set , reduced the number of beta-turn types to six categories with designated ϕ , ψ limits of the second and third residues: I , I' , II , II' , VIa , VIb , and the seventh miscellaneous category , Type IV . They divided the type VI turns of Lewis et al . with a β conformation at residue 3 and a cis peptide bond at residue 3 into two types: Type VIa with an α conformation at the cis-proline at residue 3 and Type VIb with a β conformation at the cis-proline . They merged type III turns ( with φ , ψ = -60° , -30° for residues 2 and 3 ) with type I turns ( with φ2 , ψ2 = -60° , -30° and φ3 , ψ3 = -90° , 0° for residue 3 ) because they occupy similar regions of the Ramachandran map at positions 2 and 3 , and many type III turns that could be identified at that time were part of 310 helices . The similarity of 310 helices of length 3 to two consecutive Type I turns has been analyzed by Pal et al . [12] . They found that the first two residues of these short 310 helices have φ , ψ values close to -60° , -30° , while residues 2 and 3 resemble a classical Type I beta turn with φ , ψ = -30° , -60° and -90° , 0° respectively . In 1988 Wilmot and Thornton [3] scanned a dataset of 58 protein structures for turns , and expanded Richardson’s categories by adding type VIII turns , consisting of α and β conformations at the 2nd and 3rd residues respectively [3] . To match each turn to its type , for each residue one of two backbone dihedrals had to be within 30° while the other one was allowed to be within 45° of the canonical values for that turn category . In 1990 , the same researchers assigned and named all turns by the Ramachandran plot regions ( αR , βE , βP , αL , γL , and ε ) of the two central residues [4] . They found 16 types that were observed at least once in the total of 910 turns of 58 protein structures . However , eight types were observed less than 10 times ( 1% ) and three types only once . The turn type definitions of Richardson et al . [2] and Wilmot et al . [3] have also guided the design of many turn prediction algorithms [13–19] . For example , in Kountouris et al . , the prediction includes types I , II , IV , VIII , and "non-specific” [15] . More recently , de Brevern performed clustering of the miscellaneous beta-turn type IV representing one third of all beta turns [20] , those not fitting the criteria for types I , I’ , II , II’ , VIa1 , VIa2 , VIb , and VIII [2] . This additional clustering resulted in placing about half of the type IV turns in four groups of turns adjacent to the existing Type I and Type II turn boundaries . However , they do not appear to represent peaks in the density of beta-turn conformations , but rather occur at the periphery of Type I and Type II turns after the residues in a 30° x 30° box around the Type I and Type II definitions are removed . The residue preferences for the most common turn types have been described in previous reports [2 , 17 , 21 , 22] . Hutchinson and Thornton in 1994 analyzed the residue preferences in 2 , 233 examples of the common turn types in a set of 205 protein chains [17] . They noted the preference for proline at positions in turn types that require ϕ of approximately -60° , such as position 2 of Type I , Type II , and Type VIII turns , and the preference for Gly , Asn , Asp and Ser when ϕ>0° ( position 3 of Type II turns , position 2 of Type II’ turns , and position 2 and 3 of Type I’ turns ) . Hutchinson and Thornton observed the preference for Asp , Asn and Ser at positions that require a residue in the bridge region of the Ramachandran map ( ϕ = -80° , ψ = 0° ) , for instance at position 3 of Type I and Type II’ turns . Some residues are preferred because they make hydrogen bonds to other residues within the turn or even to the residue before or after the turn . Some hydrophobic residues are preferred when the turn is a tight beta turn connecting two beta-sheet strands , particularly for positions 1 and 4 of Type I’ and II’ turns , in some cases due to their role as part of hydrophobic cores of proteins . They also noted that preference for Pro at position 4 of Type VIII turns , which prevents residue 3 from being in the alpha region of the Ramachandran map [23] . Type VIII turns have a beta conformation residue at this position , while a Type I turn contains an alpha conformation at position 3 . Although many analyses of beta turns have been performed , there are a number of questions that remain outstanding . First , what is the nature of the Type IV beta turns , and can the existing nomenclature be extended to reduce their number ? Second , what are the true modes of the density in dihedral angle space for each beta turn ? For highly skewed populations , the most common conformation ( i . e . modes in the density ) for each beta turn would be useful in protein structure determination , refinement , and prediction . Third , what are the amino acid distributions of each beta-turn type and can they be rationalized ? These have not been reported in detail since the 1990s . Fourth , is the Cα1-Cα4 distance cutoff of 7 Å justified ? And finally , what is the frequency of beta turns in loops of various lengths ? This information would be useful in the modeling of longer protein loops , which remains a challenging problem [24] . With a much larger number of ultra-high resolution structures now available , we have undertaken a fresh analysis of the nature of beta turns in protein structures . We have classified all of the beta-turn conformations in a data set of 1 , 074 non-redundant protein chains from high-quality crystal structures of 1 . 2 Å resolution or higher . In keeping with previous analyses [2 , 11] , we define beta turns as four-residue segments with the Cα atoms of residues 1 and 4 having a distance of ≤ 7 . 0 Å and secondary structure of the 2nd and 3rd residues different from sheet ( E ) , helix ( H ) , π-helix ( I ) and 310-helix ( G ) according to DSSP [10] . Our clustering is performed with a distance measure based on the seven dihedral angles that connect the Cα atoms of residues 1 and 4: ω2 , φ2 , ψ2 , ω3 , φ3 , ψ3 and ω4 , similar to what we used for the clustering of antibody CDRs [25] . To identify clusters that truly represent peaks in the density of data points in the space spanned by these dihedral angles , we have utilized a density-based clustering algorithm , DBSCAN [26] . Franklin and Slusky have recently used a similar method to cluster 4 and 6-residue turns in outer-membrane beta barrels . After optimizing the clustering parameters of DBSCAN , we obtained a set of 11 clusters from the loop residues in our data set of 1 , 074 proteins . From kernel density estimates of the Ramachandran maps of residues 2 and 3 , we identified multiple peaks in the density consistent with the existence of multiple clusters in some of the DBSCAN clusters . We were able to subdivide several of these turn types with k-medoids based on the number of peaks in the density , resulting in a set of 18 distinct turn types . We apply a refined division of the Ramachandran map into distinct populated regions developed by Hollingsworth and Karplus [27] to derive a simple nomenclature for our 18 turn types . For our turn types we provide modal conformations and residue preferences at the first , second , third and fourth positions . We analyze the distribution of the Cα1-Cα4 distance in our beta-turn types , and find that some turn types have median Cα1-Cα4 distances slightly larger than 7 Å , if the 7 Å cutoff is relaxed . We also developed a cross-platform Python script for determining the beta-turn types in an input PDB structure .
With the PISCES server [28 , 29] , we compiled a set of 1 , 074 protein chains with a resolution equal to or better than 1 . 2 Å and pairwise sequence identity less than 50% ( S1 Data set ) , comprising protein sequences with 232 , 197 residues ( Table 1 ) . For clustering purposes a total of 16% of all residues were excluded due to missing backbone , Cβ , or γ heavy atom coordinates , chain breaks , presence of alternative conformations , or missing secondary structure information . The resulting data set is named RefinedSet with 195 , 322 residues suitable for turn type analysis . If we define beta turns as four consecutive residues where the central residues 2 and 3 are not in regular secondary structure ( alpha helix , 310 helix , π helix or beta strands ) and the Cα1-Cα4 distance is less than or equal to 7 Å , we can locate 13 , 030 beta turns in this data set built on 43 , 162 residues . This is less than 13 , 030 x 4 or 52 , 120 residues because 49% of beta turns in RefinedSet share residues with one or more other beta turns . Beta-turn conformations form overlapping clusters of varying density . Application of a single clustering method such as k-means , k-medoids , or DBSCAN failed to identify all known turn types and created merged clusters with multiple modes in the density . It would either ( 1 ) detect most known turn types but fail to separate turns of Type I and Type VIII or ( 2 ) separate these two types as two clusters but in addition produce too many small clusters and noise points . Ultimately , a procedure consisting of applying DBSCAN with optimized parameters followed by k-medoids to divide the initial DBSCAN clusters that clearly contained multiple peaks in the dihedral-angle space density proved fruitful . A single application of DBSCAN on all available 13 , 030 turns produced 11 clusters with optimized clustering parameters , eps and minPts ( Methods ) . Kernel density estimates ( KDEs ) of residues 2 and 3 indicated that four of these clusters could productively be subdivided into more than one cluster ( Fig 1 ) . The most populous DBSCAN cluster contains 8 , 455 turns of the classical Type I and VIII turns but in fact exhibits five peaks in the KDE of residue 3 ( Fig 1A ) . Repeated application of k-medoids to subdivide this cluster , produced 5 clusters that correspond to well-known features in the Ramachandran map of most residue types , including the alpha-helical region , the gamma-prime or inverse gamma turn region ( φ , ψ = -84° , 68° ) [30] , the prePro zeta conformation ( φ , ψ = -137° , 76° ) [31] , and two conformations in the beta region , roughly equivalent to parallel ( φ , ψ = -120° , 125° ) , and anti-parallel beta sheet ( φ , ψ = -134° , 157° ) , regions . Each of these 5 sub-clusters exhibited different amino acid distributions indicating that they are indeed distinct turn types . The classical Type II turn cluster from the DBSCAN run contained two peaks , which could easily be subdivided by k-medoids at opposite ends of the left-handed alpha helical region in the Ramachandran map for residue 3 ( Fig 1B ) . The upper peak of these conformations preferred glycine at residue 3 , while the other was dominated by non-glycine residues at position 3 ( see Figs 2 , 3 and 4 for comparison of amino acid profiles in all clusters ) . The cluster from DBSCAN in Fig 1C , consisting of some of the structures in RefinedSet with a cis peptide bond at residue 3 , exhibited two peaks in residue 2 that produced an equivalent to the previously defined Type VIb turn and a new turn type . Finally , DBSCAN produced a new turn type cluster with left-handed helical residues at position 2 and right-handed helical residues at position 3 . This cluster contained two dominant peaks in the density for residue 3 ( Fig 1D ) . We subdivided it into 2 separate clusters with k-medoids . k-medoids is a similar algorithm to k-means but instead selects actual data points as the cluster exemplars instead of a vector of average values in each dimension . In principle , one of these clusters could be further subdivided but we did not find distinct amino acid profiles , and the smaller peak contained a small number of points . Our clustering procedure results in a set of 18 turn types ( Table 2 and Figs 2 , 3 and 4 ) among the 13 , 030 beta turns detected in RefinedSet . We define the noise points identified by DBSCAN as a group called “Other” , which consists of 319 turns or 2 . 4% . Table 2 contains the populations among our 13 , 030 beta turns and the modal values of φ and ψ for residues 2 and 3 . Among our 18 turn types and the Other group: 6 types are new; 8 types are created by splitting existing Type II turns ( 2 types ) , Type VIII turns ( 4 types ) , and Type VIb turns ( 2 types ) ; and the remaining 4 types are updated versions of the remaining classical turn types ( I , I’ , II’ , VIa1 ) . We did not find a cluster of the classical VIa2 type with a cis peptide bond at residue 3 and φ2 , ψ2 = [-120° , 120°] and φ3 , ψ3 = [-60° , 0°] . Instead , a small number of points near these values occur in the Other group , but they are too spread out in dihedral angle space and not numerous enough to produce a cluster . By contrast , we do find two types within a cis peptide bond and a β-like conformation at residue 3 . One of these is close to the classical VIb turn; the other is new . Rather than the Roman numeral system in use since 1973 with abandoned numerals ( e . g . , III , V , and VII ) and a set of intricate prime symbols and alphanumeric sub-indices ( e . g . , I’ , II’ , VIa1 ) , we propose a new nomenclature using the Latin alphabet with type names assigned according to the one-letter region codes of Ramachandran map for the 2nd and 3rd residues of each beta-turn type ( Fig 5 ) . The figure shows our designations and the positions of the medoids of residues 2 and 3 ( orange dots ) . Our scheme is based on a detailed analysis of the distribution of residues from very high-resolution structures by Hollingsworth and Karplus [27] . Most of the one-letter Ramachandran codes are familiar to structural biologists . For convenience both in computer code and for people , we converted their Greek designations to the Latin alphabet , for example α to A , β to B , γ to G and so on . We reserve upper-case letters for the left side of Ramachandran map with negative φ and lower-case letters for positive φ . The upper and lower case letters have a point symmetry relative to the map center ( 0° , 0° ) . This is similar to nomenclatures developed by Dasgupta et al . [32] and Hollingsworth et al . [33] A non-standard designation in this scheme is to denote the gamma turn region at φ = -84° , ψ = 68° as “G” in upper case . Originally , this region was designated gamma-prime ( G’ or g ) and the corresponding region with φ > 0° was designated as gamma ( G ) . We also defined a new region , N , to the left and below the A-D density axis , which occurs in the dN cluster . This “plateau” region was determined to be sparsely populated but valid by Lovell et al . [34] . In only one case ( AB1 and AB2 ) we need a third character to distinguish the two turn types . AB1 is the upper one the Ramachandran map and AB2 is the lower one ( Figs 1A and 3 ) , which is an easy way to remember them . In Table 2 , we also provide a correspondence between the proposed and classical turn type nomenclatures . Figs 2 , 3 and 4 contain the amino acid distributions at all four positions of each beta-turn type , including the Other group . As expected , glycine and proline play important roles in many turn types . Proline is required for the two cis2 turn types , cisDA and cisDP , and the three cis3 turn types , BcisP , PcisP , and PcisD . It is also important in position 4 of the AZ turn where it is responsible for the zeta conformation at position 3 , which occurs for residues immediately before proline [27] . Proline also prevents the alpha conformation at the residue before it [23] , and is therefore significant in the AB1 and AB2 turns at position 4 and completely absent from AD turns at positions 3 and 4 . Glycine allows amino acids to occupy the “d” and “p” regions of the Ramachandran map , as exhibited by the ad , Pd , dD , dN , and pG type turns . As noted above , the former Type II turns have been divided into Pa and Pd; glycine is dominant at position 3 in the Pd cluster but not in the Pa cluster , which is instead dominated by Asn and Asp at position 3 . Asp and Asn are also dominant at position 2 of the ad turn ( former Type I’ ) and position 3 of the AG and AZ turns . There are sometimes subtle but statistically significant differences in the sequence profiles of very similar clusters . For instance , residue 3 of AB1 and AB2 has slightly different positions in the Ramachandran map . The upper peak ( AB1 ) prefers Gly , Ala , Ser , and Thr at position 3 , while the lower peak ( AB2 ) prefers Asn and Asp . The dD and dN clusters are different at position 3 , with dD preferring Asn and Asp , which are almost absent from position 3 of the dN turns . The latter contain hydrophobic beta-branched residues at position 3 ( Val and Ile ) . The new turn types , PDB entry , chain , and residue identifiers , dihedral angles of medoids and modes , and secondary structure context are provided in BetaTurnLib18 turn library: S1 Text ( text format ) and S1 Table ( Excel format ) . We validated the new turn types by examining their electron density both visually and quantitatively . To analyze the electron density of atoms in beta turns , we utilized the EDIA program ( Electron Density Support for Individual Atoms ) [35] , which integrates electron density in a sphere around each heavy atom and penalizes both positive and negative density in electron density difference maps . EDIA demonstrates that turns in each cluster contain only a few structures ( 0–2 . 4% ) with substantial inconsistencies ( Fig 6 ) . The Other group has a higher rate of substantial inconsistencies with 6 . 1% of these turns having poor electron density . Nevertheless , the majority of Other group turns are valid conformations , but sufficiently spread out that they do not form a new cluster with at least 25 points ( 0 . 2% of RefinedSet ) . The mean EDIA scores for each cluster are provided in Table 3 , where the EDIA score for each turn is defined as the minimum value for the backbone atoms defining the turn , i . e . atoms from Cα1 to Cα4 , including the carbonyl oxygens . The electron densities of representative structures near the modes in the seven-dimensional probability distribution are shown for all 18 beta-turn types in Fig 7 . In addition , the figure shows two structures from the Other group that have good electron density , demonstrating that turns in the Other group are valid conformations . These were chosen to be far away from any of our cluster modes . Both of these structures have a cis proline at residue 4 , but different Ramachandran regions for residues 2 and 3 from each other . There are 21 turns with a cis proline at residue 4 in the Other category . They do not cluster well with our seven-dimensional dihedral angle metric . The results of clustering procedures can be validated with a number of measures [36] . Most such measures compare the distances of points within clusters to the distances between points in different clusters in some way . Density-based clustering presents some challenges for standard validation measures , since clusters may be shaped irregularly and have different densities and variances about their medoids compared to distance-based clustering algorithms such as k-means [37] . Nevertheless , we utilized the silhouette score [38] which compares the average distances of points to other points in the same cluster to the average distance of points to the nearest neighboring cluster for each point . For each point i , we calculate the average distance to the points in each cluster ( one value for each cluster including the cluster i belongs to ) . If a ( i ) is the average distance to points in the same cluster as point i and b ( i ) is the distance to the nearest cluster different from that of point i , then the silhouette score is defined as Si=bi-ai/maxai , bi . If point i is near other points in its own cluster , but very far away from the nearest other cluster , then S ( i ) is close to 1 . 0 . If point i is equally close to its own cluster and another cluster , S ( i ) is close to 0 , and if point i is closer to points in another cluster than its own cluster , S ( i ) is negative . This can occur if point i is misplaced or if point i’s cluster has a high variance . A graph of silhouette scores for our 18 clusters and the Other group is shown in Fig A in S1 Supporting information . Each of our clusters has an average positive silhouette score ranging from 0 . 27 to 0 . 93 . Eleven clusters have values of 0 . 6 or higher . Some of the clusters that arose from sub-clustering of the original DBSCAN clusters with k-medoids have relatively low values of the average silhouette score , including Pa ( 0 . 27 ) , AB2 ( 0 . 29 ) , and AZ ( 0 . 32 ) . This is not surprising , since they are very close to other clusters ( Pd , AB1/AZ , and AB2/AD/AZ respectively ) , but as shown in Fig 1 , these clusters represent peaks in the density of the Ramachandran maps of residues 2 and/or 3 , and they have distinct sequence profiles . The silhouette score has difficulty in scoring sub-clusters that are close together but far from other clusters [36] . We repeated the silhouette analysis by merging AB1 with AB2 , AZ with AG , Pd with Pa , and dD with dN to see whether the subclusters are distinct from the rest of our clusters even if they are near related subclusters ( Fig B in S1 Supporting information ) . The AB1-AB2 , AZ/AG , Pd/Pa , and dD/dN merged clusters now have higher average silhouette scores of 0 . 49 , 0 . 48 , 0 . 80 , and 0 . 70 respectively . The robustness of our clusters may also be assessed by reassigning points in our data set to the cluster with the closest medoid to that point . If we define a distance cutoff for assigning points to a cluster or the Other category , and if points are mostly assigned to the correct cluster , then our cluster medoids can be used successfully to assign beta turns in structures not in our data set , including new structures deposited in the PDB . To detect beta turns , we developed a Python script that either assigns one of 18 turn types based on the closest cluster medoid or flags it as Other if the distance between the input turn and the closest medoid is above a preselected distance threshold . This maximum distance is equal to the mean plus 3 standard deviations of 12 , 711 distances between turns of one of 18 types ( Other excluded ) to their cluster medoids , which is equal to 0 . 2359 in units of our distance metric or 28 . 1° in units of the average angle equivalent of our metric . We tried different statistics , target thresholds and individual thresholds for each of 18 turn types; a simple single threshold at 3 standard deviations works as well as cluster-specific means and standard deviations in terms of reclassifying beta turns in our set of structures . The results are presented in Table 3 . To understand how the assignment tool performs , we need to account for how the original assignments of turn types of 13 , 030 beta turns are transformed by reassignment . Most turns are reassigned to the same turn type: ( 13 , 030–311–255 ) / 13 , 030 = 95 . 7% . Some turns will be reassigned to one of the other 18 clusters ( 311 ) ; some assigned turns will be reassigned to Other ( 255 ) ; some turns will be reassigned from one of the other 18 clusters ( 256 ) ; and some turns will be reassigned from the Other category ( 65 ) . All of these numbers are presented in Table 3 for each cluster and the Other category . We can assess the assignment tool for each cluster in the way that binary predictors are evaluated . We calculated true positive rate ( TPR ) as the fraction of the original clustering that is preserved in the reassignments . This is equal to 100 . 0* ( Column_4 + Column_5 + Column_6 ) / ( Column_4 ) . The TPR values range from 91% to 100% for the regular clusters , and 79 . 6% for the Other category . We calculated the positive predictive value ( PPV ) as the percentage of turns in the reassigned set that came from the original assignments . From the table , this is equal to 100 . 0* ( Column_4 + Column_5 + Column_6 ) / ( Column_9 ) . For 12 of our 18 clusters , this value is well over 90% . It is lower for clusters that are close to other clusters , widely dispersed , or very small clusters , including AZ ( 81 . 8% ) , AG ( 79 . 9% ) , pG ( 72 . 7% ) , and cisDP ( 69 . 4% ) . The value is 49 . 9% for the Other category because of the number of points moved from regular clusters into the Other group ( 255 ) . These points are far from the medoids of non-spherically shaped clusters . They represent only 2% of all the data points . Finally , as a part of validation of the clusters , since a high threshold of 1 . 2 Å crystal structure resolution was applied to RefinedSet in order to produce more reliable data , we used the tool to determine whether there is a distribution bias in the established turn types as a function of resolution . The frequencies for the most turn types remained the same when proteins at 1 . 0-1 . 2 Å and 2 . 0-2 . 2 Å ( Fig 8 ) are compared . The relative frequencies of some turn types do change with resolution: Pd ( 9 . 7% to 8% ) , ad ( 5 . 0% to 4 . 0% ) , AG ( 1 . 3% to 2% ) , and Other turns ( 3 . 2% to 4 . 5% ) We used a value of 7 . 0 Å for the Cα1-Cα4 distance to define beta turns within our data set . This value has been used in almost all previous studies on beta turns [2 , 4 , 11 , 17 , 20 , 39] . It is slightly larger than the minimum at about 6 . 5 Å between two peaks on the distribution of Cα1-Cα4 distances in all four-residue segments in loops in our data set ( Fig 9 , bottom right ) . In Table 2 , we provide the median Cα1-Cα4 distance for each of our clusters when beta turns are defined with the classical 7 Å Cα1-Cα4 cutoff . However , we were curious if some beta turns might have a wider distribution of this distance if a larger cutoff or no distance cutoff is used , while maintaining a turn-like conformation . We assigned all four-residue segments in our data set of loops ( those residues not in regular secondary structures ) to one of our clusters , the one with the closest medoid , if the distance to the closest medoid was less than or equal to the cutoff distance described above ( 0 . 2359 in units of our distance metric ) . If the distance was greater than this value , we put the segment into the Other category . Kernel density estimates of the Cα1-Cα4 distance when no Cα1-Cα4 cutoff is applied are shown in Fig 9 for all of our 18 clusters , the Other group ( four-residue segments not near one of our cluster medoids in dihedral angle space ) , as well as All four-residue segments in the loop data set ( the union of all 18 clusters and the Other group ) . The maximum possible Cα1-Cα4 distance is about 11 . 4 Å ( 3 x 3 . 8 Å per Cα-Cα distance across a single peptide bond ) . In beta sheets , the average Cα1-Cα4 distance is over 10 Å . After examining many structures , we observed that four-residue segments with distance of about 9 Å are L-shaped with three extended residues and the fourth residue hooking left or right . Structures at 8 Å are shaped like turns but wider and shallower than turns with Cα-Cα distances less than 7 Å . All of our 18 clusters have a median Cα1-Cα4 distance of 8 . 0 Å or less , but several of them have significant density above the canonical distance cutoff of 7 Å , especially AB2 , dD , dN , and pG . By contrast , the majority of segments in the Other category have Cα1-Cα4 distances of 9 Å or more and are not turns at all but rather extended structures . The median values for the Cα1-Cα4 distance for each of our clusters are provided in Table 3 . We produced Ramachandran plots for all 18 clusters and the Other category without a Cα1-Cα4 distance cutoff . An example is provided in Fig 10 for AD turns . The rest are provided in S2 Supporting information . In these plots , we show the Ramachandran distribution of residues 2 and 3 for 4-residue segments with Cα1-Cα4 distance ≤ 7 . 0 Å in blue and > 7 . 0 Å in red , as well as histograms of the distances of each group separately and together . Fig 10 indicates that for AD turns , the distance rises above 7 . 0 Å in the region where φ2 ≤ -90° and ψ2>0° . Given these results , we decided to perform DBSCAN clustering without a Cα1-Cα4 distance cutoff to check whether there are additional turn-like clusters hiding in our data set . We found several common Ramachandran combinations for residues 2 and 3 , such as BB , aB , and BA , but none of these had average Cα1-Cα4 distances less than 9 Å , and none of them had density below 7 Å . At different values of DBSCAN parameters eps and minPts , we could find all of our 18 clusters ( sometimes merged as in Fig 1 ) . We conclude that our clustering with the 7 Å cutoff located all significant beta-turn types in our data set , and that running our beta-turn assignment tool , BetaTurnTool18 , with different cutoffs up to 8 . 5 Å may be useful in some circumstances ( see below ) . With a high-resolution data set and a new set of beta-turn types , we were interested in the frequency and distribution of beta turns in protein loops of various lengths . This kind of information may be useful in loop structure prediction . For this purpose , we cannot exclude beta turns based on poor electron density or multiple conformations . For residues in our set of 1 , 074 proteins with alternative conformations , we selected a primary conformation with the highest occupancy . This dataset is named CompleteSet containing 224 , 250 residues with reported coordinates ( Table 1 ) . To identify loops , we needed to consider how to treat 310 helices since three-residue 310 helices occur frequently within longer loops and might be considered part of the loop rather than significant elements of regular secondary structures . To investigate this , we examined kernel density estimates of the Ramachandran maps of residues in 310 helices of different lengths ( Fig 11 ) . A large majority of 310 helices are very short– 81% are length 3 and 12% are length 4 ( Table 4 ) . 310 helices of length 1 or 2 are impossible in DSSP . The kernel density estimates demonstrate an interesting phenomenon . At positions 1 , 2 , and 3 of the length-3 310 helices , the mode in the φ-ψ density moves progressively upwards and leftwards: φ , ψ = [-57° , -37°] , [-67° , -17°] , and [-93° , 0°] . This is roughly equivalent to two consecutive AD turns with the middle residue in an intermediate position between A and D . The same is true of length-4 310 helices . For the longer 310 helices , the last residue is always clearly in a delta position [-90° , 0°] and the first residue is clearly alpha [-60° , -30°] , while the intervening residues are in between , sometimes with points spanning both populations . This is consistent with the early analysis of beta turns by Lewis et al . that defined Type I turns as [φ2 , ψ2 = -60° , -30°; φ3 , ψ3 = -90° , 0°] and Type III turns as [φ2 , ψ2 = -60° , -30°; φ3 , ψ3 = -60° , -30°] [11] , which were later identified by Richardson as mostly restricted to 310 helices [2] . The pattern we observe is slightly more complicated with the modal value shifting from A to D along the 310 helix . The exact modal φ , ψ values for 310 helices of varying length are reported in Table 4 . Similar distributions of φ , ψ were found by Pal et al . for short 310 helices [12] . We were interested in whether including 310 helices would alter our clusters of beta turns , so we ran our clustering protocol for 3 cases: 1 ) without GGG ( as defined by DSSP ) ( as already presented above ) ; 2 ) with GGG; and 3 ) with both GGG and GGGG . There are no major changes in the clusters except for the AD cluster ( Fig C in S1 Supporting information ) . With the inclusion of GGG and/or GGGG , there are significantly more beta turns in the AD cluster overall . The distribution of φ3 , ψ3 in the AD cluster changes with the inclusion of GGG; a second peak forms near φ3 , ψ3 = [-70° , -20°] . However , we refrained from forming an AA cluster , since there is no significant variation in the amino-acid profiles of the AD and AD/AA clusters in the three cases ( Fig D in S1 Supporting information ) and the differences in dihedral angles are only about 20° in φ3 and ψ3 . Given these data , we established two criteria with regard to 310 helices for our definition of protein loops . First , short 310 helices often directly abut alpha helices in DSSP , and represent distortions detected in the i , i+4 hydrogen bonding pattern of the alpha helix [40]; therefore we exclude such 310 helices immediately adjacent to alpha helices from loops . For example , a continuous fragment with DSSP code ( H = helix; G = 310 helix; T = turn; C = coil ( other ) ) , HHHHGGGGCCTTCCCGGGHHHH , has only CCTTCCC as a loop while the flanking Gs are still a continuation of an alpha helix region before and after the loop . Second , we treat 310 helices of length 3 not adjacent to alpha helices as loop residues , since they are identified by DSSP with a single internal i , i+3 hydrogen bond within the loop between the residue that precedes the 310 helix and the last residue of the 310 helix . These short 310 helices are common in long loops and may productively be considered part of the loop rather than an intervening element of regular secondary structure . They are not likely to contribute more than one amino acid to the hydrophobic core , and in many cases not even that . Indeed , in some definitions of secondary structure , a 310 helix requires two or more hydrogen bonds [6] and these three-residue 310 helices would not be identified as such . For example , GGG in HHHHCCCCGGGCCCCHHHH is still considered a part of the loop with DSSP designation CCCCGGGCCCC . We define longer 310 helices ( ≥4 ) as elements of regular secondary structure , which therefore separate their neighboring segments into two loop regions . For instance , GGGGG in HHHHCCCCGGGGGCTTCHHHH produces two loops , CCCC and CTTC . To contain a beta turn , a loop must be at least two residues long . With this loop definition , in CompleteSet we detected 21 , 454 turns with the canonical 7 Å Cα1-Cα4 cutoff ( 65% more than in RefinedSet ) and assigned them to the closest turn type in Table 2 with the distance measure in the seven-dimensional dihedral angle space and the distance cutoff for the Other category . Beta turns comprise 29% of residues in the CompleteSet and 63% of the residues in loops between regular secondary structures ( Table 1 ) . In CompleteSet , we located 17 , 176 loops of length from 1 to 81 . The frequency of a loop exponentially decreases with loop length; approximately there is a 10-fold reduction every time a loop becomes 10 residues longer ( Fig 12 ) . 95% of all loops are 1 to 16 residues long . Very long loops are rare; only 0 . 59% of loops are 30 or more residues long ( the 81-residue “loop” is the snow flea antifreeze protein in PDB entry 3BOG ( chain A ) which has no regular secondary structure as defined by DSSP ) . The distribution of loop lengths with all 310 helices considered to be regular secondary structure ( and not a part of loops ) is shown in Fig E in S1 Supporting information . We calculated an average number of beta turns identified in each loop of a particular length and estimated ± one standard deviation confidence range ( Fig 13 ) . There is on average one beta turn per 4 . 8 residues . If we consider longer loops with 9 or more residues , there are 11 , 270 beta turns among 49 , 299 loop residues , leading to approximately the same value of one beta turn per 4 . 4 loop residues . The results with 310 helices considered regular secondary structure are shown in Fig F in S1 Supporting information . If 310 helices are considered regular secondary structure and not part of loops , there is on average one beta turn per 5 . 5 loop residues for all loops . A total of 64% ( Table 1 ) of beta turns overlap other beta turns by one or more residues . 310 helices of length 3 plus the preceding residue usually comprise two overlapping beta turns . In addition , other beta-turn types can also combine to produce immediately overlapping , consecutive beta turns . We define double turns as a set of 5 residues with residues 1-2-3-4 and 2-3-4-5 forming individual beta turns . These double turns occur most frequently; we found 5 , 372 double turns having 82 combinations of our 18 turn types ( Table 5 ) . The most common double turn ( 62% ) is AD-AD made of two consecutive AD turns , which are the most prevalent turn type in the data set ( 49% , Table 2 ) . The 10 most frequent turn type combinations account for 90% of all double turns . For 1 , 000 triple turns ( Table 6 ) defined as a stretch of 6 residues with turns at positions 1-2-3-4 , 2-3-4-5 , and 3-4-5-6 , the 7 most frequent combinations out of the observed 65 comprise 82% of triple turns . As for single and double turns , AD-AD-AD is again the most common triple turn , representing half of all cases . Of course turns can overlap by one or two residues ( e . g . 1-2-3-4 and 3-4-5-6 ) but we did not study this separately . We have compiled BetaTurnLib18 files that contain data on all 18 established beta-turn types in S1 Text ( tab-delimited text format ) and S1 Table ( Excel format ) . For each turn type we provide the following information: the absolute number and percentage of dataset points in each cluster , the older nomenclature for existing types , our new nomenclature , median and mean Cα1-Cα4 distances ( both with and without the 7 Å cutoff ) , medoids and modes for dihedral angles pulled from RefinedSet with the following information: <ω2 , φ2 , ψ2 , ω3 , φ3 , ψ3 , ω4> , <aa1 , aa2 , aa3 , aa4> , <sec1 , sec2 , sec3 , sec4> , res_id ( 1 ) , chain_id and pdb_id . Our library , BetaTurnLib18 is freely available to be incorporated into any third-party software for detection , type prediction , and modeling of beta turns in proteins . In addition , we provide Python software , BetaTurnTool18 supported on Linux , Mac , and Windows platforms coded with back-compatible Python 2 . It reads a PDB-formatted or mmCIF-formatted coordinate file of a protein structure , automatically runs DSSP to assign secondary structure in the input file , and parses our turn library file . With this information available , the Python script detects beta-turn positions and types . Beta-turn location is determined by verifying chain connectivity of each four-residue stretch , satisfying the default 7 . 0 Å Cα1-Cα4 distance constraint calculated from the Cartesian coordinates and checking the secondary structure of the 2nd an 3rd residues assigned by the DSSP program [10] . The program also allows the user to set a different Cα1-Cα4 distance cutoff , which may be useful for some beta-turn types that possess a broader distribution of Cα1-Cα4 distances ( Fig 9 ) . In addition , BetaTurnTool18 has two options to search for beta turns within loops either with or without inclusion of isolated 310 helices . Since some beta turns are similar to more than one of our beta-turn types , the first and second most probable turn types are reported based on the distance to the first and second closest medoids calculated with the same distance metric as used in the clustering , unless the closest medoid is greater than 0 . 2359 units in our distance metric , in which case the turn is reported as Other . We estimate confidence levels for the first and second most probable types , which sum up to 1 , as follows: Confidence ( 1sttype ) =1−D ( closestturntype ) D ( closestturntype ) +D ( 2ndclosestturntype ) Confidence ( 2ndtype ) =1−D ( 2ndclosestturntype ) D ( closestturntype ) +D ( 2ndclosestturntype ) Our script prints these confidence levels as integers in the [0 , 9] range . 0 and 9 correspond to the [0 , 10 ) % and [90 , 100]% confidence intervals respectively . A border-case flag ( “+” ) is returned when the integer confidence level is 6 or below , and a “ . ” ( dot ) is returned when it is 7 or higher . In border cases we recommend considering both turn types for modeling . Our tool accounts for chain breaks in coordinates when it prints a single-character alignment of the complete amino acid sequence , its DSSP secondary structure , turn types , integer confidence levels , and border-case flags . Downloads and complete documentation of our tool or library are available at dunbrack . fccc . edu/betaturn and github . com/sh-maxim/BetaTurn18 .
From a set of 13 , 030 beta turns identified in 1 , 074 proteins of resolution 1 . 2 Å or better , we have identified 18 beta-turn types , compared to the 8 beta-turn types in common use ( I , I’ , II , II’ , VIa1 , VIa2 , VIb , and VIII ) . Our nomenclature is based on positions in the Ramachandran map of residues 2 and 3 . Six of the turn types are essentially new: dD , dN , Dd , pG , cisDA , and cisDP . Among them there are two new types of turns with left-handed residue 2 and right-handed residue 3 ( “dD” and “dN” ) , and the new complementary turn type with right-handed residue 2 and left-handed residue 3 ( “Dd” ) . There is a new turn type “pG” and two new turn types with a cis peptide bond at position 2: “cisDA” and “cisDP . ” Four of the existing turn types remain essentially unchanged but have been renamed: Type I is now “AD” and its complement Type I’ is now “ad”; Type II’ is now “pD”; and Type VIa1 with a cis peptide bond at residue 3 is now “PcisD” . Eight turn types are subtypes of the classical turn types: Type VIII turns have been replaced with 4 subtypes ( “AB1” , “AB2” , “AZ” , and “AG” ) with distinct peaks in the density in the Ramachandran map of residue 3 and distinct residue preferences at one or more of the 4 turn residue positions . Type II turns now consist of two subtypes , “Pd” and “Pa” , defined by the presence or absence of Gly at residue 3 respectively . Type VIb turns have been split into two: BcisP ( close to the original definition ) and PcisP with a change in φ2 . Turns of Type VIa2 with a cis peptide bond at residue 3 , φ2 , ψ2 = [-120° , 120°] and φ3 , ψ3 = [-60° , 0°] , were too spread out and contained fewer than 25 points in RefinedSet ( 0 . 2% ) , too few to form a cluster; they were left in the Other group . Our decisions in clustering were based on two criteria: keeping the total number of turn types at a minimal practical level for existing and future applications; and justifying new cluster formation either by a significant distance from other formed clusters or uniqueness of a four-residue turn profile . Thus , we abandoned formation of a new cluster if its amino acid turn profile was not unique enough or its conformation spread could be justified by continuous , low-energy torsion angle variation . After running DBSCAN and subdividing some clusters , 2 . 4% of the turns were left as Other . We conclude that the 18 types cover 98% of turn conformations and that we have not overlooked any major turn conformations . We have also taken a census of beta turns in loops in our data set and determined that there is one turn per 4 . 8 residues , regardless of loop length . Many turns overlap other turns , such that 63% of residues in loops are in beta turns . We provide a tab-delimited library file , BetaTurnLib18 of the new 18 turn types . For each type , we list proposed and existing names , percentage , and geometrical conformation expressed with torsion angles of a real dataset sample . For each cluster , two verified sample conformations with reliable electron density are given: closest to a mode and closest to a medoid . This library may be used in third-party software for modeling and prediction of beta turns . In addition , we prepared an easy-to-use Python tool , BetaTurnTool18 ( supported on Linux , Mac and Windows platforms ) that reads a PDB-formatted or mmCIF-formatted coordinate file of a protein structure as input . The tool assigns secondary structure with included redistributable DSSP program , uploads our turn library , and finally locates turns and identifies their types . The output includes the first and second most probable types with assigned confidence levels for each detected beta turn . Beta turns in proteins have been studied for 50 years and many classification schemes have been presented . Ultimately a consensus has arisen consisting of 8 turn types: I , I’ , II , II’ , VIa1 , VIa2 , VIb , VIII , and a catch-all “Other” turn type IV . We have found that some of these turn types represent more than one mode in the density and should be subdivided into further turn types . One of the more interesting results of the new clustering are the turn types that represent combinations of right-handed helical and left-handed helical conformations of residues 2 and 3 . We find both combinations: left-right ( types dN and dD ) and right-left ( Dd ) . These are not the same as the type V and V’ turns defined by Lewis et al . [11] , which had dihedrals of ( -80° , +80° ) and ( +80° , -80° ) for type V and ( +80° , -80° ) and ( -80° , +80° ) for type V’ . The dihedral angle modes for turn types dN , dD , and Dd are substantially different from the Type V and V’ values ( Table 2 ) . Pal and Chakrabarti performed an extensive analysis of cis peptide bonds in proteins , and in particular they classified turns with cis peptide bonds at position 3 [41] . They divided the turns based on the presence of hydrogen bonds rather than the dihedral angles connecting the Cα1 and Cα4 atoms as we have done . This resulted in a set of seven turn types , with different dihedral angle values provided depending on whether residues 2 and 3 were both proline ( Pro-Pro ) , both not proline ( Xnp-Xnp ) or the more common Xnp-Pro type . Because their data set was very small ( 147 proteins ) , only 4 of the types had more than 10 examples: VIa1 , VIa2 , VIb1 , and VIb2 . The others are VIb3 , VIc , and VId . Their VIa1 and VIa2 types are distinguished by the presence or absence respectively of a hydrogen bond between the carbonyl oxygen of residue 1 and the NH of residue 4 . The VIa2 turns have a more negative value of φ2 than VIa1 turns for both Xnp-Pro and Xnp-Xnp cases . We have 125 PcisD turns which are equivalent to VIa1 turns , two of which are Xnp-Xnp and 15 of which are Pro-Pro turns . Our Xnp-Xnp and Pro-Pro PcisD turns are closer to the medoid of PcisD than they are to the values quoted by Pal and Chakrabarti . As described above , even in our much larger data set , we only have 15 beta turns within ± 30° of the VIa2 type ( as defined by Hutchinson and Thornton [17] ) , all of which are of the Xnp-Pro type . They are distributed within a 50° by 50° region in φ2 , ψ2 . These are classified as Other in our set of turn types . The VIb1 and VIb2 beta turns defined by Pal and Chakrabarti are distinguished by the absence or presence of a hydrogen bond of the residues immediately before and after the beta turn . The VIb3 turn has a CH-O hydrogen bond between residues 2 and 3 and a φ2 near 180° . We find the Xnp-Pro VIb1 and VIb2 turns of Pal and Chakrabarti are well represented in our data . Turns near their VIb1 values are a mix of our BcisP and PcisP clusters; turns near their VIb2 values are all BcisP . We have six Pro-Pro turns in PcisP , three closer to their VIb1 and three closer to their VIb2 but all closer to our PcisP medoid . We find only five Xnp-Pro turns close to the dihedral values of VIb3 turns , but they are also closer to our medoid than VIb3 . We find no VIc or VId turns in our data set . Our new clustering will be useful in determining and refining structures with X-ray crystallography , NMR , and cryo-EM methods . Structure determination , especially at low resolution , is dependent on templates of common conformations and statistical distributions of backbone and side chains [42] . Loops are particularly difficult to model at low resolution [43] and our clear definitions of beta turns may be useful in refining these elements of protein structures . Long loops are also difficult to model during the process of structure prediction by comparative modeling [44 , 45] , and better sampling and modeling of beta turns may prove useful . Finally , the interpretation of missense mutations , either inherited or those that arise somatically in cancer , remains challenging [46] . Structural approaches have contributed to solving this problem , and mutations in beta turns may be particularly disruptive to loop conformations [47] . Given the ubiquitous nature of beta turns , the clustering , nomenclature , and identification script we have developed should provide useful tools for protein structure determination , refinement , and prediction programs as well as useful tools for protein structure , function , and evolutionary analysis .
A dataset of high-resolution 1 , 074 protein chains was compiled with PISCES [28 , 29] . It consists of PDB chains of length 40 or more amino acids , from X-ray structures with resolution 1 . 2 Å or better , R-factor of 0 . 2 or better , and 50% or lower inter-chain sequence identity , which is now determined by local HMM-HMM alignment with HHSearch [48] . Their 8-label DSSP secondary structure was established from a large file holding DSSP secondary structure for the entire PDB which was downloaded from the RCSB website . This raw dataset had 232 , 197 residues in amino acid sequences , and 224 , 250 residues with known backbone coordinates . From this raw dataset we compiled two datasets: CompleteSet and RefinedSet . For CompleteSet we extracted residues with at least backbone coordinates for N , Cα , and C atoms and chose a primary conformation for each residue . For a single-conformational residue , there is only one conformation available and it was pulled into CompleteSet . For a multi-conformational residue , a primary conformation was pulled into CompleteSet . The primary residue conformation has the highest occupancies for its atoms ( in PDB files , it is predominantly the ‘A’ alternative conformation but not always ) . CompleteSet was used for beta-turn statistical analysis which is representative of the general protein population . Next we generated RefinedSet which is a subset of CompleteSet . RefinedSet contains a set of higher-quality residues to generate a set of higher-quality beta turns for clustering to identify reliable beta-turn types . The following steps were taken to generate RefinedSet: ( 1 ) we skipped all residues with any missing backbone , Cβ , and/or γ heavy atoms; ( 2 ) we skipped residues with more than one conformation and required the atom occupancy to be 1 . 0 for the backbone atoms; ( 3 ) we combined the DSSP secondary structure information with a set of PDB coordinates and skipped any residues with undefined DSSP secondary structure . These actions produced RefinedSet of a total of 195 , 332 residues . We detected beta turns in RefinedSet according to the following beta-turn definition . For the clustering procedure , a contiguous four-residue segment is a beta turn if the Cα atoms of the 1st and 4th residues have a distance of ≤ 7 . 0 Å and the 8-label secondary structure of the 2nd and 3rd residues is different from beta-sheet ( E ) , alpha-helix ( H ) , π-helix ( I ) and 310-helix ( G ) according to DSSP [10] . For the census of beta turns within loops in CompleteSet , ( 1 ) we counted three-residue 310-helices within loops ( CCCCGGGCCCC ) as part of the loop and the residues were allowed to be labeled beta turns; ( 2 ) we excluded 310-helices immediately adjacent to alpha helices ( HHHHGGG , GGGHHHH ) . For beta-turn conformation clustering , we have to define a distance metric–a discriminative measure of how different two beta-turn conformations are . Our beta turn definition is defined by the Cα ( 1 ) -Cα ( 4 ) distance . Starting from Cα ( 1 ) , the Cα ( 4 ) atom can be built from standard bond lengths and bond angles and seven dihedral angles: ω ( 2 ) , φ ( 2 ) , ψ ( 2 ) , ω ( 3 ) , ω ( 3 ) , ψ ( 3 ) , ω ( 4 ) . Inclusion of two flanking torsion angles: ψ ( 1 ) and φ ( 4 ) created too many sub-clusters with no distinct amino acid signatures . Since torsion angles are periodic , we used a metric from directional statistics [49] , which is equal to the squared chord length between a pair of torsion angle values placed on the unit circle: D ( θ1 , θ2 ) =2 ( 1−cos ( θ1−θ2 ) ) The factor of 2 is unnecessary but we have included it as we did in earlier work on the clustering of conformations of complementarity-determining regions of antibodies [25] . For two beta turns , the distance between them is: D=D ( Δω2 ) +D ( Δϕ2 ) +D ( Δψ2 ) +D ( Δω3 ) +D ( Δϕ3 ) +D ( Δψ3 ) +D ( Δω4 ) where the differences in angles are taken between the dihedral angles in the two turns . Clustering of RefinedSet was performed with the DBSCAN algorithm . We manually optimized the eps and minPts in order to minimize the number of Other points and to produce clusters with [φ ( 2 ) , ψ ( 2 ) ] and [φ ( 3 ) , ψ ( 3 ) ] restricted to well-known regions of the Ramachandran map for each cluster . This was possible for all the clusters returned by DBSCAN with parameters eps = 14° and minPts = 25 , with the exception of Type I and Type VIII turns . These turns could not readily be separated by DBSCAN since the density for residue 3 is more or less continuous from the alpha to the beta regions of the Ramachandran map . Kernel density estimates of this cluster showed 5 peaks in the density . Three other clusters also showed more than one peak in the density of residue 3 . The k-medoids algorithm was applied sequentially to these four clusters in order to find clusters associated with each peak in the [φ ( 3 ) , ψ ( 3 ) ] density . For each of resulting 18 clusters , we identified and reported both medoid and mode of a cluster density in the seven-dimensional ω2-φ2-ψ2-ω3-φ3-ψ3-ω4 dihedral space . The medoid and mode serve as representative conformations for each turn type . We scanned a distance matrix of each cluster to find a sample with the most number of neighbors within the smallest radius covering at least 20% of cluster members and at least 20 data points . We approximate a mode with such a turn sample closest to it . The medoid conformation is more sensitive for turn type detection while the mode conformation is more useful in modeling of an unknown protein structure . We calculated 1D and 2D kernel density estimates ( KDE ) for a single circular variable ( a single torsion angle ) and two circular variables ( two torsion angles ) respectively with a von Mises kernel which is suitable for circular statistics . For example , for the Ramachandran map distribution , we use a two-dimensional kernel density estimate that we developed for the backbone-dependent rotamer library [50]: ρ^ ( φ , ψ ) =1N∑i=1NKh ( ||φ−φi|| ) Kh ( ||ψ−ψi|| ) =14π2N∑i=1N1 ( I0 ( κ ) ) 2exp ( κ ( cos ( φ−φi ) +cos ( ψ−ψi ) ) ) We utilized a 10-fold maximum-likelihood cross-validation to establish the concentration parameter κ for each kernel density estimate . For 36 medoid / mode representatives , we report beta turns satisfying these conditions: 1 ) close to medoid / mode by our distance metric , 2 ) atomic EDIA backbone and side-chain atom electron density better than the median electron density in each cluster , 3 ) passing a visual inspection of protein geometry and 2Fo-Fc and Fo-Fc electron density maps for any inconsistencies in the placement of backbone and side-chain atoms of the i-1 to i+4 residues . We manually selected turns that satisfy these criteria . Our Python tool for beta-turn type assignment depends on widespread python2 language , numpy [51] and biopython [52] libraries and DSSP software [10 , 53] . These are free redistributable software and are easy to download and install . In addition to biopython and numpy libraries we used sklearn [51] and scipy [54] libraries for dataset preparation , residue pruning , and clustering . These libraries allowed PDB parsing , bond and torsion angle calculation , clustering , and scatter plotting . We downloaded electron density maps from European Bioinformatics Institute at www . ebi . ac . uk and ran EDIA software to analyze agreement between turn conformations and their electron density [35] . | Folded proteins consist of elements of regular secondary structure , such as alpha helices and beta sheets connected by irregular structures called loops . Loops have a varying length and typically contain U-shaped beta turns which abruptly change the direction of the chain . Beta turns are formed by four sequential amino acid residues and adopt specific conformations which have been classified into eight types since the 1970s . Based on a larger set of very detailed protein structures and thorough statistical data analysis , the previous set of beta-turn types was revised to include 7 existing turn types , 5 subtypes of the existing turns , and 6 new types . Their properties and amino-acid sequence propensities are analyzed . We propose a self-explanatory turn nomenclature , based on the conformations of residues 2 and 3 of the beta turn , that is much easier to remember than the old nomenclature . We developed a library of 18 turn types and software to assign them to an input protein structure . The software and new turn types should advance fundamental understanding of protein structure as well as benefit applications for protein structure prediction , determination , and refinement . | [
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| 2019 | A new clustering and nomenclature for beta turns derived from high-resolution protein structures |
The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals . Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences , models often assume that contacts are chosen at random and thereby ignore the sociological , temporal and/or spatial clustering of contacts . Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics . To model population structure , we generalize the configuration model which has a tunable degree distribution ( number of contacts per node ) and level of clustering ( number of three cliques ) . To model epidemic dynamics for this class of random graph , we derive a tractable , low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic . We find that the interaction between clustering and the degree distribution is complex . Clustering always slows an epidemic , but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size . We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous , and the magnitude of this bias increases with the amount of clustering in the network . We apply this approach to model the high clustering of contacts within households , using contact parameters estimated from survey data of social interactions , and we identify conditions under which network models that do not account for household structure will be biased .
Contacts sufficient for transmission of infectious disease occur repeatedly within stable relationships such as between sex partners or within households and workplaces . Epidemiologists increasingly use random network models that explicitly capture such interactions to study disease dynamics [1] . This work has shown that infectious disease dynamics can be profoundly influenced by two key network properties– the distribution in the number of contacts per individual ( the degree distribution ) [2] and the transitivity or clustering of contacts , such as within households [3] , [4] . However , we lack a general framework for studying the combined epidemiological impacts of clustering and degree distribution . For public health , such understanding may be critical to predicting epidemiological events across diverse populations and tailoring control strategies appropriately . As epidemiological models grow in complexity , we face the question of how much complexity is necessary and useful . For example , which features of network structure significantly influence disease dynamics and which can we ignore without introducing large biases ? In some cases , mass action models that assume panmixis may be adequate and thus we can ignore network structure altogether . In others , incorporating realistic degree distributions and/or clustering may be important . A published simulation-based study [5] suggests that clustering affects epidemic dynamics when transmissibility is low and contacts between two individuals are highly autocorrelated . However , there remains a clear need for general , systematic model selection rules . The impact of the degree distribution on epidemics in the absence of clustering is complex , but has received considerable attention and is relatively well understood [1] , [2] , [6] , [7] . For example , in networks with power law degree distributions ( so-called scale free networks ) , as the variance of the degree distribution diverges to infinity , the reproduction number for a given pathogen also diverges to infinity while the minimum transmissibility necessary for epidemics to occur approaches zero ( meaning even diseases with very low infectiousness have the potential to cause epidemics ) . In contrast , the effects of clustering on epidemics are still unclear . Some studies suggest that clustering decreases epidemic thresholds , making an epidemic more likely to occur after an initial introduction [8] . Others studies suggest that the relationships between clustering and the epidemic threshold is subtle [9]–[11] , and depends on the nature of clustering in the population . The effects of clustering on the timescale of an epidemic are less ambiguous , with most studies suggesting that clustering decreases the rate of epidemic propagation . Here , we describe and analyze a versatile model that allows extensive exploration of the interactive impacts of clustering and degree distribution on epidemic dynamics . Although clustering always retards an epidemic , the timescale of the epidemic is more sensitive to the variance of the degree distribution than to clustering . Following the approach introduced in [12] , [13] , we model the spread of infectious disease through structured host populations using networks that are straightforward generalizations of the configuration model [14] . Our model is designed so that one can easily tune the parameters describing the degree distribution and the number of cliques in the network ( a clique is a completely connected subgraph ) , which is closely related to the clustering coefficient . Although these networks are not tree-like locally , they can be analyzed using branching processes and percolation theory , as shown in [12] , [13] , and more recently in [15] and [16] . Our epidemic model generalizes the approaches recently introduced in [17] , [18] for modeling the dynamics of epidemics in networks . These models exactly predict epidemic spread in a class of random networks . The resulting model consists of a low dimensional system of ordinary differential equations that describes the prevalence of infection over time . Recently , an alternative system of approximate ODEs was independently developed [19] which describes epidemics in networks with arbitrary degree distributions and clustering coefficients . This heuristic approach is intended to be fairly generic , and it is not clear if there are clustered networks for which this model is exact . Our complimentary approach allows straightforward analytical solutions ( using percolation theory and branching process methods ) for a simple class of random networks . In some cases , our model agrees closely with the one presented in [19] , but it can differ substantially around epidemic thresholds . This result suggests that the clustering coefficient ( a single value for the entire network ) alone is not always sufficient to determine the full epidemiological impact of clustering . We also revisit one of the early , pioneering approaches to modeling disease transmission through complex contact networks: approximating the final size of an epidemic ( the giant component of the network ) using bond percolation [12] , [13] . A recent paper introduces a method that correctly accounts for variation in infectious periods when making such calculations [16] . In contrast to what is found in unclustered networks , in which such variation does not significantly impact epidemic sizes [20]–[23] , we find that in highly clustered networks ignoring variation in infectious periods can introduce considerable bias . In addition , we model a realistic population by estimating network parameters from a large diary-based survey of social interactions [24] . We quantify the amount of network clustering that occurs within households and show that ignoring household clustering can lead to significant prediction errors including overestimation of both prevalence and , somewhat counter-intuitively , the epidemiological significance of households .
Random graphs [12] , [13] can be algorithmically generated by assigning a random number of lines and triangles to a set of nodes from the distribution . Edges can then be created by This algorithm may produce loops and double-edges , but the frequency of such edges will be negligibly small for large graphs [27] , and we simply delete them if they do occur . The ODEs that describe epidemic dynamics in clustered networks can be expressed in several equivalent forms and derived from at least two different perspectives . Below , we present two systems of equations that respectively describe the change in the number of cliques with susceptible and infectious nodes and the probability that a susceptible node is connected to such a clique . Both of these systems can also describe the dynamics of the number of infected and susceptible individuals in the population as a function of time . First we present the system of equations based on the probabilities that a random node is connected by a line to a node in state and the probabilities that is connected in a triangle to two nodes in states and . Below we present an alternative derivation based on the numbers of cliques with different configurations . The derivation of this system is very similar to what was presented in [28] , but is less mathematically parsimonious than the system of equations in this section , which requires only 7 ODEs . And , below we show how this system can be extended to networks with generalized distributions of clique sizes , that is , networks that include cliques larger than size three . We follow the recently introduced edge-based compartmental modeling approach of [18] . This approach is based on the consideration of the fate of a single randomly chosen node in the network . The probability this node is susceptible is equal to the proportion of nodes that are susceptible , and the probability it is infected or recovered is similarly the proportion of nodes that are infected or recovered . If we know the probability the node is susceptible as a function of time , then we can calculate its probability of being infected or recovered , so we focus our attention on calculating , the probability the randomly chosen test node is susceptible . Following [18] we modify the test node so that it does not transmit infection once infected . This does not alter the probability it is susceptible , but eliminates some conditional probability arguments we would have to consider otherwise . Assume is a member of lines and triangles . Then the probability it is susceptible is where is the probability that a random line has not transmitted to the test node and is the probability that neither of the other nodes in a triangle has transmitted to the test node . So assuming we can calculate and as functions of time , we have as a function of time . From this we use and to find and . Let us first consider . We divide into , , and , the probabilities that a neighbor along a line has not transmitted infection to and is either susceptible , infected , or recovered respectively . The probability the neighbor has not transmitted is ( 4 ) and is the probability that it has transmitted . We create compartments for these states and display the flux between them in Figure 1 . The fluxes from to and are proportional to each other , and each begins as zero , so we can show that . We find by a different approach , similar to the calculation of . A neighbor found along a randomly chosen line will tend to have more lines than a node chosen uniformly at random . The random number of such lines is described by the excess degree distribution [29] , and we calculate the generating function for this distribution as follows . Denote to be the probability that there are lines and triangles connected to a susceptible node that we reach by following a line from an infectious to a susceptible node not counting the line by which we arrived . Similarly , is the probability that if we follow a triangle to a susceptible node , there are lines and triangles connected to that node , not counting the one by which we arrived . Then we have the generating functions ( 5 ) ( 6 ) Equations 5 and 6 generate the excess degree distributions for lines and triangles . A neighbor reached by following a line connected to is susceptible with probability ( recall that does not cause infection ) where is a realization of the excess degree distribution . Summing over values of , we find . Now we rearrange equation 4 which gives . We can finally calculate by noting that Figure 1 shows . We find ( 7 ) To complete the system , we need a corresponding equation for . Here the system is more complicated . For the line case , if the neighbor had not transmitted , there were just three states to consider . But when considering triangles , if neither neighbor has transmitted , there are states to consider . We define to be the probability both neighbors are susceptible , to be the probability one neighbor is susceptible , while the other is infected but has not transmitted to , to be the probability both are infected but neither has transmitted to , and similarly define , , and . Figure 1 shows the compartments and flux between them . We do not have a simple relation for and , so our derivation changes mildly . The starting point will be , which satisfies To calculate the right hand side , we first find , the probability that both neighbors in a triangle are still susceptible . Under the assumption that transmissions have not happened in the triangle , the probability that one neighbor is still susceptible is . Since we require both be susceptible , We take to be the rate that a neighbor in a triangle is infected from outside the triangle . Then . After some simplification , we findWe are now ready to find equations for , and . We will also need to find to complete the system , but we will not need . We find ( 8 ) This completes our system of equations . We are able to calculate and as functions of time , which in turn leads to , from which we can find and as well: ( 9 ) It is straightforward to generalize the derivation for triangles ( 3-cliques ) to larger clique sizes , and to furthermore allow the transmission rate to be a function of clique size . Let denote the number of cliques of size with susceptible and infectious nodes . We will generalize the preceding model to allow transmission rates to vary between cliques of different sizes . The transmission rate for edges within a clique of size will be denoted . We consider clique sizes from to a maximum of . Having multiple clique sizes requires us to introduce additional dummy variables into the generating function . The vector of dummy variables with elements correspond to each of the clique sizes and unclustered edges . Note that the element is the dummy variable corresponding to lines , previously denoted . Then the following will generate the degree distribution: will be the vector of survivor functions with elements . Letting the derivative of with respect to the dummy variable be denoted , the number of cliques of size in the network is proportional to ( because there are nodes for every clique ) . In addition , the number of links from susceptible nodes to cliques of size is . We find the dynamics of by tabulating the flux to and from cliques with similar configurations . A clique with susceptible and infectious nodes will have edges between susceptible and infectious nodes , so that transmissions within cliques will occur at the rate . The rate of transmissions that occur within cliques of size isThe rate of transmissions by unclustered edges will be , and nodes in cliques of size with susceptible and infectious nodes will be infected from outside of the clique ( i . e . by an edge with an infectious node not in the clique ) at a ratewhere is the average number of cliques of a node selected by randomly choosing a susceptible member of a random clique:A clique with infectious nodes will have recovery events at the rate . Putting these terms together yields the following solution for the dynamics of . These equations are defined for all and such that . ( 20 ) The survivor functions will be determined by the following set of differential equations: ( 21 ) The equations for and will be the same as equations 16 and 16 , except that indirect transmissions by cliques larger than three must be taken into account . ( 22 ) Calculation of the survivor functions only requires cliques such that and , so it is not necessary to solve for all possible configurations of susceptible and infectious nodes . In general , if cliques range in size from to , this will require equations . For an infectious disease spreading in a population in which all individuals have the same susceptibility and the same infectiousness and all transmissions are independent , the epidemic process can be exactly represented through a bond percolation process . Consider an individual chosen to be the initial infection . Assume the per-contact probability of transmission is . If we delete each edge of the network with probability , then the probability that is in the same component of the residual network as a given set of nodes is equal to the probability that that set of nodes is infected in the epidemic [20] , [23] , [31] . However , if there is variable infection duration or some other cause of heterogeneity in infectiousness , this is no longer the case: those individuals with longer infectious period are more infectious . Assuming the only heterogeneities are due to variable infectiousness , it has been shown [22] that in networks without short cycles the final size of large outbreaks depends only on the average infectiousness in the limit of large networks . When there are short cycles , the size of epidemics does depend on how infectiousness is distributed . The assumption that all individuals have the average infectiousness only gives an upper bound on epidemic size [23] , [31] . This bound is often a reasonable approximation [9] . Recently , an alternative percolation technique was developed [16] which accounts for variable infectious periods and can accurately calculate final sizes in some clustered networks . Taking the transmission rate to be and the recovery rate to be , the average probability of infecting a neighbor is . First , we investigate how closely the bond percolation approach reproduces epidemics with constant transmission and recovery rates for the clustered networks considered here . Second , we present an alternative simple solution for final size in clustered networks that takes variable infectious periods into account . The original bond percolation method for clustered networks [12] , [13] can be used to determine the probability that there would be zero , one or two secondary infections following an initial infection in a triangle . If the transmission probability is constant , the probability of having one or two secondary infections in a triangle is ( refer to [12] , [13] ) : In fact , these probabilities are functions of the infectious period of the initial case in a triangle , which is itself an exponentially distributed random variable . We can solve for the true probabilities by integrating over the infectious period ( in this case denotes time ) . Conditional on the infectious period being , the probability of transmission by single infected to a single neighbor of that infected is . When the infectious period is exponentially distributed with rate , we have the following: This distribution is generally different from the one based on and , and the expected number of secondary infections is strictly less with variable infectious periods . To see this , we denote the averages and , and note that only second order terms of will differ between and . We have , and ( 23 ) It is straightforward to see that . Furthermore , if we collect all terms involving in the equation for , we find a leading factor of . Consequently , these terms will be negative and will have larger magnitude in the expression for than for , so . Now we present an asymptotically exact solution for final epidemic size . Let be a random node . Let be the probability that a neighbor of along a line is not infected from another node at the end of the epidemic . Then following the methods described in [12] , [13] , this probability must satisfy ( 24 ) Similarly , let be the probability that a neighbor in a triangle never receives an infectious dose from outside that triangle . ( 25 ) We need to calculate and at in order to calculate final epidemic size . It suffices to find and in terms of and and then solve the system . We have is the probability that a line does not transmit to . Clearly this can be calculated by considering the probability the neighbor is never infected plus the probability the neighbor is infected , but does not infect . This is ( 26 ) Finding is slightly harder . This is the probability that neither neighbor in a 3-clique is infected from outside , or exactly one receives infection from outside , or both receive infection from outside and transmission does not reach . As above , is the probability that a node in a triangle will lead to exactly one further transmission within the triangle , and will be the probability it will cause no transmissions . The probability that an infected neighbor in a triangle recovers prior to transmitting to either of its neighbors is . Then ( 27 ) The first term means neither neighbor is infected . The second term has exactly one neighbor infected ( factor of because there are two choices ) , with the neighbor either infecting the other neighbor , but nothing further or the neighbor infects no one . The third term is both getting infected from outside; we do not need to consider the correlations in this case . Equations 24–25 can be solved numerically by iteration from small initial values of and [12] , [13] . Given and , the final size can be calculated: ( 28 ) In the SI , we show how these calculations can be extended to models with generalized distributions of clique sizes . To validate the model assumptions , we compare solutions of the system given by equations 13–17 to stochastic simulations in continuous time based on the Gillespie algorithm [32] . Random networks are generated as described above . At time , a number of initial infections are selected uniformly at random within the network . When a susceptible is infected , new transmission and recovery events are queued with exponentially distributed waiting times . We also compare our model to a similar model consisting of ODEs based on moment-closure [19] . This model was developed for networks with a given degree distribution generated by and a clustering coefficient . Unlike our model , this system does not specify a joint distribution for the number of lines and triangles . Rather , this system is based on the concept that potential triangles , of which a degree node will have , will exist with independent probability . This system also uses PGFs within a low-dimensional system of ODEs , and proposes that , with , where is the number of half-edges from a susceptible node that terminates at an infectious node . Equations for are derived in terms of the number of connected triples , or 2-paths , of nodes that pass through a susceptible . This model makes the approximation that the number of 2-paths connecting two susceptibles and an infected is a simple function of the clustering coefficient :The number of 2-paths connecting a susceptible with two infecteds isWe will subsequently refer to this as the House-Keeling ( HK ) model .
Many respiratory diseases such as influenza spread through networks of close-proximity contacts . Transmission can be especially intense within households , where contacts are highly clustered . The clustering of close-proximity contacts that occurs within households is an important factor in the spread of such diseases and such clustering has been the subject of many mathematical models [16] , [33] . In this section we illustrate how the model in equations 19–21 can be parameterized from real data that includes household contacts . The model developed below is designed for didactic purposes; it does not provide a realistic representation of a specific disease spreading in a specific population . This model excludes a number of complexities , such as age structure , clustering of non-household contacts , and dynamic partnerships . Nonetheless , the model illustrates the conditions under which it is important to include clustering of household contacts . Model misspecification can bias both model predictions and model-based estimates of parameter values . To parameterize this model , we used data from the POLYMOD study [24] , which consists of a sample of 7 , 290 individuals in eight European countries . These data are diary-based estimates of the number and type of contacts sufficient for transmission of a respiratory pathogen over a 24 hour period . Crucial for our purposes , the data provide a breakdown of contacts made both inside and outside of households . After pooling the data from each country , we find that the number of contacts outside of households was well described by a geometric distribution , which is generated by ( 31 ) with . The geometric distribution was selected by the minimum AIC criterion in comparison with Poisson and negative binomial distributions fit to the data using maximum likelihood . For household sizes , we used the empirical distribution rather than fitting the data to an idealized distribution . To ensure that the system is computationally tractable , we limited the maximum household size at eight , and rounded down any households of larger size; only 2% of households included more than eight individuals . Letting the vector of dummy variables correspond to household sizes , the following generates the household size distribution: ( 32 ) The first term in accounts for the probability of living alone . This model assumes that the household size is independent of the number of contacts made outside the household . This approximation is supported by the data , which shows very low correlation between the number of contacts reported within and outside of households ( Pearson correlation coefficient ) . Consequently , the generating function for the entire system is the product of marginal PGFs . ( 33 ) For most respiratory diseases , it is reasonable to assume that the transmission rate within households , , is greater than the transmission rate outside of households , [34] . Applying the PGF 32 to the system of equations 19–21 and using the transmission rates and completes the model . Figure 4 shows the final epidemic size ( cumulative number of infections ) for the clique model over a range of transmission probabilities both within and outside of households . The transmission probability is the per-edge probability that an infected will transmit prior to recovery , and is within households and outside of households . The final size is much more sensitive to than because the mean number of non-household contacts is much greater than household contacts ( 10 . 9 versus 3 . 3 ) and the household contacts only occur within cliques . To determine the epidemiological significance of household clustering , we compared the clique model to a null model that had an identical degree distribution but no clustering . The null model retains household contacts with the transmission rate , but in the null model , such edges do not appear in cliques . In general , the null model without clustering will over-estimate epidemic size . Consequently , null model-based estimates of the epidemiological importance of household contacts will tend to be inflated . The following discussion is oriented around the estimation of epidemic size given epidemic parameters . However , model misspecification will also bias estimates of transmission rates and other parameters made by fitting the network models to empirical epidemic data . We have identified two sources of bias in the null model without clustering: The second factor accounts for most of the bias in this example; clustering alone introduces little bias . For example , comparisons of the null model and clique model with and show that true final size is 29% , and the null model is biased by less than 0 . 36% . The bias is greatest when transmissibility is high within households , but low outside of households ( Figure 3 ) . Outside of this small region , the null model can provide good approximation . Nevertheless , there is good reason to believe that for many real epidemics , the parameters will lie close to the region of high bias . For example , the per-day transmissibility of influenza within households has been estimated to be around 5% [34] , and based on a 6 day infectious period , this implies a cumulative transmission probability of 20–30% . If transmission rates per edge outside of households are an order of magnitude less than household transmission rates , the network model without clustering may be biased by more than 25% .
We have investigated the interactive effects of clustering and the degree distribution of contact networks on the timescale and final size of infectious disease epidemics . For this purpose , we developed a model that generalizes the one presented in [17] . This model has previously been generalized in other dimensions [18] , including the incorporation of simultaneous network dynamics , such as edge swapping [30] , [35] , populations with heterogeneous contact rates [36] , multiple edge types with distinct transmission rates [28] , preferential attachment [28] , and growing networks with natural birth and mortality [37] . These extensions can be combined and extended further to model , for example , epidemics in clustered networks that also have dynamically rearranging ties , or networks in which larger clique sizes or other network motifs are prominent [15] . Model selection for epidemic dynamics in networks is a challenging problem; and our work has made two contributions to understanding the biases introduced by model misspecification . We have shown that when infectious periods vary among individuals , models that assume homogeneous transmissibility across all edges in a clustered network can be very biased; and the magnitude of this bias increases with the amount of clustering in the network . In contrast , bond percolation models that neglect variable infectious periods suffer negligible bias in configuration model networks without clustering [21] . The impact of clustering and degree distributions on SIR epidemic dynamics was previously investigated with the HK model [19] . We have compared that model to ours by calibrating the clustering coefficient of the HK model to match the fraction of links to triangles in ours . Our comparison indicates that the models are in close agreement when the variance of the degree distribution is high , but substantial differences in the expected final size and timescale of the epidemic exist when the degree distribution is homogeneous and clustering is extensive . This suggests that epidemic dynamics depend not only on the clustering coefficient , but also on the specific nature of clustering in the network . While the HK model is easy to parameterize when a population has a known clustering coefficient , our model facilitates parameterization using data with well defined cliques , such as human populations with household structure [16] , [34] . This model allows the number of cliques of different sizes connected to a node to be correlated , but assumes that no two cliques connected to a node share other members . For example , it is not possible for two triangles connected to a node to share any nodes except for . However , this feature of the model could be relaxed without much difficulty . A motif-based generalization of the configuration model was recently presented in [15] which provides one way of allowing triangles and other cliques to share more than one node . Contact data increasingly provide the information necessary to parameterize network models including the one presented here . Social network studies often ascertain degree distributions and clustering coefficients [38] , [39] and epidemiological surveillance data often provide partnership durations and measures of concurrency [28] , [40] . We have demonstrated how such data can be used to parameterize the network structure parameters of our model , with a focus on the clustering introduced by household structure , and we have shown the value of explicitly considering this component of human contact patterns in epidemiological models . Without it , models may overestimate both the epidemiological risk of a population and the extent to which household contact contribute to that risk . | The transmission dynamics of infectious diseases are sensitive to the patterns of interactions among susceptible and infectious individuals . Human social contacts are known to be highly heterogeneous ( the number of social contacts ranges from few to very many ) and to be highly clustered ( the social contacts of a single individual tend also to contact each other ) . To predict the impacts of these patterns on infectious disease transmission , epidemiologists have begun to use random network models , in which nodes represent susceptible , infectious , or recovered individuals and links represent contacts sufficient for disease transmission . This paper introduces a versatile mathematical model that takes both heterogeneous connectivity and clustering into account and uses it to quantify the relative impact of clustered contacts on epidemics and the prediction biases that can arise when clustering and variability in infectious periods are ignored . | [
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| 2011 | Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics |
Trachoma is the commonest infectious cause of blindness worldwide . Recurrent infection of the ocular surface by Chlamydia trachomatis , the causative agent , leads to inturning of the eyelashes ( trichiasis ) and blinding corneal opacification . Trachoma is endemic in more than 50 countries . It is currently estimated that there are about 1 . 3 million people blind from the disease and a further 8 . 2 million have trichiasis . Several estimates for the burden of disease from trachoma have been made , giving quite variable results . The variation is partly because different prevalence data have been used and partly because different sequelae have been included . The most recent estimate from the WHO placed it at around 1 . 3 million Disability-Adjusted Life Years ( DALYs ) . A key issue in producing a reliable estimate of the global burden of trachoma is the limited amount of reliable survey data from endemic regions .
Trachoma is the leading infectious cause of blindness worldwide . Overall it is the eighth commonest blinding disease [1] . Trachoma is caused by the obligate intracellular bacterium C . trachomatis . Recurrent episodes of conjunctival infection and the associated chronic inflammation it causes initiate a scarring process that ultimately leads to irreversible blindness . There is a worldwide effort underway to try to control blinding trachoma; this is lead by the World Health Organization ( WHO ) with the Global Alliance for the Elimination of Blinding Trachoma ( GET2020 ) . It is estimated that approximately 1 . 3 million people are blind from this disease and probably a further 1 . 8 million have low vision [2] . Trachoma is endemic in more than 50 countries , predominantly in sub-Saharan Africa , the Middle East , and Asia [3] . The burden of trachoma on affected individuals and communities can be huge both in terms of the disability it causes and the economic costs that result . In this paper we review the available data on the prevalence of the disease and estimates of its burden .
Endemic trachoma is caused by the four ocular serovars of C . trachomatis ( A , B , Ba , and C ) . Although the genital serovars ( D to K ) of C . trachomatis can infect the conjunctiva causing either ophthalmia neonatorum in infants or inclusion conjunctivitis in adults these are usually isolated episodes for the individual , which do not lead to blinding sequelae . For endemic trachoma the average age of acquisition of the first episode of C . trachomatis infection is probably related to the prevailing level of infection in the community . In hyperendemic settings infection may be acquired in early infancy , whereas in meso- and hypo-endemic regions it is probably on average later . Infection is probably usually acquired through living in close physical proximity to an infected person , with the family as the principle unit for transmission [4] , [5] . Conjunctival infection with C . trachomatis is largely confined to the epithelium , with little evidence of deeper spread . The infection triggers an immune response characterised by a marked inflammatory cell infiltrate and the release of pro-inflammatory cytokines in the conjunctiva [6] , [7] . Clinically it causes papillary and/or follicular inflammation of the tarsal conjunctiva , referred to as active trachoma ( Figure 1A ) . The WHO Simplified Trachoma Grading System ( Table 1 ) , which is used by trachoma control programmes , subdivides active trachoma into two often coexisting clinical phenotypes: Trachoma Inflammation Follicular ( TF ) and Trachoma Inflammation Intense ( TI ) [8] . Eventually the infection resolves and the clinically visible inflammation gradually subsides . Animal models for C . trachomatis infection and limited data from humans indicate that the resolution of infection is probably through an interferon-γ ( IFN-γ ) –dependent cell-mediated immune response [9] , [10] . Studies from trachoma endemic communities have found that the prevalence and duration of conjunctival chlamydial infection decline with increasing age , suggesting that there is a maturation of the immune response as individuals are repeatedly exposed to infection [11] , [12] . However , in the early vaccine trials using whole C . trachomatis organisms the acquired immunity appeared to be strain specific and relatively short-lived [13]–[15] . As a result of the relatively ineffective immune response , repeated infection of the individual by Chlamydia trachomatis is common within an endemic environment . This leads to a recurrent chronic inflammation , which is associated with the development of scar tissue within the conjunctiva over many years ( Figure 1B ) [16] , [17] . As the scar tissue accumulates it also contracts , causing the eyelids to roll inwards towards the eye ( entropion ) and the eyelashes to scratch the ocular surface ( trichiasis , Figure 1C ) . The degree to which conjunctival scarring develops probably depends on a complex interaction between the pressure of infection ( load and frequency ) and host specific immunogenetic factors [18]–[20] . It is possible that a failure of the immune response to adequately control the chlamydia leads to prolonged infection episodes , which provokes more severe inflammation , tissue damage ( through the release of proteases ) , and aberrant repair [7] . The most serious disease sequela from trachoma is blinding corneal opacification ( CO , Figure 1D ) . The main aetiological risk factor for corneal damage is the presence of trichiasis , however , a number of other factors probably contribute such as bacterial infection and chronic conjunctival inflammation [21] .
Today , blinding trachoma is believed to be endemic in 56 countries ( Figure 2 ) [22] . The countries with the highest prevalence of disease are in sub-Saharan Africa , particularly in the Sahel belt and East Africa . In addition , there are countries in the Middle East , the Indian sub-Continent , and Southeast Asia where trachoma is endemic , although the distribution is patchier . One hundred years ago trachoma was widespread in Europe and North America , but faded away during the first half of the 20th century as living conditions improved [23] . The clinical manifestations of trachoma change with age . Active trachoma is predominantly seen in young children , becoming less frequent and shorter in duration with increasing age [11] , [12] . Conjunctival scarring accumulates with age , usually becoming evident in the second or third decade of life [24] , [25] . Entropion , trichiasis , and CO develop later . The onset of the blinding complications of trachoma can occur in children living in regions where the pressure of infection is high [26] . Epidemiological surveys have generally found trichiasis and CO to be more common in women than men [25] , [27] . This difference has been attributed to the greater life time exposure of women to C . trachomatis infection , through closer contact with children , the main reservoir of infection . The transmission of C . trachomatis from infected to noninfected individuals is necessary to sustain trachoma in endemic communities . Several routes of transmission are probably involved including direct spread ( close contact ) , fomites , and eye-seeking flies . In common with other Neglected Tropical Diseases ( NTDs ) , trachoma is generally a disease of resource-poor rural communities . Risk factors for trachoma are generally things that favour the transmission of C . trachomatis from one person to another [28] . The presence of secretions around the eyes has consistently been associated with active trachoma , attracting flies , and providing a vehicle for transmission . Similarly , water scarcity probably promotes transmission , because less water is available to use for face washing . Limited access to latrines increases faecal contamination of the environment , providing breeding material for the fly Musca sorbens , which is implicated in trachoma transmission [29] . Crowded living conditions , for example with several young children sleeping in the same bed , probably promotes transmission . For many trachoma endemic countries the socioeconomic developments that might promote the disappearance of the disease are likely to be very slow in arriving , which in the light of demographic trends and in the absence of effective control programmes was predicted to lead to an increase in the total numbers blind from trachoma [30] .
Several estimates of the number of people affected by trachoma worldwide have been made by the WHO and others ( Table 2 ) [2] , [3] , [31]–[35] . These estimates have generally been produced with models that have relied on the results of a limited number of surveys conducted in a few endemic countries . The results have been extrapolated across these countries and to other countries in the same region . However , there are problems . The list of countries included has sometimes been incomplete or contained countries without endemic trachoma . The survey data used is sparse , often old , and some of it is of questionable reliability . In the WHO estimate from the 1990s there was probably a significant overestimate of the number blind from trachoma ( 6 million ) , because part of the estimate was based on questionnaires reporting numbers of people who might become blind without treatment [31] , [33] . Moreover , there are big gaps . In particular , there are limited data from both India and China , where pockets of the disease are thought to exist; even with a low prevalence the contribution from these two most populous countries could make a profound difference in the global burden of trachoma . More recent estimates have been more stringent , using only surveys with a national sampling frame and the updated WHO list of endemic countries [33] . However , despite the limitations and potential unreliability of the available data , there does appear to be an encouraging downward trend in the numbers ( Table 2 ) . The most recent estimate , released in 2008 , suggests that there are currently about 40 million people with active trachoma and 8 . 2 million with trichiasis [36] . The significant downward revision in this estimate was attributed in large part to significant overestimates in the number of cases in China and India in the 2004 figures . The highest prevalence of trachoma is reported from Ethiopia and Sudan , where active trachoma is often found in more than 50% of children under 10 y , and trichiasis is found in up to 19% of adults [37] , [38] .
The disabling sequelae of trachoma are visual impairment and trichiasis . These have a major advantage over the disease sequelae reported for many other NTDs as they can be readily measured or observed clinically and do not require special investigations . Visual impairment is subdivided into Blindness and Low Vision ( Definitions: Box 1 ) . Visual impairment can have a profound impact on many aspects of the life of an individual ( mobility , psychological , social , financial , mortality ) , their family , and the wider community . Trichiasis can also be disabling in the absence of visual impairment by causing pain and photophobia . A study from Tanzania found that women who had trichiasis without visual impairment suffered a degree of disability that was comparable to that caused by visual impairment ( without trichiasis ) from causes other than trachoma [39] . Moreover , when both trichiasis and visual impairment are present the degree of limitation rises to roughly double that of either of these two elements alone . However , despite the evidence of disability caused by trichiasis , only one estimate of the burden of disease from trachoma has included it [40] . The first attempt to calculate the burden of trachoma was in the Global Burden of Disease study ( GBD ) [41] . The GBD study developed a new measure of the burden of disease: Disability-Adjusted Life Years ( DALYs ) . This measures the gap , in terms of healthy life lost , between an “ideal” healthy population and the reality caused by a specific disease in terms of premature mortality and disability in a particular society . DALYs and the GBD study have been discussed in detail elsewhere [42] . In essence there are two major components in the calculation of DALYs: a measure of premature mortality , years of life lost ( YLL ) ; and a measure of years of healthy life lost through disability caused by the disease , years of life lived with a disability ( YLD ) . Different disabilities are weighted so that the more severe the disability the greater the number of YLDs that are lost . In order to make the calculation it is necessary to have estimates of the number of people dying from the disease or living with the disability for a given year . The GBD study estimated the burden of trachoma to be about 1 . 0 million DALYs annually , the vast majority of which was due to YLD ( Table 3 ) . This estimate used WHO figures for the number of people affected by trachoma at that time , which were probably somewhat less accurate than subsequent estimates , as discussed above . Moreover , the list of endemic countries used in the process probably left out some now known to be endemic and included others where there is not thought to be a problem . Subsequently , as part of the ongoing updates to the GBD project there have been further estimations of the burden of trachoma by the WHO included in the World Health Report using updated estimates for the global prevalence of the disease ( Table 3 ) . There have been two other calculations of the burden of disease from Trachoma , in addition to the GBD programme . The first of these , by Evans and Ranson , was published in 1995 and was an estimate of the Handicap-Adjusted Life Years ( HALYs ) for the year 1990 ( Table 3 ) [43] . This measure is similar in concept to the DALY , consisting of a composite of the number of years lost through an early death and the number of years lived in a handicapped state . Instead of using disability weights the authors developed a new measure , the handicapped weight . The handicapped weights were estimated from self-assessment questionnaires of people with trachomatous visual impairment . In addition , to produce this estimate the investigators performed a fresh calculation for the number of people blind or with low vision from trachoma worldwide [34] . The value of handicapped weight for blindness they used was similar to that for DALY calculations ( 0 . 58 ) . However , the value for the low vision handicapped weight ( 0 . 59 ) was higher than the disability weight for low vision used in other estimates ( 0 . 27 ) . They also performed a sensitivity analysis that found a high degree of uncertainty around the mean estimate of 79 . 5 million lifetime HALYs , with the 95% confidence interval ( CI ) ranging from 15 to 500 million . This reflects the high degree of uncertainty both in the estimates of prevalence and other parameters . The second estimate was made by Frick and colleagues for the year 2000 ( Table 3 ) [33] . In this calculation the investigators excluded YLL , because they did not consider its estimate to be sufficiently reliable . They used a relatively low disability weight for low vision ( 0 . 245 ) . They re-estimated the number of people with blindness from trachoma worldwide on the basis of a reassessment of survey data collected since 1980 . On the basis of previous published data they calculated that for each person blinded by trachoma there were a further 1 . 4 with low vision , and extrapolated the number of people with low vision on this basis . Their estimate of the annual DALYs was 3 . 6 million , with 72% of these DALYs occurring within sub-Saharan Africa and 80% of lifetime DALYs occurring in women . This figure was significantly higher than the GBD estimate for the same year ( 2 . 2 million ) . Estimates of the burden of trachoma suffer from several weaknesses . The first is the limited supply of reliable data on the prevalence of disease sequelae in endemic populations . There are relatively few robust population-based surveys that can be used to estimate the number of affected people . The more recent estimates of burden have benefited from the work done by the WHO/GET2020 in defining the list of countries that currently have endemic trachoma . Secondly , there is uncertainty over whether to include trichiasis as a disabling disease sequela , independent of visual impairment . One report has suggested that the additional disability caused by trichiasis , independent of visual impairment , may add as much as 50% to the burden of disease caused by trachoma [40] . Finally , it remains unclear , due to an absence of data , what degree of premature mortality trachoma-related blindness may cause . There are two studies from rural communities in sub-Saharan Africa that have examined the question of excess mortality due to visual impairment [44] , [45] . Both of these found an increase in mortality amongst blind people compared to sighted controls . As the potential for confounding in assessing mortality related to visual impairment is high , it is necessary to conduct carefully designed studies to investigate this question specifically for trachoma .
Two estimates of the economic cost of trachoma have been made by Frick and colleagues [33] , [40]; these are framed in terms of lost productivity . The economic cost of one disabled person was calculated by multiplying the value of the disability weights by the individual economic productivity value for each country considered . In the first estimate only the burden caused by visual impairment was considered and the productivity lost was estimated at US░ 2 . 9 billion ( 1995 ) [33] . In the second estimate it was found that the economic loss was higher at US░ 5 . 3 billion ( 2003 ) [40] . This second estimate used the adjusted dollar value for 2003 , it considered the productivity lost from blindness to be 100% ( instead of 60% ) and it added a 10% cost for each blind person for a carer . The investigators also examined the effect of including trichiasis and found that the lost productivity rises significantly to US░ 8 billion . There are significant challenges in producing meaningful estimates of the economic cost of trachoma . Estimates are made of the cost to the world economy as a whole , rather than the relative cost to the affected individual . They are greatly influenced by the per capita GDP of each country involved . A particular difficulty is determining what average individual productivity value to use . In the estimates described above the authors used ( where available ) the “average agricultural value added per worker . ” However , this was not available for each country . It is also assumed that the affected individual would always be in full-time paid employment in the absence of their disability and no adjustment of potential earnings is made for age or sex . It is also difficult to include a measure for the productivity lost outside the formal work place or the indirect cost to the carer of a visually impaired person . Finally there are issues around the choice of which disabling sequelae to include and the disability weights these should be given , discussed above .
There have been organised trachoma control programmes in many endemic countries for decades , which have met with variable success . In 1998 the World Health Assembly resolved to eliminate blinding trachoma by the year 2020 [46] . To this end the GET2020 was formed , including representatives from the WHO , national blindness control programmes from endemic countries , nongovernmental organisations working in the field , industry , and academic institutions . The GET2020 alliance adopted the SAFE Strategy as its favoured approach to controlling trachoma [47] . The four components of SAFE are surgery for trichiasis , antibiotics for infection , facial cleanliness , and environmental improvements to reduce transmission . There is a growing body of published evidence supporting the clinical effectiveness of each component of the SAFE strategy [48] . In the case of trichiasis surgery there are currently nine published randomised controlled trials investigating various aspects of management including the optimal type of operation , level of surgeon , where surgery should be done , and whether peri-operative antibiotic improves the outcome [49] . However , there are major problems both with the outcome of surgery , with high trichiasis recurrence rates reported under operational conditions , and the effective delivery of the service on the ground in many endemic countries [21] , [50] . Over the last 60 y there have been many studies testing different antibiotics for active trachoma . The WHO currently recommends the use of either oral azithromycin ( single dose ) or topical tetracycline ( twice daily for 6 wk ) . Both of these antibiotics have been demonstrated to be effective in clinical trials at reducing the prevalence of both active disease and C . trachomatis infection [51] . The clinical signs of active trachoma have a relatively low sensitivity for the identification of C . trachomatis infection . In addition , C . trachomatis has the potential to rapidly reemerge in communities where some infected cases are left untreated . Therefore , the current recommendation is for mass drug administration ( MDA ) of entire endemic communities to be conducted annually for several years , until the prevalence of follicular trachoma ( TF ) in children ages 1–9 y drops below 5% [3] . It is likely that , with such a low threshold for treatment , very large numbers of uninfected people will be treated with antibiotic in order to catch all those harbouring infection . The evidence base supporting the effectiveness of face washing and environmental interventions in reducing trachoma is more limited [48] . However , the historical epidemiology of trachoma strongly supports the view that general improvements in hygiene can have a profound long-term effect on this disease . Several investigators have produced estimates of the cost-effectiveness of trachoma control programmes or individual components of the SAFE strategy . The only study evaluating the long-term cost-effectiveness of an entire national trachoma control programme was made by Evans and colleagues for Myanmar ( Burma ) over a 30-y period ( 1964–1993 ) [52] . This programme predated the introduction the SAFE strategy; however , it contained several elements of today's trachoma control programmes: surgery , mass antibiotic distribution , and community education . During this period there was a marked decline in the disease in Myanmar . The overall cost-effectiveness of the programme was estimated at US░54 per case of visual impairment prevented . Two factors may have lead to an overestimate of the cost-effectiveness: ( 1 ) it was assumed that all the visual impairment from trachoma prevented was due to the activities of the programme , rather than to any underlying secular trend due to socioeconomic changes , ( 2 ) only the direct costs to the national programme were included . Three analyses for the cost-effectiveness of trichiasis surgery have been produced , which consistently found it to be very cost effective . In the analysis of the Myanmar programme the average cost ( over the 30-y period ) per case of visual impairment prevented was estimated to be US░193 , although in the last 10 y of the programme there was a marked rise in the cost-effectiveness of trichiasis surgery to US░41 per case of visual impairment prevented [52] . The cost per HALY saved was on average US░10 , dropping to US░3 for the final 10 y . In The Gambia the cost of surgery was estimated to be US░6 . 13 per operation ( 1998 ) , whilst the estimated life-time loss of economic productivity was US░89 [53] . In a separate analysis the cost-effectiveness of surgery was estimated for seven trachoma endemic world regions [54] . The cost of trichiasis surgery was estimated to be about International Dollars ( I░ ) 19 per case in Africa . It was estimated that if surgery was carried out on 80% of the current cases of trichiasis this would save 11 million DALYs globally each year . Surgery was found to be very cost effective with estimates ranging from I░13 to I░78 per DALY , depending on the region . The cost-effectiveness of antibiotic treatment has also been considered in a number of analyses . In the evaluation of the Myanmar programme it was found that the cost of nonsurgical interventions ( mostly antibiotic treatment ) was US░47 per case of visual impairment prevented [52] , which gave a cost-effectiveness of US░3 per HALY averted . This seems to be a remarkably low cost and there may have been some major methodological biases that attributed the vast bulk of the DALYs to the nonsurgical as opposed to the surgical components of the programme . In contrast , the more recent projection of the cost-effectiveness of trachoma control in seven world regions found antibiotic treatment to be relatively cost ineffective [54] . For example in Africa the cost of mass antibiotic distribution of azithromycin to children aged 1–10 y was I░9 , 012 per DALY saved if the azithromycin had to be purchased at the standard cost price . Azithromycin is currently donated by the manufacturer , Pfizer Inc . , to trachoma control programmes in 15 endemic countries . Even if the drug is donated , the authors concluded that costs remain high at I░3 , 922 per DALY . However , this study made a number of questionable assumptions that cast doubt on these figure . For example , the authors assumed that mass treatment would need to be given annually for 10 y , which is probably much longer than would be needed if high coverage levels are achieved . The effectiveness of mass azithromycin treatment is variable , although several studies have suggested that C . trachomatis infection can be well controlled with one or two rounds of mass treatment [55] , [56] . The authors further assumed that the reduction in trichiasis prevalence due to mass treatment would not be seen for 45 y , and that the proportionate reduction in trichiasis and blindness would be the same as the reduction in the prevalence of active trachoma seen after 10 y . It is not known how effective controlling C . trachomatis infection will be on the development of the scarring sequelae in people who have previously been repeatedly infected , however it is anticipated that as the prevalence of infection drops so the drive to disease progression lessens . There are major logistical and financial obstacles , even with donated azithromycin to repeatedly conducting mass drug administration , especially in remote rural settings . In response to this there has been a move to try , where appropriate , to combine mass drug administration with azithromycin for trachoma with treatments for other NTDs .
Several attempts have been made to estimate both the burden and cost of trachoma . It remains a significant problem with a high burden of disability . Encouragingly the reported numbers of people affected by trachoma appears to be steadily declining . However , current burden estimates are limited in reliability because of the paucity of survey data available on which to base estimates of the total number of cases . There is also variability over whether to include trichiasis , in the absence of visual impairment . In order to develop more robust estimates of the burden of trachoma there needs to be a coordinated effort to conduct population-based surveys with a national sampling frame in representative countries from endemic regions . Clarification of the situation within India and China is particularly important , given the size of their populations . A consensus also needs to be reached on whether trichiasis should be included in the calculation of DALYs and what weight this should be given . There is limited evidence of premature mortality due to blindness in general . Further studies on this specifically in relation to trachoma would be of value . | This review examines the various attempts to estimate the burden of disease from trachoma , the commonest infectious cause of blindness worldwide . Reports vary considerably because of differences in methodology and changing estimates of the number of people affected . Currently about 1 . 3 million are blind from trachoma and it causes about 1 . 3 million Disability-Adjusted Life Years ( DALYs ) . The limited amount of survey data available from endemic regions remains a problem in generating accurate estimates . The effect of the disease may be underestimated as some of the disabling sequelae are not included in the calculation . | [
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| 2009 | The Global Burden of Trachoma: A Review |
Although information theoretic approaches have been used extensively in the analysis of the neural code , they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects . Here , we present a novel method to estimate the cumulative information for stimuli or categories . We further define a time-varying categorical information index that , by comparing the information obtained for stimuli versus categories of these same stimuli , quantifies invariant neural representations . We use these methods to investigate the dynamic properties of avian cortical auditory neurons recorded in zebra finches that were listening to a large set of call stimuli sampled from the complete vocal repertoire of this species . We found that the time-varying rates carry 5 times more information than the mean firing rates even in the first 100 ms . We also found that cumulative information has slow time constants ( 100–600 ms ) relative to the typical integration time of single neurons , reflecting the fact that the behaviorally informative features of auditory objects are time-varying sound patterns . When we correlated firing rates and information values , we found that average information correlates with average firing rate but that higher-rates found at the onset response yielded similar information values as the lower-rates found in the sustained response: the onset and sustained response of avian cortical auditory neurons provide similar levels of independent information about call identity and call-type . Finally , our information measures allowed us to rigorously define categorical neurons; these categorical neurons show a high degree of invariance for vocalizations within a call-type . Peak invariance is found around 150 ms after stimulus onset . Surprisingly , call-type invariant neurons were found in both primary and secondary avian auditory areas .
Information theoretic analyses are well suited to the study of neural representation since this mathematical framework was developed to quantify and optimize the encoding of informative signals in communication channels [1] . In sensory systems , Information Theory ( IT ) has been applied extensively as a complementary approach to the estimation of stimulus-response functions such as tuning curves , spatio-temporal or spectro-temporal receptive fields or other higher-level encoding models [2] . Information theoretic approaches have been particularly powerful in explorations of the nature of the neural code and its redundancy or efficiency [3–6] . For example , IT was used in early studies in the visual system to demonstrate that spike patterns contain information beyond average rate both for static images [7] and dynamic visual stimuli [8] . IT was also used to show that spike doublets can contain synergistic information that cannot be explained by an analysis of successive single spikes [9] and that , although information can only decrease in a signal processing chain , the neural coding efficiency increases as one moves to higher levels of sensory processing [10] . Finally , IT investigations also revealed that neural efficiency is higher when sensory systems process natural stimuli versus synthetic stimuli [11–13] , in support of ethological theories of optimal sensory processing [14] . In sensory systems , the mutual information between a stimulus and the neural responses has often been estimated in a stimulus reconstruction framework and for continuous dynamic stimuli in stationary conditions , where time averages can be performed . In the stimulus reconstruction framework , one attempts to estimate the information about all aspects of the stimulus; for example , in audition , the stimulus would be represented by its exact sound pressure waveform . As long as the stimulus set is rich ( i . e . has very large entropy ) , the mutual information can be an estimate of the maximum information that can be transmitted by a neural communication channel , also known as the channel capacity [3 , 4] . For instance , one can obtain the mutual information of an adapted auditory neuron processing white noise or colored noise sounds [11] . As long as the stationary assumption is valid , using continuous stimuli is also beneficial as it provides large data sets that are needed to estimate the joint probability of stimuli and neural responses , both of which can have high dimensions . Even in these conditions , it is noteworthy that a direct estimation of information is only possible when many repeats of the same stimulus can be obtained [15] or when simplifying assumptions are made [9] . Ultimately , the calculation of information based on stimulus reconstruction gives a single number corresponding to the information transmitted by a single neuron or an ensemble of neurons for a particular stimulus ensemble . By repeating the calculation for different stimulus ensembles , one can investigate how the channel capacity of particular neurons or neural ensembles might depend on the stimulus statistics ( e . g . for natural vs synthetic stimuli ) . Furthermore , by repeating the calculation using different symbols to represent the response , the potential nature of the neural code ( e . g . time patterns vs . rate ) can be revealed . Here we are using IT in a different sensory encoding context: the accumulation of information in a recognition task , such as face recognition in the visual system [16] or word recognition in the auditory system [17] . Recognition or identification is one of the key computations performed by higher sensory areas as opposed to the task of efficient stimulus representations that is performed in lower sensory areas and that might therefore be well quantified by information values based on stimulus reconstruction . In the recognition tasks , each stimulus is described by a simple label , such as the word corresponding to a given speech sound or that is used to label a given visual object . The relevant value of information in that task is then the information about these discrete labels and the information capacity of the system in its ability to identify the stimulus as a whole . In the recognition framework , one can ask how the information about the stimulus identity or label changes as a function of time relative to the stimulus onset and to what extent that time-varying information is redundant and , thus , how it accumulates over time . For example , one could ask at what time after stimulus onset does the performance of single neurons or ensemble of neurons match a behavioral performance of word recognition . Such an IT analysis has been performed in the primate visual system using a delay-matching to sample paradigm , and using spike counts , estimated in progressively longer windows , as the neural symbols [18] . In information studies based on continuous stimulus reconstruction , the neural code can be investigated in terms of its temporal resolution ( i . e . letter size ) and its integration time ( i . e . word length ) . While the same properties of the neural code can be deciphered in the recognition framework , one can also examine the relationship between spikes at different points in time and time-varying information . This analysis is meaningful because a time zero corresponding to stimulus onset can be clearly defined and is behaviorally relevant . Moreover , stimulus-response functions for such discrete stimuli are not time-invariant . Responses in sensory neurons , in vision [19 , 20] and in audition [21 , 22] , are often characterized by an onset response ( or on-response ) and a sustained response , where both the precision of spikes and the information coded might be different [23 , 24] . For example , a first spike latency code has been proposed as a fast encoding scheme in vision [25] , audition [26] and somato-sensation [27] . Rolls et al . tested this hypothesis , by quantifying the fraction of information that is present in the first spike relative the on-going response [28] . Finally , in the recognition framework , one can also compare the information values obtained when different labelling schemes are used for identifying the stimuli as objects . For example , speech sounds could be labeled hierarchically as unique utterances , as phonemes , as syllables , as words , etc . One can then compare time-varying information about each of the levels in such hierarchical labelling scheme and gain insight on the neuro processing involved in object categorization . Although such a hierarchical representation of stimulus features has been used in encoding models for studying human processing [29] , it has not yet been used in an IT analysis . In this study , we developed a new approach for estimating time-varying information and cumulative information for sensory object identification task . Our approach assumes that time-varying neural responses can be modeled as inhomogeneous Poisson processes and generalizes well to large number of stimulus categories and to long integration times relative to the dynamics of the time-varying response . Our motivation for developing this methodology was to gain additional understanding on the neural representation of communication signals in high level auditory areas . Animal communication calls , just as speech sounds in humans , are categorized into behaviorally meaningful units . Significant progress has been made in identifying brain regions involved in categorizing sounds , in particular in the primate brain , where neural responses that are correlated with progressively more abstracts concepts are found in primary auditory cortex , the lateral belt of the auditory cortex and the prefrontal cortex [30] . However , the neural computations involved in generating categorical responses remain poorly described [31] and only a small number of studies have examined the neural categorization of natural communication calls in non-human species [32–36] . We and others have been developing an avian model system to study the neural processing of relatively large and complex vocal repertoires [37 , 38] . Our prior studies include a detailed bioacoustical analysis of the features that define each call-type of the complete vocal repertoire of the zebra finch [39] and the first characterization of neural responses to the calls from that large repertoire in primary and secondary avian auditory cortical areas [40] . In that study , we found that approximately 45% of auditory neurons encode information about call-type categories . Among those , a minority show strong selectivity for single call-type categories and invariance for calls within that category . Here , we investigated the processing in time that could lead to those observed categorical responses by comparing the time-varying information for stimuli labelled as individual utterances to the time-varying information for the same stimuli labelled by their call-type category . With that analysis , we were able to obtain values of temporal integration for stimulus identification and call-type category identification . We also analyzed the relationship between the time-varying firing rate and the time-varying information and , in particular , examined differences in selectivity in the onset versus sustained response . Finally , we used anatomical data to examine the distribution of neurons in primary and secondary avian auditory cortical areas with distinct responses properties as revealed by this IT analysis .
At a given time t , the instantaneous mutual information between the stimulus S and the response Yt can be written as a difference in Shannon entropies: It=H ( Yt ) -H ( Yt|S ) Here , H ( Yt ) is the response entropy for a window at time t , while H ( Yt|S ) corresponds to the entropy of the response given the stimulus or the conditional response entropy . H ( Yt|S ) can also be called the neural noise since it represents the variability in the neural response to the same stimulus . For spiking neurons , yt represents the number of spikes in the window at time t ( Note: in our notation , capitals are used for random variables and lower case for a sample from that random variable ) . Similarly , the cumulative mutual information in neural responses that are discretized into time intervals is given by: CIt=H ( Yt , Yt-1 , Yt-2 , … , Y0 ) -H ( Yt , Yt-1 , Yt-2 , … , Y0|S ) The entropies now include the time course of the neural responses starting at t = 0 and up to time t . The reference time t = 0 is set to the stimulus onset in our analyses but could be any arbitrary reference point . The conditional response entropy and the response entropy are obtained from the distribution of the conditional probability of neural responses given the stimulus , p ( yt|s ) , and the distribution of probability of each stimulus p ( si ) : H ( Yt|S ) =∑ip ( si ) ∑yt=0RMax-p ( yt|si ) log2p ( yt|si ) H ( Yt ) =∑yt=0RMax-p ( yt ) log2p ( yt ) with p ( yt ) =∑ip ( si ) p ( yt|si ) yt , the neural response at time t , is measured in spike counts and takes values from zero to a maximum rate value , RMax ( for example , as dictated by the neuron’s refractory period or numerically as p log p becomes infinitely small for high spike counts that have very small probability of occurring ) . The probability of the stimulus p ( si ) is usually taken as 1/ns , where ns is the number of stimuli , unless the study incorporates natural stimulus statistics . The probability of spike counts at time t given a stimulus , ( yt|si ) , could be estimated empirically by recording hundreds or thousands of responses of the same neuron to the same stimulus . Although this approach has been shown to be possible in certain preparations [15] , it severely limits the number of stimuli that can be investigated in most neurophysiological experiments . Here we propose a parametric approach where we model the distribution of neural responses to a given stimulus si as an inhomogeneous Poisson process . The conditional probability of response ( spike count ) given the stimulus is then: p ( yt|si ) =μsi ( t ) ytyt ! e-μsi ( t ) where μsi ( t ) is the mean response at time t for stimulus si . This mean rate was estimated empirically using the time varying kernel density estimation ( KDE ) proposed by Shimazaki and Shinomoto [42] . The instantaneous information estimated in this fashion is relatively straightforward , as long as the Poisson assumption is valid and a sufficient number of trials ( i . e . stimulus presentations ) is obtained to estimate μsi ( t ) ( see Methods ) . The validity of the Poisson assumption for our dataset is assessed and discussed further below . Note that the Poisson distribution corresponds to the distribution with maximum entropy for unbounded count probabilities given a mean rate [43] . Thus , the value of entropy calculated for the empirical data , p ( yt|si ) , constitutes an upper-bound when using the Poisson distribution assumption . If the empirical data follows a different distribution , the true conditional response entropy , H ( Yt|S ) , will be lower and , thus , the actual mutual information higher . In other words , the assumption of a Poisson distribution yields a lower bound for the estimation of the mutual information . The estimation of the cumulative information then extends this approach to joint probabilities of responses in successive time windows , ( yt , yt−1 , yt−2 , … ) . Due to what has been labelled as the “curse of dimensionality” , the numerical estimation of the unconditional probability becomes exponentially more expensive as the integration time increases . We evaluated multiple approaches based on different assumptions and found that Monte Carlo with importance sampling yielded the best results ( see Methods and S3 Fig for comparison to alternative approaches ) . If the Poisson assumption also holds for successive time-windows , the conditional probability of response at t is independent of the conditional response at previous times . Given this probabilistic independence , it can be shown that the joint conditional response entropy is simply the sum of the conditional response entropies at each time point ( see Methods ) : H ( Yt , Yt-1 , Yt-2 , …|S ) =H ( Yt|S ) +H ( Yt-1|S ) +H ( Yt-2|S ) +⋯ Thus , the estimation of the conditional response entropy is straightforward and not affected by the integration time . The problem of dimensionality arises in the estimation of the unconditional probabilities of response and the corresponding response entropy . The probability of the time varying response is the joint probability of observing ( yt , yt−1 , yt−2 , … ) . This joint probability cannot be expressed as the product of the probabilities at different times because these are not independent . More intuitively observing a particular yt−1 will affect the probability of observing yt . This is true because the time varying means of the Poisson distributions μsi ( t ) are correlated in time; for example , if μsi ( t-1 ) is high we might expect μsi ( t ) to also have high values . These high values could be true for one particular stimulus si but not for the other stimuli . Then observing a high value of yt−1 would predict a higher value than expected for yt ( and an increase in probability that it was caused by si ) . The joint unconditional probability distribution is: p ( yt , yt-1 , yt-2 , … ) =∑sip ( si ) [p ( yt|si ) p ( yt-1|si ) p ( yt-2|si ) …]≠p ( yt ) p ( yt-1 ) p ( yt-2 ) … Given the lack of independence , the response entropy must then be calculated from the joint probability distribution: H ( Yt , Yt-1 , Yt-2 , … ) =-∑yt=0RMax∑yt-1=0RMax∑yt-2=0RMax…p ( yt , yt-1 , yt-2 , … ) log2p ( yt , yt-1 , yt-2 , … ) The estimation of this entropy was performed using Monte Carlo with importance sampling . In Monte Carlo , random samples of a vector ( yt , yt−1 , yt−2 , … ) are drawn from a proposal distribution q ( yt , yt−1 , yt−2 , … ) and used to estimate the expected value of log2 p ( yt , yt−1 , yt−2 , … ) by an algebraic average weighted by the likelihood ratio of p ( yt , yt−1 , yt−2 , … ) /q ( yt , yt−1 , yt−2 , … ) . The sampling stops when entropy estimations reach an equilibrium . Information estimations are also known to suffer from positive bias [44] . Here , biased-corrected estimates and errors on information values were obtained from Jackknifing the estimation of the time-varying rates and bootstrapping the Monte Carlo samples . For these calculations , one has also to determine the size of the time window used for estimating the instantaneous information and correspondingly the steps for the cumulative information . This time window is used to estimate the spike counts and the average rate μsi ( t ) and depends both on the dynamics of the stimulus and on the response properties of the neurons . By performing a coherence analysis on spike trains and a power spectral analysis on time varying rate in response to natural stimuli , we found that 10 ms ( or 50 Hz ) captured between 97% and 99% of the dynamics in our system ( see Methods and S1 Fig ) . Finally , we also estimated the information values for stimulus categories by performing the weighted sum of probabilities for stimuli belonging to each category . The information about categories at time t: It=H ( Yt ) -H ( Yt|C ) . is obtained from the conditional probability of response given the category ck , which is in turn calculated as the average conditional probability of response for the stimuli belonging to that category: p ( yt|ck ) =∑skp ( sk ) p ( yt|sk ) Here , p ( sk ) is the probability of occurrence of stimulus sk within the category ck . In controlled playback experiments ( as here ) , p ( sk ) is 1/nsk where nsk is the number of stimuli used to sample the category ck . Similarly , p ( ck ) , the probability of occurrence of vocalizations in category ck was taken as 1/nc where nc is the number of categories . In our system , the stimuli are individual renditions of vocal communication calls that fall into 9 call categories of the zebra finch vocal repertoire . We will contrast stimulus information to categorical information , both instantaneous and cumulative for these behaviorally relevant categories of call-types . To validate our approach and to illustrate the behavior of time-varying information values , we calculated instantaneous and cumulative information for model neurons with simple and stereotyped response properties . Fig 1 shows the firing rates , raster plots and information values for 3 model neurons in response to 4 stimuli ( S1-S4 ) . One model neuron responds to the 4 stimuli with different mean firing rates that are constant in time ( Rate Neuron ) . A second model neuron responds to the four stimuli with the same fixed firing rate but with different latencies ( Onset Neuron ) . The third model neuron also responds with equal average firing rates to the four stimuli but the response occurs at different times ( Temporal Neuron ) . These simulations illustrate some very basic principles of neural coding . First , many different response profiles can lead to very similar rates of information: in all three simulations , the cumulative information approaches the maximum possible value ( 2 bits ) . Second , the coding capacities of neurons are a function of both the range of firing rates that can be achieved ( as in the rate neuron ) and the modulations in time of this neural activity . Third , the estimation of the instantaneous information gives an incomplete picture of the neural coding of a neuron as it does not incorporate the redundancy or independence of the neural representation over time . For example , on the one hand , when comparing the instantaneous information in the Rate neuron to that of the Onset neuron , one might erroneously conclude that the Rate neuron has more information while , in fact , the cumulative information shows that the Onset neuron is more informative at short time scales . On the other hand , one can also observe that the cumulative information in the Rate neuron continues to increase while the firing rate and the instantaneous information are constant; additional time points allows for a better assessment of that firing rate by time-averaging out neural noise . These simulations also allowed us to validate our methods . The KDE for the empirical estimation of the time-varying rate based on the generated spike rasters gave very good predictions ( solid lines Fig 1A–1C ) of the actual model rates ( dashed lines Fig 1A–1C ) : over all stimuli and model neurons , the average error was less than 0 . 02 spikes/ms . Not surprisingly then , using the actual rate versus the estimated rate yielded practically identical results in the information calculations ( dashed vs solid lines , Fig 1D–1F ) . We also checked that the bias corrected estimates were accurate: the instantaneous information was indeed centered at zero when the response to the 4 stimuli was identical . We verified that the actual values of instantaneous and cumulative information were correct . For example , in the Temporal neuron ( Fig 1C & 1F ) a peak instantaneous information of 0 . 5 bits is expected as 1 out 4 stimuli will be almost perfectly discriminated . Finally , we also assessed the limitations of Monte Carlo with importance sampling for the estimation of the cumulative information . For neurons , with continuously high firing rates , this estimation can become unreliable at longer integration times as illustrated by the calculation for the Rate neuron ( Fig 1A & 1D ) . However , in those cases , the estimate of the standard error also increased drastically and allowed us to define end points for the calculation of the cumulative information . Our estimation of the time-varying information values is based on a Poisson parametrization for the distribution of spike counts in each time window of analysis and on the independence of the conditional probability of spike counts , p ( yt|si ) , between successive time windows . Here , we assessed the limits of these assumptions . First , we examined for each neuron , for each stimulus and for each time bin of 10 ms , whether the empirical distribution of spike counts across stimulus presentations was different than the one expected from a Poisson distribution whose mean was given by the kernel density estimation ( resampling test; see Methods ) . For each neuron , we obtained the proportion of time bins where the Poisson assumption was rejected . Fig 2A shows the distribution of this proportion across the neural population . For the large majority of cells ( 240/404 ) , the empirical spike count could not be distinguished from a Poisson distribution for 90% of the time bins . For a small number of neurons ( 5/404 ) non-Poisson spike count statistics could be observed in 40% of the time bins . The mean number of rejected time bins was 10 . 5% . Thus , the Poisson statistics assumption for spike is warranted in more than half of our neurons . For the other neurons , the deviation is relatively small . As stated above , the Poisson assumption used for non-Poisson data yields a lower bound on information . Given the small number of presentations of each stimulus ( n = 10 ) , one can argue that the test of conformity to a Poisson distribution is relatively conservative ( i . e . we might fail to reject the Poisson statistic because of small sample size ) . Thus , we also assessed whether the first and second moments ( mean and variance ) obeyed Poisson statistics . Fig 2C shows the Fano Factor estimated at each consecutive time bins after stimulus onset , both for individual stimuli ( insets ) and average across stimuli ( solid line ) . The mean Fano factor for the population was 1 . 04 with SEM of 0 . 04 ( Fig 2D ) . This second analysis further quantifies the degree with which our neural data follow Poisson statistics and also shows that for a majority of neurons the assumption is warranted . The second assumption used in the calculation of cumulative information is that the conditional probability of spike counts , p ( yt|si ) , is independent across successive time bins . To assess the validity of this assumption , we estimated the Pearson correlation coefficient of spike counts obtained in successive time windows in responses to the same stimulus , also known as the noise correlation . Fig 2B shows the distribution of noise correlations for the 404 neurons examined . Significant noise correlations were observed ( red bars versus non-significant values shown in blue ) , but they were small , centered around zero and their distribution showed a slight positive skew . These positive values correspond to cases were a slight increase of the spike count above average at a given stimulus presentation and time bin is somewhat giving a higher chance of greater than average spike count in the successive time bin of the same stimulus presentation . Ignoring noise correlations can lead to either under or over estimates of mutual information depending on the functions that describe the mean time-varying response to each stimulus [45 , 46] . In the majority of cases , not taking into account noise correlations leads to an under-estimate of mutual information . For instance , if responses in successive time bins decrease for one stimulus and increase for another stimulus , positive or negative noise correlations will result in lower conditional response entropies , H ( Yt , Yt−1 , Yt−2 , …|S ) , and thus higher mutual information . In the general case of stimulus time-varying rates that have different and random shapes depending on the stimulus , one can also show that noise correlations increase mutual information . In some cases , however , noise correlations can increase the conditional response entropies and not taking them into account lead to an over-estimate of the mutual information . Over-estimation of mutual information commonly happens when the responses for stimuli change in the same manner in successive time bins ( increase or decrease in mean rates for all sounds ) , then positive noise correlations can lead to an increase in conditional response entropies and thus a decrease in mutual information . In our data , the time varying rates of the different stimuli are positively correlated at the onset response , but mostly uncorrelated at other times ( see Figs 3–5 ) . We might therefore slightly overestimate mutual information at stimulus onset , but for most of our analysis we might be slightly underestimating cumulative information . Given that the noise correlations are small , this estimation bias is small . While we can neglect noise correlations in our calculations and assume independence of conditional probability of spike counts , p ( yt|si ) , across successive time bins , we are not assuming independence of the unconditional probability , p ( yt ) , across successive time bins . Indeed , the distribution of stimulus correlation ( correlation of mean rate between pairs of consecutive time bins in the trial average response of each stimulus ) across the neural population show high positive values centered around 0 . 4 ( Fig 1B ) . These values indicate that the change in the spike counts in successive time bins is highly driven by the stimulus identity . As such , the unconditional probability distributions in successive time windows are not independent . We do take into account these large stimulus correlations in our estimation of the cumulative mutual information . In Figs 3–5 , we show the time-varying firing rates , the time-varying instantaneous information and the cumulative information for 3 zebra finch auditory neurons with distinct response properties . The neuron in Fig 3 responded robustly to all communication calls with high and reliable firing rates . It also responded in a time locked fashion sometimes at multiple time points for single calls . Although this neuron was not selective for a particular stimulus or call-type category , by combining rate and temporal codes it reached very high levels of instantaneous information . Moreover , this instantaneous information showed little redundancy yielding very high cumulative information . One can also observe , that for this particular neuron , the average time-varying firing rate is not correlated with instantaneous information . This neuron shows a strong onset response in its firing rate while the instantaneous information is almost constant and even slightly lower during the onset response . The neurons in Figs 4 and 5 have much lower firing rates and exhibit selectivity for a call-type , the Distance Call ( DC ) and the Wsst Call ( Ws ) respectively . The neuron in Fig 4 exhibits both an onset and sustained response both of which are selective for DC . The neuron in Fig 5 has a much longer latency for response with correlated peaks in firing rate and instantaneous information found between 100 and 300 ms after stimulus onset ( Example of spike rasters from single trials for these three neurons are shown in S4 , S5 and S6 Figs ) . The cumulative information curves were fitted with an exponential function that allows us to quantify the time constant of information accumulation ( τ ) , the saturation level ( k ) relative to the maximum information that could be achieved ( IMax ) and the latency ( dt ) : CImod ( t ) =kIMax ( 1-e- ( t-dt ) /τ ) Most of the calls in the zebra finch repertoire have durations that are shorter than 300 ms [39] . In order to investigate the cumulative information accumulated at this behaviorally relevant fixed point in time , we estimated the relative cumulative information as the value of the model at 300 ms relative to IMax . ; k300=CImod ( 300 ) /IMax . The results of the fit are shown in dashed lines in the cumulative information plot for each neuron . One can observe that the neuron in Fig 3 has exceptionally high values of saturation: with sufficiently long integrations time , single spike trials could be used to perfectly assess what stimulus ( out of those used in the experiment ) was heard and , thus , which call-type category it belonged to . The selective neurons in Figs 4 and 5 have much lower saturation levels as expected since they mostly respond to stimuli from a single call-type category . The neuron in Fig 5 has longer latency in its categorical cumulative information but this is not the case for the neuron in Fig 4 that has a rapid yet selective onset response . The high firing neuron ( Fig 3 ) also has a faster time constant τ for the stimulus information than the selective neurons shown in Figs 4 and 5 . All three neurons have similar and relatively slow time constants for the categorical information in the 200–300 ms range . Fig 6A shows the time course of the firing rate averaged across all stimuli and its relationship with the instantaneous information . On average , avian cortical auditory neurons show an onset response followed by a sustained response as observed at many stages of auditory processing and in many sensory systems . The instantaneous information , however , remains almost constant during the entire time . This is true both for the information about individual stimuli or the information about categories . Thus , the onset response is only less informative than the sustained response in bits/spike but not in bits/s; when processing natural vocalizations , the onset and sustained response are equally informative . Moreover , the information in the sustained response continues to provide new information as reflected by the continuous and relatively fast increase in cumulative information shown in Fig 6B . The non-redundancy of the information in the sustained period is also well captured by examining the derivative or slope of the cumulative information shown in Fig 6C . That plot shows how much new information is acquired at each time bin . The new information for categories is relatively constant ( red line Fig 6C ) and similar to the instantaneous information about category ( yellow line Fig 6A ) , while the spike rate is drastically decreasing between 50 and 150ms . Thus , as time progresses , individual spikes carry more new information for categories: there is very little redundancy in the code for call-type categories up to 200 ms and during that time the coding efficiency in bits/spike increases for the new information that is acquired about categories . In contrast , the new information for individual stimuli decreases and is , on average , remarkably correlated with spike rate ( Fig 6C ) . Thus , while , in terms of instantaneous information , the coding efficiency ( in bits/spike ) is greater in the sustained versus onset period , in terms of non-redundant information , the coding efficiency is remarkably constant throughout the response . This result suggests that an integrating average spike rate measure ( i . e . the time running average spike count over stimuli ) could serve as a relatively good proxy of cumulative information on individual stimuli . However , note that this cumulative information needs to take into account time varying spike rates ( or stimulus locked spike patterns ) as in our calculation . Indeed , taking into account time varying spike rates for each stimulus as opposed to the mean firing rate across the entire time-window for each stimulus is crucial in the accurate calculation of time-varying cumulative information . As seen in Fig 6B , the increases in the cumulative information for the time-varying rate code ( solid lines ) is much larger than the increases obtained with the fixed rate code ( dashed lines; the information still increases because of noise averaging as explained above ) . The time-varying rates observed in neural responses ( as illustrated in Fig 3A ) provide additional information . How much more ? For the coding of individual stimuli ( comparing the dashed blue line to the solid blue line in Fig 6B ) , a fixed rate code ( or assumption ) captures only 24% of the information at 100 ms and 21% at 300 ms . The effect for categorical information is smaller because some of the coding dynamics in time-varying responses to stimuli belonging to the same category effectively become neural noise: for categorical information , a fixed rate code captures 50% of the information at 100 ms and 41% at 300 ms . The distributions of time constants , τ for the cumulative information for stimuli and call-type categories are shown on Fig 7A . The distributions of relative cumulative information at 300ms ( k300 ) are shown on Fig 7B . The range of time constants observed across the population of neurons was large ( 100 ms-600 ms ) with average time constants of 459 ms for cumulative stimulus information and 372 ms for cumulative categorical information . This difference in means of time constants is statistically significant ( Paired t-test t ( 214 ) = 3 . 49 , p = 0 . 00058 ) ; the ongoing time-varying rate changes continue to provide more information for decoding stimuli and less so for decoding categories of stimuli . There is also a wide distribution of relative cumulative information values ( k300 ranging from close to zero to 0 . 6 ) . On average across neurons , the k300 was 0 . 2 ( or 20% ) for stimuli and 0 . 15 ( or 15% ) for categories . These differences in relative information values are highly significant suggesting that , single neurons , capture more variability in stimuli than in categories . Note however that measures of relative information depend on the number of stimuli or categories . Therefore , a direct statistical comparison is not warranted . The comparison between stimulus representation and category representation requires estimations of expected values of categorical information given stimulus information which is performed below . Relative cumulative information values can , however , be used for stimulus and categories independently to assess other coding properties: although average time-varying rates and instantaneous time varying information are not well correlated within single neurons ( as shown in Figs 3 and 6 ) , the relative cumulative information is correlated with average firing rates . For cumulative stimulus information , one finds an increase in k300 of 1% per spike/s ( Adj R2 = 0 . 36 , F ( 1 , 213 ) = 120 , p = 1 . 82 10−22 ) and , for cumulative categorical information , an increase in k300 of 0 . 8% per spike/s ( Adj R2 = 0 . 34 , F ( 1 , 213 ) = 112 , p = 2 . 26 10−21 ) . We are interested in identifying neurons that could play an important role in categorizing vocalizations . We had previously identified example neurons that were highly selective for particular call-types and showed a high degree of invariance such as those shown in Figs 4 and 5 [40] . Here , we attempted to quantify the neural invariance for call renditions within call-type categories along time . For this purpose , we computed a Categorical Information Index ( CII ) . The CII compares the actual categorical cumulative information to three potential values ( see cartoon probability distributions of neural responses in Fig 8A ) : a floor or minimum value ( set at 0 ) , an expected value for shared information between stimuli and categories ( set at 1 ) and a ceiling or maximum value ( set at 2 ) . The floor is the categorical information that one would obtain if stimuli are randomly grouped . The shared-information value is the information that one would obtain if the information about stimuli is equally shared across all stimuli and the neural responses for stimuli are perfectly sorted for each natural call-type category; for example , at a given point in time the 10 renditions of DC would elicit the 10 highest rates , the 10 renditions of LT Call the next 10 higher firing rates and so forth . This shared-information value does therefore assume that , for a given neuronal signal to noise ratio , neural responses segregate categories maximally while also preserving the maximum discrimination between stimuli within categories . The ceiling value is the categorical information value that one would obtain if a maximum amount of information about stimuli was used for the discrimination of categories and a minimum for discriminating stimuli within categories; it assumes maximum invariance to variations within a category . The ceiling value is equal to the stimulus information until it reaches log2 ( nc ) , where nc is the number of categories , corresponding here to the 9 call-types . Fig 8B shows the time-varying floor ( dashed-green ) , shared ( dashed-orange ) and ceiling ( dashed-red ) values of cumulative categorical information along with the actual stimulus ( solid blue ) and categorical ( solid red ) cumulative information for 3 neurons chosen to illustrate CIIs that are below 1 , around 1 and above 1 . Fig 9A shows the distribution of CII values across the neural population ( see also S7 Fig for results of this analysis in absolute information units ) . The thin colored lines correspond to CII curves for single neurons and they are colored according to the time average CII ( see legend Fig 9A ) . The average CII over neurons ( bold black line ) is very close to the shared value of 1 as one might expect if acoustical differences across stimuli drive neural responses in a linear fashion along some acoustical feature and call categories segregate perfectly along that same acoustical feature . However , the average CII is also slightly ( and significantly ) above 1 between 120 and 260 ms showing some small degree of average invariance for call renditions within a call-type category during those times . In addition it is clear that there is a wide distribution of CII around the shared value of 1 . And this distribution is positively skewed towards values higher than 1 as exemplified by the density plots calculated at 150ms . This distribution includes many neurons that exhibit a high degree of invariance for call renditions within call-type categories as also shown by the average CII for the top quartile ( red solid line ) . Focusing on these 25% of neurons with the highest CII , we found a maximum value of CII at 175 ms , indicating that , relative to the maximum invariance achievable given their individual stimulus information values , these neurons are maximally invariant around that time . In terms of absolute value of additional categorial cumulative information relative to the expected shared information value , these same neurons reach a maximum of 0 . 16 bits at 320 ms ( S7 Fig ) . Do these high CII neurons exhibit other characteristic response properties ? In the scatter plots of Fig 9B and 9C , we examined the relationship between CII ( color coded ) and , respectively , time constants and relative level of the cumulative information . It can be seen from Fig 9B , that neurons with high CII have relatively long stimulus time constants in comparison to their corresponding time constant observed for categorical information . This relationship is also significant for the entire population of neurons ( Linear Regression explaining CII from τcat/τstim: Adj R2 = 0 . 11 , F ( 1 , 213 ) = 28 . 5 , p = 2 . 37 10−7 ) . As shown in Fig 9C , neurons with high CII also have higher relative levels of categorical information in comparison to their relative values for stimulus information although this result is expected given our definition of CII ( Linear Regression explaining CII from k300 , cat—k300 , stim: Adj R2 = 0 . 76 , F ( 1 , 213 ) = 675 , p = 5 . 5 10−68 ) . Finally , one can also notice that neurons with high CII have low values of relative stimulus cumulative information ( Linear Regression explaining CII from kstim ( 300 ) : Adj R2 = 0 . 28 , F ( 1 , 213 ) = 84 , p = 3 . 96 10−17 ) . This effect is caused by the correlation between invariance and selectivity as we have shown previously [40]: neurons that show the highest degrees of invariance also tend to respond to a small number of call-type categories and thus a small number of stimuli . As such , high invariance and high selectivity goes hand in hand with lower values of information . We examined whether neurons were organized in the avian auditory cortex based on their CII , cumulative information time constants ( τ ) and the relative cumulative information at 300 ms relative to the maximum ( k300 ) . Most of our recording sites were identified histologically and could be assigned to avian cortical areas that had been segregated into the thalamic recipient area , L2; intermediate primary auditory regions , L1 , L3 , CLM and L; and secondary areas , CMM and NCM [47–50] ( n = 303/337 neurons ) . We also obtained spatial x , y , z coordinates of the recording sites relative to the midline , the position along the rostral-caudal axis where the lamina pallio-subpallialis ( LPS ) is the most dorsal , and the top of the brain ( Fig 10 ) . In all regions , we found a range of CII and thus only weak anatomical effects across areas . Using the L2 dorsal-ventral oblique axis as reference point , CII was slightly higher as one moved rostrally or caudally to higher regions of auditory processing ( Linear regression: Adj R2 = 0 . 036 , F ( 2 , 195 ) = 8 . 39 , p = 0 . 0042 ) . However , an ANOVA also suggested that both regions NCM and L2 had slightly higher mean CII ( Adj R2 = 0 . 027 , F ( 4 , 185 ) = 2 . 77 , p = 0 . 042 ) . We also observed an increase in the time constant ( τ ) for the stimulus cumulative information that parallel the increase in CII as one moved away from the L2 axis ( Linear regression: Ajd R2 = 0 . 04 , F ( 2 , 195 ) = 9 . 32 , p = 0 . 00258 ) . The saturation constant at 300 ms ( κ300 ) for stimulus information was also higher in the central auditory region and smaller as one moved away from the L2 axis ( Linear Regression: Ajd R2 = 0 . 035 , F ( 2 , 195 ) = 8 . 18 , p = 0 . 0047 ) . Thus , there is more invariance in higher auditory areas and longer time constants but less absolute information about the stimulus . Note again that these significant anatomical trends were characterized by very small effect sizes . We did not find , however , any effects of anatomy on the time constant , ( τ ) , or saturation constants ( κ300 ) for the cumulative categorical information; in this data set , the subset of neurons that have high invariance have similar response properties in both lower and higher regions of the avian auditory pallium .
We modeled responses that are observed in the auditory system as inhomogeneous Poisson processes in order to estimate the time-varying instantaneous and cumulative information for vocalizations used in communications . We showed that using Kernel Density Estimation for the time-varying firing rate and Monte Carlo with importance sampling for estimating probabilities , we were able to obtain accurate and bias-free estimates of these time-varying information values . This parametric approach is powerful because a relatively small number of trials can be used to estimate the time-varying response and thus information values can be estimated in response to a relatively large set of stimuli ( here over 100 distinct vocalizations ) given typical recording times . The auditory avian cortical neurons recorded here and stimulated with natural communication calls had approximate Poisson statistics with small violations that mostly resulted in our estimation of information being a tight lower bound . More generally , the same procedures could also be used with spike trains statistics that can be parametrized with probability functions that depend only on a time varying rate such as inhomogeneous gamma or inhomogeneous inverse Gaussian [12 , 51] and could also be extended to ensemble of neurons . Although Poisson statistics are often observed in neural data and will correctly fit any data set obtained from pooling responses across a large number of trials [52] , we realize that they make the strong assumption that the firing rate for a particular trial and for a particular neuron depends only on time . Refractory period in spiking neurons and other correlations in single neurons or in an ensemble of neurons that are not phased locked to the stimulus ( i . e . noise correlations ) are common violations of this assumption . In our data , we found small positive noise correlations reflecting the fact that neural noise effects extend beyond our analysis windows of 10 ms . Taking noise correlations into account can increase stimulus decoding accuracies [23] . In cases where noise correlations are informative , the method proposed here would only yield lower bound estimates of information theoretic values . Alternatively , one should try the use of spike metrics measures that can capture noise correlations [53 , 54] in combination with stimulus decoding approaches to then obtain measures of information from the confusion matrix of predicted versus actual stimuli [13 , 40 , 55 , 56] . If spike metrics can be estimated accurately ( with limited data ) , repeating the stimulus decoding procedure for progressively longer time windows would yield cumulative information values such as those calculated here but which could consider noise correlations . Beyond our methodological contribution , the principal goal of this analysis was to characterize the neural code in higher auditory areas for communication calls . We found that , on average , auditory cortical neurons responded to these natural stimuli with time-varying firing rates that exhibited an onset and a sustained component of the response . Although in some situations the auditory cortex appears to respond only transiently [26 , 57] , our data supports findings from mammalian species that have showed strong sustained responses when neurons are driven by preferred stimuli [21] . Similarly , in the human superior temporal gyrus , the sustained responses to speech have been shown to be more informative for decoding speech phonemes [58] . Natural sounds in general have also been shown to be particularly efficient at driving auditory areas [12 , 59–64] and thus the presence of informative sustained responses is not surprising . Indeed , in some of our most selective neurons , such as the neuron in Fig 5 , the onset response is missing and only the sustained response is observed . More generally , and on average , we found that both the transient and sustained response had information about the stimulus identity and that the information in these two response phases was not redundant: the stimulus space of natural vocalizations is very large and although the initial response provides some clue as to the nature of the vocalization , new and additional information is observed in the sustained response . On the one hand , one might argue that , in natural vocalizations , the sound itself is changing with time . These stimulus changes occur both within calls that are made of composite notes or in call bouts and song motifs made of multiple syllables . Sustained responses in such cases could be interpreted as multiple onset responses ( but with decreasing amplitude ) . On the other hand , from a behavioral perspective , these communication calls correspond to a single auditory object: a message from a particular individual about a particular state . From either perspective , these observations and analyses illustrate the importance of using behaviorally relevant stimuli when analyzing the nature of the neural code . The information about stimulus identity was shown to be approximately equal in bits/s in the onset and sustained response . Given that the average onset response ( in spikes/s ) is greater than the sustained response , one might conclude that the onset coding ( in bit/spikes ) is not as efficient . Although , this is true for instantaneous information , we also found that the coding efficiency for novel information was remarkably constant for stimulus information while increasing in the first 200 ms for categorical information . The information efficiency in the onset versus sustained response might also be different when an ensemble of neurons is considered; the relative timing of the first spike has been shown both in audition [26] and in other sensory modalities [25 , 27] to be highly informative . Moreover , in addition to stimulus identity other stimulus features are also encoded in neural responses; the timing of the stimulus is clearly marked by the onset response or transient response [22 , 58 , 65 , 66] and other stimulus attributes such as the location , fundamental and loudness/distance of the sound source are also processed in auditory cortex [67–69] . It is therefore very likely that the onset response contains information about stimulus features other than stimulus identity or category and , potentially to a greater extent than in the sustained response ( e . g . relative timing of onset ) . We note , however , that the opposite was found for the neural discrimination of two vowel sounds in the ferret auditory cortex where the onset response or early response was the most informative for decoding vowel identity relative to other attributes [69] . Our IT analysis also revealed the importance of stimulus locked spike patterns even in relatively short neural responses: in just 100 ms , the mean rate captured only a quarter of the information present when time-varying firing rates are considered . Thus , our analyses provide additional evidence that spiking patterns carry a significant amount of stimulus information and should therefore not be ignored in the analysis of neural responses [70 , 71] . The spiking precision analyzed here was relatively coarse ( 10 ms windows ) and matched to the time scales of the relevant dynamics in the stimulus: although zebra finch calls are much longer than 10 ms , they are complex sounds with fast spectro-temporal features [39] . Therefore , although the neural code observed here uses fast varying spike rates , it cannot be labelled as a temporal code . In a rigorous definition of a temporal code , temporal information in spike patterns must code stimulus attributes other than the stimulus dynamics [72] . The cumulative information for stimulus identity or for categories increased for sustained periods of time before saturating . These saturating curves were well fitted with exponentials and yielded relatively long-time constants of approximately 460 ms for stimuli identity and 370 ms for call categories . These information time constants are long in comparison to the integration times that are usually found for auditory cortical neurons; the spectro-temporal receptive fields of avian auditory neurons rarely extend beyond 50 ms [73–76] although adaptive responses on longer time scales have also been described [77–79] . Information time constants depend on the integration and adaptation time constants of neurons but also on the stimulus dynamics: although stimulus identity and stimulus category are fixed in time , the sound itself has time varying features that can provide additional information as time goes on . It is the triple combination of the dynamics of the natural stimulus statistics , the neuronal integration time and the neuronal signal to noise levels that are going to affect the information time constant . Natural sounds and in particular communication calls are informative objects not only because of their spectral structure but also their rich dynamical structure . The importance of time in the neural code used in the auditory system has been emphasized multiple times [80–84] and our cumulative information analysis further stresses the importance of using natural sounds or synthetic sounds that carefully match natural dynamics when probing auditory neural encoding . Ultimately , it is an information time constant that includes stimulus dynamics that should be compared to behavioral responses [18] . A behavioral assay of reaction time for all the calls in the zebra finch repertoire has not been performed but the values of a few hundred of ms correspond to the shortest time intervals between call and call back in anti-phonal calling in paired zebra finches [85] . Although , faster times could be obtained in reaction to any sounds ( e . g . startle or orientation ) , the processing of stimulus identity to extract the information on who is calling and what is being said might require these longer processing times . Finally , we quantified the fraction of the information about stimulus identity that could be used for extracting the call-type category . We used that analysis to characterize neurons not only in terms of their absolute coding capacities for call-type categories but also in terms of their invariance in their response to different stimuli belonging to the same call-type category . We found that , on average , information for categories is close to what one would expect if neural representation for stimulus identity is also segregated along call-type categories . Nonetheless , the invariance for categories was significantly above that expected value between 120 and 260 ms after stimulus onset . In addition , the distribution of our categorial index also had a positive skew with a long tail: many neurons had a high degree of invariance and could therefore be classified as categorical . We have shown in previous work that this highly invariant neurons could not be simply the result of sampling; when invariance metrics are calculated using stimuli that are grouped randomly but that preserve the acoustical distance found in the natural categories , one does not find the similar highly invariant neurons [40] . To better understand the response properties that give rise to this category specific invariance , one could compare the empirical distribution of Categorical Information Indices ( CII ) to the distribution of CII obtained either with a bank of linear modulation filters or with models that include particular non-linearities as was done in [86] . The properties of the model neurons that yield the closest CII distribution to the one empirically observed could then be compared to the actual non-linear receptive fields of the neurons that could be estimated by a multiple filter decomposition [87] . Are the categorical neurons in our study anatomically organized ? On the one hand , the categorical and invariant neurons described here had even longer time constants for cumulative stimulus information than the non-invariant neurons in our dataset suggesting that they could be higher in the auditory processing stream . On the other hand , we did not find a separate population of invariant neurons across all of our recordings of neurons informative for categories: the distribution of our categorical index was unimodal . Moreover , we found only weak anatomical correlations with very small increases in the Categorical Information Index along an anatomical axis corresponding to lower vs higher auditory areas . Higher avian auditory cortical areas have been associated with more complex spectro-temporal receptive fields [73 , 76 , 88] , increase robustness to noise [89–91] and more specificity for processing natural sounds [12 , 92] . It is possible that we failed to find a large anatomical effect because our sampling of higher auditory areas was relatively sparse; in particular we have a small number of recording sites in CLM and in the more ventral and medial regions of CMM . It is also possible that , although high-invariance neurons are found throughout the avian auditory pallium , this invariance is achieved differently and potentially for different purposes in different regions . A better description of the non-linear receptive fields of these neurons would also allow us to investigate this possibility . However , it is also striking to see that both in our study and in previous research that investigated invariant properties of avian auditory neurons to classes of natural sounds [92 , 93] there is a high degree of heterogeneity in the neural responses within each area . For example , although small differences across auditory areas were reported ( and of similar effect size than the spatial effect described here ) , Meliza and Margoliash [92] also found neurons that were tolerant to different renditions of song types throughout the auditory pallium of the starling . Here , we found that neurons with high Categorical Information Indices could be found anatomically next to neurons with low Categorical Information Indices . Some of this functional heterogeneity could be associated with different cell types [75 , 92] and a better understanding of the micro-circuitry in the avian auditory cortical areas is clearly needed [30] . It is interesting to note , however , that this mixing of low level and high-level response properties is not unique to the avian auditory system as similar heterogeneity has been found in the mouse [94] and ferret auditory cortex [95 , 96] . Contrary to the visual system , the auditory system might preserve a higher mixture of low-level and high-level sensory responses properties at multiple stages of processing including the higher auditory areas involved in auditory object recognition . If this is true and universally found in vertebrates , it might be a necessary property of the computations needed for auditory object recognition , potentially related to the fact that complexity in auditory signals is in time-varying spectral patterns that quickly disappear; the fleeting nature of sounds could prevent higher processing stages from subsequently accessing lower-level representations for additional information .
All animal procedures were approved by the Animal Care and Use Committee of the University of California Berkeley ( AUP-2016-09-9157 ) and were in accordance with the NIH guidelines regarding the care and use of animals for experimental procedures . Birds subjects were euthanized after neural recordings by overdose of Isoflurane . Four male and two female adult zebra finches ( Taeniopygia guttata ) were used for the electrophysiological experiments . The birds were bred and raised in family cages until they reached adulthood , and then maintained in uni-sex groups . Although birds could only freely interact with their cage-mates , all cages were in the same room allowing for visual and acoustical interactions between all birds in the colony . All birds were given seeds , water , grid and nest material ad libitum and were supplemented with eggs , lettuce and bath once a week . Vocalizations used as stimuli during neurophysiological experiments were recorded from 15 adult birds and 8 chicks ( 20–30 days old ) . The vocalization bank obtained contains 486 vocalizations that included for each bird most of the calls in the Zebra finch repertoire: 7 call-types in adults and 2 in chicks . The adult calls included the following affiliative calls: Song ( So ) , Distance Call ( DC ) , Tet call ( Te ) and Nest call ( Ne ) ; and the following non-affiliative calls: Wsst or aggressive call ( Ws ) , the Distress call ( Di ) and one of the two alarm calls , the Thuk ( Th ) . The juvenile calls included the Begging call ( Be ) and the Long Tonal call ( LT ) . Additional information about these stimuli and their behavioral meanings can be found in [39 , 40 , 97] . For the neurophysiological experiments , a new subset of the vocalization bank was used at each electrophysiological recording site . This subset was made from a representative number of vocalizations from the repertoire of individuals: three adult females , three adult males , two female chicks and two male chicks . From each individual caller , we randomly chose 3 call bouts from each category or fewer if fewer than 3 call bouts were obtained for that particular call-type and individual . The maximum number of stimuli that could be selected in that procedure was therefore 3x7x3 ( adult males x repertoire x renditions ) + 3x6x3 ( adult females x repertoire x renditions ) + 4x2x3 ( juveniles x repertoire x renditions ) = 141 . The average number of stimuli played for each single unit was 114 ( sd = 22 , min = 34 , max = 123 ) . Ten trials were acquired for each stimulus with a few exceptions ( min = 9 , max = 11 ) . Sounds were broadcasted in a random order using an RX8 processor ( TDT System III , sample frequency 24414 . 0625 Hz ) connected to a speaker ( PCxt352 , Blaupunkt , IL , USA ) facing the bird at approximately 40cm . The sound level was calibrated on song stimuli to obtain playbacks at 75dB SPL measured at the bird’s location using a sound meter ( Digital Sound Level Meter , RadioShack ) . Extra-cellular electrophysiological recordings were performed in 6 urethane anesthetized adult zebra finches . The birds were placed in a sound-attenuated chamber ( Acoustic Systems , MSR West , Louisville , CO , USA ) and sound presentation and neural recording were performed using custom code written in TDT software language and TDT hardware ( TDT System III ) . Sounds were broadcasted in a random order as described above . Neural responses were recorded using the signal of two ( 5 subjects ) or one ( 1 subject ) electrode arrays , band-pass filtered between 300Hz and 5kHz and collected by an RZ5-2 processor ( TDT System III , sample frequency 24414 . 0625 Hz ) . The electrode arrays consisted of two rows of 8 tungsten electrodes with row separation of 500 μm and inter-electrode separation within row of 250 μm ( Tucker Davis Technologies , FL ) . Electrode impedances were approximately 2 MOhms . When two electrode arrays were used , they were placed each in one hemisphere . Spike arrival times and spike shapes of multiple units were obtained by voltage threshold . The level of the threshold was set automatically by the TDT software using the variance of the voltage trace in absence of any stimuli . Electrodes were progressively lowered and neural responses were collected as soon as auditory responses to song , white noise , Distance call or limited modulation noise could be identified on half of the electrodes in each hemisphere ( the stimuli used to identify auditory neurons were different from the stimuli used in the analysis ) . Several recording sites were randomly selected by progressively deepening the penetration of the electrodes and ensuring at least 100 μm between two sites . This distance between recordings insured that we did not record from the same neurons at separate sites . On average 4 . 2±2 sites ( mean ± sd ) were recorded per bird and per hemisphere at a depth ranging from 400 μm to 2550 μm . After the last recording site , the subject was euthanized by overdose of isoflurane and transcardially perfused . Coronal slices of 20μm obtained with a cryostat were then alternatively stained with Nissl staining or simply mounted in Fluoroshield medium ( F-6057 , Fluoroshield with DAPI , Sigma-Aldrich ) . While Fluoroshield slices were used to localize electrode tracks , Nissl stained slices were used to identify the position of the 6 auditory areas investigated here: the three regions of Field L ( L1 , L2 and L3 ) , 2 regions of Mesopallium Caudale ( CM ) : Mesopallium Caudomediale ( CMM ) and Mesopallium Caudolaterale ( CLM ) ; and Nidopallium Caudomediale ( NCM ) . By aligning pictures , we were able to anatomically localize most of the recording sites and calculate the approximate coordinates of these sites . Since we could not localize the Y-sinus on slices , we used the position of the Lamina Pallio-Subpallialis ( LPS ) peak as the reference point for the rostro-caudal axis in all subjects . The surface of the brain and the midline were the reference for respectively the dorsal-ventral axis and the medial-lateral axis . The approximate coordinates of units were used to build 3-D reconstructions of all single unit positions in an hypothetic brain . We were not able to recover all the electrode traces in one of the six birds and excluded that data from the anatomical analysis . Table 1 shows the number of units from each bird for which we had an anatomical location and that had significant stimulus cumulative information ( 303/337 neurons; see below ) . Single unit isolation was performed off-line using custom software that used a combination of supervised and unsupervised clustering algorithms . These clustering algorithms used the spike-snippets shape as described by a PCA . Sorted units where declared to be single units based on spike shape reliability across snippets . The spike shape reliability measure was a signal to noise ratio ( SNR ) where the signal was the difference between the maximum and the minimum of the average snippet and the noise was the standard deviation of this measure across all snippets . Single units in our data set have an SNR > 5 . Additional details on these experimental procedures can be found in [40] . The data of neural responses from 404 out of 914 isolated single units were used in this study . The 404 neurons were selected based on a prior analysis that showed that this subset of units were not only auditory but also contained information about call-types , in the sense that call-types could be decoded above chance level from neural responses ( see [40] ) . Here , we analyzed the neural response in the first 600ms after stimulus onset . Calculations of cumulative information requires the estimation of very large distributions which sizes grow exponentially with the number of time points investigated . It is therefore imperative to find the optimal width of the time bin for the analysis: as small as possible to capture information in spike patterns but as large as possible to reduce the dimensionality for the estimation of mutual information . It is also well known that the relevant time scale can be different in different epochs of the responses ( e . g . more precise spike timing at the onset phase of the response and less precise during the sustained phase ) [98] . Here , we addressed this issue by first estimating the time-varying rate of our neurons using methods of density estimation based on adaptive kernels . In this methods , narrower kernels are used when spike precision is high and wider kernels at times when the spike precision is low . We then estimated the largest fixed time window that could capture most of the fastest changes in those average time-varying spike rate . Alternative methods based directly on measures of information have also been proposed [98] . For each stimulus , the 9 to 11 raw spike patterns of 600ms , sampled at 10kHz , were combined to obtain the corresponding time varying spike rate ( sample frequency set at 1kHz ) by applying a locally adaptive kernel bandwidth optimization method [42] . In cases where the neuron did not respond to any of the presentations of the stimulus or responded only once over all presentations , the rate was estimated as being constant for the 600ms duration of the neural response . For those two unresponsive cases , the rate was set to be 1/ ( 2*Ntrials*NTimes ) in the absence of any spike or 1/ ( Ntrials* NTimes ) in case of one spike , with Ntrials the number of stimulus presentations ( 9–11 ) and NTimes the number of time points at which the rate was estimated ( here 600 , for a 600ms neural response section with a sampling rate set at 1kHz ) . To investigate cumulative information values up to 600ms after stimulus onset , time-varying rates were sampled at 10ms ( Nyquist limit frequency of 50Hz ) . To estimate , the amount of information potentially lost by this low-pass filtering , we estimated an information value based on coherence analysis of the signal to noise ratio in the raw spike train . The coherence between a single spike train ( R ) and the actual time-varying mean response ( A ) γAR2 can be derived from the coherence between the peristimulus time histogram ( PSTH ) obtained from half of the trials and the PSTH obtained from the other half [99] . γAR2=[1-M2× ( 1-1γR-1 , M/2R-2 , M/22 ) ] where M the total number of trials ( presentations of the stimuli ) and γR-1 , M/2R-2 , M/22 the coherence between the two PSTHs calculated on half of the trials . The coherence between two responses is a function of frequency ( ω ) . An estimate of the mutual information ( in bits per second ) between R and A responses can then be obtained by integrating over all frequencies [4 , 99]: IAR=-∫0∞log2[1-γAR2 ( ω ) ]dω For each unit , we estimated the percentage of information preserved as the ratio between IAR calculated up to 50Hz and IAR calculated over all frequencies . Over all units , 96 . 7% ±6 . 9% ( mean ± SD ) of information was conserved by a lowpass filtering at 50Hz and only 88 out of 404 cells had information losses greater than 5% . For each unit , we also calculated the proportion of cumulative power across frequencies in the time varying spike rate estimation obtained with the KDE before low-pass filtering and down-sampling ( averaged periodogram in overlapping 200 ms Hanning windows and 1 kHz sampling rate ) . The cumulative sum of the power was calculated across frequencies and normalized by the maximum power value to obtain the proportion of cumulative power . On average across units , the cumulative power reached 98 . 8%±1 . 6% ( mean ± SD ) at 50Hz , further validating our choice of the temporal resolution ( S1 Fig ) . To estimate values of mutual information , we assumed that spike counts in our 10 ms windows had a Poisson distribution . To assess the validity of this assumption , we calculated for each neuron , for each stimulus si and for each time bin t , the log-likelihood function from the 10 spike counts yj each obtained in 1 trial j assuming a Poisson distribution with the mean response μsi , j given by the delete one estimate . We then generated a bootstrap distribution of 1000 of these likelihood measures each obtained for 10 samples from a Poisson with mean rate given by our KDE estimate . Finally , we calculated the number of times the log-likelihood value obtained for the data was greater or smaller than the bootstrap values to generate a p-value ( two-tailed test ) . Given the very large number of statistical tests , a False Discovery Rate correction , the Benjamini–Hochberg procedure , was used for each neuron to estimate the number of bins for which the Poisson hypothesis could be rejected with p = 0 . 05 . In our estimations of mutual information , we also assumed that the conditional probability of the response given a stimulus was independent between time bins . In other words , knowing that the spike count is greater ( or smaller ) than the mean in one time bin provides no information about probability of spike counts in the same trial in another time bin . To test this assumption , we calculated the noise correlation . The noise correlation is simply the Pearson correlation coefficient obtained from successive time bins . A single noise correlation was obtained for each neuron by averaging across pairs of time bins and stimuli . As described in the results , the time-varying instantaneous mutual information between the stimulus S and the response Yt can be written as a difference in Shannon entropies: It=H ( Yt ) -H ( Yt|S ) and the cumulative mutual information for neural responses that are discretized into time intervals is given by: CIt=H ( Yt , Yt-1 , Yt-2 , … , Y0 ) -H ( Yt , Yt-1 , Yt-2 , … , Y0|S ) In the present paper we calculated 4 different types of information: the stimulus instantaneous information , the categorical instantaneous information , the stimulus cumulative information and the categorical cumulative information . Stimulus instantaneous and cumulative information were calculated for all 404 units , while categorical instantaneous and cumulative information were calculated on a restricted set of 337 neurons that presented at least one time point with a significant value of stimulus cumulative information ( significant threshold set as 3 times the local error , see below for error calculations ) . While we verified our assumptions ( minimal information loss with spike rate binning , Poisson distributions of spike counts and maximum value of spike counts ) on the full set of 404 units , the population analysis of time-varying information presented in the results section only include the relevant dataset of 337 neurons . The custom Matlab code used to calculate time-varying information values is available at https://github . com/julieelie/PoissonTimeVaryingInfo along with a tutorial on how to use the core functions . We computed a Categorical Information Index ( CII ) that compared the empirical categorical cumulative information for call-type categories , CCI , to three hypothetical values: a floor ( CCIFloor ) , an expected value ( CCIExp ) and a ceiling value ( CCICeil ) . The floor value is the categorical cumulative information obtained from random categories . The expected value is the categorical cumulative information that would be achieved if the stimulus information was 1 ) equally distributed for each stimulus and 2 ) could be used for classifying stimuli into groups . Note that the second assumption is not necessarily true in the actual data because the categorical information is based on averaging the probabilities for stimulus from the same category and thus effectively averaging time-varying rates . If time-varying rates are not grouped by categories , then it is possible that two stimuli from two different categories are distinguishable based on their time-varying rate , but that , the average time-varying rates for the two categories are not distinguishable , or less so than expected from the average pair-wise distances . The ceiling value corresponds to the case where all the cumulative information about stimuli is used for the categorization and none to discriminate stimuli belonging to the same category: CCICeil . The CII is a number between 0 and 2 that is then calculated as: CII=CCI-CCIFloorCCIExp-CCIFloorifCCI<CCIExp CII=1+CCI-CCIExpCCICeil-CCIExpifCCI≥CCIExp The following three steps were taken to calculate the expected categorical information ( CCIExp ) from the stimulus cumulative information . First , the stimulus mutual information , mi , was expressed as the conditional probability of correct stimulus decoding , p , for any given stimulus ( and assumed to be equal for all stimuli ) . Given a confusion matrix of size nxn obtained from a decoder for n stimuli , with p as the conditional probability given a stimulus of correct decoding ( diagonal terms ) and thus ( 1-p ) / ( n-1 ) as the conditional probability of error ( off-diagonal terms ) , the mutual information is equal to: mi=p[2logp-logpn]+ ( 1-p ) [2log1-pn-1-log1-p ( n-1 ) n] The above equation was inverted numerically to solve for p given mi . Second , a new matrix was generated by grouping rows and columns of joint probabilities ( and not conditional ) to form a confusion matrix for categories . The number of stimulus in each category was matched to the actual values in the neurophysiological data on a unit per unit basis . Third , the expected mutual information for categories was then estimated from this new confusion matrix by subtracting the total entropy obtained from the joint probabilities , from the sum of the entropies of the marginal distributions for the rows and columns: micat=HRow+HCol-HTot | Just as the recognition of faces requires neural representations that are invariant to scale and rotation , the recognition of behaviorally relevant auditory objects , such as spoken words , requires neural representations that are invariant to the speaker uttering the word and to his or her location . Here , we used information theory to investigate the time course of the neural representation of bird communication calls and of behaviorally relevant categories of these same calls: the call-types of the bird’s repertoire . We found that neurons in both the primary and secondary avian auditory cortex exhibit invariant responses to call renditions within a call-type , suggestive of a potential role for extracting the meaning of these communication calls . We also found that time plays an important role: first , neural responses carry significantly more information when represented by temporal patterns calculated at the small time scale of 10 ms than when measured as average rates and , second , this information accumulates in a non-redundant fashion up to long integration times of 600 ms . This rich temporal neural representation is matched to the temporal richness found in the communication calls of this species . | [
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| 2019 | Invariant neural responses for sensory categories revealed by the time-varying information for communication calls |
During experimental cerebral malaria ( ECM ) mice develop a lethal neuropathological syndrome associated with microcirculatory dysfunction and intravascular leukocyte sequestration . The precise spatio-temporal context in which the intravascular immune response unfolds is incompletely understood . We developed a 2-photon intravital microscopy ( 2P-IVM ) -based brain-imaging model to monitor the real-time behaviour of leukocytes directly within the brain vasculature during ECM . Ly6Chi monocytes , but not neutrophils , started to accumulate in the blood vessels of Plasmodium berghei ANKA ( PbA ) -infected MacGreen mice , in which myeloid cells express GFP , one to two days prior to the onset of the neurological signs ( NS ) . A decrease in the rolling speed of monocytes , a measure of endothelial cell activation , was associated with progressive worsening of clinical symptoms . Adoptive transfer experiments with defined immune cell subsets in recombinase activating gene ( RAG ) -1-deficient mice showed that these changes were mediated by Plasmodium-specific CD8+ T lymphocytes . A critical number of CD8+ T effectors was required to induce disease and monocyte adherence to the vasculature . Depletion of monocytes at the onset of disease symptoms resulted in decreased lymphocyte accumulation , suggesting reciprocal effects of monocytes and T cells on their recruitment within the brain . Together , our studies define the real-time kinetics of leukocyte behaviour in the central nervous system during ECM , and reveal a significant role for Plasmodium-specific CD8+ T lymphocytes in regulating vascular pathology in this disease .
CM is a severe complication of Plasmodium falciparum infection and is responsible for an estimated 627 , 000 deaths annually , particularly among children [1] . CM is strongly associated with parasitised red blood cell ( pRBC ) sequestration in the vasculature of the brain [2] , [3] . The resultant obstruction of blood flow has been proposed as a potential pathological mechanism leading to ischemia and hypoxia of the central nervous system ( CNS ) [4] , [5] , [6] . Although direct visualisation of the retinal vasculature in infected humans supports this notion [7] , most neuropathological observations in humans are limited to post-mortem analysis of brain tissues . In addition , imaging approaches such as MRI do not resolve details of microcirculatory dysfunction or neuropathology in the intact brain [6] , [8] . Therefore , we lack a dynamic view of the events leading to progressive CNS damage during CM . The murine model of PbA-infection has proven useful in studying ECM pathogenesis [9] , [10] , [11] , [12] . In the PbA model , leukocytes accumulate in the brain microcirculation when mice develop NS , similarly to human disease [12] , [13] , [14] , [15] . However , to date no detailed real-time microscopic investigations have been conducted to assess the spatio-temporal dynamics of leukocyte behaviour in the brain during ECM development . Leukocyte imaging has been limited to visualising circulating cells using non-specific dyes or fluorescent antibodies [16] , [17] . Thus , it is not known whether and how local inflammation within the brain microvasculature correlates with disease progression , and what factors control the sequestration of leukocytes in the blood vessels . In the present work , we used 2P-IVM [18] to quantitate and characterize specific leukocyte subsets in situ within the brain microvasculature in a dynamic manner . Observations that athymic nude mice are protected from CM have implicated T cells in the pathogenesis of disease [19] . Indeed , several studies have documented that the CD8+ T cell subset does not protect but rather promotes NS during PbA infection . Mice depleted of or deficient in CD8+ T cells , β2-microglobulin , perforin or granzyme B are all protected from ECM establishing the role of effector T cell cytotoxicity in its pathogenesis [9] , [13] , [20] , [21] , [22] , [23] . Further , antigen-specific cytotoxic T lymphocytes ( CTL ) sequester within the brain vasculature where they produce granzyme B to promote NS [20] , [21] , [24] . Nevertheless , the precise effects of CD8+ T cells on vascular neuropathology during CM are not entirely clear . The primary goal of this study was to investigate the time-dependent development of vascular neuropathology during ECM in situ using 2P-IVM and to dissect the role of CD8+ T cells in regulating leukocyte trafficking within the CNS . Our study describes some of the precise events that lead up to vascular neuropathology during ECM development . We show that monocytes are a prominent cell type adhering to the microvasculature of the brain during ECM . These cells enhance recruitment of CD4+ and CD8+ T cells to the CNS vasculature but are not essential for disease . Further , we show that adhesive behaviour and rolling velocity of monocytes change depending on the disease stage and are regulated by the presence of Plasmodium-specific CD8+ T cells , which in contrast , are crucial for driving clinical disease .
To characterize and quantify brain sequestered leukocytes ( BSL ) during ECM , we performed flow cytometric analysis of brain single cell suspensions harvested from uninfected ( UI ) and PbA-infected C57BL/6 mice . PbA-infected mice included in this analysis had all developed NS ( see Materials and Methods and Fig . S1 for disease scoring ) . CD11b+Ly6G−Ly6Chi monocytes constituted 15 . 5±1 . 5% of total CD45hi BSL in PbA-infected mice as compared to 4 . 3±0 . 6% in uninfected mice ( Fig . S2A , C ) . CD8+ T cells constituted 51 . 9±2 . 6% and 10 . 9±1 . 7% and CD4+ T cells 6 . 4±0 . 7% and 9 . 3±1 . 5% of total CD45hi BSL in PbA-infected and uninfected mice , respectively ( Fig . S2B , C ) . This translated to a 33-fold increase in absolute monocyte numbers and a 66-fold and 8-fold increase in absolute CD8+ T and CD4+ T cell numbers , respectively , in PbA-infected mice ( Fig . 1 ) . CD8+ T cell numbers were 9-fold higher than CD4+ T cells in PbA-infected mice . No change in neutrophils and NK cell numbers was observed ( Fig . 1 ) . Collectively , our data are consistent with previous reports showing that there is a significant increase in the number of Ly6Chi monocytes [25] and CD8+ T cells , and to a lesser degree CD4+ T cells [13] , [15] , [21] , [26] recruited to the brain during ECM . We have recently established an intravital imaging model that allows us to visualize the superficial intact pial vasculature in living mice in situ ( Fig . S3 ) [27] . In order to gain a better understanding of how leukocytes contribute to vascular inflammation during ECM , we adopted this approach for mice infected with PbA . The main obstacle with imaging ECM is that , often , mice may progress to severe disease within only a few hours thereby leaving a short time window for surgical preparation and intravital imaging . We therefore opted for an open skull preparation , whereby a cranial window is prepared by skull bone removal under the least possible traumatic conditions and the shortest possible time frame ( ∼70 min ) . We observed that mice that developed NS resisted anaesthetic action . To overcome this , we allocated increased anaesthesia time for infected mice ( 3 times longer than healthy mice ) . Further , mice with an NS score of <7 were largely selected for our studies because these mice tolerated anaesthesia , surgical procedure and intravital imaging . A template for anaesthesia monitoring is provided in Table S1 . Our procedure was further optimized for providing lasting pain relief with buprenorphine , a non-steroidal opiate that has no known immunosuppressive function [27] . This is important as the immunosuppressive action of other anti-inflammatory drugs and analgesics potentially interferes with the development of immune responses during ECM [28] . Mice were maintained at a core body temperature of 37°C in order to obviate the introduction of artefacts during the surgical procedure and intravital imaging , as reported [27] . Core body temperature was regulated by using a heat pad connected to a rectal probe and covering mice with a thermal blanket . For uniformity in imaging conditions , mice with ECM were maintained under the same optimised conditions as healthy mice . Under these conditions , mice with ECM did not develop hypothermia , which is normally observed during the progression of disease [29] . To determine the kinetics of vascular leukocyte accumulation in the course of ECM , we made use of MacGreen transgenic reporter mice in which GFP expression in the circulation is confined mainly to monocytes and granulocytes [30] , [31] . GFP expression can also be seen in other immune cells in the brain parenchyma [30] , [31] such as perivascular myeloid cells . MacGreen mice were infected with PbA and monitored for clinical signs of ECM . MacGreen×RAG−/− mice , which were derived by crossing MacGreen mice with lymphocyte deficient RAG-1−/− mice [32] , were also infected with PbA . PbA-infected MacGreen×RAG−/− mice did not develop any severe clinical signs at the pre-defined clinical end-point of day 7 p . i . , as reported previously [24] , [33] , and served as a negative control ( Fig . 2A ) . On the other hand , 65% of the PbA-infected MacGreen mice that had developed minor clinical signs on day 5–6 p . i . ( early stage , ES ) progressed on to develop severe clinical signs ( NS ) on day 7 p . i . ( Fig . 2A , B ) . Mean parasitemia levels progressively increased in MacGreen mice ( Fig . 2C ) with levels peaking on day 7 p . i . ( p<0 . 005 ) . Peak parasitemia in MacGreen mice was not significantly different ( p = ns ) from that of MacGreen×RAG−/− mice on day 7 p . i . We then used 2P-IVM to track the behaviour of GFP+ leukocytes in PbA-infected MacGreen mice in the three experimental groups ( UI , ES and NS ) . First , a baseline value of the average number of rolling and firmly adherent GFP+ cells in the vasculature of UI mice was determined . Rolling cells were defined as single , round-shaped endothelium-interacting cells moving in the direction of the blood flow at a lower speed than free flowing cells . In UI mice , circulating cells were mostly non-interactive , with leukocytes largely adhering at a baseline value of 0–300 cells/mm2 min−1 . Next we arbitrarily graded leukocyte accumulation in the diseased animals relative to UI mice as follows: nil ( 0–300 cells/mm2 min−1 , in the same range as UI ) , moderate ( 300–1000 cells/mm2 min−1 ) and severe ( 1000–10 , 000 cells/mm2 min−1 ) interactions . Examples of the different grades are shown in Fig . 2D–F ( Movies S1 , S2 , S3 ) . To assess leukocyte accumulation during ES , mice with minor clinical signs were assessed on day 5–6 p . i . We found that 31% of venules had moderate and 15% had severe levels of leukocyte accumulation during ES ( Fig . 2G ) . With the onset of NS , leukocyte accumulation further increased with 10% of venules showing moderate and 79% showing severe levels ( Fig . 2G ) . This was paralleled by an increase in the absolute number of endothelium-interacting GFP+ leukocytes in the CNS vasculature , with cells rolling along ( 116±60 cells/mm2 min−1 ) or adhering to the endothelium ( 283±74/mm2 ) as early as day 5–6 p . i . ( Fig . 2H ) . With the progression of disease from ES to NS , the number of adherent leukocytes increased significantly to an average of 1432±200/mm2 , while rolling cell numbers did not change ( Fig . 2H ) . To determine the lineage of GFP+ leukocytes that accumulate in the venules of MacGreen mice during NS , we administered antibodies against Ly6C or Ly6G or an isotype control antibody intravenously ( i . v . ) [34] . The Gr1 and Ly6C surface markers are expressed by both GFP+ granulocytes and monocytes , whereas Ly6G is expressed selectively by neutrophils . We found that the adherent GFP+ leukocytes did not stain for the isotype control ( Fig . 3A ) ( Movie S4 ) , but that the majority were Ly6C+ ( Fig . 3B ) ( Movie S5 ) . Several rolling leukocytes also stained for Ly6C . Stationary Ly6G+ cells were not seen ( Fig . 3C ) ( Movie S6 ) . We validated the used antibodies also in an ear skin-imaging model in Lysozyme-GFP mice , where GFP+ neutrophils are known to roll along non-inflamed dermal post-capillary venules [34] , [35] , unlike the non-inflamed pial vasculature where such events are almost entirely absent [36] . While no staining was observed with the isotype control ( Fig . 3D ) ( Movie S7 ) , GFP+ cells were positive for both Ly6C ( Fig . 3E ) ( Movie S8 ) and Ly6G ( Fig . 3F ) ( Movie S9 ) . Further confirmation of the identity of adherent GFP+ cells was obtained with antibody staining of brain sections ( Fig . S4 ) . Thus , along with the flow cytometric data presented in Fig . 1 , these results show that the adherent GFP+ cells in the CNS vasculature during ECM are Ly6Chi monocytes . Concomitantly with increased monocyte adherence , we observed a transient decrease in total circulating monocyte numbers during ES , which was restored during the later NS ( Fig . 3G ) . Notably , Ly6Clo monocytes largely disappeared from the circulation , corresponding with a shift towards a Ly6Chi subset ( Fig . 3G , H ) . Our data show that there is an overall switch to Ly6Chi monocytes within circulation during infection , and this translates to increased adherence to the vascular endothelium of the brain during ECM . As another measure of endothelial cell activation during ECM development , we measured rolling velocities of GFP+ cells in MacGreen mice . As reported , GFP+ cells were largely non-interactive in UI animals [36] , whereas during ES they were observed rolling along the endothelium ( Fig . S5A; Vmean: 22 . 9±1 . 3 µm/sec ) ; which is within the range of rolling velocities described for leukocytes in other vascular beds ( 20–60 µm/sec ) [37] ) . During NS , the majority of the GFP+ cells became firmly adherent , with the remaining rolling cells slowing down considerably ( Vmean: 6 . 3±1 . 8 µm/sec; Fig . S5B ) . Three representative tracks that depict the instantaneous velocities of monocytes during ES and NS are shown in Fig . S5C and D . The average velocity of rolling monocytes in severely inflamed venules ( Vmean: 4 . 7±1 . 2 µm/sec ) was significantly lower than that in moderately inflamed venules ( Vmean: 23 . 0±1 . 3 µm/sec ) ( Fig . S5E ) . Studies have shown that myelomonocytic cell recruitment by CTL and their extravasation leading to CNS injury plays an important role in viral infection [38] . Therefore we asked whether monocytes also extravasated during ECM . We did not observe extravasation events of rolling/adhering monocytes during the observation period of up to 1 . 5 hours ( Fig . S6A ) ( Movie 10 ) . GFP+ cells typically travelled in the same direction as that of the blood flow ( Fig . S6B ) and exhibited very little deviation from their path during ES or NS as evident from the meandering index [MI] [ES: 0 . 92±0 . 01] [NS: 0 . 87±0 . 02] ( Fig . S6C ) . To further immunophenotype GFP+ cells residing in the perivascular space of UI and PbA-infected MacGreen and MacGreen×RAG−/− mice , we used confocal microscopy ( Panel i–iii , Fig . S6D ) . We injected wheat germ agglutinin ( WGA ) conjugated to Alexa 594 to mark circulating monocytes [39] , and then prepared sections of the brain . Round GFP+ WGA+ intravascular monocytes were mostly F4/80−/lo ( yellow overlay ) whereas stellate-shaped GFP+ perivascular cells were mostly F4/80+ ( blue-green overlay ) . We concluded that monocytes by and large do not extravasate in the course of ECM , which is consistent with our previous histopathological studies [3] , [40] . GFP+ cells in the perivascular space most likely represent perivascular macrophages in MacGreen mice [41] , [42] . The role of CD8+ T cells in promoting CM is well established [13] , [15] , [21] , [24] , [43] , [44] . Therefore , we investigated whether CD8+ T cells regulate monocyte accumulation in mice during ECM . To this end , MacGreen and MacGreen×RAG−/− mice were infected with PbA or left uninfected . In MacGreen×RAG−/− mice , approximately 21% of the venules had moderate and 8% had severe levels of monocyte accumulation ( Fig . S7A , B ) , with the total numbers of endothelium-interacting monocytes averaging 332 ( ±74 . 5 ) /mm2 min−1 as compared to MacGreen mice with 1432 ( ±200 ) /mm2 min−1 interacting cells ( Fig . S7C , p<0 . 0001 ) . To directly examine the effects of T lymphocyte subsets on ECM development , we adoptively transferred enriched CD8+ ( 80% ) or CD8− splenocytes isolated from UI or PbA-infected C57BL/6 donor mice into MacGreen RAG−/− recipients ( Fig . 4A , B ) . When MacGreen×RAG−/− recipient mice were infected with PbA ( Fig . 4A ) , we found that CD8+ T cells from PbA-infected donor mice induced severe clinical signs in all animals ( Fig . 4C , D , E ) . Recipients of primed CD8+ T cells had a higher mean clinical score compared to all other groups , at least during the observation period of 7 days ( Fig . 4C ) . Uninfected recipients that received primed CD8+ T cells did not develop ECM ( data not shown ) . Of recipients receiving naïve T cells , 25% developed severe clinical signs , whereas all other groups of recipients were resistant ( Fig . 4D , E ) . To determine the effects of T cells on monocyte behaviour within the brain vasculature , we performed 2P-IVM on groups of MacGreen×RAG−/− mice that had received saline , CD8− splenocytes , naive CD8+ T cells or primed CD8+ T cells ( Fig . 4A ) . The four recipient groups were infected with PbA and blood vessels assessed by intravital imaging on day 7 p . i . As in wildtype MacGreen mice , several degrees of monocyte accumulation could be observed in the various groups of MacGreen×RAG−/− recipient mice ( representative examples of nil , moderate and severe accumulation are shown in ( Fig . 5A–C ) ( Movie S11 , S12 , S13 ) . As expected , saline infusion alone resulted in low levels of inflammation ( Fig . 5D ) . Adoptive transfer of CD8− splenocytes from PbA-infected recipients resulted in an average 46% and 22% of venules showing moderate and severe levels of GFP+ cell accumulation , respectively . Transfer of naïve CD8+ T cells resulted in 40% of venules showing moderate , and 35% showing severe levels of GFP+ cell adhesion . Primed CD8+ T cells induced an average of 37% of venules showing moderate and an average of 52% showing severe levels of monocyte accumulation . In terms of absolute numbers of GFP+ cells adhering to the vessel wall , saline alone or CD8− splenocytes only induced low levels of monocyte accumulation ( 332±74/mm2 and 479±68/mm2 respectively ) ( Fig . 5E ) . Adoptive transfer of naïve CD8+ T cells did not change the phenotype significantly from that induced by saline or CD8− splenocytes with monocytes adhering to the endothelium at 767±117 . 7/mm2 . On the other hand , primed CD8+ T cells led to significantly more monocytes adhering within the vasculature compared to groups receiving other treatments , at an average of 1513±225 . 2/mm2 ( Fig . 5E ) . Mice with this phenotype developed NS . Rolling cell numbers did not change between the three groups ( Fig . 5F ) . Transfer of primed CD8+ T cells caused monocytes to slow down considerably as compared to naïve CD8+ T cells ( not shown ) . The average velocity of rolling monocytes in severely inflamed venules ( Vmean: 18 . 9±1 . 03 µm/sec ) was lower than in moderately inflamed venules ( Vmean: 29 . 9±2 . 2 µm/sec ) ( Fig . S8 ) . Collectively , our data show that the clinical signs of NS induced by primed CD8+ T cells are associated with high levels of monocyte accumulation in venules as well as slowing down of the rolling monocytes . Next , to determine the critical number of primed effector CTL that are required to induce monocyte adhesion and disease , we performed dose titration experiments . Primed CD8+ T cells ( 10 , 4 . 5 , 3 and 0×106 ) were transferred into PbA-infected MacGreen×RAG−/− recipients and on day 7 p . i . , the blood vessels in each of the four groups were assessed by intravital imaging . Mice that received 4 . 5 , 3 and 0×106 T cells did not develop NS ( Fig . S9A ) with approximately 23% and 50% of venules lacking monocyte accumulation in mice that received 4 . 5×106 and 3×106 T cells respectively ( Fig . S9B ) . In contrast , 10×106 CTL consistently induced NS , with only 11% venules lacking monocyte accumulation . Of interest , recipients of 10×106 naïve CD8+ T cells lacked monocyte accumulation in 25% of venules and were also protected from NS ( Fig . 5D ) . Furthermore , transfer of 40×106 naïve CD8+ T cells into PbA-infected MacGreen×RAG−/− mice failed to increase monocyte accumulation or induce NS ( not shown ) . Taken together , these data show that a critical number of primed CD8+ T cells is required in order to induce high levels of monocyte adhesion and clinical disease . Given the observation of inflammatory monocyte recruitment to the brain during ECM , we investigated their role in the development of neuropathology . Circulating monocytes , and other phagocytic populations that are in contact with the peripheral blood , were depleted by i . v . injection of clodronate liposomes ( CL ) [45] either at two days prior to infection ( day −2 ) , two days p . i . ( day +2 ) , or 5 days ( day +5 ) p . i . Administration of CL prior to infection led to complete protection from ECM , with mice ultimately succumbing to high parasitemia and low haematocrit ( Fig . 6A , 6B , S10A , S10B ) . There was no difference in disease course between PbA-infected mice that were depleted of monocytes at day +2 or day +5 p . i . when compared to vehicle treated mice ( Fig . 6A ) . Nevertheless , mice that received CL late in the disease course ( day +5 ) showed decreased recruitment of BSL compared to sham-depleted controls . As expected , treatment with CL resulted in an 80% decrease in brain-recruited monocytes following CL treatment ( Fig . 6C ) . In addition , CD4+ and CD8+ T cells and NK cell numbers were also decreased by 1 . 8-fold , 2 . 8-fold and 4 . 6-fold , respectively , compared to sham-treated control animals , indicating the role of monocytes/macrophages in lymphocyte recruitment to infected brain during ECM .
Discussion on the type of leukocytes that sequester inside the blood vasculature has gained significance due to discrepancies that have been reported between the pathological features of the ECM model and human CM [46] . A recent report tries to look beyond the differences in the cell type that sequester to the endothelium during human CM and ECM [47] . It attempts to unify the two by suggesting that the outcome of the neuropathological syndrome may be similar ( i . e . impaired blood flow , altered hemodynamics and tissue necrosis ) . A consensus has since emerged within the field that experimental studies in the mouse model of ECM must be directed towards understanding the pathological processes within the brain that lead to NS and the relevance of these localised events to human CM . As a step in this direction , we performed real-time imaging of leukocyte behaviour in the CNS microvasculature in order to understand the role of leukocytes in ECM development . To achieve this , we optimised a brain-imaging model specifically designed for imaging mice with ECM [27] . Using this model we assessed the dynamics of the leukocyte responses during ECM in the absence of anti-inflammatory drugs . Another recent report has utilized a chronic cranial window approach generated in healthy mice 1–2 weeks before they develop ECM [48] . Anti-inflammatory drugs were used to suppress inflammation induced by the cranial window preparation on the basis that drug effects will subside during the recovery period prior to imaging . Nevertheless , anti-inflammatory drugs can potentially impact on immune responses during the early stages of disease , given the difficulties of maintaining a chronic cranial window in a drug-free environment [49] , [50] . Another factor that significantly impacted on the quality of intravital images was the anaesthetic state of mice . Undoubtedly , intravital imaging studies in the ECM model have been hampered until now due to technical difficulties [51] such as anesthetising mice prior to surgery . To overcome this , a majority of our recordings were acquired when mice were most responsive to anaesthesia ( mild clinical signs of ES on day 5–6 p . i . or <7 severe clinical signs of NS on day 7 p . i . ) . This strategy increased the survival rate of mice during and after surgery and obviated tracking cells within an immunosuppressed microenvironment . Over the past decade , the role of CD8+ T cells in driving NS has become well established , however the other populations of leukocytes sequestering within the vasculature have not been adequately characterised . Macrophages , neutrophils , CD4+ and CD8+ T cells have all been reported to increase in numbers during ECM [14] , [15] . While using T cell-deficient mice or depleting CD8+ T cells in mice abrogated clinical signs and neuropathology [13] , [19] , [20] , [52] , depleting F4/80+ macrophages ( with clodronate liposomes ) and antibody-mediated depletion of neutrophils in the late stages of ECM failed to confer protection from NS [14] , [15] . This was further demonstrated using the MAFIA mice wherein conditional ablation of about 80% of the total CD115+ myeloid cells did not affect the parasite biomass in the head or prevent ECM [53] . Similarly , depleting brain-sequestered FcεRI+ granulocytes from day 5 p . i . did not protect mice from ECM although this population was reported to play a crucial role in the lead up to disease [54] . The above studies are contradicted by an earlier report showing that BSL are not granulocytes but Ly6C+ monocytes , which also sequester to the endothelium of PbA-infected mice that do not develop ECM [55] , [56] . Although no clear functional role has been attributed to these leukocytes in inducing NS , one study found that adherent leukocytes reduce the luminal diameter of the blood vessels and function as barriers to blood flow during ECM [57] . This is a reminder that functional studies can sometimes be limited by a requirement for sensitive cellular and imaging tools to help delineate complex mechanisms that contribute to clinical disease . We therefore undertook a comprehensive phenotypic and functional analysis of leukocytes in the CNS vasculature using multiple technical approaches . Our studies show that the majority of the BSL are Ly6Chi monocytes or CD8+ T cells as assessed by flow cytometry with no increase in granulocyte numbers . Thus , the molecules that promoted myeloid cell adhesion to the vascular endothelium appear to be monocyte-specific since neutrophils did not adhere , consistent with previous reports [55] . Using intravital imaging , we further confirmed that adherent leukocytes were Ly6C+Ly6G− monocytes . Confocal microscopy of whole mount brain sections revealed that the intraluminal monocytes not only accumulated within the superficial pial vessels but also in the deeper vessels of the cerebral cortex ( not shown ) . Studies have proposed that monocytes can transform into macrophage-like antigen presenting cells within the blood vessels and engage in intravascular antigen presentation to T cells [58] . Our data show that , during ECM , F4/80−/lo monocytes remain mostly single , round , discrete , non-vacuolated cells that can be clearly distinguished from the stellate morphology of F4/80+ vacuolated , perivascular myeloid cells that flank the blood vessels [30] , [31] . We are unable to formally exclude the possibility of perivascular myeloid cells originating from extravasated monocytes that underwent differentiation; however this is unlikely as perivascular myeloid cells are detected in uninfected MacGreen mice and their numbers did not increase significantly following PbA-infection . Further , it is also unlikely that , during NS , all monocytes that extravasate undergo differentiation into perivascular myeloid cells within a short span of only a few hours . Historically , leukocyte extravasation has not been documented in the ECM model and our histological studies support these observations [3] , [40] . We observed that monocytes begin interacting with the endothelium as early as day 5–6 p . i . when mice have only minor clinical signs of ES . The factors that modulate the vascular endothelium during ECM could thus be produced far earlier than envisaged . As the disease progressed , we saw a shift in behaviour with increasing numbers of monocytes adhering to the endothelium of post-capillary venules . This was accompanied by a significant decrease in the velocity of rolling monocytes . Vascular obstruction was unlikely to be a reason for the decreased velocity as we assessed only blood vessels that supported high velocity , free flowing cells [57] . In support of our view , recent reports have shown that a decrease in the velocity of circulating pRBC during ECM is rarely accompanied by vascular occlusion [59] . However , it should be pointed out that we did not directly correlate red blood cell ( RBC ) velocities and rolling behaviour of monocytes . Thus it is conceivable that in severely inflamed blood vessels a decrease in blood flow may have contributed to the observed reduction in the velocity of rolling monocytes [59] . To understand the cellular mechanism that regulates monocyte behaviour , we set up an adoptive transfer model . Primed CD8+ T cells transferred from PbA-infected mice were mostly differentiated effector cells ( data not shown and [13] , [52] ) that had a T cell repertoire specific to Plasmodium antigen . We found that , following adoptive transfer , CD8+ T cells promoted monocyte adhesion to the vascular endothelium and led to a significant decrease in the rolling velocity of monocytes . Primed T cells were more effective than CD8− splenocytes or naïve T cells in promoting monocyte adhesion , slowing down rolling monocytes and inducing disease and this coincided with a significant number of primed CD8+ T effector cells being recruited to the brain ( not shown ) . These data are in line with reports of CD8+ T cell recruitment to and activation of CTL function in the CNS vasculature leading to inflammation and disease [16] , [21] , [24] , [60] , [61] . Further , primed T cells induced ECM in MacGreen×RAG−/− recipient mice within the same time frame as wild type MacGreen mice suggesting that primed T cells , unlike naïve T cells , can be recruited directly to the brain while bypassing the requirement for priming . Furthermore , uninfected recipients that received primed CD8+ T cells did not develop ECM ( data not shown ) , suggesting a requirement for Plasmodium antigen at the effector phase , as reported [21] , [24] , [62] . While the recruitment of CD8+ T cells to the brain during ECM is dependent upon IFNγ driven production of the CXCR3-binding chemokines CXCL9 and CXCL10 [16] , [44] , [60] , the origin of this IFNγ is debated . Either NK cells [63] or CD4+ T cells [64] , [65] have been proposed to be the cellular source of this critical IFNγ . In our hands , transfer of purified primed CD8+ T cells into infected RAG-1−/− recipients ( which retain NK but lack CD4+ T cells ) resulted in the development of ECM . These data echo those of Nitcheu et al . , who transferred CD8+ T cells of >98% purity into RAG-2−/− mice and demonstrated the capacity of CD8+ T cells to drive neuropathology and disease in the absence of CD4+ T cells [13] . Similarly , we found that transfer of OT-I effector cells ( >98% purity ) into RAG−/− recipient mice infected with OVA-transgenic parasites ( PbTG ) led to disease , as reported ( data not shown and [24] ) . Nevertheless a small number of CD4+ T cells were recruited to the brain of mice that developed NS ( primed CD8+ T cell recipients ) as well as in mice that were protected from disease ( CD8− splenocyte recipients ) therefore we can not formally exclude that small numbers of CD4+ T cells contributed to CD8+ T cell recruitment . Although some studies have suggested that monocytes are essential for the induction of ECM [66] , [67] , others have argued that these cells are dispensable [68] . We addressed the role of monocytes by administration of CL . Since CL administration does not result in complete depletion , unequivocal conclusions about the functional role of these cells could not be drawn at this stage; however our data did reveal an influence of phagocytic cells , including monocytes , in both early and late processes underlying ECM . Depletion prior to infection resulted in diversion of the disease course away from cerebral pathology . This observation is consistent with involvement of a phagocytic population in induction of immune processes that normally culminate in ECM . The most likely phagocytic population is Clec-9a+ DCs that are responsible for cross-presentation of Plasmodium antigens to CD8+ T cells [69] . When monocytes were depleted by CL administration late in the course of infection , there were significant decreases in sequestered CD8+ T , as well as CD4+ T and NK cells within the brain compared to sham depleted controls . Consistent with monocyte contribution to this process , we have previously shown that , during ECM , cells sequestered within the vasculature express CXCL9 mRNA [44] . Nevertheless , monocyte depletion did not alter the course of disease progression . This phenomenon is potentially explained by results from our adoptive transfer model where we found that recruitment of a very small number ( ∼35 , 000 ) of CD8+ T cells recruited to the brain is sufficient to induce neuropathology ( not shown ) . Thus although monocytes contribute to CD8+ T cell recruitment , in their absence sufficient T cells are still recruited to the brain to initiate ECM . Although monocytes themselves were not essential to ECM pathology , their recruitment to the brain vasculature appeared to be a sensitive readout that correlated with end stage pathological processes . Our adoptive transfer model enabled us to track precisely how primed CD8+ T cells regulated monocyte trafficking in the microvasculature of the brain during disease . We found that even a small increase in the numbers of severely inflamed venules ( 17% increase in mice that received primed T cells as compared to naive T cells ) was associated with a strikingly different clinical outcome ( Fig . 5D ) . Thus monocyte accumulation served as a useful surrogate marker for visualising the unfolding of vascular inflammation in the CNS . Altogether our data support the view that NS manifestation correlates with CD8+ T effector cells promoting a critical threshold of monocyte accumulation in the venules . To this end , a critical number of CD8+ T effector cells were required to attain this threshold . The underlying molecular mechanism/s by which CD8+ T cells promote monocyte accumulation are unclear; however specific blockade of LIGHT-LTβR interactions during PbA infection was shown to decrease monocyte sequestration in the brain [70]; a potential role for LIGHT or LT-alpha/beta expressed on activated CD8+ T cells [71] can therefore be envisaged in facilitating monocyte sequestration . Similarly , IFNγ along with TNF and LT-alpha is a candidate cytokine that can promote monocyte sequestration by increasing the expression of adhesion molecules such as ICAM-1 , VCAM-1 , P-selectin and E-selectin on mouse brain endothelial cells [72] . Altogether , our observations are consistent with a model in which damage to the blood-brain barrier ( BBB ) and vascular leakage caused by CD8+ T cell-mediated cellular/molecular factors is ultimately responsible for disease and death during ECM [59] , [73] .
This study was carried out in accordance with the guidelines from The National Health and Medical Research Council of Australia . All animal procedures were carried out under the protocol number K75/11-2009/3/5195 , approved by the University of Sydney Animal Ethics Committee . All mice were maintained with environmental enrichment in specific pathogen-free ( SPF ) conditions at the Centenary Institute Animal Facility . The surgical procedures and intravital imaging experiments were carefully optimised [27] and performed by trained specialists . The anaesthetic drugs Ketamine , Xylazine and Buprenorphine were used for anaesthetising mice prior to surgery . All mice used in this study were on a C57BL/6 background . Lysozyme-GFP mice [74] , and c-fms-GFP ( MacGreen ) mice [30] , [31] were described previously . C57BL/6 and RAG-1−/− mice were obtained from the Australian Resource Centre ( ARC ) [75] . Wildtype MacGreen+/+ were outcrossed with RAG-1−/− mice to derive F1 MacGreen×RAG+/− heterozygous mice . Heterozygous parents were intercrossed to derive MacGreen×RAG−/− homozygotes . Litters underwent prior screening for GFP and CD3 ( Fig . S11A ) as well as B220 expression by flow cytometry and for RAG-1 gene expression by PCR ( Fig . S11B ) . Only CD3− GFPhi MacGreen×RAG−/− mice were used in this study . PbA strain was maintained and used as previously reported [76] . All wildtype , transgenic and knockout mice were infected intraperitoneally ( i . p . ) with 106 pRBC . Parasitemia was determined by thin blood smears prepared from tail bleeds . Smears were stained using the Diff-Quick kit ( Lab Aids , Narrabeen , NSW , Australia ) . PbA-infected MacGreen mice that developed minor clinical signs were referred to as being in the early stage or ES ( Fig . S1 ) . ES was defined by reduced activity , hair standing on end , prominent vertebral spinous processes/scapulae/pelvis , sunken eyes and/or hunched posture . Each minor clinical sign was given a score of 1 with mice receiving a maximum ES score of 2 . Mice with ≥3 minor clinical signs on days 1–6 p . i . were euthanized in accordance with ethics guidelines and given a maximum score of 8 the following day to denote death . Mice were allowed to develop severe clinical signs on day 7 p . i . defined by ataxia , immobility , isolation from group , reduced searching behaviour , muscular weakness , hemiparesis , paraparesis , inability to grab the cage grid and/or hold on to a rope , fitting , inability to roll and/or spasmodic backbend ( Fig . S1 ) . These mice were referred to as having neurological signs or NS . Each severe clinical sign was given a score of 1 with mice receiving a maximum NS score of 8 . All mice in the experimental study were sacrificed at the pre-defined clinical end-point of day 7 p . i . A modified clinical scoring system was used for monocyte depletion experiments , with animals being euthanized if they manifested with any of the following signs: decreased stimulated movement with unsteady gait , hemiplegia , seizures or inability to grab the cage grid . Fluorochrome-labelled antibodies were purchased from BD Biosciences and Biolegend and included anti-CD3 , CD4 , CD8 , CD11b , CD19 , CD45 , CD335 , NK1 . 1 , B220 , F4/80 , Ly6C and Ly6G ( Table S2 ) . For in vivo labelling , WGA-A594 ( Life Technologies ) was administered i . v . in mice just prior to sacrifice . Mice were perfused intracardially and brains were harvested . Similarly one hour prior to intravital imaging , 5 µg of anti-Ly6C , Ly6G or isotype antibody ( Biolegend ) was administered i . v . in mice via the lateral tail vein to label cells in vivo . CL containing 5 mg/ml clodronate were purchased from www . clodronateliposomes . com . Monocytes were depleted by a single i . v . injection of 200 µl of CL on the day indicated in the main text . To purify leukocytes from the brain , mice were sacrificed and perfused via the heart with 20 ml of perfusion fluid ( 0 . 05% w/v EDTA in ice cold PBS ) to remove blood contaminants and non-sequestered cells from the cerebral vasculature . Brains were initially mashed between frosted glass slides in PBS . Subsequently , they were digested with collagenase type IV ( Sigma ) and DNase I ( Sigma ) for 20 min at room temperature , and triturated through a Pasteur pipette before a second 20 min collagenase/DNase incubation . Low-density dead cells/debris/myelin were removed by resuspension in 30% Percoll and centrifugation at 500 g for 10 min at 4°C . Contaminating RBCs were then lysed using Tris-ammonium chloride buffer and leukocytes were washed and enumerated . To process brains harvested from RAG−/− mice , whole brains weighing up to 600 mg were placed in 2650 µl of PBS containing 0 . 05% v/v FCS , collagenase type IV ( 2 mg/ml ) and DNAse I ( 0 . 5 mg/ml ) . For brains weighing more than 600 mg , suspension volume was scaled up accordingly . Brains were homogenized using a gentleMACS dissociator ( Miltenyi ) . Tissue suspension was digested by rotational mixing at 37°C for 60 min and , after a wash , debris/myelin were removed by Percoll centrifugation as above . For flow cytometry on blood leukocyte populations , 20 µl of blood was collected from the tail using EDTA as an anticoagulant and red blood cells lysed using Tris-ammonium chloride buffer . Following lysis , cells were washed and stained as described below . Cells obtained from the brain were suspended in 2% ( v/v ) FCS in PBS ( FACS wash buffer containing 2 mM EDTA and 0 . 01% w/v sodium azide ) containing anti-CD16/32 ( 2 . 4G2 ) ( BD ) to block Fc receptors . Cells were stained by incubating with fluorochrome-labelled antibodies diluted in FACS buffer for 30 min on ice . Thereafter , cells were washed and stained with 0 . 5 µg/ml DAPI ( Invitrogen ) for dead cell exclusion . For blood leukocyte counts , samples were spiked with a known concentration of Accucount polystyrene microspheres ( Spherotech , Lake Forest , Illinois ) to determine absolute numbers of cells in the original sample . Data were acquired on a FACS Canto II ( BD ) , LSRFortessa ( BD ) or LSR-II flow cytometer ( BD ) and analysed using FlowJo software ( Treestar ) . All cell isolations were performed in a biological safety cabinet under sterile conditions to facilitate transfer into immunodeficient mice . CD8+ T cells were positively selected from disaggregated splenocytes using CD8-conjugated immunomagnetic beads ( Miltenyi ) and MACS columns ( Miltenyi ) . Positive fractions were further enriched by passing cells through a second MACS column as suggested by the manufacturer . Approximately 80% of the cells in the positive fraction were enriched for CD3+CD8+ T cells as assessed by flow cytometry . Approximately 5% of the CD8− splenocytes constituted CD3+CD8+ T cells that had eluted into the negative fraction . MacGreen×RAG−/− recipient mice aged 5–8 weeks were first infected with 106 pRBC via the i . p . route . CD8+ T cells isolated by MACS positive selection and CD8− splenocytes constituting the negative fraction were adoptively transferred into recipient mice 4–6 hours later . Either 3×106 , 4 . 5×106 or 107 cells were administered into the tail vein . PbA-infected mice were monitored as above . Mice were administered WGA-A594 i . v . , just prior to sacrifice . Vascular perfusion was performed as above using 10% neutral buffered formalin ( Sigma ) in PBS . Brains were harvested and further fixed in 10% neutral buffered formalin and 4% w/v sucrose for 16 hours at 4°C . Longitudinal or superior transverse sections of the brain were prepared using a vibratome and then blocked in 10% FCS . Immunohistochemistry was performed by treating brain sections with anti-Ly6C , anti-Ly6G or anti-F4/80 antibody . For some sections , blood vessels were stained with purified anti-CD31 antibody , which was conjugated to Alexa594 using the monoclonal antibody labelling kit ( Life Technologies ) . Both WGA-A594 and CD31 delineated the blood vessels . Sections were mounted in mounting medium ( DAKO ) and a series of single Z-stack images were acquired by confocal microscopy using a 20× dry or 63× oil objective ( Leica SP5 ) . Image analysis was done using Volocity ( Perkin-Elmer ) . The presence of typical histopathological features of ECM was determined in paraffin embedded haematoxylin and eosin stained brain sections . Slides were scored in a blinded manner for 3 features: ( 1 ) the presence of sequestered cells in the vasculature , ( 2 ) tissue oedema and ( 3 ) haemorrhages . Each feature was scored from 0–3: 0 = not present; 1 = isolated ( 1–2 ) occurrences throughout a single brain section; 2 = 2–4 occurrences; 3 = >4 occurrences . Values for each feature were added to provide a single histopathological score . Mice were prepared for intravital imaging of the brain as previously described [27] . Briefly , animals were anaesthetised by i . p . injection of Ketamine ( 100 mg/kg of body weight ) ( Cenvet ) and Xylazine ( 10 mg/kg of body weight ) ( Cenvet ) . Buprenorphine ( Cenvet ) was administered at 100 µg/kg of body weight i . p . for lasting pain relief . Animals were monitored for awareness signs such as whisker twitching , palpebral ( blink ) reflex , pedal withdrawal reflex and respiration rate , and surgical procedures were initiated only after the animal entered a deep state of anaesthesia ( Table S1 ) . A primary dose of Ketamine/Xylazine mixture was given increased time for anaesthetic action ( 3 times longer than healthy mice ) and a booster dose was administered only after this time . Importantly , anaesthetic strategy was individually tailored to each mouse—for example , mice with paraparesis ( partial paralysis in the lower limbs ) were expected to take 3 times as long as healthy mice to lose their pedal-withdrawal reflex . Loss or regain of reflexes was closely monitored throughout the procedure and booster doses of Ketamine and Xylazine were administered as required . Anaestethized mice were placed on a heat pad ( Fine Science Tools ) and core body temperature was monitored using a rectal probe ( Fine Science Tools ) , as described [27] . The head of the mouse was restrained in a stereotaxic frame , and the skull was exposed by making a mid-line incision in the scalp . The periosteum was removed and a circular incision was made in the parietal bone using a pneumatic dental drill to yield a cranial flap . The flap was lifted gently without damaging the dura mater underneath . Minor bleeding was controlled using gelfoam bits and the cranial window was bathed in pre-warmed artificial cerebrospinal fluid ( aCSF ) ( 132 mM NaCl , 2 . 95 mM KCl , 1 . 71 mM CaCl2 . 2H20 , 1 . 4 mM MgSO4 , 6 . 7 mM Urea , 24 . 6 mM NaHCO3 , 3 . 71 mM glucose , pH 7 . 4 ) [27] . The chamber was sealed with a coverslip held in place with vacuum grease . All the intravital imaging experiments in this study were performed for up to 1 . 5 hours , as described previously [27] . Where extended recordings of more than 1 . 5 hours are required , we recommend the addition of a superfusion chamber containing aCSF as shown in Fig . S3 [27] . Imaging was performed using a LaVision Biotec Trimscope II single-beam 2-photon microscope ( Bielefeld , Germany ) attached to an Olympus BX-51 fixed-stage microscope equipped with 20× ( NA0 . 8 ) water-immersion objective . The setup included external non-descanned dual-channel/fluorescence detectors and a diode-pumped , wide-band mode-locked Ti∶Sapphire femtosecond laser ( MaiTai HP; Spectraphysics , 720–1050 nm , pulse length 140 fs; 90 MHz repetition rate ) . To label blood vessels , 800 µg of TRITC-conjugated dextran ( Invitrogen ) dissolved in saline or Evans blue conjugated to BSA was injected i . v . just prior to imaging . For data acquisition , firstly a suitable field of view was selected in the upper left region of the cranial window . The brain was exposed to polarized laser light at a wavelength of 900 nm , and x-y-t data of a 300 µm×300 µm plane at a resolution of 0 . 6 µm pixel−1 was captured at the rate of 1 frame per second . A minimum 120 to a maximum 600 frames were collected and sometimes combined with 3-dimensional z stacks to create x-y-z-t time-lapse images . The next field of view was recorded by moving horizontally across to the next 300 µm×300 µm plane within the cranial window . Only fields of view with at least 1 blood vessel were recorded . A minimum 29 to a maximum 123 blood vessels were assessed for each group of mice . Post-acquisition image analysis was carried out using Volocity software . Firstly , each blood vessel was annotated with a unique identity code . For the analysis of leukocyte behaviour , each blood vessel was initially assessed for intact blood flow present for the entire duration of the recording . Intact blood flow was defined by the presence of high velocity , freely flowing leukocytes ( at least one free flowing cell during the observation period ) . The area of each blood vessel was derived by measuring its length and diameter . The phenotype of the blood vessels and its tributaries were assessed for brightness , size , central reflex , wall thickness and direction of flow [77] . As relying solely on phenotypic features can lead to misclassification of arteries and veins , we used a functional parameter that is widely utilised to distinguish the two . Diverging vessels with outflow of blood were classified as arteries and converging vessels with inflow of blood were classified as veins . Data were collated only from large post capillary venules . Leukocytes were tracked as they entered the field of view and over the entire observation period . Rolling cells were defined as single , round-shaped cells moving in the direction of the blood flow at a lower speed than free flowing cells . Adherent cells were defined as single cells that remain stationary for 30 seconds or longer . To normalise for variability in blood vessel diameter , the average number of rolling cells per mm2 of blood vessel over a period of 1 min and the number of adherent cells per mm2 of blood vessel were calculated . The mean rolling velocity ( Vmean ) of leukocytes was defined as the distance travelled by rolling cells per second . The percentage of blood vessels within the cranial window with varying degrees of leukocyte infiltration , as defined in results below , was calculated . Differences in survival of treatment groups were analysed using the Mantel-Cox log-rank test . Correlations were calculated and plotted using Prism ( Graphpad Prism software ) . For comparison of two groups , the Student's t-test ( normally distributed ) or the Mann-Whitney U test ( not normally distributed ) were used . For multiple comparisons , one-way ANOVA was used . A difference between groups was considered significant if p<0 . 05 . | Cerebral malaria ( CM ) is a severe complication of Plasmodium falciparum infection that takes a significant toll on human life . Blockage of the brain blood vessels contributes to the clinical signs of CM , however we know little about the precise pathological events that lead to this disease . To this end , studies in Plasmodium-infected mice , that also develop a similar fatal disease , have proven useful . These studies have revealed an important role for leukocytes not so much in protecting but rather promoting pathology in the brain . To better understand leukocyte behaviour during experimental CM , we established a brain-imaging model that allows us to ‘peek’ into the brain of living mice and watch immunological events as they unfold . We found that worsening of disease was accompanied by an accumulation of monocytes in the blood vessels . Monocyte accumulation was regulated by activated CD8+ T cells but only when present in critical numbers . Monocyte depletion resulted in reduced T cell trafficking to the brain , but this did not result in improved disease outcome . Our studies reveal the orchestration of leukocyte accumulation in real time during CM , and demonstrate that CD8+ T cells play a crucial role in promoting clinical signs in this disease . | [
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| 2014 | Real-Time Imaging Reveals the Dynamics of Leukocyte Behaviour during Experimental Cerebral Malaria Pathogenesis |
Phlebotomus tobbi is a vector of Leishmania infantum , and P . sergenti is a vector of Leishmania tropica . Le . infantum and Le . tropica typically cause visceral or cutaneous leishmaniasis , respectively , but Le . infantum strains transmitted by P . tobbi can cause cutaneous disease . To better understand the components and possible implications of sand fly saliva in leishmaniasis , the transcriptomes of the salivary glands ( SGs ) of these two sand fly species were sequenced , characterized and compared . cDNA libraries of P . tobbi and P . sergenti female SGs were constructed , sequenced , and analyzed . Clones ( 1 , 152 ) were randomly picked from each library , producing 1 , 142 high-quality sequences from P . tobbi and 1 , 090 from P . sergenti . The most abundant , secreted putative proteins were categorized as antigen 5-related proteins , apyrases , hyaluronidases , D7-related and PpSP15-like proteins , ParSP25-like proteins , PpSP32-like proteins , yellow-related proteins , the 33-kDa salivary proteins , and the 41 . 9-kDa superfamily of proteins . Phylogenetic analyses and multiple sequence alignments of putative proteins were used to elucidate molecular evolution and describe conserved domains , active sites , and catalytic residues . Proteomic analyses of P . tobbi and P . sergenti SGs were used to confirm the identification of 35 full-length sequences ( 18 in P . tobbi and 17 in P . sergenti ) . To bridge transcriptomics with biology P . tobbi antigens , glycoproteins , and hyaluronidase activity was characterized . This analysis of P . sergenti is the first description of the subgenus Paraphlebotomus salivary components . The investigation of the subgenus Larroussius sand fly P . tobbi expands the repertoire of salivary proteins in vectors of Le . infantum . Although P . tobbi transmits a cutaneous form of leishmaniasis , its salivary proteins are most similar to other Larroussius subgenus species transmitting visceral leishmaniasis . These transcriptomic and proteomic analyses provide a better understanding of sand fly salivary proteins across species and subgenera that will be vital in vector-pathogen and vector-host research .
Sand flies are bloodsucking nematoceran Diptera that transmit the protozoan parasites of the genus Leishmania . Similar to that of other bloodsucking arthropods , sand fly saliva comprises antihemostatic , immunomodulatory , and antigenic components . The saliva is deposited into the host skin every time the sand fly ingests a blood meal to facilitate feeding . Also during the bite by an infected sand fly , Leishmania parasites are egested into the wound with the saliva . Sand fly saliva can enhance Leishmania infection in naive mice [1] , [2] . Conversely , pre-exposure of mice to sand fly saliva conferred a protective effect against Leishmania infection [3] , [4] . Even single salivary proteins have been characterized as potential Leishmania vaccine candidates in mouse , hamster , and dog models of cutaneous or visceral leishmaniasis [5]–[9] . The potent effects of sand fly saliva stimulate a protective host cellular immune response [3]–[9] , and the antigenic nature of saliva also provides a humoral immunity measurement of host exposure to sand fly bites already used in several human epidemiological studies [10]–[18] . Identifying markers of vector exposure based on anti-saliva antibodies are essential in epidemiologic and vector control surveillance [15] , [16] , [18] , [19]–[21] . However , anti-saliva antibodies are highly specific [12] , [16] , [22] and with over 80 species of sand flies implicated in Leishmania transmission , it is vital to continue describing the salivary proteins in the search for markers of exposure as well as vaccine candidates . Sand fly salivary gland proteins have been well studied in Lutzomyia longipalpis [23] , [24] and Phlebotomus papatasi [6] . Recently , transcriptomic and proteomic data have been published for several other sand fly species , vectors of visceral ( P . ariasi , P . argentipes , and P . perniciosus ) and cutaneous ( P . arabicus , P . duboscqi ) forms of leishmaniasis [25]–[28] . To broaden the repertoire of subgenus Larroussius salivary proteins and provide the first report from a subgenus Paraphlebotomus sand fly , we prepared and analyzed the transcriptomes and proteomes of P . tobbi and P . sergenti , both proven vectors in the Old World . Phlebotomus sergenti , subgenus Paraphlebotomus , is the main vector of Le . tropica , principally an agent of cutaneous leishmaniasis [29]–[31] . Phlebotomus tobbi , on the other hand , is an important vector of Le . infantum [32] together with the taxonomically related P . ariasi and P . perniciosus , sand flies of the subgenus Larroussius [29] , [33] , [34] . In contrast to other members of the subgenus , P . tobbi transmits the cutaneous form of the Le . infantum [32] . Additionaly , we characterized P . tobbi antigens , glycoproteins , and hyaluronidase activity; the later one compared with 6 sand fly species belonging to vectors of cutaneous or visceral leishmaniases .
Colonies of P . tobbi ( originating from Turkey ) , P . papatasi ( Turkey ) , P . sergenti ( Israel ) , P . argentipes ( India ) , P . arabicus ( Israel ) , P . perniciosus ( Spain ) , and L . longipalpis ( Brazil ) were kept in the insectary of Charles University in Prague as described in [35] . The P . sergenti colony , originating from Turkey , was reared in similar conditions at the Laboratory of Malaria and Vector Research , National Institutes of Health ( Rockville , MD , USA ) . For mRNA extraction , salivary glands ( SGs ) from non-bloodfed 1- to 2-day-old female sand flies were dissected and stored in RNA Later ( Ambion , Inc . , Austin , TX , USA ) . For other assays and analysis , SGs of non-bloodfed 5- to 7-day-old females were stored at −70°C; SGs were stored in NuPAGE LDS sample buffer ( Invitrogen , Carlsbad , CA , USA ) for proteome analysis and in Tris buffer ( 20 mM Tris , 150 mM NaCl , pH 7 . 8 ) for hyaluronidase assays , affinity blot , and immunoblot . Before use , samples were homogenized by three freeze-thaw cycles in liquid nitrogen . Protein concentration in resulting SG homogenate ( SGH ) was measured on Qubit Fluorometer ( Invitrogen ) following manufacturer's guidelines . An SG cDNA library was constructed from P . sergenti ( Turkey ) and P . tobbi . MicroFastTrack mRNA isolation kit ( Invitrogen ) was used to isolate SG mRNA from 40 SG pairs dissected into 20 µl of RNA Later ( Ambion ) . A cDNA library was constructed using SMART™ cDNA Library Construction Kit ( BD Clontech , Palo Alto , CA , USA ) following the manufacturer's protocol , with some modifications as described in [36] . For each species , three cDNA libraries were constructed according to PCR product size – large , medium , and small . PCR amplicons were washed and concentrated to 4–7 µl on Microcon YM-100 columns ( Millipore , Billerica , MA , USA ) . Concentrated samples ( 3 µl ) were ligated into the λTripleEx2 vector and packed into the phage particles with Gigapack III Gold Packaging Extract ( Stratagene , La Jolla , CA , USA ) . Phage libraries were used to infect the log-phase XL-1 Blue Escherichia coli ( Clontech ) plated onto four LB agar plates per each library size . Transfected plaques were randomly selected and transferred into 96-well V-shape plates with 75 µl of ultrapure water per well . Four 96-well plates of phage were picked per each library size , resulting in 12 plates ( 1 , 152 clones ) per sand fly species . Phages ( 3 µl ) were subjected to PCR using FastStart PCR Master Mix ( Roche , Molecular Biochemicals , Indianopolis , IN , USA ) and vector-specific primers ( PT2F1 5′-AAGTACTCTAGCAATTGTGAGC-3′ and PT2R1 5′-CTCTTCGCTATTACGCCAGCTG-3′ ) . Amplification conditions were as follows: 1 hold of 75°C for 3 min , 1 hold of 94°C for 2 min , 34 cycles of 94°C for 1 min , 49°C for 1 min , and 72°C for 2 min . The final elongation step lasted for 10 min at 72°C . Products were cleaned using ExcelaPure 96-well UF PCR Purification Plates ( Edge Biosystems , Gaithersburg , MD , USA ) and cleaned PCR products were used as a template for cycle-sequencing reaction using BigDye Terminator v3 . 1 cycle sequencing kit ( Applied Biosystems , Fullerton , CA , USA ) and a vector-specific forward primer ( PT2F3 5′-CTCGGGAAGCGCGCCATTGT-3′ ) . Products of cycle-sequencing reaction were cleaned using Sephadex and MultiScreen HV Plates ( Millipore ) , dried , resuspended in formamide , and stored at −20°C until sequenced on an ABI 3730XL 96-Capillary DNA Sequencer ( Applied Biosystems ) . For mass spectrometric ( MS ) analysis , SGH samples of P . sergenti ( Turkey ) and P . tobbi were dissolved in Laemmli sample buffer in parallel with or without 2-mercaptoethanol and electrophoretically separated on 12% polyacrylamide SDS minigel with initial voltage 80 V and 120 V upon entry of sample to the gel . Gels were stained for total proteins with Coomassie Brilliant Blue R-250 . Individual bands were cut , destained and digested as was described in [37] . Samples ( 0 . 5 µl ) were transferred to a 384 spot stainless steel MALDI target ( AB Sciex , Framingham , MA , USA ) and let to dry . Dried droplets were covered with a 0 . 5 µl drop of alpha-cyano-hydroxycinnamic acid ( Fluka , Switzerland ) solution ( 2 mg/ml in 80% acetonitrile ) and allowed to dry . Spectra were acquired with 4800 Plus MALDI TOF/TOF analyzer ( AB Sciex ) equipped with a Nd∶YAG laser ( 355 nm; firing rate 200 Hz ) . Voltages were set as follows: source1 20 kV , grid1 16 kV , source1 lens 10 kV , lens1 5 kV , mirror1 14 . 085 kV , mirror2 20 . 3 kV and reflector detector 1 . 905 kV . Digitizer bin size was set to 0 . 5 ns , vertical scale 0 . 5 V , vertical offset 0 . 0 , input bandwith 500 MHz . Spectra were externally calibrated using ProteoMass peptide MALDI calibration kit ( Sigma-Aldrich ) . Spectra were recorded in the range 700 to 4000 Da , focus mass 2100 Da . Spectra were summed from 40 positions per 50 shots , 2000 shots in total . Spectra were processed by 4000 Series Explorer version 3 . 5 . 3 ( AB Sciex ) without smoothing; baseline subtraction was performed with peak width set to 50 . Spectra were deisotoped and peaks with a local signal-to-noise ratio greater than 5 were picked and searched by local Mascot v . 2 . 1 ( Matrix Science , Boston , MA , USA ) against a database of protein sequences derived from the cDNA library . Database search criteria were as follows: enzyme: trypsin; taxonomy: none; fixed modification: carbamidomethylation; variable modification: methionine oxidation; peptide mass tolerance: 80 ppm; one missed cleavage allowed . Only hits that were scored as significant ( P<0 . 05 ) were included . The data associated with this manuscript may be downloaded from ProteomeCommons . org Tranche using the following hash: mCZfFsOLaBtSfR+Jh6o8OwEgjrqDp4m3VntpJdAPqPGFNzNpTPry8IhEuGeLw9 TmpHcTRMSiiuiNNRL/6xP65TLvyNwAAAAAAAADGQ = = . The hash may be used to prove exactly what files were published as part of this manuscript's data set , and the hash may also be used to check that the data has not changed since publication . Expression sequence tags ( ESTs ) were analyzed using the dCAS software ( Desktop cDNA Annotation System , version 1 . 4 . 3 ) [38] with all third-party components recommended: CAP3 assembler program [39] , Phred [40] , [41] , and BLAST programs [42] . Sequences with Phred quality scores lower than 25 were removed , as well as primers and vector sequences . Resulting sequences were grouped based on nucleotide homology of 90% identity over 100 residues and aligned into consensus transcript sequences ( contigs ) using the CAP3 sequence assembly program . BLAST programs were used to compare contigs and singletons ( contigs with a single sequence ) to the non-redundant protein database of the NCBI , the Gene Ontology database ( GO ) [43] , to COG conserved domains database [44] , Protein Family database ( Pfam ) [45] , Simple Modular Architecture Tool database ( SMART ) [46] , and to rRNA Nucleotide Sequences , and Mitochondrial and Plasmid Sequence ( MIT-PLA ) databases available from NCBI . The three frame translations of each dataset were submitted to the SignalP server [47] to detect signal peptides . The grouped and assembled sequences , BLAST results , and SignalP results combined by dCAS software in an Excel spreadsheet were manually verified and annotated . Additionally , glycosylation sites were determined in selected sequences using NetNGlyc prediction server [48] . For phylogenetic analysis , protein sequences without signal peptide were aligned using ClustalX ( version 2 . 0 ) [49] with related sequences obtained from GenBank and manually refined in BioEdit 7 . 0 editing software . For each alignment , best substitution matrix was determined by ProtTest software , version 2 . 0 [50] . This matrix was then used by TREE-PUZZLE 5 . 2 [51] to reconstruct maximum likelihood phylogenetic trees from the protein alignments using quartet puzzling with 1000 puzzling steps . Resulting trees were visualized in MEGA 4 [52] . All protein and nucleotide accession numbers mentioned in the text , tables and figures are listed in Text S1 . Hyaluronidase activity was compared between seven sand fly species: P . tobbi , P . sergenti ( Israel ) , P . papatasi , P . argentipes , P . arabicus , P . perniciosus , and L . longipalpis . Hyaluronidase activity in SGs was quantified using a sensitive assay on microtitration plates coupled with biotinylated HA ( bHA ) . bHA , prepared as described in [53] , was immobilized onto Covalink NH microtiter plates ( Nunc , Placerville , NJ , USA ) using the method in [54] at a final concentration of 1 µg bHA per well . The plates were incubated overnight at 4°C and washed three times in PBS ( containing 2 M NaCl and 50 mM MgSO4 , pH 7 . 2 ) . Plates with immobilized bHA were blocked with 1% BSA in PBS for 45 min , washed and equilibrated to pH 5 . 0 ( 0 . 1 M acetate , 0 . 1 M NaCl , 0 . 1% Triton X-100 , pH 5 . 0 ) , the pH optimum for sand fly salivary hyaluronidase [53] . SGHs were incubated for 45 min at 37°C in triplicate at a final concentration of 0 . 5 gland per well . As a standard , bovine hyaluronidase ( Sigma ) at a concentration of 0 . 01 Turbidity Reducing Units ( TRU ) /µl was serially diluted in 0 . 1 M acetate buffer ( 0 . 1 M NaCl , 0 . 1% Triton X-100 , pH 4 . 5 ) . Wells without bHA or enzyme were used as controls . The reaction was terminated by 6 M guanidine 200 µl/well . Plates were washed in PBS ( containing 2 M NaCl , 50 mM MgSO4 , 0 . 05% Tween 20 , pH 7 . 2 ) and then equilibrated with PBS , 0 . 1% Tween 20 , pH 7 . 2 . Avidin-peroxidase ( Sigma ) was added at a final concentration of 0 . 2 µg/well and incubated for 30 min at room temperature . Color reaction was developed with o-phenylenediamine substrate in 0 . 1 M citrate-phosphate buffer ( pH 5 . 5 ) . After 10 min in dark , plates were read at 492 nm ( Tecan-Infinite M 200 Fluorometer; Schoeller Instruments , Prague , Czech Republic ) . The obtained results were expressed as relative TRU ( rTRU ) . Three independent experiments were performed with a different set of SGH samples in each experiment . For hyaluronidase zymography , 8% polyacrylamide gels ( 0 . 75 mm thick ) were copolymerized with 0 . 002% hyaluronic acid ( HA ) . As the hyaluronidase activities and band patterns varied among sand fly species , different loads were used per lane to obtain bands of equal intensity . The equivalent of 1/2 gland ( L . longipalpis and P . sergenti ) or 1/20 gland ( other tested species ) was loaded for zymography under non-reducing conditions , and the equivalent of 2 . 5 glands ( L . longipalpis and P . sergenti ) or 1/4 gland ( other tested species ) was loaded for zymography under reducing conditions . The total protein content per lane was as follows ( non-reducing/reducing conditions ) : L . longipalpis = 110/550 ng; P . papatasi = 12 . 5/62 . 5 ng; P . sergenti = 140/700 ng; P . argentipes = 14/70 ng; P . arabicus = 10 . 5/52 . 5 ng; P . tobbi = 10/50 ng; P . perniciosus = 10 . 5/52 . 5 ng . For reducing conditions , samples were treated with 3% 2-mercaptoethanol for 40 min at 45°C . SDS-PAGE electrophoresis was carried out using Mini-Protean II apparatus ( Bio-Rad , Hercules , CA , USA ) and constant voltage at 150 V . After electrophoresis , gels were rinsed 2×20 min in 0 . 1 M Tris , pH 7 . 8 , and 20 min in 0 . 1 M acetate buffer , pH 5 . 5 ( both with 1% Triton X-100 to wash out SDS ) and then incubated in 0 . 1 M acetate buffer ( without detergent ) for 120 min at 37°C . The gels were then washed in water , soaked in 50% formamide for 30 min and stained in Stains-all ( Sigma , St . Louis , MO , USA ) solution ( 100 mg/ml in 50% formamide ) for 24 h in the dark . Hyaluronidase activity was visible as a pink band on a dark blue background . Immunoblot was performed using P . tobbi SGH separated by SDS-PAGE on 10% polyacrylamide gel under non-reducing conditions using the Mini-Protean III apparatus ( Bio-Rad ) . Separated proteins were electrotransferred onto nitrocellulose ( NC ) membrane by iBlot Dry Blotting System ( Invitrogen ) . After transfer , the NC membrane was cut into strips with the equivalent of four glands/strip and free binding sites were blocked by 5% low-fat dried milk in 20 mM Tris buffer with 0 . 05% Tween ( Tris-Tw ) overnight at 4°C . The strips were then incubated with serum obtained from rabbit repeatedly exposed to P . tobbi females . Serum was diluted 1∶250 in Tris-Tw and incubated with P . tobbi proteins for 1 h , followed by 1 h incubation with peroxidase-conjugated swine anti-rabbit IgG ( Sevapharma , Prague , Czech Republic ) diluted 1∶1 , 000 in Tris-Tw . Substrate solution contained Tris buffer , diaminobenzidine , and H2O2 . Affinity blot was performed using P . tobbi SGH separated and electrotransferred as described for Immunoblot . After transfer , free binding sites on NC membrane were blocked by 5% BSA in 20 mM Tris-Tw overnight at 4°C . The strip was then incubated for 1 h at room temperature with biotinylated lectin from Canavalia ensiformis ( Concanavalin A , Sigma ) diluted 0 . 2 µg/ml in Tris-Tw . To control the reaction specificity , another strip was incubated with lectin preincubated for 30 min with the ligand , 0 . 5 M methyl-α-D-mannopyranoside . Avidin-peroxidase ( Sigma ) was added at a final concentration of 2 . 5 µg/ml and incubated for 1 h at room temperature . Substrate solution contained Tris buffer , diaminobenzidine , and H2O2 . All animals used in this study were maintained and handled strictly in accordance with institutional guidelines and legislation for the care and use of animals for research purpose Czech Act No . 246/1992 coll . on Protection Animals against Cruelty in present statues at large that complies with all relevant European Union and international guidelines for experimental animals . The experiments were approved by the Committee on the Ethics of Animal Experiments of the Charles University in Prague ( Permit Number: 24773/2008-10001 ) and were performed under the Certificate of Competency ( Registration Number: CZU 934/05 ) in accordance with the Examination Order approved by Central Commission for Animal Welfare of the Czech Republic . All efforts were made to minimize suffering of experimental animals within the study .
Phlebotomus tobbi and P . sergenti cDNA libraries were constructed from SGs of female sand flies dissected one day after emergence . From each cDNA library , 1 , 152 randomly selected clones were sequenced . Obtained ESTs were deposited in the NCBI dbEST database under accession numbers GW814275–GW815416 ( 1 , 142 sequences ) for P . tobbi and GW813185–GW814274 ( 1 , 090 sequences ) for P . sergenti . High-quality sequences were grouped together based on sequence homology , and resulting assembled sequences were analyzed using the dCAS cDNA annotation software [38] and verified by manual annotation . In the P . tobbi cDNA library , 997 high-quality sequences were grouped into 68 contigs and 125 singletons ( one sequence in cluster ) ; in P . sergenti , 853 high-quality sequences were grouped into 56 contigs and 196 singletons . Similar to other sand flies studied so far , the most abundant transcripts in both libraries were those coding for putative secretory proteins . BLAST comparison of translated nucleotide sequences with the non-redundant ( NR ) protein database showed high similarity with other sand fly secreted salivary proteins . In P . tobbi , 81 clusters containing 863 sequences ( average 10 . 7 sequences per cluster ) matched to sand fly salivary proteins . Of them , we found 62 clusters ( 796 sequences ) with predicted signal peptide sequence . In P . sergenti , 50 clusters containing 553 sequences ( average 11 . 1 sequences per cluster ) matched to sand fly salivary proteins . Of them , 32 clusters ( 482 sequences ) with predicted signal protein sequence were found . Tables 1 and 2 list representative secreted salivary proteins from P . tobbi and P . sergenti , respectively , deposited into NCBI GenBank database . The tables show GenBank accession numbers , putative mature protein features , best match to NR protein database , and presence in the proteome analysis as confirmed by MS ( Figure 1 ) . Additionally , Figure 1A and 1B show detailed analysis of MS results for P . tobbi and P . sergenti , respectively , including cluster name , Gen Bank accession number , and molecular weight of mature proteins under reducing and non-reducing conditions . The putative secreted salivary proteins of P . tobbi and P . sergenti could be divided into ten main protein families ( Figure S1 ) : antigen 5-related protein , apyrase , hyaluronidase , D7-related and PpSP15-like protein ( odorant-binding proteins superfamily ) , ParSP25-like protein , PpSP32-like protein , yellow-related protein , the 33-kDa salivary proteins , and the 41 . 9-kDa superfamily . The following paragraphs describe these families in detail , focusing on protein family characteristics , possible function , biochemical , immunomodulatory , and antigenic properties , and phylogenetic analysis in context with related proteins from other sand flies . Antigen 5-related proteins ( Ag5r ) are present in saliva of all sand fly species studied so far [55] , [56] , including P . tobbi ( PtSP77/HM140620 , PtSP78/HM140621 , PtSP79/HM140622 ) and P . sergenti ( PsSP52/HM537134 ) . Sand fly Ag5r proteins are members of CAP superfamily consisting of mammalian Cysteine-rich secretory proteins ( CRISPs ) , Antigen 5 ( Ag5 ) originally described from wasp venom , and plant Pathogenesis-related 1 proteins ( PR-1 ) [55] . Proteins with CAP domain occur across all living organisms , including prokaryotes [57] , and are mostly extracellular/secreted . All sand fly Ag5r proteins have similar predicted molecular mass ( ranging from 28 . 8 to 31 . 2 kDa ) and are alkaline ( Table S1 ) . In P . tobbi and P . sergenti , the predicted molecular mass corresponded well with the one measured in proteomic analysis ( Figure 1 , Tables 1 and 2 ) suggesting single-domain protein and negligible post-translational modifications . We identified 14 highly conserved cysteine residues proportionally distributed through the whole sequence length ( Figure 2 ) , possibly involved in disulfide bonding . Although the members of this family were described in sialotranscriptomes of all bloodsucking arthropods characterized [55] , [56] , their role is mostly unknown with a few exceptions . In Stomoxys calcitrans , Ag5r protein possesses immunoglobulin Fc binding activity [58] . In Tabanus yao , members of the Ag5r protein family can probably serve as an inhibitors of angiogenesis ( RTS disintegrin motif ) [59] or a potent platelet inhibitor ( RGD motif ) [60] . The Ag5r proteins are not specific for salivary glands thus they may possess other functions not associated with feeding [23] , [56] . Several studies showed antigenic properties associated with Ag5r proteins . Plasmid coding for Ag5r protein from P . ariasi ( ParSP05/AAX44092 ) induced a cell-mediated immune response in Swiss Webster mice [27] , showing that sand fly Ag5r proteins might modulate cell-mediated host immune response . This presumption is also supported by several T cell epitopes predicted for P . duboscqi Ag5r proteins [26] that include regions highly conserved among sand flies ( Figure 2 ) . Antibody response to sand fly Ag5r proteins was demonstrated in P . perniciosus; Ag5r protein ( PpeSP07/ABA43055 ) reacted with IgG antibodies from sera of P . perniciosus bitten dogs [21] . In other bloodsucking diptera , Ag5r proteins are mostly associated with IgE antibody response . Ag5r protein of Simulium vittatum seems to be the major allergen for insect bite hypersensitivity sharing common IgE-binding epitopes with Ag5r protein from Culicoides nubeculosus [61] , [62] . Specific anti-Ag5r IgE antibodies were also observed in Ugandan individuals bitten by Glossina morsitans [63] . Phylogenetic analysis of Ag5r proteins from sand flies and other insects showed a strongly supported distinct clade of sand fly Ag5r proteins ( Figure 3 ) similar to a previous analysis by [28] . The relationship within the sand fly clade reflected phylogenetic relationship within phlebotomine sand flies [33] , showing three distinct branches: clade I with species belonging to subgenera Euphlebotomus , Larroussius , and Adlerius; clade II with Phlebotomus and Paraphlebotomus species ( P . papatasi , P . duboscqi , and P . sergenti ) ; and Lutzomyia in clade III ( Figure 3 ) . Apyrase ( EC 3 . 6 . 1 . 5 ) appears to be a universal enzyme used to prevent blood coagulation by diverse hematophagous animals such as bloodsucking leeches , ticks , triatomine bugs , fleas , and mosquitoes . This enzyme hydrolyses both ATP and ADP to AMP , thus destroying an important physiologic stimulus of platelet aggregation released from damaged tissues and blood cells . Apyrases of bloodsucking insects are divided into three families: CD-39 ( the actin/heat shock 70/sugar kinase superfamily ) ; 5′-nucleotidase; and Cimex-type [55] , [56] . Sand flies are not an exception; transcripts coding for apyrases have been found in the saliva of all tested species [6] , [23]–[28] , including P . tobbi ( PtSP4/HM135951 , PtSP10/HM135952 ) and P . sergenti ( PsSP40/HM560860 , PsSP41/HM560862 , PsSP42/HM560861 ) ( Tables 1 and 2 ) . The predicted molecular mass of the translated molecules is uniform for all sand fly species , varying between 35 and 36 kDa ( Table S1 ) . All sand fly apyrases deposited in GenBank have also been found in the proteomic analysis ( Table S1 ) . In P . tobbi and P . sergenti , the predicted molecular mass corresponds well with the molecular weight measured under non-reducing conditions ( 33 . 0–37 . 6 kDa ) ( Figure 1; Tables 1 and 2 ) . Sand fly apyrases belong to the Cimex-type apyrase family . They hydrolyze ADP at a faster rate than ATP [64] and , similar to Cimex lectularius , the activity strictly depends on Ca2+ but not Mg2+ ions [6] , [23] , [64]–[66] . Apyrase activity has been demonstrated in the saliva of L . longipalpis [23] , [64] , P . argentipes [65] , P . colabaensis [65] , P . duboscqi [66] P . papatasi [65] , [67] , P . perniciosus [65] , and as well as in recombinant apyrases of P . papatasi ( PpApy/AF261768 ) [67] and P . duboscqi ( PduApy2/DQ834331 ) [66] . Bacterially expressed P . duboscqi apyrase inhibited ADP- as well as collagen-induced platelet aggregation [66] , indicating that post-translational modifications such as glycosylation are not necessary for apyrase activity . Orthologs of the Cimex apyrase family have also been identified in vertebrates and termed calcium-activated nucleotidases ( CANs ) [68] . In contrast to sand flies , human soluble CAN-1 ( SCAN-1 ) preferentially hydrolyses UDP and GDP; however , the engineered SCAN-1 mutant Glu92Tyr shows five times and seven times higher hydrolysis activity for ADP and ATP , respectively [69] . This mutated tyrosine is conserved among species of the genus Phlebotomus ( Figure 4 ) , supporting its key role in substrate specificity for phlebotomine apyrases [69] . In human SCAN-1 , other amino acid residues essential for binding nucleotide and Ca2+ were identified [70] , some of them being absolutely conserved among the analyzed apyrase proteins ( Asp44 , Ser100 , Asp114 , Glu216 , Arg232 , Ser277 ) , while others were uniformly mutated within sand fly apyrases ( Asp101Glu , Gly160Ser , Ile214Trp ) ( Figure 4 ) . Besides hydrolyzing activity , sand fly apyrases also possess antigenic properties . Antibodies from dogs experimentally or naturally exposed to P . perniciosus strongly recognized PpeSP01 ( ABB00906 ) and PpeSP01B ( ABB00907 ) apyrases [21] . In humans naturally exposed to sand flies , anti-sand fly saliva IgG antibodies recognized a protein band corresponding , in molecular weight , to apyrase [11] , [12] . Moreover , antibodies elicited by P . duboscqi saliva also recognized bacterially expressed P . duboscqi apyrase [66] , indicating that not all antibodies are specific for possible glycan modifications of sand fly apyrases . Phylogenetic analysis of sand fly apyrases reflects the same taxonomic relationship as Ag5r proteins . Figure 5 shows three distinct clades separating species in clade I ( P . arabicus , P . argentipes , P . ariasi , P . perniciosus , P . tobbi ) from Phlebotomus and Paraphlebotomus subgenera in clade II ( P . papatasi , P . duboscqi , and P . sergenti ) , and genus Lutzomyia in clade III . This analysis showed a very close relationship within the Larroussius species , P . tobbi and P . perniciosus ( Figure 5 ) . Hyaluronidase is an enzyme that catalyzes the hydrolysis of hyaluronic acid , a major component of the extracellular matrix in vertebrates . It is an ubiquitous enzyme found in mammals , bacteria and in the venom of bees , wasps , spiders , and snakes [71] . In bloodsucking Diptera , hyaluronidase activity has been found primarily in the saliva of telmophagic insects: horse flies , black flies , biting midges , and sand flies [72] . Thus , hyaluronidase is believed to decreases host skin tissue viscosity , assisting other salivary components to diffuse and create a pool of blood [60] , [72] , [73] . Sand fly hyaluronidase belongs to the same family as mammalian and Hymenopteran hyaluronidases ( endo-β-N-acetyl-hexosaminidases , E . C . 3 . 2 . 1 . 35 ) and is different from that of bloodsucking leeches and nematodes ( endo-β-glucuronidases , E . C . 3 . 2 . 1 . 36 ) [71] , [74] . Hyaluronidase activity has been detected in all eight sand fly species studied to date ( [23] , [28] , [53] , [73] , Figure 6 ) . Our zymographic analyses of P . tobbi ( Figure 6 ) and P . sergenti originating from Israel ( Figure 6 ) and Turkey [53] showed the potent activity of sand fly hyaluronidase . Based on the microplate method , P . tobbi hyaluronidase activity is one of the highest measured ( Figure 6A ) . In contrast , hyaluronidase of P . sergenti had the lowest activity among the species of the genus Phlebotomus ( Figure 6A ) . Under non-reducing conditions , P . tobbi and P . sergenti hyaluronidase revealed diffuse bands with the molecular weight of around 110 and 135 kDa , respectively ( Figure 6B ) . Hyaluronidase of P . sergenti is probably a homodimer , because under reducing conditions , the activity was observed at about half of the molecular weight , both in the Israeli ( Figure 6B ) and Turkish strains [53] , while hyaluronidase of P . tobbi was monomeric with similar molecular weight under non-reducing and reducing conditions and the activity reduced to minimum when denaturated and treated with β-mercaptoethanol ( Figure 6B ) . Similar features were observed for the hyaluronidase of P . perniciosus , the other Larroussius species ( [53] , Figure 6 ) , which suggests common biochemical characteristics of this enzyme between closely related species . In general , the remarkably high activity of salivary hyaluronidase may aid the spread of other salivary components as well as transmitted pathogens . Indeed , hyaluronidase coinjected with Le . major promotes infection in BALB/c mice [72]; however , no association was found between hyaluronidase activity and the sand fly capacity to vector either cutaneous or visceral leishmaniasis ( Figure 6A ) . Although sand fly hyaluronidase is a very potent enzyme , it is scarcely found in transcriptomic and proteomic approaches probably due to the low abundance of transcripts combined with the large size of the protein . Hyaluronidase transcripts have been reported in only two of seven salivary cDNA libraries , namely in L . longipalpis and P . arabicus [23] , [24] , [28] . In P . sergenti , no transcript was found , and in P . tobbi , only one 3′-truncated transcript was identified ( PtSP125/JN192442 ) . Amino acid residues that constitute the catalytic site ( Asp111 , Glu113 , and Glu247 ) and form disulfide bridges ( Cys22–Cys313 and Cys189–Cys201 ) in bee hyaluronidase [75] are conserved among the sand fly hyaluronidase sequences ( Figure 7 ) . Based on the NetNGlyc prediction server [48] , several putative glycosylation sites were predicted in sand fly hyaluronidases , including one highly conserved among aligned sequences ( Figure 7 ) . Allergenic properties of sand fly hyaluronidase are not known , although it has been identified or suspected as the main allergen in the saliva of other bloodsucking Diptera , namely biting midges and horseflies [59] , [76] . However , there is no record of typical IgE-mediated allergic reaction to sand fly saliva; only negligible amount of anti-saliva IgE was measured in hosts repeatedly bitten by sand flies [11] , [19] , [77] . Two sand fly salivary protein families , D7-related proteins and PpSP15-like proteins , are related to the arthropod pheromone/odorant binding protein superfamily ( OBP , protein domain PBP-GOBP , pfam01395 ) [56] , [78] . D7-related ( D7r ) proteins are named after the D7 protein , originally described in Aedes aegypti as a major salivary protein exclusively synthesized in bloodsucking females [79] , [80] . Salivary proteins related to D7 have also been found in black flies and biting midges [56] , [78] and all sand fly species studied to date . In the P . tobbi SG cDNA library we found seven clusters homologous to D7r sequences ( HM164145–HM164151 ) and three clusters in the P . sergenti cDNA library ( PsSP4/HM560863 , PsSP5/HM569360 , PsSP7/HM560864 ) ( Tables 1 and 2 ) . Within all sand flies , D7r proteins have similar predicted molecular mass ( 25 . 3–28 . 1 kDa ) and wide range of pI ( 4 . 82–9 . 5 ) ( Table S1 ) . Based on the results from NetNGlyc prediction server [48] , we found a mixture of putative glycosylated and non-glycosylated D7r sequences in most of the sand fly species studied with the exception of L . longipalpis , P . sergenti , and P . papatasi , where no N-glycosylation sites were found ( Figure 8 ) . All sand fly D7r predicted proteins contain nine highly conserved cysteine residues ( Figure 8 ) , implying there is a single non-disulphide-bond-forming cysteine . The other family related to OBPs , PpSP15-like proteins , is closely related to larger D7-related proteins [25] , [56] and are named after 15-kDa salivary protein of P . papatasi ( PpSP15/AF335487 ) [6] . They have not been identified in any Diptera other than sand flies [25] , [56] . It is the most abundant family among sand fly salivary proteins , and P . tobbi and P . sergenti are not exceptions; six and seven members of this family were found in each cDNA library , respectively ( Tables 1 and Table 2 ) . Several members were also detected by proteomic analysis , having similar molecular mass as predicted based on the amino acid sequences ( Tables 1 and Table 2; Figure 1 ) . Within the sand flies , PpSP15-like proteins have a similar predicted molecular mass ( 12 . 2–17 . 1 kDa ) and surprisingly wide range of pI ( 6 . 33–9 . 44 ) ( Table S1 ) . In accordance with previous reports [25] , [28] , all sand fly PpSP15-like proteins show high degree of variability of around six conserved cystine residues ( Figure S2 ) . In mosquitoes , some salivary D7 strongly bind biogenic amines and leukotrienes as well as components of the coagulation cascade , thus promptly antagonizing the host defense system [81]–[83] . D7r and PpSP15-like sand fly salivary proteins have not yet been characterized functionally; however , the motif [ED]-[EQ]-x ( 7 ) -C-x ( 12 , 17 ) -W-x ( 2 ) -W-x ( 7 , 9 ) -[TS]-x-C-[YF]-x-[KR]-C-x ( 8 , 22 ) -Q-x ( 22 , 32 ) -C-x ( 2 ) -[VLI] , found in mosquito D7 salivary proteins that bind cysteinyl leukotrienes [83] , is also found in the sand fly D7r proteins ( Figure 8 ) . Sand fly PpSP15-like proteins and D7r proteins possess antigenic properties . PpSP15-like proteins were reported as promising anti-Leishmania vaccine candidates [6] , [27] , [84] . Phlebotomus papatasi SP15 protein is able to protect mice against Le . major challenge , and a DNA vaccine containing the PpSP15 cDNA provided the same protection [6] . ParSP03 ( AAX56359 ) , a PpSP15-like protein from P . ariasi , elicited similar delayed-type hypersensitivity and humoral immune responses upon DNA vaccination [27] . D7r could serve as a marker of exposure to sand fly bites . In humans , all tested serum samples from individuals naturally exposed to P . papatasi strongly bound to a P . papatasi protein band with a molecular mass corresponding to PpSP30 D7r protein ( AAL11049 ) [12] , [18] . As an ideal marker of exposure , this protein was recognized by both IgE and IgG antibodies , including all tested IgG subclasses [18] . D7r proteins seem to be applicable also for measurement of dog exposure , the main reservoir host for visceral leishmaniasis , since IgG antibodies from animals bitten by P . perniciosus [21] or L . longipalpis [16] , [85] recognized D7r proteins of the respective species ( PpeSP4/DQ150623 , PpeSP04B/DQ150624 , PpeSP10/DQ153104 , LJL13/AF420274 ) . Moreover , L . longipalpis-bitten dogs bind also to the LJL13 D7r recombinant form [16] . Phylogenetic analysis of D7r proteins showed several major clades ( Figure 9 ) . Phlebotomus sergenti sequences clustered together forming a distinct subclade within clade III that contains P . papatasi and P . duboscqi . In contrast , P . tobbi D7r protein sequences are divided among clades I and II , which contain sequences from P . arabicus , P . ariasi , P . argentipes , P . perniciosus , and L . longipalpis . Interestingly , clade II only contained sequences with predicted N-glycosylation sites , which may suggest a unique functional characteristic of D7 molecules within this clade that have arisen after gene duplication . Similarly , phylogenetic analysis of PpSP15-like proteins ( Figure 10 ) revealed several separated groups , consistently clustering P . sergenti sequences with P . duboscqi and P . papatasi , and P . tobbi sequences with those from P . perniciosus and other sand flies studied to date , including a single member from L . longipalpis . PpSP15 could be a multicopy gene , as more than two alleles were found in several P . papatasi individuals , some of them unique to the population origin [86] . This family is named PpSP32 from the original identification in P . papatasi ( AAL11050 ) [24] and due to the lack of homology to a conserved protein domain . PpSP32-like proteins have been described solely in sand flies and are found in all species studied so far; we identified homologous sequences also in P . tobbi ( PtSP27/HM173642 , PtSP28/HM173643 , PtSP29/HM173644 ) and P . sergenti ( PsSP44/HM569368 ) . The predicted molecular mass of P . tobbi PpSP32-like proteins ( 24 . 5 kDa ) is slightly lower than what was measured in proteomic analysis ( Figure 1 , Tables 1 and 2 ) . All sequences have a wide range of predicted molecular mass ( ranging from 22 . 5 to 34 . 9 kDa ) , no protein domain match , and are alkalic ( pI ranging from 9 . 3 to 10 . 6 ) ( Table S1 ) . An interesting common feature of this protein family is that it possesses highly conserved N- and C- terminal regions with extremely variable internal sequence ( Figure 11 ) . Within the genus Phlebotomus there are predicted N-glycosylation sites in the variable and C-terminal regions ( Figure 11 ) . To date , no function has been associated with sand fly PpSP32-like proteins , although L . longipalpis and P . perniciosus proteins have been hypothesized to possess collagen binding activity [24] , [25] and in P . papatasi , PpSP32 transcripts are expressed independently of either diet or age [87] , indicating a vital role for these molecules in feeding . Phylogenetic analysis of PpSP32-like proteins reflects again the taxonomic relationship within Phlebotomine sand flies [33] . True to form , phylogenetic position of P . tobbi PpSP32-like proteins are within a subclade I with P . perniciosus and the P . sergenti PpSP32-like protein is within the Phlebotomus and Paraphlebotomus clade II ( Figure 12 ) . Phlebotomine yellow-related proteins are characterized by the presence of major royal jelly protein domain ( MRJP; pfam03022 ) . Originally , MRJP proteins were described from honeybee larval jelly , making up to 90% of the protein content [88] . Sequences related to MRJP proteins were described in Drosophila , where it is related to cuticle pigmentation and , when mutated , it produced a yellow phenotype and thus named Yellow proteins [89] , [90] . In bloodsucking Diptera , salivary yellow-related proteins have only been described in sand flies [55] , [56] and black flies [91] . Yellow-related proteins are found in all sand fly species studied to date . In the P . sergenti cDNA library , five different clusters were found ( PsSP18/HM569361 , PsSP19/HM560865 , PsSP20/HM560866 , PsSP22/HM560867 , PsSP26/HM569362 ) compared with P . tobbi , where only two clusters were found ( PtSP37/HM140618 and PtSP38/HM140619 ) ( Tables 1 and 2 ) . Sand fly yellow-related proteins have a similar predicted molecular mass ( 41 . 5–45 . 2 kDa ) , wide range of pI ( 4 . 75–9 . 8 ) , and contain four conserved cysteine residues shown to form two disulfide bonds in LJM11 ( AAS05318 ) [9] ( Table S1 , Figure 13 ) . Yellow-related proteins are modulated on a transcriptional level [87] and are likely post-translationally modified , as variants with different mobility have been detected on SDS-PAGE [6] , [25] ( Figure 13 ) . Ribeiro and Arca [55] proposed that in Phlebotomines , salivary yellow-related proteins work as kratagonists , the binders of biogenic amines . Indeed , Xu et al . [9] proved that the bacterially expressed L . longipalpis yellow-related proteins ( LJM11 , LJM17/AAD32198 , and LJM111/ABB00904 ) bind biogenic amines , namely serotonin , catecholamines , and histamine . The proteins differed in affinity to the particular ligand , suggesting functional divergence within the family [9] . The midgut yellow protein in Aedes aegypti is involved in the melanization pathway as a dopachrome conversion enzyme [92]; however , in sand flies the yellow-related proteins found in the midgut lumen probably originating from swallowed saliva [93] and researchers failed to detect dopachrome convertase activity in salivary yellow-related proteins [28] , [56] . In Glossina morsitans , the ubiquitous tissue expression of the protein suggests also a housekeeping role for yellow-related proteins [91] . Sand fly salivary yellow proteins possess antigenic properties as they are recognized by serum antibodies of experimentally bitten mice [12] and dogs [19] , [21] , as well as naturally exposed dogs , humans , and foxes [11] , [16] , [18] , [21] , [77] , [85] . Additionally , a combination of recombinant LJM17 and LJM11 successfully substituted L . longipalpis whole SG sonicate in probing sera of individuals for vector exposure [16] , [20] . Yellow proteins are also under consideration for anti-Leishmania vector-based vaccines . LJM17 from L . longipalpis elicited leishmanicidal Th1 cytokines in immunized dogs [8] , and LJM11 protected laboratory animals against both Le . major and Le . infantum [7] , [9] . In contrast , mice immunized with P . papatasi yellow-related proteins PpSP42 or PpSP44 ( AAL11052 and AAL11051 , respectively ) elicited Th2 cytokines and exacerbated Le . major infection [84] . It remains to be elucidated whether the protection induced by yellow-related proteins is related to particular protein immunogenicity , to sand fly species , or to the vector-Leishmania-host combination , as all of these factors can contribute to vaccine efficacy . Recently , Xu et al . [9] showed that L . longipalpis LJM11 but not LJM111 produces a DTH response in mice challenged by SGH . The authors related this immunogenicity to electrostatic potential on the protein surface , which is positive in LJM11; thus the protein is probably more attractive to antigen-presenting cells [9] . Yellow-related proteins are highly conserved among sand flies . Phylogenetic analysis produced three major clades combining Larroussius , Adlerius and Euphlebotomus ( clade I ) ; Phlebotomus and Paraphlebotomus ( clade II ) ; and Lutzomyia ( clade III ) , while subclades discerned each subgenus ( Figure 14 ) . Interestingly , P . sergenti illustrates a gene duplication event that preceded speciation and was followed by a clear gene duplication expansion that is seen in one of the subclades . Gene duplication in bloodsucking arthropod salivary molecules is fundamental for the functional diversification of proteins , as can be seen with the range of substrates bound by the L . longipalpis yellow-related proteins [9] . Within clade II , two subclades can be seen distinguishable by the presence of putative N-glycosylation sites . Moreover , sequences in clade IIa have a slightly higher predicted isoelectric point than the glycosylated sequences in clade IIb ( Figure 14 , Table S1 ) , indicating another feature that might be responsible for functional diversification . ParSP25-like transcripts were found in P . tobbi but not in P . sergenti SG library . Phlebotomus tobbi ParSP25-like molecules ( PtSP73/HM173639 , PtSP75/HM173640 , and PtSP76/HM173641 ) have predicted molecular mass ranging from 27 . 8 to 38 . 8 kDa and contain a large proportion of acidic residues resulting in a pI of 4 . 5±0 . 1 . The sequences share similarity with eight other sand fly salivary proteins from three sand fly species [25] , [27] , [28] ( Figure 15 ) , all of them with predicted pI between 4 . 4 and 5 . 0 ( Table S1 ) . Analysis of the putative protein sequences revealed highly conserved regions rich in amino acid residues such as Asp , Tyr , Glu , and Ser and no predicted N-glycosylation sites ( Figure 15 ) . Though the function is not known , some members of this family were shown to be highly antigenic . Mice immunized with a plasmid coding for ParSP25 ( AAX55664 ) elicited high levels of anti-P . ariasi IgG1 and a strong DTH reaction when challenged with P . ariasi saliva [27] . Moreover , dogs exposed to P . perniciosus bites strongly bind to protein band characterized as PpeSP08 ( ABA43056 ) [21] . Sand fly ParSP25-like proteins are most likely genus-specific because , so far , the sequences have been found only in Adlerius ( P . arabicus ) and Larroussius species ( P . ariasi , P . perniciosus , P . tobbi ) and not in representatives of the other subgenera ( Figure 15 ) . These proteins , named by Anderson et al . [25] as members of the 33-kDa family , have not yet been found in any Diptera other than sand flies . PsSP49 ( HM569369 ) and PtSP66 ( HM173645 ) share sequence similarity with seven other sand fly salivary proteins from six sand fly species both from both New and Old World sand flies [24]–[28] ( Figure 16 ) . All sand fly 33-kDa family proteins have similar predicted molecular weight ( 32 . 3–34 . 5 kDa ) and alkalic pI ( 8 . 2–9 . 1 ) ( Table S1 ) . PsSP49 and PtSP66 were both identified in the proteomic analysis ( Figure 1 ) . Two highly conserved N-glycosylation sites were predicted among all sand fly sequences ( Figure 16 ) and both PsSP49 and PtSP66 were found above the predicted molecular weight in the proteomic analysis ( Figure 1 , Tables 1 and 2 ) , indicating a post-translational modification . Indeed , the two proteins from P . arabicus ( PabSP32/ACS93510 and PabSP34/ACS93511 ) showed glycosylation by ProQ Emerald staining [28] . The function is unknown; however , P . perniciosus PpeSP06 ( ABA43054 ) and the L . longipalpis LJL143 ( AAS05319 ) were identified as antigens for dogs living in endemic areas of Le . infantum [16] , [21] , the later one also shown to be a candidate for vaccine against canine leishmaniasis [8] . 41 . 9-kDa protein superfamily is specific to bloodsucking Nematocera encompassing members of mosquitoes , biting midges , black flies , and sand flies [56] . The P . sergenti and P . tobbi members of this superfamily , PsSP82 ( HM569371 ) and PtSP49 ( HM173648 ) , share sequence similarity with five other sand fly salivary proteins from five sand fly species ( Figure 17 ) . These sand fly proteins have a wide range of predicted molecular weight ( 27 . 5–56 . 6 kDa ) and pI ( 4 . 3–8 . 5 ) ( Table S1 ) but only one of them , P . perniciosus PpeSP19 ( ABA43063 ) , has been found by proteomic analysis [25] . All sequences are rich in putative N-glycosylation sites ( Figure 17 ) and the function is not known . Several other putative salivary proteins were identified in the transcriptomes of P . tobbi and P . sergenti SGs . They are smaller than 15 kDa , their function is not known , and are , thus far , unique to sand flies . Additionally , none of these small proteins have been found in the proteomic analysis ( Figure 1 , Tables 1 and 2 ) . PsSP28 ( HM569370 ) , PtSP8 ( HM173646 ) , and PtSP81 ( HM173647 ) share sequence similarity with P . ariasi ParSP23 ( AAX55663 ) and P . perniciosus PpeSP15 ( ABB00905 ) ( Figure 18 ) . The proteins have a low predicted molecular weight ( 2 . 4–5 . 0 kDa ) and an alkalic pI ( 9 . 2–10 . 7 ) . PsSP98 ( HM569366 ) has a predicted molecular weight similar to PpSP15-like proteins ( 14 . 3 kDa ) but is highly acidic ( pI = 4 . 73 ) . The protein sequence is related to 16-kDa proteins from P . arabicus ( PabSP64/ACS93507 , PabSP63/ACS93506 ) and P . argentipes ( PagSP73/ABA12153 ) ( Figure 19 ) . PsSP73 ( HM569367 ) has a predicted molecular weight 12 . 2 kDa and is highly acidic ( pI = 4 . 51 ) . The predicted protein sequence is related to proteins found in P . arabicus ( PabSP75/ACS93508 ) and P . ariasi ( ParSP13/AAX55657 ) ( Figure 20 ) . PtSP71 ( HM173638 ) has a low predicted molecular weight ( 4 . 5 kDa ) and an alkalic pI ( 10 . 6 ) . The protein sequence is related to molecules identified in P . perniciosus ( PpeSP12/ABA43060 , PpeSP13/ABA43061 ) and P . ariasi ( ParSP15/AAX55658 ) ( Figure 21 ) , indicating these sequences might be unique to Larroussius species . To identify antigens and glycoproteins in P . tobbi SGH , electrophoretically separated proteins were incubated with anti-P . tobbi rabbit serum and a lectin Concanavalin A ( ConA ) , respectively ( Figure 22 ) . When compared with the proteome analysis in Figure 1 , the protein bands visible by silver staining are most likely yellow-related proteins ( PtSP37 and PtSP38 ) , apyrases ( PtSP4 and PtSP10 ) , antigen 5-related proteins ( PtSP77 and PtSP79 ) , PpSP32-like proteins ( PtSP28 and PtSP29 ) , D7-related proteins ( PtSP58 and PtSP60 ) , and PpSP15-like proteins ( PtSP9 , PtSP23 , and PtSP32 ) . Anti-P . tobbi antibodies recognized all identified bands as well as other six high molecular weight proteins not visible by silver staining ( Figure 22 , lane 2 ) . Most of the P . tobbi proteins reacted with ConA , indicating they are N-glycosylated . The lectin binding was specific , as the reactivity was totally inhibited when ConA was preincubated with specific monosaccharide methyl-α-D-mannopyranoside . The most intense reaction was observed with the high molecular weight band not visible by silver staining , and with the bands of molecular weight similar to one yellow-related protein ( PtSP38 ) and both apyrases . Among the nine silver-stained bands , three did not react with ConA , namely bands with molecular weight similar to D7-related proteins , PpSP15-like proteins , and one yellow-related protein ( PtSP37 ) ( Figure 22 , lane 4 ) . The reactivity with ConA is in agreement with N-glycosylation as predicted by NetNGlyc server [48] , with the exception of PtSP10 apyrase ( Table 3 ) . We can speculate that the most glycosylated band with the highest molecular weight might be hyaluronidase . Although producing a minor unstainable band , it is predicted to be highly glycosylated ( Figure 7 ) and its activity is clearly visible around 135 kDa in zymography analyses ( Figure 6 ) . Within sand fly yellow-related proteins , it is common that glycosylated and non-glycosylated forms occur in the same species . As proved for P . papatasi [93] and predicted for protein sequences of Phlebotomus ( P . papatasi and P . duboscqi ) and Paraphlebotomus ( P . sergenti ) species , at least one form is glycosylated , forming a well supported subclade with glycosylated sequences from other species ( Figure 14 ) . Glycosylated and non-glycosylated forms are also present in P . tobbi , as proven by blot analysis ( Figure 22 ) , although the closely related P . perniciosus possesses only glycosylated forms . Interestingly , in sand fly species within the clades I and III ( Figure 14 ) , all published sequences are glycosylated with an exception of P . tobbi and P . ariasi , which has at least one non-glycosylated form . Further research is needed to investigate whether the presence of sugar side chains may contribute to the antigenicity of the yellow-related proteins . With over 80 species of sand flies implicated in Leishmania transmission , it is vital to continue describing their salivary proteins in the search for vaccine candidates and markers of exposure . In this study , we prepared and analyzed the transcriptome and proteome data of P . tobbi and P . sergenti to broaden our knowledge on the repertoire of Larroussius salivary proteins and provide the first report from a Paraphlebotomus sand fly , respectively . P . tobbi has been reported to transmit Le . infantum that causes cutaneous leishmaniasis [32] . Interestingly , the salivary proteins of P . tobbi are highly homologous to those of P . perniciosus , a vector of Le . infantum that causes visceral disease . It is likely that , in this instance , the salivary proteins of P . tobbi are not the determining factor for these different disease manifestations . However , in general , it is possible that the divergence , diversity or amount of sand fly salivary proteins or non proteinaceous components of the saliva correlate with different disease manifestations of the same species of Leishmania . The transcriptome data can be utilized to prepare recombinant proteins that can be used to test their potential as anti-Leishmania vaccines or in epidemiologic studies to develop more specific and efficient methods for measurement of vector exposure . Finally , recombinant salivary proteins may also help us to understand the mechanism of blood sucking or find biological activities of many of these novel sequences . | Phlebotomine female sand flies require a blood meal for egg development , and it is during the blood feeding that pathogens can be transmitted to a host . Leishmania parasites are among these pathogens and can cause disfiguring cutaneous or even possibly fatal visceral disease . The Leishmania parasites are deposited into the bite wound along with the sand fly saliva . The components of the saliva have many pharmacologic and immune functions important in blood feeding and disease establishment . In this article , the authors identify and investigate the protein components of saliva of two important vectors of leishmaniasis , Phlebotomus tobbi and P . sergenti , by sequencing the transcriptomes of the salivary glands . We then compared the predicted protein sequences of these salivary proteins to those of other bloodsucking insects to elucidate the similarity in composition , structure , and enzymatic activity . Finally , this descriptive analysis of P . tobbi and P . sergenti transcriptomes can aid future research in identifying molecules for epidemiologic assays and in investigating sand fly-host interactions . | [
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| [
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| 2012 | Salivary Gland Transcriptomes and Proteomes of Phlebotomus tobbi and Phlebotomus sergenti, Vectors of Leishmaniasis |
Chikungunya virus ( CHIKV ) is a mosquito-transmitted RNA alphavirus causing major outbreaks of infectious chronic inflammatory rheumatisms ( CIR ) . Recently , methotrexate ( MTX ) , a disease modifying anti-rheumatic drug has been used successfully to treat patients suffering from rheumatoid-like arthritis post-CHIK but its immunomodulatory activity in the context of viral persistence has been a matter of concerns . We herein used a model of primary human synovial fibroblasts ( HSF ) and the synthetic molecule polyriboinosinic:polyribocytidylic acid ( PIC ) to mimic chronic infectious settings in the joints of CHIKV infected patients . The innate antiviral immune and inflammatory responses were investigated in response to MTX used at the therapeutic concentration of 1 μM . We found that MTX did not affect cellular viability as indicated by the LDH release assay . By quantitative RT-PCR , we observed that HSF responded robustly to PIC by increasing ISG15 and IFNβ mRNA levels . Furthermore , PIC upregulated the mRNA expression of two of the major pattern recognition receptors , RIG-I and MDA5 involved in the innate immune detection of viral RNA . MTX did not impact the antiviral response of PIC on ISG15 , IFNβ , RIG-I and MDA5 mRNA expressions . MTX alone or combined with PIC did not affect the expression of proinflammatory CCL2 and CXCL8 chemokines . PIC strongly upregulated the mRNA and protein expression of osteoclastogenic factors ( IL-6 , GM-CSF but not RANKL ) . Critically , MTX treatment alone or combined with PIC did not affect the expression of all three tested osteoclastogenic cytokines . We found that MTX alone did not increase the capacity of CHIKV to infect and replicate in HSF . In conclusion , our study argues for a beneficial effect of MTX to treat CIR post-CHIKV given that it does not critically impact the antiviral , the proinflammatory and the bone tissue remodeling responses of synovial cells .
Alphaviruses , transmitted by bites of infected mosquitoes , are globally distributed and capable to cause significant inflammatory diseases including arthritis and encephalitis [1] . Old World alphaviruses , such as Chikungunya virus ( CHIKV ) , Ross River virus ( RRV ) and O’Nyong-Nyong virus ( ONNV ) , are associated with rheumatic diseases in humans which can be chronic and severely debilitating [2] . Classically , patients acutely infected with CHIKV present with a febrile illness , polyarthralgia , myalgia and maculopapular rash that can last for several days [3 , 4] . Remarkably , severe complications in adults such as persistent arthralgia and destructive arthritis have been reported , consistent with chronic inflammatory rheumatisms ( CIR ) [5–8] . Symptoms can persist for months or even years following initial infection . The immunopathological mechanisms responsible for CHIK-CIR are poorly understood but may be due to viral persistence leading to chronic expression of viral RNA and with local inflammatory responses to drive osteoclastogenic activities [9–12] . Conceptually , the CHIK-CIR might be favored by different mechanisms: CHIKV can replicate in very high load and was reported to block type I interferon ( IFN ) regulatory pathway involved in the antiviral response [13 , 14] . Induction of type I Interferon ( IFN α and β ) by intracellular sensors also called pattern recognition receptors ( PRR ) such the Toll-like receptors ( TLRs ) and RIG-I-like receptors ( RLRs ) , namely retinoic acid-inducible gene I ( RIG-I ) and melanoma differentiation-associated gene-5 ( MDA5 ) represent an early innate immune response against viruses [15] . RIG-I and MDA5 can detect cytoplasmic dsRNA generated during viral replication and are able to bind polyriboinosinic:polyribocytidylic acid ( PIC ) , the synthetic analog of viral dsRNA , and to mediate type I IFN responses [16] . IFN-stimulated genes ( ISGs ) code for antiviral proteins to inhibit virus replication [17] . ISG15 was reported to be a central player in the control of CHIKV infection [18] . The joint destructive process may also be mediated at least in part by fibroblast synoviocytes [19] . Interestingly , human synovial fibroblasts ( HSFs ) infected by CHIKV are able to promote differentiation of monocytes/macrophages into osteoclasts involved in bone erosion [20] . The differentiation of osteoclasts is regulated in response to various cytokines , including the receptor activator of nuclear factor-Kappa B ligand ( RANKL ) , macrophage colony stimulating factor ( M-CSF ) , granulocyte-macrophage colony-stimulating factor ( GM- CSF ) , interleukins ( IL ) -6 and IL1β [21 , 22] . RANKL , which is a member of tumor necrosis factor ( TNF ) family , has been identified as a key mediator of osteoclast activation and maturation in the presence of M-CSF [23] . It has already been reported that CHIKV patients presented high levels of RANKL and IL-6 which could participate in macrophage-derived osteoclast appearance in the joints [24 , 25] . During rheumatoid arthritis ( RA ) , high levels of IL-6 , IL1β and GM-CSF were described in the inflamed joints [26] . High plasma GM-CSF concentrations were also reported in patients displaying chronic symptoms after CHIKV infection [27] . Recruitment of inflammatory cellular infiltrates to the joint of infected patients has been reported during CHIKV induced arthropathy [28 , 29] . Chemokines such as CCL2 and CXCL8 are involved in modulating the recruitment of immune cells such as monocytes and neutrophils , in the inflamed joint and have been found to be upregulated in the serum and synovial fluid of CHIKV-infected patients [28 , 30] . Treatment of alphavirus-infected mice with Bindarit , an inhibitor of CCL2 , CCL8 and CCL7 was able to ameliorate cellular infiltration in joints and attenuate the joint swelling , which suggest the major role of chemokines in joint damage and inflammation [24] . Methotrexate ( MTX ) , a disease modifying anti-rheumatic drug ( DMARD ) , has been successfully used to treat patients experiencing rheumatoid arthritis ( RA ) -like CIR months to years post CHIK [6 , 28 , 31 , 32] . Originally developed as a clinical chemotherapeutic agent for malignancies such as leukemia [33] , MTX has become the first DMARD prescribed in patients with RA [34] . MTX is a potent competitive inhibitor of dihydrofolate reductase ( DHFR ) and is currently used once-weekly at low dose for treatment of inflammatory diseases due to its beneficial anti-inflammatory and immunosuppressive activities [33] . Immunosuppressive medications using MTX and others such as hydroxychloroquine , etanercept , adalimumab , and sulfasalazine could be detrimental in the context of viral persistence . Indeed , it has been hypothesized that these treatments may interfere with immune-mediated control of infection and the resolution of inflammation [2] . Hence , our aim has been to develop an in vitro model to ascertain whether or not MTX may affect the innate immune , inflammatory and osteoclastogenic responses of synovial fibroblasts in the context of CHIKV persistence in the joint of patients suffering from CIR . To mimic cytoplasmic viral dsRNA generated in synovial tissue of chronically CHIKV infected patients , we used the synthetic analog of viral dsRNA PIC . Importantly , it has already been shown that PIC alone can drive a pro-arthritic inflammatory response in animal models of RA [35] .
The primary cultures of HSF were obtained from ScienCell Research Laboratory ( ScienCell , 4700; Clinisciences ) . Cells were grown in Minimum Essential Medium eagle ( MEM eagle , PAN Biotech P0408500 ) supplemented with 10% of decomplemented fetal bovine serum ( FBS ) ( PAN Biotech , 3302 P290907 ) and completed with L-glutamine 2 mM ( Biochrom AG , K0282 ) , 100U/mL– 0 . 1 mg/mL penicillin- streptomycin ( PAN Biotech , P0607100 ) , 1 mM sodium pyruvate ( PAN Biotech , P0443100 ) and 0 . 5 μg/mL fungizone ( PAN Biotech , P0601001 ) . We used primary cultures of HSF to investigate the cellular response to PIC and the immunoregulatory effect of MTX treatment . MTX was used at the concentration of 1μM which corresponds to the maximal plasma concentration after the ingestion of a 15 mg tablet of MTX recommended for the treatment of RA [36] . Lipopolysaccharide ( LPS ) , a TLR4 agonist ( cat . no . L2762 ) was purchased from Sigma-Aldrich . IL-1β ( cat . no . 200-01B ) and Tumor necrosis factor-α ( TNFα ) ( cat . no . 300-01A ) were purchased from Peprotech . LPS , and the recombinant cytokines were used as canonical proinflammatory activators of synovial fibroblasts [37] . The double-stranded polyribonucleotide PIC ( cat . no . 27-4732-01 ) was purchased from Amersham Biosciences . We used a viral isolate ( clone CHIKV 4 . 2 ) amplified from a patient’s serum sample ( isolated during the 2006 epidemic ) after two passages on Vero cells [28] . HSF were placed in a six-well tissue culture plate and maintained at 37°C in a humid atmosphere with 5% CO2 . The medium was replaced twice a week . Cells were allowed to grow to 80–90% confluence . Infections were performed with CHIKV clone 4 . 2 in a BSL3 facility and HSF were treated or not with MTX to evaluate MTX effects on CHIKV replication and cellular response to CHIKV infection . To analyze the expression profile of HSF innate immune genes , proinflammatory chemokine genes and osteoclast-related cytokine genes , cells were stimulated with PIC100 μg/mL in the presence or not of MTX . After treatment , supernatants were collected and frozen at -20°C until analyzed . The concentration of MTX used in the different experiments was 1μM and the culture periods were from 6h to 72h of continuous exposure to MTX . On the basis of pharmacokinetic analysis , the ingestion of a 15mg tablet of MTX produces plasma MTX concentrations of approximately 0 . 7μM ( Cmax ) after 1 . 5 hours [36] . MTX can distribute to the synovial fluid , in which the level of MTX is comparable with that in plasma [38] . MTX was also used at the concentration of 10μM to evaluate potential cytotoxic effect on HSF . Total RNA was extracted directly from harvested cell culture ( in six well plates ) using a QIAamp RNA Blood Mini Kit ( QIAGEN , Cat No 52304 ) . 350μL of lysis buffer from the kit was added to each well , collected after 5 min and kept at -80°C until use . qRT-PCR experiments were done using the One Step Prime Script Syber Green RT-PCR kit from TAKARA ( Cat No RR066A ) . qRT-PCR was performed in a final volume of 5μL containing 1μL of extracted total RNA per reaction , 2 . 7μL of enzyme mix and 1 . 3μL of primers mix with final primer concentration of 250nM . The specific primers used for qRT-PCR are listed in Table 1 . qRT-PCR was carried out in Labgene Biometra T Optical thermocycler with the following steps: a reverse transcription at 42°C for 5 minutes and 40 cycles comprising a denaturation step at 95°C for 5 sec , annealing step at 58°C for 15sec and extension step at 72°C for 15 sec . Fluorescence data were collected at 520 nm during the extension step . Relative gene expression was calculated using GAPDH as a reference gene . Experiments were done in triplicate or in quadruplicate . For measurement of CD90 , CD13 , CD55 and CD59 surface expression , HSF were detached from 6-well plates with EDTA 5mM , washed with PBS/BSA , and incubated for one hour with the following monoclonal antibodies: phycoerythrin ( PE ) -conjugated anti-CD55 ( 1:100 , BioLegend ) , Fluorescein isothiocyanate ( FITC ) conjugated anti-CD90 ( 1:100 , BioLegend ) , PE anti-CD59 ( 1:100 , BioLegend ) , PE anti-CD13 ( 1:100 , BECKMAN COULTER ) or isotype control antibodies: IgG1-FITC ( 1:100 , BECKMAN COULTER ) and IgG1-PE ( 1:100 , BECKMAN COULTER ) . Stainings were visualized by flow cytometry with BD ACCURI flow cytometer . Cytokine and chemokine concentrations in supernatants of HSF were measured using commercially available ELISA kits for CCL2 ( Peprotech: cat . no . 900-T31 ) , CXCL8 ( Peprotech; cat . no . 900-T18 ) , IL-6 ( Peprotech; cat . no . 900-T16 ) , GM-CSF ( Peprotech; cat . no . 900-K30 ) , and RANKL ( Peprotech; cat . no . 900-K142 ) , according to the manufacturer’s instructions . Samples were analyzed from three to four independent experiments . The Cytotox96 assay from Promega ( cat . no . G1781 ) is a colorimetric-based cytotoxicity assay that quantitatively measures the release of lactate dehydrogenase ( LDH ) from damaged cells . CytoTox 96 Non-Radioactive Cytotoxicity Assay was used following treatment with MTX 1μM and 10μM . After treatments , culture medium was recovered , and then cells were lyzed following the manufacturer's instructions . Released LDH in culture medium was measured for detection of cell damage following treatments . Intracellular LDH ( induced by the addition of the lysis buffer ) was measured for determination of the maximum LDH release . The percentage of cellular injury was calculated using the formula: % cytotoxicity = 100 × experimental LDH release / maximum LDH release . Statistical analyses were performed with GraphPad Prism software version 6 . 01 using a Student unpaired t test . p-values ≤ 0 . 05 were considered statistically significant . Significance was indicated in the figures as follow: p-values ≤ 0 . 05 ( * ) , p-values ≤ 0 . 01 ( ** ) , p-values ≤ 0 . 001 ( *** ) and p-values ≤ 0 . 0001 ( **** ) . Results are expressed as mean ± standard error “SEM” and as percentage .
Control cells ( medium alone ) showed a basal level of cell toxicity as indicated by background levels of LDH released in HSF cell culture supernatants . MTX treatment at the concentration of 1μM and 10μM did not affect significantly the level of LDH release compared to control cells ( Fig 1A ) . As shown in Fig 1B , we observed that MTX treatment did not induce cell shrinking and failed to induce necrotic activities . We have evaluated by Sybergreen qRT-PCR the expression of several antiviral genes . We screened for the expression of RLRs ( RIG-I , MDA5 ) , IFN β and ISG15 . We used GAPDH as a housekeeping gene and to establish the relative expression of each mRNA . Early ( 6h ) and late ( 24h ) regulatory mechanisms were analyzed . In response to PIC stimulation ( Fig 2A ) , the relative expression of RIG-I mRNA was significantly increased in HSF at 6 h ( 1 . 14x10‐1 ± 2 . 99x10-2 , p<0 . 001 ) corresponding to a fold change of 13 . MDA5 was also highly expressed in response to PIC , ( 4 . 61x 10‐1 ± 2 . 63x10-1 , p<0 . 05 ) with a fold change of 262 when compared to control untreated cells . At 24h post PIC treatments , the levels of expression were ( 6 . 89x10-2 ± 3 . 26x10-2 , p<0 . 05 ) for RIG-I ( 10 fold ) and ( 2 . 3x 10‐1 ± 8 . 05x10-2 , p<0 . 05 ) for MDA5 ( 197 fold ) when compared to control conditions . The levels of expression in control cells were for RIG-I: 6 . 71x10-3±4 . 68x10-3; and for MDA5: 1 . 17x 10‐3 ± 2 . 59x10-4 . After MTX 1μM treatment alone , the relative expression of RIG-I and MDA5 was not significantly affected in HSF at 6h . In contrast , the expression of MDA5 was increased at 24h with a fold change of 2 . More importantly , MTX did not affect the expression of RIG-I and MDA5 in response to PIC . The level of IFN β mRNA ( Fig 2B ) was not significantly affected at 6h after PIC . A more significant increase up to 27 fold was observed at 24h ( 3 . 99x 10‐2 ± 3 . 75x10-3 , p<0 . 0001 ) versus ( 1 . 48x 10‐3 ± 6 . 69x10-4 ) in control cells . The relative expression of ISG15 was significantly higher at 6h and 24h in response to PIC ( more than 200 fold at 6h and 800 fold at 24 h ) . MTX did not affect significantly the relative expression of IFN-β and ISG15 in all tested conditions: alone or together with PIC . Chemokines play an important role in the pathogenesis of aseptic and septic arthritides through their ability to recruit and activate a wide range of leukocytes [39] . We therefore decided to evaluate the capacity of dsRNA PIC to induce the expression of CCL2 ( MCP1 ) and CXCL8 ( IL8 ) and the potential of MTX to affect proinflammatory chemokines expression . We have first investigated by qRT-PCR the effects of individual and combined treatments of PIC and MTX on mRNA levels of CCL2 and CXCL8 in HSF ( Fig 3A ) . The relative expression of CCL2 was not significantly affected at 6h after PIC exposure . In contrast , we observed a significant increase up to 20 fold of CCL2 mRNA levels at 24h ( 5 . 8x 10+0 ± 2 . 74x10+0 , p<0 . 01 ) versus ( 2 . 92x 10‐1 ± 1 . 54x10-1 ) in control cells . CXCL8 gene expression was significantly increased at 6h and 24h in response to PIC stimulation . More than 40 fold increase was observed at 6h ( 9 . 98x 10‐1 ± 6 . 47x10-1 , p<0 . 05 ) versus ( 2 . 28x 10‐2 ± 1 . 80x10-3 ) and more than 400 fold increase was observed at 24h ( 9 . 37x 10+0 ± 5 . 75x10+0 , p<0 . 05 ) versus ( 2 . 08x 10‐2 ± 2 . 53x10-2 ) in CXCL8 mRNA levels after PIC exposure . When HSF were treated with MTX alone , no significant difference in CCL2 and CXCL8 mRNA levels was noticed at 6h and 24h . Moreover , MTX did not affect the induction of CCL2 and CXCL8 mRNA levels after PIC treatment . We next decided to investigate whether PIC and MTX treatment can affect CCL2 and CXCL8 protein secretion from HSF . Cells were exposed to PIC in the presence or not of MTX treatment and the production of proinflammatory chemokines CCL2 and CXCL8 in cell culture supernatants was monitored by ELISA . When exposed to PIC ( Fig 3B ) , HSF significantly increased CCL2 and CXCL8 release in cell culture supernatants at 6h and 24h . At 6 hours , CCL2 protein levels were ( 9383pg/mL ± 1893 , p<0 . 001 ) with a fold change of 7 compared to control ( 1279 pg/mL ± 563 ) . PIC treatment also increased CXCL8 ( 456pg/mL ±2 41 , p<0 . 05 ) with a mean fold change of 5 , when compared to control cells ( 84pg/mL ± 1 . 6 ) . At 24 hours , we observed a 6 and 32 fold increases in CCL2 and CXCL8 protein levels , respectively following PIC treatment . After MTX 1μM treatment alone , CCL2 and CXCL8 protein expressions were not affected at 6h and 24h . Moreover , MTX treatment did not modulate the PIC-dependent upregulation of CCL2 and CXCL8 release by HSF . The role of osteoclast formation in arthritis and bone erosion has been well described and it was already reported that HSF played an important role on bone erosion through their ability to secrete a large panel of cytokines such as IL1β , IL-6 , M-CSF , GM-CSF and RANKL [21 , 26 , 40] . We first tested and validated that the expression of three major osteoclastogenic factors by HSF , RANKL , M-CSF and GM-CSF was upregulated in response to IL1β stimulation ( S1 Fig ) [40–42] . As a mean of controlling for IL1β and PIC stimulatory activities on HSF , we tested and validated that both treatments increased the expression of CD55 by HSF as previously described [43] ( S2 Fig ) . Exposure to PIC strongly increased IL-6 gene expression in HSF at 6h and 24h ( Fig 4A ) . IL-6 mRNA levels were ( 6 . 4x 10−2 ± 2 . 16x10-2 , ≤ 0 . 01 ) at 6h and ( 1 . 66x 10+0 ± 2 . 55x10-1 , ≤ 0 . 001 ) at 24h when compared to control cells ( 6h , 2 . 98x 10‐4 ± 2 . 24x10-4 ) ; ( 24h , 4 . 61x 10‐4 ± 7 . 15x10-5 ) . No significant difference in IL-6 mRNA levels was observed after MTX treatment alone or combined with PIC exposure . The upregulation of IL-6 expression after PIC exposure was confirmed at the protein level by ELISA assay at 6h and 24h ( Fig 4B ) : For instance , PIC stimulation significantly increased IL-6 concentration ( 13609 pg/mL ± 1720 , ≤ 0 . 0001 ) compared to unstimulated cells ( 305pg/mL ± 10 ) with a fold change of 45 at 24h after treatment . MTX treatment had no significant effect on IL-6 release in HSF culture supernatant in control cells as well as after PIC stimulation at 6h and 24h . IL1β relative expression was also highly upregulated at 24h in response to PIC stimulation ( 2 . 27x10+0 ± 2 . 92x10-1 , p≤ 0 . 001 ) versus ( 9 . 37x10-4±1 . 8x10-4 ) in control cells , corresponding to more than 2400 fold increase ( S3 Fig ) . M-CSF and GM-CSF , the main factors of monocytes/macrophages survival , were shown to be involved in synovial inflammation and joint destruction [40 , 44 , 45] and higher levels of GM-CSF have been reported to be associated with persistent arthralgia during CHIKV infection [27] . We have evaluated the effects of PIC and MTX treatments on M-CSF and GM-CSF mRNA expression by qRT-PCR and on GM-CSF protein expression by ELISA assay ( Fig 5 and S3 Fig ) . PIC exposure did not affect GM-CSF relative mRNA expression at 6h whereas we observed a significant upregulation in GM-CSF and M-CSF mRNA levels at 24h ( 1 . 32x10‐1 ± 7 . 33x10-2 , , p<0 . 05 ) and ( 1 . 19x 10+0 ± 2 . 49x10-1 , , p<0 . 01 ) , respectively as compared to control cells ( 5 . 87x10-4 ± 1 . 07x10-4 ) and ( 6 . 44x10-2 ± 2 . 18x10-2 ) , respectively . MTX did not significantly affect M-CSF and GM-CSF gene expression when used alone or together with PIC . We next investigated kinetic changes of GM-CSF production in response to PIC stimulation . Protein levels were monitored in HSF culture supernatants from 6h to 72h . We found that GM-CSF production started to increase at 24h ( 85 pg/mL ± 22 , ≤ 0 . 01 ) compared to unstimulated cells ( 16pg/mL ± 1 ) with a fold change of 5 and reached higher levels at 48h ( 176pg/mL ± 9 , ≤ 0 . 0001 ) corresponding to a fold increase of 12 and at 72h ( 302pg/mL ± 116 , ≤ 0 . 05 ) with a fold change of 18 . When HSF were exposed to MTX 1μM treatment alone , no significant effect was observed on GM-CSF release in cell culture supernatant at 6h , 24h , 48h and 72h . MTX treatment did not affect the upregulation of GM-CSF release after PIC exposure . RANKL has been identified as a crucial regulator and promoter of osteoclastogenesis and bone erosion . RANKL is made as a membrane-bound molecule that can be released from the cell surface after proteolytic cleavage by ADAM17 [46 , 47] . We investigated the ability of PIC and MTX to affect RANKL and ADAM17 expression by HSF . RNA levels of RANKL were assessed by qRT-PCR at 6h and 24h after PIC exposure in presence or absence of MTX 1μM treatment . RANKL release in cell culture supernatant was measured by ELISA assay ( Fig 6 ) . Unexpectedly , PIC was not able to induce RANKL mRNA expression in HSF at 6h and 24h . In contrast , ADAM17 gene expression was significantly upregulated by PIC at 24h ( 1 . 61x10+0 ± 4 . 39x10-1 , , p<0 . 05 ) with a fold change of 3 compared to control cells ( 4 . 97x10‐1 ± 2 . 03x10-1 ) ( S3 Fig ) . MTX 1μM treatment alone or combined with PIC did not affect RANKL mRNA levels . We next evaluated RANKL secretion in HSF culture supernatants at 24h , 48h and 72h after PIC 100μg/mL exposure in the presence or not of MTX 1μM . PIC had no effect on RANKL protein levels and MTX did not affect significantly RANKL release in all tested conditions , alone or together with PIC . We next questioned whether MTX treatment could directly affect viral replication in HSF infected by CHIKV following exposure at different MOIs . As shown in Fig 7 , no significant difference in NSP1 and E2 RNA levels was noted after MTX 1μM treatment of HSF exposed to CHIKV . To study the effect of MTX treatment on the antiviral innate immune responses of HSF exposed to CHIKV , we evaluated by qRT-PCR the expression of IFN β and ISG15 antiviral genes at 24H post-infection . In response to CHIKV MOI of 1 , the relative expression of IFN β was significantly increased in HSF at 24H post-infection ( 1 . 54x10‐2 ± 2 . 75x10-3 , p<0 . 01 ) with a fold change of 10 compared to mock-infected control cells ( 1 . 48x10‐3 ± 6 . 69x10-4 ) ( Fig 8 ) . Moreover , CHIKV infection significantly upregulated the expression of ISG15 mRNA at 24H post-infection with a fold change of 14 . MTX 1μM did not affect significantly the relative expression of IFN β and ISG15 in mock-infected control cells as well as after CHIKV MOI1 infection .
We argued that PIC treatment could be used essentially to mimic the effect of viral dsRNA present in the joint during the chronic phase of CHIKV for the following reasons [11] . First , and of critical note , the presence of viral dsRNA in the joint during the chronic phase of CHIKV was revealed in a human clinical case report 18 months post-infection [11] . A similar observation was reported in a macaque model of CHIKV infection , with the persistence of CHIKV RNA up to 44 days post-infection [10] . Using a murine model of a long lasting CHIK infection established by Hawman et al . , the authors detected the release of dsRNA up to 112 days post-infection [48] . In addition , it is now well established that PIC can induce arthritis in 3 days post-treated mice [35] and that dsRNA has been detected in the synovial fluid and serum of patients suffering from RA [42] . While the source of dsRNA was not identified by Bokareva et al . , it was established that patients with erosive disease had significantly higher levels of dsRNA in synovial fluid than patients diagnosed with non-erosive RA . MTX has been initially identified as an anti-metabolite drug . Several reports have described the effects of MTX on the inhibition of synovial fibroblast invasion and proliferation [49 , 50] . To examine whether MTX may induce HSF cell death we used one cytotoxic assay . We demonstrated that MTX treatment at the concentration of 1μM ( therapeutic dose of RA as well as CHIKV-induced chronic arthritis ) or even at 10μM did not cause HSF necrosis . We next studied the effect of PIC on the antiviral response of HSF . First , we observed that HSF constitutively expressed dsRNA receptors , ie . MDA5 and RIG-I . In agreement with our findings , previous studies have shown that FLS expressed MDA5 , and RIG-I [43 , 51] . PIC is an agonist particularly of MDA5 and has been shown to stimulate synovial cells to induce interferon type I production [43 , 52] . We found that the transcription levels of MDA5 and RIG-I were upregulated in response to PIC stimulation . Moreover , PIC induced the expression of high levels of IFNβ . We next questioned whether MTX treatment may affect the aforementioned antiviral immune response . We found that MTX increased MDA5 mRNA levels . However , MTX treatment had no effect on RIG-I , IFNβ and ISG15 mRNA levels . CCL2 and CXCL8 are major chemokines involved in modulating the recruitment of immune cells in the inflamed joint [39] . We demonstrated that HSF constitutively express CCL2 and CXCL8 albeit at low levels . These results are in line with earlier studies showing that synovial fibroblasts display low constitutive expression of CCL2 and CXCL8 [53 , 54] . We observed that PIC significantly upregulated CCL2 and CXCL8 mRNA levels and protein secretion by HSF . Moon et al . showed that PIC stimulation increased CXCL8 expression at both the mRNA and protein levels in RA synovial fibroblasts [55] . Interestingly , CCL2 and CXCL8 were detected in the synovial tissue of RA patients and CXCL8 expression correlated with the development of clinical signs and synovial inflammation [56] . We herein demonstrated that MTX at 1μM did not affect CCL2 and CXCL8 expression in HSF in basal conditions as well as after PIC exposure and hence may not promote inflammatory functions of synovial cells . A plethora of pro-inflammatory cytokines such as IL1β , IL-6 and M-CSF were documented to promote osteoclast differentiation and bone resorption [21] . They may act by increasing production of RANKL , identified as key mediator of osteoclastogenesis , or by inducing the development of osteoclast precursors . Here , we demonstrated that HSF express low mRNA and protein levels of IL-6 in basal conditions and that IL-6 expression was strongly enhanced in response to PIC stimulation . MTX 1μM had no inhibitory effect on IL-6 mRNA expression and protein secretion by HSF stimulated or not with PIC . We also demonstrated that GM-CSF was expressed constitutively at low levels by HSF and its expression was significantly upregulated by PIC . These results are in line with previous studies showing that synovial fibroblasts expressed weakly GM-CSF at basal conditions [40] . The involvement of GM-CSF in joint inflammation and destruction has been demonstrated in animal model of RA [44] . We found that MTX 1μM did not modulate constitutive and PIC-induced GM-CSF expression by HSF . It has been reported that MTX can inhibit GM-CSF production in whole blood culture from RA patients [57] . When applied on human synovial sarcoma cell line , used as an in vitro model of RA , MTX significantly decreased GM-CSF secretion in culture supernatants . However , these results are difficult to compare to ours because MTX was used at much higher concentrations of 0 . 1 and 1mg/mL corresponding respectively to 220 μM and 2200 μM [58] . RANKL is the central mediator of osteoclast development . It is considered as an essential factor for osteoclast activation and survival [23] . Here we demonstrated that our HSF constitutively express RANKL mRNA and protein at low levels . A similar observation was found by Tunyogi-Csapo and colleagues . They demonstrated that HSF express RANKL mRNA and are sources of RANKL production [59] . They also reported that RA HSF may significantly contribute to bone resorption through the modulation of RANKL production in inflamed joints . An interesting finding of our study was the observation that PIC alone was not able to increase RANKL mRNA and protein expression in cultured HSF . IL-6 and IL1β were reported to induce RANKL expression in synovial fibroblasts [41] . Although we found that PIC upregulated IL1β and IL-6 expression , these events did not translate into an increased RANKL expression by HSF . Of note , Kim et al . have found that PIC significantly upregulated RANKL mRNA levels in RA HSF but not in OA HSF and normal skin fibroblasts [60] . We have been using primary HSF and it will be interesting to address the fine mechanisms by which PIC can nevertheless control RANKL expression and which may involve specific signaling pathways present in inflamed RA fibroblasts but not in naïve conditions . We found that MTX at the concentration of 1μM did not modulate RANKL expression in HSF and , hence , may not affect the bone tissue repair mechanisms . We also analyzed the capacity of MTX to modulate CHIKV infection and replication in HSF . We tested CHIKV at different MOI and particularly at very low MOI in order to mimic in situ tissue settings of patients chronically infected . Our data showed that MTX did not affect CHIKV replication . These in vitro data are in agreement with results obtained in mice where it was shown that the CHIKV load was not increased in target tissues when mice were treated with the immunosuppressive drug MTX ( 0 . 3 mg/kg , intraperitoneally ) [61] . In contrast , this situation might be different for another alphavirus as shown in one study by Taylor et al [62] . It was shown that MTX caused a rapid development of severe disease in treated mice with a significant increase in viral titer in sera and quadriceps . It is possible that MTX may have an effect on the infectious process during the acute phase of alphaviral infection . Our paradigm has been to address the role of MTX in chronic settings at the distance from the initial infection and to better address the therapeutic window of MTX which is clearly in chronic but not acute phases of chikungunya . In conclusion , we consider that we have been able to model the context of CHIKV persistence in the joint of patients suffering from chronic injuries using the PIC molecule . Moreover , we have been addressing for the first time the role of the immunosuppressive drug MTX in the overall antiviral , inflammatory and pro-osteoclastogenic responses which may all be in action in the joint of patients suffering from CIR . Scientists and clinicians have been concerned that the immunosuppressive drug could contribute to the resurgence of the virus in patients . Critically , our study revealed for the first time that MTX treatment is likely to be safe and did not affect the antiviral immune and inflammatory responses of HSF . MTX had no modulatory effect on the expression of several pro-osteoclastic cytokines by HSF and which are involved in bone tissue repair . Further studies are warranted to address whether MTX could affect the expression of inflammatory and co-stimulatory molecules involved in the recruitment and activation of T cells and monocytes and which are present in the synovial tissue of patients with CIR post-CHIK . All of our findings are summarized and illustrated in Fig 9 . | Chikungunya is a mosquito-borne virus ( CHIKV ) and has been incriminated in the development of arthralgia ( pain of the joint ) and arthritis particularly in elderly patients . Methotrexate ( MTX ) has been used widely to effectively treat these chronic rheumatic symptoms . Using a model of primary human joint fibroblasts ( HSF ) , we investigated the capacity of the MTX immunosuppressive drug to affect the immune antiviral and inflammatory responses essential to clear the virus while allowing bone tissue repair . This study is important given that CHIKV and its RNA were shown to persist in the joint for months to years post infection and leading to injuries through ill-characterized mechanisms . The molecule PIC was used to mimic the effect of viral RNA . Interestingly , we found that MTX did not affect the expression of several proinflammatory and bone repair factors by HSF . Remarkably , MTX did not also impair the antiviral response of synovial fibroblasts . Our study revealed for the first time that MTX treatment should be considered as safe even in the context of viral persistence associated with chronic inflammation . MTX will not affect the capacity of the synovial tissue to maintain antiviral mechanism , to control inflammation and to promote bone tissue repair . | [
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| 2018 | Immunomodulatory drug methotrexate used to treat patients with chronic inflammatory rheumatisms post-chikungunya does not impair the synovial antiviral and bone repair responses |
Diagnosis of intestinal schistosomiasis in low endemic areas is a problem because often control measures have reduced egg burdens in feces to below the detection limits of classical coproparasitological methods . Evaluation of molecular methods is hindered by the absence of an established standard with maximum sensitivity and specificity . One strategy to optimize method performance , where eggs are rare events , is to examine large amounts of feces . A novel diagnostic method for isolation of Schistosoma mansoni eggs in feces , and an initial evaluation of its performance is reported here . Known amounts of S . mansoni eggs were seeded into 30 g of normal human feces and subjected to a sequence of spontaneous sedimentation , sieving , Ritchie method , incubation and isolation through interaction with paramagnetic beads . Preliminary tests demonstrated the efficacy of lectins as ligands , but they also indicated that the paramagnetic beads alone were sufficient to isolate the eggs under a magnetic field through an unknown mechanism . Eggs were identified by microscopic inspection , with a sensitivity of 100% at 1 . 3 eggs per gram of feces ( epg ) . Sensitivity gradually decreased to 25% at a concentration of 0 . 1 epg . In a preliminary application of the new method to the investigation of a recently established focus in southern Brazil , approximately 3 times more eggs were detected than with the thick-smear Kato-Katz method . The novel S . mansoni detection method may significantly improve diagnosis of infections with low burdens in areas of recent introduction of the parasite , areas under successful control of transmission , or in infected travelers . It may also improve the evaluation of new treatments and vaccines .
In areas with recent introduction of Schistosoma mansoni transmission or areas where control efforts have reduced parasitic burden , the classical parasitological methods for finding eggs in stools do not demonstrate sufficient sensitivity [1] , [2] , [3] . Travelers may have light S . mansoni infections of difficult definitive diagnosis [4] . Molecular diagnostic tools are an alternative detection method , but more extensive validation in areas of low endemicity is still lacking . Very sensitive egg detection systems are desirable as methods for definitive diagnosis and are the standard for evaluation of other indirect diagnostic methods , such as serological tests . Since S . mansoni eggs are large and have a peculiar shape and a lateral spine , they are easily recognized visually , using a microscope , leading to a definitive diagnosis with few possibilities of false-negative results . Increased amounts and/or numbers of fecal samples , the use of differential fecal concentration methods or even application of mathematical modeling have been tested in order to improve sensitivity of parasitological diagnosis , but none of these approaches have been convincing enough to warrant extensive field trials [5] , [6] . Paramagnetic beads coupled to a variety of ligands are available for different applications such as purification of whole cells , organelles , nucleic acids , proteins and other molecules [7] , [8] , and may be used for S . mansoni antigen detection [9] . Magnetic separation has also been applied for parasitological diagnosis , for example , in detection of cryptosporidiosis and giardiasis [10] . Here we report the preliminary evaluation of a highly sensitive method for isolation and detection of S . mansoni eggs from large amounts of feces which is based on their interaction with paramagnetic beads in a magnetic field .
Stock solutions ( 1 mg/mL ) of five biotinilated lectins were prepared with a pH 7 . 6 “lectin buffer” ( 6 . 057 g TRIS , 8 . 7 g NaCl , 0 . 203 g MgCl2 , 0 . 111 g CaCl2 and 0 . 02% sodium azide , per Litre ) [11] . The five lectins ( SIGMA , USA ) , reported in the literature as ligands to the surface of S . mansoni eggs [12] , [13] , [14] , were: Triticum vulgaris ( L5142 ) , Concavalin A ( C2272 ) , Ulex europeaus ( L8262 ) , Arachis hypogea ( L6135 ) , and Lycopersicum esculentum ( L0651 ) . Microtubes containing 100 eggs in 100 µL PBS plus 50 µL of lectins at several concentrations ( 5 µg/mL , 10 µg/mL and 20 µg/mL ) were incubated at room temperature for 1 h . After a washing step with PBS , the volume was adjusted to 1 . 5 mL and paramagnetic beads covered with streptavidin ( Bangs Labs , USA ) were added to a final concentration of 1 . 4% ( v/v ) . Incubation was performed in an orbital shaker , at room temperature , for 1 h . Eggs and beads , without lectins , were incubated as a negative control . The microtubes containing the preparations were connected to a magnet ( Dynal , Oslo , Norway ) for 3 min , supernatants were removed , and the sediments retained at the wall were collected and examined under a microscope for counting of S . mansoni eggs . Several combinations of lectins were also tested under the same conditions and at a final individual concentration of 40 µg/mL . Paramagnetic beads coupled with 1 ) anti-rabbit-IgG ( Dynal , Oslo , Norway ) ; 2 ) a monoclonal antibody anti-Cryptosporidium ( Dynal , Oslo , Norway ) ; 3 ) streptavidin ( Bangs Lab , USA ) ; 4 ) streptavidin followed by biotinylated lectin ( SIGMA , USA ) , were incubated with eggs in distilled water , under the conditions described above . A preparation with eggs and without beads ( negative control ) and another with eggs and a latex ( non-magnetic ) bead coupled to Protein A ( Dynal , Oslo , Norway ) were also tested . Based on the results from the previous experiment , 10 S . mansoni eggs were seeded in 30 g of normal human feces . The fecal sample was suspended and stirred in 250 mL of water , filtered through 8 layers of surgical gauze into a conical cup , left for 1 h at RT , re-suspended in distilled water and the spontaneous sedimentation repeated until a clear supernatant was obtained . Sediment was sieved through 100 ( S1 ) , 200 ( S2 ) and 325 ( S3 ) meshes per square inch metal sieves . The fraction retained at S3 was submitted to the method of Ritchie [15] and the procedure was repeated usually twice or as necessary to get a clear supernatant without a ring of debris . The final sediment was incubated either with or without 20 µg/mL of biotinylated Triticum vulgaris as previously described in the lectin experiments . Paramagnetic beads were coupled with streptavidin ( Bangs Lab , USA ) . Eggs were added at different concentrations ( 60 , 40 , 30 , 20 , 10 , 7 and 3 ) per 30 g of normal human feces , corresponding respectively to 2 . 0 , 1 . 3 , 1 . 0 , 0 . 6 , 0 . 3 , 0 . 2 , 0 . 1 eggs per gram of feces ( epg ) . The fecal samples were processed as described above , except that lectins were not employed and uncoated paramagnetic beads ( BioMag , BM547/7065 , Bangs Lab , USA ) were used to isolate the eggs . The novel method was employed for the first time in the investigation of two infected individuals from a potential new focus of transmission in Porto Alegre , the capital of Brazil's southernmost State . For each sample ( whole evacuation ) 2 thick fecal smears or Kato-Katz method [16] were prepared and 30 g of feces were processed with the new method . The method and kit , including the reagents , magnet and sieves , have been named Helmintex ( patent pending ) . The study protocol was approved by the ethics committee of the Public Health Central Laboratory of Rio Grande do Sul ( LACEN/RS ) . The normal feces used in the seeding experiments were donated by three of the authors ( C G-T , CFT , and JR ) . Informed consent was obtained from the two infected individuals who donated feces for the study .
Table 1 shows the percentage of eggs found in the supernatant ( SN ) and in the pellet ( SD ) formed at the wall of the microtube in contact with the magnet . Both the negative control and the several preparations with lectins contained most of the eggs in the pellet . The results indicated that both negative control and several combinations of lectins were effective at promoting isolation of the eggs ( Table 2 ) . The data in Table 3 were used to compare treatments with different beads , and confirmed that lectins were not essential and that paramagnetic beads were necessary for isolation of the eggs , since latex beads and eggs alone did not migrate to the magnet . The results of the seeding experiment in feces with and without lectins ( data not shown ) , besides confirming that lectins were not essential , demonstrated that isolation of eggs occurred in the presence of fecal sediment . Based on these initial results we decided to use uncoupled beads for future testing . The recovery of seeded eggs in fecal samples is shown in Table 4 . Sensitivity was 100% with egg burdens of 2 and 1 . 3 epg and gradually decreased to 20% as the egg burden was reduced to 0 . 1 epg . The investigation of the new focus in Porto Alegre revealed one infection ( Patient A ) out of 6 individuals of the family and the confirmation of infection in the index case ( Patient B ) , with total numbers of eggs per sample as shown in Table 5 .
The Kato-Katz ( KK ) method is the cornerstone for parasitological diagnosis of S . mansoni infection . The KK method has the advantage of being a simple and inexpensive procedure , which has justified its widespread use in the classical endemic areas of intestinal schistosomiasis [17] . Several efforts have been made to develop more sensitive diagnostic tools , such as immunoassays for detection of S . mansoni antibodies [1] or antigens [18] , PCR [19] methods , and examination of large amounts of feces with isolation of eggs in a Percoll gradient [5] . Molecular methods have not been properly evaluated for sensitivity since current parasitological methods themselves lack sensitivity and few of them have undergone extensive field evaluations [17] . Assays for detection of antigens may not have the expected high sensitivity and specificity when employed for diagnosis in low endemicity areas or in light infections of travelers [18] , [20] . In the southernmost transmission focus of schistosomiasis in Brazil , most of the infected individuals examined had less than 1 epg and were usually diagnosed only after examination of several samples with increasing amounts of feces [2] . Efforts to develop a much more sensitive detection method originated with the idea of collecting and examining the entire evacuation of individuals who are of high epidemiological risk but who have consistently tested negative by coproparasitological examinations . Attempts were made to isolate eggs from large final fecal sediments using sucrose density columns and paramagnetic beads covered with anti-S . mansoni-egg-surface antibodies raised in rabbits without success ( data not shown ) . Using the Helmintex method described here , efficient isolation was achieved when lectins were used as ligands to the paramagnetic beads . Isolation of the eggs was also unexpectedly achieved when only beads and eggs were incubated ( negative control ) , suggesting that ligands were unnecessary for isolation of the eggs . Although a detailed explanation of the mechanism of isolation is lacking , it clearly depends on the magnetic field and the presence of paramagnetic beads since eggs alone did not migrate to the magnet . It is possible that the eggs were carried along with the beads as they aligned themselves with the force of the magnetic field and moved towards the magnet . It is not a highly specific interaction since a substantial amount of fecal debris also migrated towards the magnet . However , the reduction in the volume of final sediment and the concentration of eggs in this sediment appeared to be the basis for success of this very sensitive method . The amount of feces ( 30 g ) used for this test was arbitrarily chosen . It is anticipated that ongoing field tests of the method will demonstrate whether amounts larger than 30 g should be examined . This novel method is far more sensitive then other existing coproparasitological tests . Nevertheless it is expected that additional modifications could be made that may further improve the performance of the Helmintex method . Although Helmintex is a relatively expensive ( US$ 0 . 80 per sample ) and laborious method , it is more sensitive than KK . This method was able to detect 1 . 3 epg with 100% sensitivity while studies of KK indicate that its sensitivity is reduced to 60% at egg burdens lower than 100 epg [3] . Data presented in Table 5 indicated that approximately 30 times more eggs were recovered using Helmintex , than using KK , resulting in recoveries of 91 and 25 for Helmintex compared to 3 and 0 for KK , in patients B and A , respectively . The best performance of the egg hatching method was reported in the literature to have a sensitivity of 100% with 12 epg and 80% with 1 epg [21] . This novel detection method is not meant to replace other classical methods ( e . g . , thick smear KK ) for S . mansoni screening . However , in low endemicity areas it may be part of a series of screening steps including epidemiological surveys with quantification methods of risk behavior , KK and serology . For case-control studies Helmintex will be useful as criterion of uninfected groups and , in travelers , it will improve the ability to make a definitive diagnosis in an ever increasing amount of people returning home after brief contact with transmission foci abroad resulting in a very light infection [3] . It may also serve as an extremely valuable tool to be used as the standard for evaluation of other diagnostic tests and vaccines . | Schistosomiasis mansoni is a parasitic infection that affects approximately 200 million people , mainly in the tropics . The worms live inside the veins of intestines and liver and produce eggs that are eliminated within feces . If the eggs reach water , a ciliated larva is released and enters snails to develop into a larva infective to man and other vertebrates . Most infections evolve without overt disease , but severe intestinal , hepatic , pulmonary and cerebro-medulary dysfunctions may occur after many years . Definitive diagnosis is made through the identification of eggs in stool . Classical diagnostic methods fail to detect infection when the number of eggs is low ( e . g . , in areas where control measures have decreased the intensity of infection or in the case of light infections in travelers who have had only brief exposure ) . A new and very sensitive method is reported here , in which eggs are isolated from large amounts of feces through their interaction with magnetic beads . After incubation with the fecal sediment , eggs co-migrate with the beads towards a magnet attached to the test tube . This improvement in diagnostic methodology will strengthen efforts to control schistosomiasis . | [
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| 2007 | Detection of Schistosoma mansoni Eggs in Feces through their Interaction with Paramagnetic Beads in a Magnetic Field |
Effective immune responses require the directed migration of leukocytes from the vasculature to the site of injury or infection . How immune cells “find” their site of extravasation remains largely obscure . Here , we identified a previously unrecognized role of platelets as pathfinders guiding leukocytes to their exit points in the microvasculature: upon onset of inflammation , circulating platelets were found to immediately adhere at distinct sites in venular microvessels enabling these cellular blood components to capture neutrophils and , in turn , inflammatory monocytes via CD40-CD40L-dependent interactions . In this cellular crosstalk , ligation of PSGL-1 by P-selectin leads to ERK1/2 MAPK-dependent conformational changes of leukocyte integrins , which promote the successive extravasation of neutrophils and monocytes to the perivascular tissue . Conversely , blockade of this cellular partnership resulted in misguided , inefficient leukocyte responses . Our experimental data uncover a platelet-directed , spatiotemporally organized , multicellular crosstalk that is essential for effective trafficking of leukocytes to the site of inflammation .
Directed migration of leukocytes from the vasculature to the site of injury or infection is a prerequisite for effective immune responses . Neutrophils are the first immune cells infiltrating the inflamed tissue , followed by a second wave of inflammatory monocytes ( iMOs ) amplifying the inflammatory reaction [1] . To enter the site of inflammation , leukocytes “roll” on the luminal surface of microvascular endothelial cells before these immune cells stabilize their interactions and adhere to the inner vessel wall . Extensive signaling between arrested leukocytes and the endothelium then triggers adhesion strengthening and leads to intraluminal crawling of these blood cells along the microvasculature in search for suitable sites of extravasation . Subsequently , leukocytes squeeze between adjacent endothelial cells , penetrate the perivenular basement membrane , and subendothelially locomote to gaps between pericytes from where they finally migrate into the interstitial tissue [2–6] . Whereas the basic principles of this multistep cascade have been characterized in the past decades , it remains poorly understood how leukocytes can “find” their sites of extravasation . Platelets are anucleated cell particles released from bone marrow megakaryocytes into the circulation [7] . In addition to their fundamental role in hemostasis , platelets are increasingly recognized as participants in different biological processes including immune responses [8–14] . With respect to the capability of these cells to establish interactions with leukocytes and endothelial cells [15–19] , we hypothesized that platelets guide leukocytes to their site of extravasation . Here , we identified a previously unrecognized role of platelets as pathfinders navigating leukocytes to their exit points in the inflamed microvasculature: upon onset of inflammation , platelets immediately adhere at endothelial junctions in the smallest venular microvessels and capture neutrophils via CD40-CD40L/CD154-dependent interactions . Intravascularly adherent platelets and neutrophils , in turn , cooperatively recruit iMOs to these “hot spots” through a CD40- , CD40L/CD154- , and L-selectin/CD62L-mediated intraluminal interplay of these blood cells . In this cellular crosstalk , ligation of PSGL-1 by P-selectin/CD62P induces ERK1/2 MAPK-dependent conformational changes of surface-expressed leukocyte integrins . Together with ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD106 , PECAM-1/CD31 , and JAM-A , activated integrins subsequently promote the successive extravasation of neutrophils and iMOs to the perivascular tissue . Conversely , blockade of this cellular partnership leads to misguided , inefficient leukocyte responses collectively uncovering a platelet-directed , spatiotemporally organized , multicellular crosstalk that is essential for effective trafficking of leukocytes to the site of inflammation .
In a first approach , we sought to identify the individual sites that are utilized by neutrophils and iMOs to extravasate into the perivascular tissue . For this purpose , endothelial cell interactions of these immune cells were analyzed in the inflamed microvasculature of the cremaster muscle of “monocyte-reporter mice” ( CX3CR1GFP/+ mice; exhibiting fluorescence-labeled monocytes as well as NK cells and T cell subsets [20] ) by using multichannel in vivo microscopy ( S1 Video ) . Classical/inflammatory ( GFPlow leukocytes ) and nonclassical monocytes ( ncMOs; GFPhigh leukocytes ) in CXC3CR1GFP/+ mice were differentiated by their relative fluorescence intensity as described previously [21 , 22] . This experimental approach was validated by in vivo immunostaining with fluorescence-labeled monoclonal anti-Ly6C antibodies demonstrating strong expression of Ly-6C on GFPlow leukocytes ( classical/iMOs ) and the absence of expression of Ly-6C on GFPhigh leukocytes ( ncMOs; S1A Fig ) . In the acute inflammatory response , only about 3% of intravascularly rolling GFP-positive leukocytes were NK1 . 1-positive NK cells or CD3-positive T cells , and approximately 2% of intravascularly adherent GFP-positive leukocytes were positive for NK1 . 1 or CD3 . These data collectively indicate that in postcapillary venules of the acutely inflamed cremaster muscle of CX3CR1GFP/+ mice endothelial cell interactions of GFP-positive leukocytes almost exclusively represent monocyte responses ( S1B Fig ) . In addition , interactions of platelets ( visualized by a fluorescence-labeled anti-GPIbβ antibody ) and neutrophils ( visualized by a fluorescence-labeled anti-Ly-6G antibody ) with endothelial cells were examined in our in vivo microscopy experiments ( Fig 1A and 1B ) . Cytokines such as tumor necrosis factor ( TNF ) , interleukin-1β ( IL-1β ) , or interferon-γ ( IFN-γ ) , and lipid mediators , predominantly elicit neutrophil ( but barely monocyte ) extravasation in the initial inflammatory response ( <6 h; S2 and S3 Figs ) [22] . To enable us to study the involvement of monocytes under acute inflammatory conditions , however , the C-C motif chemokine CCL2 was used as principal inflammatory stimulus in our study . This chemokine potently induces extravasation of both neutrophils and iMOs in a variety of inflammatory disorders [23 , 24] . In this context , it has been shown that CCL2 causes the degranulation of tissue mast cells and induces the synthesis of inflammatory mediators including leukotrienes and platelet activating factor [25 , 26] . This leads to an increase in microvascular permeability and causes enhanced expression of adhesion/signaling molecules on the surface of microvascular endothelial cells ultimately resulting in a massive leukocyte infiltration of the perivascular tissue [22 , 25 , 26] . To proof our key findings under different inflammatory conditions , selected experiments were repeated using the cytokines TNF , IL-1β , or IFN-γ as inflammatory stimulus ( S2 and S3 Figs ) . To ensure a broad affection of the tissue by the inflammatory response , the inflammatory mediators were applied intrascrotally prior to in vivo microscopy . In unstimulated control animals , some rolling , but only few intravascularly adherent , platelets were detected in cremasteric postcapillary venules exhibiting inner diameters of less than 40 μm . Upon stimulation with CCL2 or TNF , however , the number of intravascularly adherent platelets immediately increased , whereas the number of rolling platelets significantly decreased in this vessel segment ( Fig 1A; S2A Fig ) . In detail , more than 95% of firmly adherent platelets were found to colocalize with PECAM-1/CD31-immunoreactive endothelial cell junctions ( Fig 1B; S1 Video ) . Moreover , we observed platelets to be the first cellular blood components arresting at the inflamed endothelium ( within 30 min after onset of inflammation with CCL2 or TNF ) , whereas intravascular adherence of neutrophils ( >60 min after onset of inflammation elicited by CCL2 or TNF ) and iMOs ( >180 min after onset of inflammation; upon stimulation with CCL2 , but not with TNF ) occurred at later time points ( Fig 1C; S2B Fig ) . About 80% of firmly adherent Ly6G-positive neutrophils and more than 90% of firmly adherent iMOs ( represented by GFPlow cells ) were directly attached to endothelial cell junctions ( Fig 1B; S1 Video ) . Remarkably , in postcapillary venules with vessel diameters of more than 40 μm , only low numbers of platelets interacted with the endothelium upon stimulation with CCL2 or TNF ( Fig 1A; S2A Fig ) , whereas platelet–endothelial cell interactions were virtually absent in arterioles . Similar to platelets , endothelial cell interactions and transmigration events of neutrophils ( upon stimulation with CCL2 , TNF , IL-1β , or IFN-γ ) and iMOs ( only upon stimulation with CCL2 or IFN-γ ) in the inflamed microvasculature were predominantly observed in postcapillary venules exhibiting an inner diameter of approximately 25 μm , but not in arterioles . Interestingly , antibody-mediated depletion of platelets almost completely abolished intravascular adherence and subsequent transmigration of neutrophils ( upon stimulation with CCL2 , TNF , IL-1β , or IFN-γ ) and iMOs ( only upon stimulation with CCL2 or IFN-γ ) in this venular vessel segment , whereas leukocyte rolling was consecutively enhanced in larger venules exhibiting diameters of more than 40 μm ( Fig 2A and 2B; S2C and S3 Figs ) . In contrast , the proportion of intravascularly crawling neutrophils and monocytes to total adherent neutrophils or monocytes in response to CCL2 remained unaltered upon platelet depletion ( S4 Fig ) . Furthermore , depletion of neutrophils resulted in a shift of endothelial cell interactions and transmigration events of iMOs from smaller ( <40 μm diameter ) to larger diameter ( >40 μm diameter ) segments of postcapillary venules upon stimulation with CCL2 ( Fig 2C ) . Depletion of platelets or neutrophils ( by 95% compared with controls; S1 Table ) was confirmed in peripheral blood samples . To elaborate the molecular basis of this specific interaction profile of platelets , myeloid leukocytes , and endothelial cells in the CCL2-stimulated microvasculature , we analyzed the expression of key adhesion and signaling molecules on the luminal surface of venular microvessels by using confocal microscopy . In unstimulated control animals receiving an intrascrotal injection of phosphate-buffered saline ( PBS ) , ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD106 , JAM-A , and PECAM-1/CD31 as well as the C-C motif chemokine CCL2 were found to be nearly equally distributed throughout the entire venular microvasculature of the cremaster muscle . Whereas the expression of ICAM-1/CD54 , VCAM-1/CD106 , and CCL2 was significantly increased upon onset of CCL2-elicited inflammation , surface expression of ICAM-2/CD102 , JAM-A , and PECAM-1/CD31 was not significantly altered ( Fig 3A ) . Furthermore , the number of pericytes and perivascular macrophages as well as the number of collagen IV low expression regions ( LERs ) in the perivenular basement membrane ( through which leukocytes pass this section of the venular wall [27–29]; S5 Fig ) was not dependent on the vessel size ( Fig 3B ) . In contrast , wall shear rates decreased in postcapillary vessel segments with increasing inner vessel diameters and reached the highest values in small-caliber venules ( Fig 3A ) . Moreover , von Willebrand factor ( vWF; which serves as an endothelial interaction partner of different platelet receptors , e . g . , GPIIbIIIa ) was predominantly expressed in endothelial junctions of small-caliber venules with diameters of less than 40 μm . Induction of inflammation significantly increased the expression of vWF in these segments of the venular microvasculature ( Fig 4A ) . In a next step , the functional relevance of these adhesion/signaling molecules and their interaction partners on platelets for endothelial cell interactions of platelets in small-caliber segments of the CCL2-stimulated cremasteric microvasculature was analyzed by multichannel in vivo microscopy . To avoid distraction by cumulative effects arising from a priori blockade of a defined protein in the course of the inflammatory response , blocking monoclonal antibodies directed against the different target proteins were administered after ( and not prior to ) the onset of inflammation and in vivo microscopy in the inflamed microvasculature was immediately performed . Whereas blockade of vWF or of its interaction partner GPIIbIIIa significantly reduced the number of intravascularly adherent platelets , inhibition of CD40 , CD40L/CD154 , GPIbα , P-selectin/CD62P , PSGL-1/CD162 , ICAM-2/CD102 , or PECAM-1/CD31 did not significantly alter platelet adherence to the inflamed endothelium ( Fig 4B ) . Interestingly , more than 85% of adherent neutrophils and the majority of adherent iMOs were observed to colocalize with firmly adherent platelets ( Fig 4C ) . Blockade of CD40 , P-selectin/CD62P , or their interaction partners CD40L/CD154 and PSGL-1/CD162 ( but not of ICAM-2/CD102 or PECAM-1/CD31 ) significantly diminished the frequency of stable interactions between intravascularly adherent platelets and neutrophils or iMOs ( Fig 4D ) . To assess the role of these molecules for direct interactions between activated platelets and neutrophils or iMOs , additional in vitro experiments were performed . Platelet activation by adenosine diphosphate ( ADP ) significantly enhanced the number of neutrophils or iMOs binding to platelets . This increase was significantly reduced upon blockade of CD40 or its ligand CD40L/CD154 ( but not upon blockade of P-selectin/CD62P; S6 Fig ) . To further evaluate the effect of interactions between adherent platelets and neutrophils on the activation state of these immune cells , the expression of Mac-1/CD11b and L-selectin/CD62L ( which represent commonly used leukocyte activation markers ) was analyzed on adherent neutrophils in the inflamed microvasculature of the cremaster muscle by multichannel in vivo microscopy . Whereas the expression of Mac-1/CD11b was significantly higher on neutrophils adhering to intravascularly arrested platelets as compared to neutrophils adhering to the inflamed endothelium independently of platelets or adhering to the inner vessel wall in platelet-depleted animals , the expression levels of L-selectin/CD62L on neutrophils did not vary with their relative localization to platelets ( Fig 4E ) . Noteworthy , surface expression of P-selectin/CD62P ( which is expressed by platelets and endothelial cells ) did not differ between endothelial cells , endothelially adherent platelets , and endothelially adherent platelets capturing neutrophils or iMOs ( S7 Fig ) . Firm adherence of leukocytes to the microvascular endothelium is facilitated by interactions between leukocyte integrins in higher affinity conformation and their endothelial binding partners of the immunoglobulin superfamily ( e . g . , ICAM-1/CD54 , VCAM-1/CD106 ) [2–6] . To characterize the consequences of interactions between platelets and neutrophils or iMOs on conformational changes of integrins on the surface of neutrophils and iMOs , the effect of recombinant P-selectin/CD62P , CD40L/CD154 , L-selectin/CD62L , and PSGL-1/CD162 ( which mediate this cellular interplay; see above ) on leukocyte integrin affinity was analyzed . As a measure of conformational changes of leukocyte integrins , the binding capacity of these proteins for their interaction partners ICAM-1/CD54 ( for β2 integrins ) or VCAM-1/CD106 ( for β1 integrins ) was evaluated by flow cytometry . Static exposure to recombinant murine P-selectin/CD62P ( but not to recombinant murine CD40L/CD154 , L-selectin/CD62L , or PSGL-1/CD162 ) significantly enhanced the binding of neutrophils to ICAM-1/CD54 without altering the surface expression levels of β2 integrins . This effect was completely abolished upon blockade of PSGL-1/CD162 or upon inhibition of ERK1/2 MAPK ( by compound FR180204 ) , but not of p38 MAPK ( by compound SB203580 ) or JNK ( by compound SP600125 ) MAPK . In contrast , expression of the β1 integrin VLA-4/CD49d on the surface of neutrophils or binding of neutrophils to VCAM-1/CD106 was not significantly altered upon exposure to recombinant murine CD40L/CD154 , P-selectin/CD62P , L-selectin/CD62L , or PSGL-1/CD162 ( Fig 5A ) . In human neutrophils , exposure to recombinant human P-selectin induced the high-affinity conformation ( but not the extended conformation ) of β2 integrins , which allows binding to ICAM-1/CD54 , as indicated by increased binding of the conformation-specific antibody “mAb 24” ( Fig 5A ) . Using in vivo microscopy on the cremaster muscle of CX3CR1GFP/+ mice , intravascular firm adherence of neutrophils in small-caliber venules was found to be significantly reduced upon blockade of the integrins LFA-1/CD11a or Mac-1/CD11b as well as upon inhibition of their interaction partner ICAM-1/CD54 or of PECAM-1/CD31 . Furthermore , blockade of Mac-1/CD11b and—to a lesser extent—of LFA-1/CD11a or VLA-4/CD49d as well as of ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD106 , PECAM-1/CD31 , or JAM-A significantly decreased the extravasation of neutrophils to the inflamed peritoneal cavity ( Fig 5B ) . In addition to platelets , the majority of intravascularly adherent neutrophils were found to colocalize with adherent iMOs . These neutrophil–monocyte interactions were dependent on L-selectin/CD62L and its ligand PSGL-1/CD162 ( Fig 6A ) . Exposure to recombinant murine P-selectin/CD62P but not to recombinant murine CD40L/CD154 , L-selectin/CD62L , or PSGL-1/CD162 significantly increased the binding capacity of iMOs for ICAM-1/CD54 , whereas the binding capacity for VCAM-1/CD106 was only marginally altered . Similar to neutrophils , P-selectin-elicited ICAM-1/CD54 binding of iMOs was almost completely abolished upon blockade of PSGL-1/CD162 or upon inhibition of ERK1/2 MAPK ( but not of p38 MAPK or JNK MAPK ) . Moreover , exposure to recombinant human P-selectin/CD62P induced the extended and the high-affinity conformation of β2 integrins on the surface of human neutrophils as indicated by increased binding of the conformation-specific antibodies “kim127” ( extended conformation ) and “mAb 24” ( high-affinity conformation; Fig 6B ) . As observed by in vivo microscopy on the cremaster muscle of CX3CR1GFP/+ mice , blockade of the β2 integrin Mac-1/CD11b or its ligand ICAM-1/CD54 significantly reduced the rolling flux of iMOs , whereas intravascular firm adherence of these inflammatory cells was significantly diminished upon blockade of the β1 integrin VLA-4/CD49d or its interaction partner VCAM-1/CD106 ( Fig 6C ) . Extravasation of iMOs to the inflamed peritoneal cavity was significantly attenuated upon blockade of Mac-1/CD11b or VLA-4/CD49d or their interaction partners ICAM-1/CD54 or VCAM-1/CD106 ( but not of ICAM-2/CD102 , PECAM-1/CD31 , or JAM-A ) . To further characterize the nature of platelet-directed leukocyte extravasation , real-time and time-lapse records of multichannel in vivo microscopy experiments in the cremaster muscle of CX3CR1GFP/+ mice were analyzed . In these experiments , intravascularly adherent platelets were found to predominantly interact with neutrophils and iMOs by capturing these immune cells while rolling or crawling in the inflamed microvasculature ( Fig 7A; S2 Video ) . Occasionally , rolling neutrophils ( ~20% ) and iMOs ( ~10% ) were observed to “take up” adherent platelets ( S3 Video ) . Subsequently , neutrophils and iMOs arrested at sites of intravascularly adherent platelets and extravasated from these “hot spots” in the microvasculature to the inflamed tissue in a successive manner: more than 90% of neutrophils and about 80% of iMOs were found to transmigrate in “groups” ( Fig 7B; S4 Video ) . In order to evaluate the effect of platelets on the overall extravasation efficacy of leukocytes in the venular microvasculature , the ratio of transmigrated leukocytes and intravascularly accumulated ( rolling flux and firm adherence ) leukocytes was determined in all venular vessel segments . Antibody-mediated platelet depletion significantly decreased the extravasation efficacy of total leukocytes , neutrophils , or iMOs ( Fig 7C ) . To assure intergroup comparability , systemic leukocyte counts and microhemodynamic parameters including blood flow velocity , inner vessel diameter , and wall shear rate were determined in each experiment . No significant differences were detected among experimental groups studying the functional relevance of different adhesion and signaling molecules for interactions between platelets , neutrophils , iMOs , and endothelial cells ( S2 Table ) .
The directed recruitment of leukocytes to the site of injury or infection is indispensable for effective immune responses [2–6] . Recently , we have demonstrated that the spatiotemporal expression dynamics of selectins initiate the sequential endothelial cell interactions of neutrophils and iMOs in the acute inflammatory response [22] . How these immune cells subsequently “find” their sites of extravasation remains largely unknown . In order to address this principal question , we sought to identify the individual sites that are utilized by neutrophils and iMOs to extravasate into inflamed tissue . Employing in vivo microscopy techniques , we were able to show that both immune cell populations predominantly establish their endothelial cell interactions in vessel segments of postcapillary venules exhibiting an inner diameter of approximately 25 μm and continue their subsequent passage through the vascular wall from the same sites . Since the endothelial expression of key adhesion and signaling molecules required for leukocyte extravasation , the structural composition of the vascular wall , and the distribution of macrophages in the perivascular tissue were found to be largely homogenous throughout the entire venular microvasculature , particularly the high shear stress present in small-caliber venules might favor the extravasation of neutrophils and iMOs at these vessel sites . This assumption is in line with previous in vitro data predicting shear stress to be supportive for interactions of leukocytes with endothelial cells in the microvasculature [30 , 31] . In addition to shear stress , however , platelets have recently been reported to contribute to the recruitment of leukocytes [10 , 11 , 13–16] . Here , we demonstrate that upon onset of inflammation , platelets are the first cellular blood components adhering to the endothelial surface of postcapillary venules , immediately before neutrophils and iMOs sequentially arrest in the same vascular regions . Interestingly , adherent platelets were almost exclusively detected at junctions of adjacent endothelial cells , which represent the sites from where neutrophils and iMOs initiate their transmigration process . With regard to these observations , we hypothesized that adherent platelets serve as “sentinels” defining the exit points of leukocytes in the microvasculature . In our experiments , we found that more than 85% of intravascularly adherent neutrophils and the majority of adherent iMOs were directly associated with adherent platelets . Platelet depletion completely abolished the directed extravasation of neutrophils and iMOs from these confined sites in low-diameter microvessels . Similarly , depletion of neutrophils resulted in random endothelial cell interactions and transmigration events of iMOs scattered throughout the entire venular microvasculature . Hence , our findings uncover a previously unrecognized role of platelets as “pathfinders” guiding leukocytes to their microvascular exit points . Since these events were observed under various inflammatory conditions , platelet-directed guidance of leukocytes to their site of extravasation might represent a more general inflammatory phenomenon . Towards a more comprehensive , mechanistic understanding of this process , we systematically screened a variety of candidate adhesion molecules [32–42] for their functional relevance in mediating interactions of platelets with endothelial cells and neutrophils in this particular context . We were able to show that vWF , whose expression is pronounced in endothelial junctions of activated small-caliber venules , and GPIIbIIIa ( but not GPIbα ) , which represent interaction partners of this protein on the surface of platelets , were indispensable for intravascular adherence of platelets at these sites . These data confirm recent observations as the early recruitment of platelets to inflamed liver sinusoids occurred via GPIIbIIa , but independently of GPIbα [12] . Moreover , we found that interactions of platelets and neutrophils strictly require CD40 , P-selectin/CD62P , and their ligands CD40L/CD154 or PSGL-1/CD162 , of which only CD40 and CD40L/CD154 were directly involved in mediating firm adherence of neutrophils to platelets . Our results extend recent reports describing a role of CD40 for interactions between platelets , endothelial cells , and leukocytes in atherosclerosis [43] . In this context , it is interesting that P-selectin/CD62P and PSGL-1/CD162 initiate signaling events in leukocytes [44–46] . Furthermore , circulating platelets have recently been observed to be capable of activating leukocytes ( in 20%–30% of leukocyte–platelet interactions ) [11] . Consequently , intravascularly adherent platelets might not only determine the microvascular exit points of neutrophils but might also actively promote the extravasation process of these immune cells at these sites . Confirming this hypothesis , we found that neutrophils recruited to intravascularly adherent platelets exhibit a higher activation state as compared to neutrophils arresting on the microvascular endothelium in the absence of platelets . Specifically , ligation of PSGL-1/CD162 by P-selectin/CD62P under these static conditions induced the high-affinity conformation of β2 integrins on the surface of neutrophils ( as opposed to the induction of the extended conformation of β2 integrins in rolling leukocytes [44 , 47 , 48] ) via ERK1/2 MAPK-dependent signaling events . Moreover , neutrophil extravasation at the sites of adherent platelets was almost completely inhibited upon blockade of the integrins LFA-1/CD11a , Mac-1/CD11b , and VLA-4/CD49d or ICAM-1/CD54 , ICAM-2/CD102 , PECAM-1/CD31 , VCAM-1/CD106 , and JAM-A . In summary , our data indicate that intravascularly adherent platelets capture neutrophils through CD40 and CD40L/CD154 . These events , in turn , enable P-selectin/CD62P to induce the high-affinity conformation in neutrophil β2 integrins via PSGL-1/CD162 and ERK1/2 MAPK thereby promoting the passage of these immune cells through the vessel wall . Secretion products of emigrating neutrophils have been documented to facilitate the sequential recruitment of iMOs under prolonged or chronic inflammatory conditions [1] . In the initial inflammatory response , however , we found that intravascularly adherent platelets and neutrophils cooperatively recruit iMOs through CD40- , CD40L/CD154- , P-selectin/CD62P- , L-selectin/CD62L- , and PSGL-1/CD162-dependent interactions , of which only CD40 and CD40L/CD154 directly promoted adherence of iMOs to platelets . Ligation of PSGL-1/CD162 by P-selectin/CD62P , in contrast , led to the induction of the extended and high-affinity conformation of β2 integrins on the surface of iMOs via induction of ERK1/2 MAPK-dependent signaling events . Monocyte LFA-1/CD11a , Mac-1/CD11b , and VLA-4/CD49d together with their endothelial interaction partners ICAM-1/CD54 and VCAM-1/CD106 subsequently consolidated firm adherence of iMOs at the sites of intravascularly adherent platelets and neutrophils and promoted the extravasation of these immune cells to the inflamed tissue . Having deciphered the mechanisms underlying platelet-directed leukocyte recruitment to their sites of extravasation , we sought to characterize the biological relevance of this process . It is well documented that platelets are critically involved in the pathogenesis of acute inflammatory conditions such as ischemia- , reperfusion- , transfusion- , thermally , or acid-induced injury and sepsis [11 , 15 , 16 , 49–52] as well as of chronic inflammatory pathologies including autoimmune disorders [53] or atherosclerosis [54] . With regard to these reports and our present findings , we proposed that platelet-directed leukocyte trafficking enhances the efficacy of immune cell recruitment to the site of inflammation . The breaching of the venular basement membrane is considered as the “rate-limiting” step in leukocyte extravasation , which has to be accomplished by the first leukocytes encountering this vascular structure [55] . Accordingly , we found that in the absence of platelets , the overall recruitment of leukocytes to the inflamed tissue is severely compromised , as myeloid leukocytes interacted with the venular wall at multiple sites in the microvasculature . In contrast , platelet-directed extravasation of neutrophils and iMOs promoted successive , uninterrupted extravasation of these immune cells in “groups” by focusing leukocyte diapedesis to few , confined microvascular spots . Thus , these data clearly demonstrate that platelet-directed guidance of leukocytes to distinct sites of extravasation increases the efficacy of the leukocyte recruitment process thereby enabling effective immune responses . To what extent platelets that only transiently interact with endothelial cells ( e . g . , intravascularly rolling platelets ) or with adherent leukocytes as well as platelets taken up by rolling leukocytes contribute to leukocyte extravasation , however , cannot clearly be stated . In conclusion , our experimental data assign a previously unrecognized role to platelets as “pathfinders” guiding leukocytes to their exit points in the inflamed microvasculature: upon onset of inflammation , platelets immediately arrest at distinct sites in venular microvessels enabling these cellular blood components to capture neutrophils and , in turn , iMOs . The cellular crosstalk arising from these interactions leads to an activation of surface-expressed leukocyte integrins , which subsequently promotes the successive extravasation of neutrophils and iMOs from these “hot spots” to the perivascular tissue . Blockade of this cellular partnership results in misguided , inefficient leukocyte responses collectively uncovering a platelet-directed , spatiotemporally organized , multicellular crosstalk that is essential for effective trafficking of leukocytes to their sites of inflammation . These findings define platelet-directed guidance of leukocytes to confined sites of extravasation as a critical step in the recruitment process of immune cells ( Fig 8 ) , which might emerge as a promising therapeutic target for the prevention and treatment of inflammatory pathologies .
According to the guidelines of the local governmental authorities ( “Regierung von Oberbayern” ) , animal welfare was ensured . All animal experiments were approved by the local governmental authorities ( “Regierung von Oberbayern” ) . For all surgical procedures , animals were anesthetized with ketamine and xylazine ( see below ) . Male C57BL/6 mice were purchased from Charles River ( Sulzfeld , Germany ) . Male CX3CR1GFP/+ mice were generated as described previously and backcrossed to the C57BL/6 background for six to ten generations [56] . All experiments were performed using mice at the age of 10 to 12 wk . Animals were housed under conventional conditions with free access to food and water . The experiments were performed according to German legislation for the protection of animals and approved by the local government authorities . Recombinant murine CCL2 ( 0 . 3 μg in 400 μl PBS i . s . ; R&D Systems , Nordenstadt , Germany ) , TNF ( 0 . 5 μg in 400 μl PBS intrascrotally ( i . s . ) ; R&D Systems ) , IL-1β ( 0 . 05 μg in 400 μl PBS intrascrotally ( i . s . ) ; R&D Systems ) , or IFN-γ ( 1 . 0 μg in 400 μl PBS intrascrotally ( i . s . ) ; R&D Systems ) was used to induce expression of endothelial adhesion/signaling molecules and/or ( subsequent ) leukocyte recruitment . An anti-Ly-6G monoclonal antibody ( mAb; clone 1A8; 150 μg intravenously ( i . v . ) ; 24 h and 6 h prior to induction of inflammation; BD Biosciences , San Jose , CA , US ) was used for the depletion of neutrophils . An anti-GPIbα ( CD42b ) mAb ( clone Xia . B2; 50 μg i . v . ; 24 h and 6 h prior to induction of inflammation; emfret Analytics , Eibelstadt , Germany ) was used for the depletion of platelets . Platelet–endothelium and platelet–leukocyte interactions were analyzed upon administration of blocking Abs and inhibitors: anti-vWF polyclonal Ab ( 100 μg in 100 μl PBS intraarterially ( i . a . ) ; Dako , Dako Deutschland GmbH , Hamburg , Germany ) , anti-GPIbα Fab-fragment ( clone Xia . B2; 60 μg in 100 μl PBS i . a . ; emfret Analytics ) , GR 144053 ( GPIIbIIIa antagonist; 0 . 25 mg in 100 μl PBS i . a; Tocris Bioscience , Bristol , UK ) , anti-CD154 mAb ( clone MR1; 50 μg in 100 μl PBS i . a . ; eBiosciences ) , anti-CD40 mAb ( clone 1C10; 50 μg in 100 μl PBS i . a . ; eBiosciences ) , anti-CD62P mAb ( clone RB40 . 34; 50 μg in 100 μl PBS i . a . ; BD Biosciences ) , anti-CD162 mAb ( clone 4RA10; 50 μg in 100 μl PBS i . a . or i . v . ; BD Biosciences ) , or anti-CD31 mAb ( clone MEC 13 . 3; 50 μg in 100 μl PBS i . a . ; BD Biosciences ) . Leukocyte responses were analyzed upon administration of blocking mAbs: anti-CD11a mAbs ( clone M17/4; 50 μg in 100 μl PBS i . a . ; Biolegend , San Diego , CA , US ) , anti-CD11b mAb ( clone M1/70; 50 μg in 100 μl PBS i . a . ; Biolegend ) , anti-CD49d mAb ( clone R1-2; 50 μg in 100 μl PBS i . a . ; Biolegend ) , anti-CD54 mAb ( clone YN1/1 . 7 . 4; 50 μg in 100 μl PBS i . a . ; Biolegend ) , anti-CD102 mAb ( clone 3C4; 50 μg in 100 μl PBS i . a . ; Biolegend ) , anti-CD106 mAb ( clone 425; 50 μg in 100 μl PBS i . a . ; Biolegend ) , anti-CD31 mAb ( clone MEC 13 . 3; 50 μg in 100 μl PBS i . a . ; BD Biosciences ) or anti-JAM-A mAb ( clone BV-11; 50 μg in 100 μl PBS i . a . ; Merck Millipore , Darmstadt , Germany ) . Classical/iMOs ( and neutrophils ) were labeled with anti-Ly6C PE mAb ( clone HK1 . 4 , 5 μg in 100 μl PBS i . a . ; Biolegend ) , NK cells were labeled with an anti-NK1 . 1 PE mAb ( clone PK136 , 5 μg in 100 μl PBS i . a . ; eBioscience ) , and T cells were labeled with an anti-CD3 PE mAb ( clone 17A2 , 5 μg in 100 μl PBS i . a . ; Biolegend ) . Neutrophils were labeled with an anti-Ly6G PE mAb ( clone 1A8; 5 μg in 100 μl PBS i . a . ; BD Biosciences ) , platelets were visualized by a rat IgG derivative against the GPIbβ subunit of the murine platelet/megakaryocyte-specific GPIb-V-IX complex ( X649; DyLight 649-labeled , noncytotoxic and not interfering with platelet adhesion and aggregation in vitro and in vivo; 3 μg in 100 μl PBS i . a . ; emfret Analytics ) [57 , 58] . Endothelial junctions were visualized with a nonblocking PE-labeled anti-CD31 mAb ( clone 390; 3 μg in 100 μl PBS i . a; eBiosciences ) . Animals were assigned randomly to the following groups: In a first set of experiments , CX3CR1GFP/+ mice received neutrophil or platelet depleting mAbs or isotype control antibodies prior to 360 min of i . s . stimulation with recombinant murine CCL2 , TNF , IL-1β , IFN-γ , or injection of PBS ( n = 3 per group ) . In further experiments , CX3CR1GFP/+ mice received an i . a . injection of anti-vWF Ab , anti-GPIbα Fab fragment , GR 144053 , anti-CD40 mAb , anti-CD40L/CD154 mAb , anti-P-selectin/CD62P mAb , anti-PSGL-1/CD162 mAb , anti-PECAM-1/CD31 mAb , isotype control antibodies/Fab fragments/drug vehicle prior to 6 h of i . s . stimulation with recombinant murine CCL2 ( n = 4 per group ) . In a next series of experiments , CX3CR1GFP/+ mice received an i . a . injection of blocking mAbs directed against LFA-1/CD11a , Mac-1/CD11b , VLA-4/CD49d , ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD106 , PECAM-1/CD31 , or JAM-A or isotype control antibodies after 6 h of i . s . stimulation with recombinant murine CCL2 ( n = 5 per group ) . Finally , C57BL/6 mice received an i . v . injection of mAbs directed against LFA-1/CD11a , Mac-1/CD11b , VLA-4/CD49d , ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD106 , PECAM-1/CD31 , or JAM-A 6 h prior to intraperitoneal stimulation with CCL2 ( n = 7 per group ) . For the analysis of PECAM-1 , ICAM-1/CD54 , ICAM-2/CD102 , VCAM-1/CD102 , JAM-A , CCL2 , or vWF expression on endothelial cells of postcapillary venules , for the analysis of collagen IV expression in the venular basement membrane as well as for the analysis of the pericyte distribution in postcapillary venules , excised mouse cremaster muscles ( 6 h after intrascrotal injection of CCL2 or PBS ) were fixed in 2% paraformaldehyde . Tissues were then blocked and permeabilized in PBS , supplemented with 10% goat serum ( Sigma Aldrich ) and 0 . 5% Triton X-100 ( Sigma Aldrich ) . After incubation at 4°C for 12 h with antibodies directed against PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology , Dallas , Texas , US ) and CD54 ( rat anti-mouse; Biolegend ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and CD102 ( rat anti-mouse; Biolegend ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and CD106 ( rat anti-mouse; Biolegend ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and JAM-A ( rat anti-mouse; Merck Millipore ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and vWF ( rabbit anti-human; Dako ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and CCL2 ( rabbit polyclonal; abcam , Cambridge , UK ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and α-SMA ( rabbit antimouse; abcam ) , PECAM-1 ( CD31 goat IgG; Santa Cruz Biotechnology ) and collagen IV ( rabbit anti-mouse; abcam ) tissues were incubated for 180 min at room temperature with an Alexa Fluor 633-linked donkey antigoat antibody ( molecular probes ) and then with an Alexa Fluor 488-linked goat antirat antibody ( molecular probes ) or with an Alexa Fluor 633-linked donkey antigoat antibody ( molecular probes ) and an Alexa Fluor 488-linked chicken antirabbit antibody ( molecular probes ) . Immunostained tissues were mounted in PermaFluor ( Beckman Coulter , Fullerton , CA ) on glass slides . Confocal z-stacks typically covering 30 μm ( z-spacing 0 . 5 μm ) were acquired using a Leica SP5 confocal laser-scanning microscope ( Leica Microsystems , Wetzlar , Germany ) with an oil-immersion lens ( Leica; 63x; NA 1 . 40 ) . The fluorescence signal of CD31 , CD54 , CD102 , CD106 , JAM-A , vWF , or CCL2 was only quantified on the surface of endothelial cells of postcapillary venules not measuring the fluorescence signal of these adhesion and signaling molecules on leukocytes and platelets ( easily identified by expression of PECAM-1 and morphological characteristics ) , and the background signal was subtracted . To analyze the effect of CD40L/CD154 , P-selectin/CD62P , L-selectin/CD62L , or PSGL-1/CD162 on the expression profiles of LFA-1/CD11a , Mac-1/CD11b , or VLA-4/CD49d on murine neutrophils and monocytes , anticoagulated whole blood samples were incubated ( 30 min; 37°C ) with recombinant mouse CD40L/CD154 ( 10 μg/ml; PeproTech , Rocky Hill , New Jersey , US ) , P-selectin/CD62P ( 0 . 01 , 0 . 1 , 1 , or 10 μg/ml; R&D Systems ) , recombinant mouse L-selectin/CD62L ( 0 . 01 , 0 . 1 , 1 , or 10 μg/ml; R&D Systems ) , recombinant mouse PSGL-1/CD162 ( 0 . 01 , 0 . 1 , 1 , or 10 μg/ml; R&D Systems ) , or PBS as negative control . Additionally , the effect of CCL2 or PMA on the expression profiles of LFA-1/CD11a , Mac-1/CD11b , or VLA-4/CD49d on murine blood leukocytes was analyzed by incubation ( 30 min; 37°C ) with CCL2 ( 100 ng/ml; R&D Systems ) , PMA ( 50 ng/ml; Sigma Aldrich , St . Louis , US ) , or PBS as negative control . After washing , cells were incubated with primary antibodies directed against CD45 , CD11b , GR-1 , CD115 , CD62L , CD44 , and CD162 on ice . Isotype-matched controls were used in all experiments . After lysis of erythrocytes , stained cells were analyzed on a flow cytometer ( Gallios , Beckmann Coulter ) . Approximately 20 , 000 gated events were collected in each analysis . For the analysis of integrin activation , murine peripheral blood cells were isolated from male C57BL/6 mice , anticoagulated , and suspended in Hanks balanced salt solution containing 1 mM CaCl2 and MgCl2 ( Life Technologies , Carlsbad , US ) . Cells were exposed to PMA ( 50 ng/ml , Sigma-Aldrich ) , CCL2 ( 100 ng/ml , R&D Systems ) , or PBS as negative control , in the presence of ICAM-1/Fc ( 10 μg/ml , R&D Systems ) , VCAM-1/Fc ( 10μg/ml , R&D Systems ) , and PE-conjugated antihuman IgG1 ( Fc-specific , Southern Biotechnology ) for 5 min at 37°C . In further experiments , cells were treated with a blocking mAb against PSGL-1/CD162 or isotype control antibody , SB203580 ( 25 μM; Sigma Aldrich ) , SP600125 ( 5 μM; Sigma Aldrich ) , FR180204 ( 15 μM; Sigma Aldrich ) or vehicle . After washing , cells were labeled with antibodies directed against CD45 , CD11b , CD115 , and Gr1 . Binding of ICAM-1 or VCAM-1 was measured by a flow cytometer ( Gallios , Beckmann Coulter ) . The results were analyzed with FlowJo Software ( Treestar ) . Moreover , binding of neutrophils or iMOs to platelets was measured by flow cytometry after incubation of murine peripheral blood with the potent platelet-activating substance ADP ( 10 μM; 10 min 37°C; Sigma Aldrich , St . Louis , Missouri , US ) or PBS as well as with blocking monoclonal antibodies directed against CD62P , CD40L/CD154 , PECAM-1/CD31 , ICAM-2/CD102 , or isotype control antibodies . Myeloid leukocyte subsets were identified by their expression of CD45 , CD11b , GR-1 , and CD115 ( see above ) . Platelets were stained with a primary anti-GPIIb/IIIa antibody ( clone JON/A; emfret Analytics , Eibelstadt , Germany ) . After lysis of erythrocytes , stained cells were analyzed on a flow cytometer ( Gallios , Beckmann Coulter ) . Approximately 20 , 000 gated events were collected in each analysis . Binding of neutrophils or iMOs to ADP-activated platelets was measured by determining the number of JON/A-positive and -negative neutrophils or iMOs . In a next set of experiments , anticoagulated whole human blood was obtained from healthy human donors , stimulated with recombinant human P-selectin/CD62P ( 10 μg/ml; R&D Systems ) or PBS . Neutrophils , nonclassical ( CD14+ CD16++ ) , intermediate ( CD14++ CD16+ ) , and classical/iMOs ( CD14++ CD16- ) were distinguished by size and granularity as well as by their expression of CD45 , CD16 , and CD14 . Binding of mAb24 ( detection of high-affinity conformation of β2 integrins; mouse anti-human , monoclonal; abcam ) or KIM127 ( detection of extended conformation of β2 integrins; mouse antihuman , monoclonal; gift from M . Sperandio ) was measured by flow cytometry . Data analysis was performed with a statistical software package ( SigmaStat for Windows; Jandel Scientific ) . Rank Sum test ( two groups ) or ANOVA-on-ranks followed by the Dunnett test ( > two groups ) were used for the estimation of stochastic probability in intergroup comparisons . Mean values and SEM are given . p-Values < . 05 were considered significant . | White blood cells ( leukocytes ) are the effector cells of the immune system . The movement ( extravasation ) of leukocytes from the bloodstream to the surrounding tissue is a prerequisite for proper host defense . Platelets are anucleate cell particles that circulate in the blood and play a fundamental role in hemostasis . Here , we report a previously unrecognized function of platelets as "pathfinders" guiding leukocytes to their site of extravasation . Upon onset of the inflammatory response , platelets were found to immediately adhere to specific sites in the smallest venular microvessels . At these "hot spots" , platelets capture intravascularly crawling neutrophils and , in turn , inflammatory monocytes . The cellular crosstalk arising from these interactions leads to conformational changes of distinct adhesion molecules on the surface of leukocytes , subsequently promoting the extravasation of these immune cells to the inflamed tissue . Conversely , blockade of this cellular partnership leads to misguided and inefficient leukocyte responses . Thus , platelet-directed guidance of leukocytes to confined sites of extravasation appears to be a critical step in the recruitment process of immune cells , which might emerge as a promising therapeutic target for the prevention and treatment of inflammatory pathologies . | [
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| 2016 | Platelets Guide Leukocytes to Their Sites of Extravasation |
Human cyclophilin A , or CypA , encoded by the gene peptidyl prolyl isomerase A ( PPIA ) , is incorporated into the HIV type 1 ( HIV-1 ) virion and promotes HIV-1 infectivity by facilitating virus uncoating . We examined the effect of single nucleotide polymorphisms ( SNPs ) and haplotypes within the PPIA gene on HIV-1 infection and disease progression in five HIV-1 longitudinal history cohorts . Kaplan-Meier survival statistics and Cox proportional hazards model were used to assess time to AIDS outcomes . Among eight SNPs tested , two promoter SNPs ( SNP3 and SNP4 ) in perfect linkage disequilibrium were associated with more rapid CD4+ T-cell loss ( relative hazard = 3 . 7 , p = 0 . 003 ) in African Americans . Among European Americans , these alleles were also associated with a significant trend to more rapid progression to AIDS in a multi-point categorical analysis ( p = 0 . 005 ) . Both SNPs showed differential nuclear protein-binding efficiencies in a gel shift assay . In addition , one SNP ( SNP5 ) located in the 5′ UTR previously shown to be associated with higher ex vivo HIV-1 replication was found to be more frequent in HIV-1-positive individuals than in those highly exposed uninfected individuals . These results implicate regulatory PPIA polymorphisms as a component of genetic susceptibility to HIV-1 infection or disease progression , affirming the important role of PPIA in HIV-1 pathogenesis .
As an obligate intracellular parasite , HIV type 1 ( HIV-1 ) utilizes host cell factors for its replication . Human cyclophilin A ( CypA ) , also known as peptidyl prolyl isomerase A ( PPIA ) , is a ubiquitous cytoplasmic protein ( by convention , we refer to the protein as CypA and the gene as PPIA ) . CypA has long been known for its incorporation into HIV-1 virions and its important role in facilitating HIV-1 replication in host cells [1 , 2] . CypA is a member of the cyclophilin family , members of which all possess peptidyl-prolyl cis/trans isomerase activity . Peptidyl prolyl cis/trans isomerases ( PPIAses ) catalyze the cis/trans isomerization of prolyl peptide bonds and are believed to be involved in protein folding [3] . The incorporation of CypA into the HIV-1 virion capsid is mediated through the direct binding between prolyl peptide bond located in a proline-rich loop of the fourth and fifth helices of the HIV-1 capsid and the active sites of CypA [4 , 5] . Disruption of CypA incorporation , either by HIV-1 Gag mutations or by cyclosporine A , an immunosuppressive drug that prevents HIV-1 Gag binding to CypA , leads to an attenuation of HIV-1 infectivity [2 , 6] . Braaten and Luban found that HIV-1 replication was decreased in CypA-null human CD4+ T cells , in which the gene encoding CypA ( PPIA ) was deleted through homologous recombination [7] . CypA is therefore an important host factor that regulates HIV-1 replication . Recently , the role of CypA in HIV-1 has gained even greater attention with the discovery of a fusion protein of CypA and TRIM5α , a host restriction factor against HIV-1 [8] , which confers HIV-1 resistance in owl monkey [9–11] . Both TRIM5α and CypA recognize and act on the capsid of HIV-1 , but apparently confer opposite effects . TRIM5α restricts HIV-1 by promoting premature disassembly of HIV-1 capsid [12] , while CypA increases viral infectivity by facilitating proper uncoating . Although the interaction between CypA and TRIM5α is still unclear , it appears that the modulation of HIV-1 infectivity by CypA is independent of TRIM5α [11 , 13–15] . It has been postulated that binding of CypA to capsid protects HIV-1 from an unknown restriction factor in humans [15] . The study of the influence of human gene variation on susceptibility to HIV-1 infection and progression is an approach that may reveal the in vivo host factor HIV-1 interactions and their epidemiologic importance at the population level . With this approach we have identified several AIDS-modifying variants in genes TRIM5 [16] , APOBEC3G [17] , and CUL5 [18] that encode human innate HIV-1 restriction factors or related proteins . In a recent study performed in the Swiss HIV Cohort Study , a variant of PPIA was implicated as a potential factor affecting HIV-1 disease progression [19] . As CypA is an essential human protein for completion of the HIV-1 life cycle , we have assessed the influence of genetic variation in the PPIA gene on HIV-1 infection and disease progression in five United States–based HIV-1 natural history cohorts .
The PPIA gene is approximately 7 kb in length , consisting of five exons ( Figure 1A ) . Using PPIA-specific primers we resequenced virtually the entire PPIA gene to screen for single nucleotide polymorphisms ( SNPs ) in 92 European Americans ( EA ) and 92 African Americans ( AA ) . Seven polymorphisms were discovered: three in the putative promoter region , one in the 5′ UTR , one in the 3′ UTR , one in intron 4 , and one in the second Alu repeat region upstream of the putative promoter . No SNPs were found in the coding regions . These SNPs and four additional SNPs available from dbSNP database located in regions not detected by our sequencing were selected for genotyping in the AIDS cohorts ( Figure 1 and Table 1 ) . SNP1 genotypes deviated from the frequencies expected under the Hardy-Weinberg equilibrium ( p < 0 . 001 ) , probably due to its location in the Alu repetitive element; thus , SNP1 was excluded in the analysis . SNP2 was also excluded from analysis due to its rarity . The genotypic frequencies of all other SNPs conform to Hardy-Weinberg expectations . Allele frequency distributions of PPIA SNPs differed in EA and AA with Fst values ranging from 0 . 03 to 0 . 40 ( average 0 . 22 ) . The difference is particularly pronounced for SNPs 7 , 8 , 9 , 10 , and 11 ( Fst , 0 . 30–0 . 40 ) , where the minor alleles are reversed in the two populations ( Figure 1 ) . The extent of linkage disequilibrium ( LD ) among eight SNPs was assessed by calculating all pairwise D′ values , separately for AA and EA ( Figure 2A and 2B ) . Strong LD was observed among almost all SNPs in AA ( D′ range 0 . 87–1 . 0 ) and even stronger LD in EA ( D′ range 0 . 97–1 . 0 ) . SNP3 and SNP4 were in perfect LD in both populations ( D′ = 1 , r2 = 1 ) . As each of these two SNPs carries the same information content , SNP4 was selected as a proxy for SNP3 in the analysis . The eight SNPs in both population groups formed a single LD block as defined by the solid spine of LD method [20] . This provided a justification to use all eight SNPs spanning the entire region as one block for subsequent haplotype-based association analyses . Among AA , SNP6 presented intermediate level of LD ( D′ ~0 . 60 ) with the SNPs downstream ( SNP8–11 ) ; SNP6–SNP8 recombinant G-C haplotype had a frequency of 5% . Among EA , SNP5 had weak LD with SNP6–11 ( D′ 0 . 47–0 . 71 ) ; the recombinant haplotype SNP5–SNP6 ( G-G ) had a frequency of 2 . 5% . This suggests an occurrence of recombination in the region between SNP5 and SNP8 . The presence of multiple Alu elements in PPIA may have introduced the recombination event by promoting genomic instability [21] . Haplotype structure and maximum likelihood haplotype frequencies were estimated with the EM algorithm . There were , in total , eight haplotypes with minor allele frequency >1% in either population ( Figure 1B ) . Five or six haplotypes were present in EA or AA , respectively , accounting for >97% of total sampled chromosomes . Among these , only three common haplotypes ( minor allele frequency >5% ) were shared between AA and EA ( hap1 , 2 , and 4 ) . Diverse distributions of haplotype frequencies between AA and EA were seen as for SNPs . We compared the PPIA SNP allele and haplotype frequency distributions among three groups with increasing resistance to HIV-1 infection: seroconverters ( SCs ) , seronegatives ( SNs ) belonging to an HIV-1 risk group , and those with documented high-risk exposures to HIV-1 who remain uninfected ( HREU ) ( Table 2 ) . The minor allele of SNP5 ( G ) was carried in 13 . 7% of SCs , 13 . 5% of SNs , and 9 . 5% of HREU , respectively ( Mantel-Haenszel trend test , p = 0 . 21 ) . The reduced carriage of SNP5G in HREU was significant when SCs were compared to HREU ( odds ratio = 1 . 78 , p = 0 . 02 ) , suggesting that the SNP5G allele carriers may have increased susceptibility to HIV-1 infection ( Table 2 ) . No distortion of frequency distribution between risk groups were observed for all other SNPs in AA or EA ( unpublished data ) . We analyzed AA and EA separately since the SNP frequencies differed between the two groups . The Cox proportional hazards model was employed to test the potential differential impact of SNPs on the rates of progression to CD4 <200 or to AIDS clinical diseases ( Table 3 ) . To minimize the number of SNPs to be tested , we only tested the unique SNPs represented in each population , i . e . , only one SNP was tested for those in perfect LD ( r2 = 1 ) . Six and four SNPs were analyzed in AA and EA , respectively . Two SNPs in AA ( SNPs 4 and 5 ) and three SNPs in EA ( SNPs 4 , 5 , and 7 ) showed significant or near-significant effects ( Table 3 ) . Among AA SCs , the minor allele of SNP4 ( the G allele ) was associated with accelerated loss of CD4+ T cells ( relative hazard [RH] = 3 . 7 , 95% confidence interval [CI] = 1 . 59–8 . 63 ) in a Cox proportional hazards model ( Table 3 ) . After stratifying by cohort in the Cox regression analysis , this association became slightly stronger ( RH = 4 . 08 , 95% CI = 1 . 71–9 . 70 ) . Kaplan-Meier survival analysis presented a clear separation of curves stratified for the SNP4 C/C and C/G genotypes ( G/G absent ) on progression to CD4 <200 in AA ( p = 0 . 002 , log-rank test , Figure 3 ) . Notably , all SNP4 carriers progressed to CD4 <200 within 6 y . The effect on AIDS-1987 was in the same direction but short of significance ( Table 3 ) . Identical results were seen for SNP3 ( unpublished data ) . Among EA SCs , a trend of accelerated progression to AIDS-1987 was also observed , though short of significance ( RH = 1 . 34 , p = 0 . 07 , Table 3 ) . To affirm the observed genetic influence of SNP4 , we performed a categorical analysis of HIV-1-infected people that included seroprevalents ( SPs ) in the long-term survivors ( Figure 4 ) . SPs who had remained AIDS-free for greater than 7 . 5 y for AA or 10 y for EA after study enrollment ( the median time to AIDS for each ) were included in this analysis . The frequency distributions of SNP4 in six discrete time intervals progressing to disease outcome after HIV-1 infection were tested for statistical trend using a Mantel-Haenszel test . As shown in Figure 4 , the SNP4 G allele occurred more frequently among fast progressors in both AA and EA . With an increased sample size of 389 patients in AA , a significant trend toward rapid CD4 loss is still preserved ( p = 0 . 04 , Figure 4A ) . Among 970 EA patients , the trend toward rapid progression to AIDS was more pronounced ( p = 0 . 004 ) ( Figure 4B ) . Thus , the results from SC plus SP groups corroborate the findings from the SC-alone survival analyses , indicating that the accelerating effect of SNP4 is consistent in both AA and EA populations . In a Swiss Caucasian HIV-1 cohort , SNP5G , referred to as 1650A/G [19] , was reported to be associated with a rapid CD4 cell depletion and with a trend toward higher in vitro HIV-1 replication [19] . In our analysis of EA SCs , SNP5G showed a nonsignificant trend toward rapid progression to AIDS-1987 ( RH = 1 . 26 , p = 0 . 08 ) , but had no effect on CD4 cell depletion , while an opposite trend was observed in AA ( Table 3 ) . In a categorical analysis of combined samples from SCs and SPs , SNP5G also had no impact on both outcomes in both EA and AA ( p > 0 . 23 , unpublished data ) . Therefore , this study offers no convincing evidence for SNP5 association with disease progression in either EA or AA . In the haplotype analysis , we first used the Cox regression model to test the global null hypothesis of no association between haplotype frequency and AIDS progression , by comparing a model with all haplotypes to the covariants-only model . This revealed that the global distributions of haplotype frequencies were significantly or near-significantly different for the outcome of CD4 <200 in AA ( p = 0 . 01 ) and AIDS-87 in EA ( p = 0 . 08 ) . The distortion of frequency distribution was mainly attributable to one haplotype ( Hap7 ) ( Table 4 ) . Hap7 , the only haplotype carrying SNP4G ( Figure 1 ) , was associated with accelerated progression to AIDS in both AA and EA ( Table 4 ) , consistent with the results from SNP analysis . Because TRIM5α interferes with and CypA facilitates post-entry uncoating of the HIV-1 capsid , it is possible that there may be genetic interactions between two genes . Potential interactions were tested only between variants in two genes with functional plausibility or high strength of association . Specifically , PPIA SNP4 was tested for its interaction with TRIM5-rs16934386 and TRIM5-R136Q , which was previously reported using the same patient population to be associated with HIV-1 infection but not progression [22] . In the Cox regression model analysis , we observed no obvious interaction for infection or progression ( p > 0 . 15 , unpublished data ) . This suggests that CypA and TRIM5α likely act independently , consistent with recent in vitro observations [11 , 13–15 , 23] . As SNP3 , 4 , and 5 reside in the regulatory region of PPIA , we performed gel shift assays to assess whether these SNPs differentially bind to transcriptional factors ( Figure 5 ) . In this experiment , ~25-bp DNA probes containing either of the SNP alleles were incubated with the nuclear extracts from Th1 cytokine-stimulated human T lymphocytes . Both SNPs showed differential binding affinity to nuclear protein ( s ) with the same mobility ( Figure 5A , top band ) . Averaged from two independent experiments , a 4-fold decrease or 2 . 8-fold increase in the band density was observed for the minor allele of SNP3 ( G ) and SNP4 ( G ) , respectively . The cold unlabeled oligonucleotides with minor alleles were unable to compete with wild-type oligonucleotides , suggesting the bindings are specific . These results indicate that these two SNPs within the promoter alter the binding affinity with certain transcriptional protein ( s ) . On the contrary , no obvious differential binding was observed for SNP5 ( Figure 5A ) . In silico analysis of the sequence at these three SNP sites by using TESS software also predicted differential binding for SNP3 and 4 , but not for SNP5 . SNP3 and 4 were each located in one of the consensus-binding motifs of transcription factor SP1 , as shown in Figure 5B . The local sequence with SNP3C is identical to the consensus sequence of SP1 site , GGGGCC , whereas SNP3C>G change perturbs this motif . In contrast , the sequence with SNP4C has two mismatch sites compared to an alternative consensus sequence of SP1 site , GAGGCGGGGC , whereas SNP4C>G reduced the mismatch to one , predicting a stronger binding . There is , therefore , a plausible biochemical basis for the differential binding affinity observed in the gel shift experiment . These results also suggest that SNP3 and SNP4 affect transcription , likely through their interactions with SP1 . The sequence of the region of human PPIA promoter resided in by SNP3 and SNP4 was compared to the corresponding sequence from other species ( Figure 5C ) . SNP3 and SNP4 are both located in the highly conserved regions . The C allele of SNP3 is present in both primates and rodents . The C allele of SNP4 occurs only in primates and the minor allele G occurs in rodents , suggesting a possible association with speciation . These interspecies data support the hypothesis that SNP3 and SNP4 changes may have functional consequences .
The role of CypA in promoting HIV-1 replication has been well established through extensive in vitro experiments [1 , 2 , 6 , 7] . In this study , we have undertaken a systematic investigation of the association between genetic variations in the PPIA gene and susceptibility to HIV-1 infection and disease progression in HIV-1 natural history cohorts . We found that two promoter variants in perfect LD , SNP3 and SNP4 , in PPIA , were associated with rapid HIV-1 disease progression . This effect was revealed for CD4 cell depletion or for AIDS progression , reflecting early or late stage of disease progression , in AA and EA , respectively . The racial difference in the strength or timing of the effects may be due to interactions with other sequence variations that are distributed differently in two populations or to immunologic and viral variables . We further found that both SNPs had differential binding efficiency to nuclear proteins in the gel shift experiment . These findings suggest that the functional promoter SNP3/SNP4 influences HIV-1 disease progression . PPIA in human encodes a 165-amino acid protein that is highly conserved across species , with 100% and 96% sequence identity to rhesus monkey and mouse , respectively . The nucleotide sequence of PPIA is 98% identical between human and chimpanzee . No variation in the coding sequence of human PPIA was found in this study or in the dbSNP database . This suggests that differential genetic impact of PPIA SNPs and haplotypes on infection and progression are likely due to regulatory region variation affecting protein levels . The regulation and promoter function of human PPIA is still undefined . PPIA is highly expressed in almost all cell types and is considered a housekeeping gene . Sequence analysis revealed that PPIA promoter contains a TATA box and two SP1 binding sites and is highly GC-rich ( ~70% ) with overrepresented CpG dinucleotides [24] . In this study , we identified two SNPs , each located in two additional SP1 binding elements . SP1 is generally believed to function as a transactivating factor for many housekeeping genes [25] . These two SNP changes , SNP3G and SNP4G , always occurring together through LD , had opposite effects on the nuclear protein binding . Assuming the predicted stronger SP1 binding effect of SNP4 is dominant over the weaker SP1 binding of SNP3 , a higher PPIA gene expression would be predicted . This would be consistent with more rapid disease course as observed in this study . Based on our analyses of eight SNPs covering the entire gene region of PPIA and their haplotypes , we found that the PPIA disease-modifying effects were most likely afforded by promoter SNP3 or SNP4 and the associations with the downstream SNPs 7–11 were largely due to tracking of these two SNPs through LD . In a previous report , Bleiber et al . studied the ex vivo HIV-1 infectivity in CD4+ T cells from healthy individuals carrying the SNP4 and SNP5 alleles ( named as PPIA-1604 and PPIA-1650 , respectively , in the Bleiber's report ) , as well as their impact on the CD4 gradient ( from CD4+ T-cell counts 500/mm3 to 200/mm3 ) among SPs enrolled in the Swiss HIV cohort , largely comprised of Caucasians [19] . In the ex vivo experiment , both SNPs showed nonsignificant tendencies toward greater HIV-1 replication , with the effects of SNP5 being more pronounced . In the population study , both SNPs were associated with faster CD4+ T-cell depletion in a model also containing two other candidate genes ( model 2 ) ; with the SNP5 of PPIA effect again being more pronounced . Thus , the detrimental effect of SNP4 observed from these two studies is largely consistent . In contrast , no evidence supporting a role of SNP5 in AIDS progression was found , although the SNP5 G allele was carried significantly less often in HREU than in SC but was similarly distributed in SC and SN . Thus , the role of CypA polymorphism in HIV-1 infection remains inconclusive and warrants further investigation . Taken together , the association and functional results from these two studies point to a role of PPIA genetic variation in HIV-1/AIDS . In future studies , it may be informative to determine the mechanism of association between PPIA variants and host susceptibility to HIV-1 infection or disease progression in view of the interaction between CypA and the capsid domain of the Gag polyprotein . Population-based genetic association studies provide a powerful approach to uncovering host genes that confer disease susceptibility or resistance . This approach has led to identification of various genetic factors that affect HIV-1/AIDS , and has provided unique insights into the host HIV-1 interaction [26 , 27] . The successful identification of true genetic associations requires a large sample size ( to minimize the type II error ) , replication in independent studies ( to ward off type I error ) , and plausible functional evidence ( to infer causal relationship ) . In this study , the plausible genetic-modifying role of PPIA is supported by replication in two independent ethnic groups and the demonstration that SNPs 3 and 4 are each associated with altered binding affinities to transcription factors . Moreover , the previous independent study also provides supportive association and functional evidence [19] . Our comprehensive analysis of nearly all variant alleles and their haplotypes in the PPIA gene is , in essence , equivalent to a gene-based approach [28] . A major problem facing genetic association studies is the difficulty in replication , particularly for single SNP associations . One reason for failure to replicate previous studies is due to potential differences in allele frequencies and LD structure across populations . Gene-based replication has recently been advocated as a gold standard for replication studies [28] . The entire gene with prior SNP association is considered the functional unit and is examined for association with effectively all genetic variation in the gene . Nonreplication due to population differences may be minimized as local allele frequencies and LD structure from study populations are considered with this approach [28] . Thus , our comprehensive survey of PPIA genetic variation and LD structure may facilitate future comparisons of replication studies using a gene-based approach . In summary , through genetic epidemiological and functional approaches , we have identified two promoter SNPs in PPIA as potential genetic modifiers of HIV-1 disease progression . Our findings corroborate the notion that genetic variation of PPIA influences AIDS pathogenesis and provide in vivo evidence that CypA is a critical host protein in interaction with HIV-1 . Manipulation of PPIA may be considered as a plausible option for anti-HIV-1 therapeutic development as previously explored [29] .
Study participants were enrolled in five United States–based natural history HIV/AIDS cohorts . AIDS Link to the Intravenous Experience ( ALIVE ) is a community-based cohort of intravenous injection drug users in Baltimore enrolled in 1988–1989 [30] , consisting of 92% AA; Multicenter AIDS Cohort Study ( MACS ) is a longitudinal prospective cohort of men who have sex with men ( MSM ) from four United States cities: Chicago , Baltimore , Pittsburgh , and Los Angeles , enrolled in 1984–1985 [31] , consisting of 83% EA and 10% AA; the San Francisco City Clinic Study ( SFCC ) is a cohort of MSM originally enrolled in a hepatitis B study in 1978–1980 [32] , consisting of 96% EA; Hemophilia Growth and Development Study ( HGDS ) is a multicenter prospective study that enrolled children with hemophilia who were exposed to HIV-1 through blood products between 1982 and 1983 [33] , consisting of 72% EA and 11% AA; the Multicenter Hemophilia Cohort Study ( MHCS ) is a prospective study that enrolled persons with hemophilia [34] , consisting of 90% EA and 6% AA . The participant group is comprised of HIV-1 SCs ( infected after study enrollment ) , SPs ( infected at study enrollment ) , SNs , and HREU . The MACS , MHCS , SFCC , and ALIVE cohorts consist of both SCs and SPs among HIV-1-infected individuals . Due to the potential frailty bias ( missing the most rapid progressors to AIDS and death ) among SPs , only SCs from these four cohorts were used in the survival analysis . SPs were also included for allele frequency estimation , haplotype inference , and disease categorical analysis . The number of participants studied in each risk or disease category was as follows: SC = 654 EA , 295 AA; SN = 604 EA , 350 AA; HREU = 153 EA , 81 AA; and SP = 1199 EA , 773 AA . Of 290 AA SCs , 237 , 42 , 5 , and 5 were from ALIVE , MACS , MHCS , and HGDS , respectively . The date of seroconversion after study enrollment was estimated as the midpoint between the last seronegative and first seropositive HIV-1 antibody test; only individuals with less than 2 y elapsed time between the two tests were included in the seroconverter progression analysis . The censoring date was the earliest of the date of the last recorded visit , or December 31 , 1995 for the MACS , MHCS , HGDS , and SFCC or July 31 , 1997 for the ALIVE cohort to avoid potential confounding by highly effective anti-retroviral therapy ( HAART ) . The censoring date was extended in the ALIVE cohort because of delayed uptake of HAART in this group [30 , 35] . HIV-1-uninfected individuals were classified into two categories based on individual's documented exposure levels to HIV-1 . HREU individuals were those 80 AA and 145 EA with documented high-risk exposure through sharing of injection equipment [36] , who had anal receptive sex with multiple partners [37] , or transfusions with Factor VIII clotting factor prior to 1984 , when heat treatment was initiated [38] . SN individuals ( n = 420 and 571 , respectively , for AA and EA ) are those enrolled in the cohorts who remained HIV-seronegative despite ongoing or prior risk activity . The study protocols were approved by the Institutional Review Boards of participating institutions and informed consent was obtained from all study participants . Nucleotide polymorphisms were discovered in a panel of 92 EA and 92 AA , representing the extremes of the distribution for rapid and slow progression to AIDS and HREU . A nonisotopic RNA cleavage assay following PCR was employed to screen for polymorphisms [39] . PPIA has a high degree of homology to multiple processed pseudogenes that varies from 75% to 95% in the exon regions . PPIA also contains six copies of Alu repeats , each with a length of approximately 300 bp , located in the immediate upstream of the putative promoter region and introns [24] . Sequence comparison and BLAST search were performed to select PPIA-specific PCR primers . Overlapping primers covered nearly the entire PPIA gene region including the putative promoter region , 5′ and 3′ UTRs , all five exons , as well as the Alu repeat regions . A part of intron 1 was not covered due to the high GC content . Primer sequences are presented in Table S1 and are numbered according to the GenBank DNA sequence X52851 . Additional intronic SNPs were selected from NCBI dbSNP ( http://www . ncbi . nlm . nih . gov/SNP ) and HapMap databases ( http://www . hapmap . org ) , by considering location , spacing , and allele frequency at least 10% . Haplotype tagging ( ht ) SNPs were given preference in the SNP selection . The ancestral allele state of the SNPs was based on a reference chimpanzee sequence . Genotyping was performed using PCR-restriction fragment length polymorphism ( PCR-RFLP ) assay or TaqMan assays . PCR-RFLP was carried out with 35 cycles of denaturing at 94 °C for 30 s , annealing at 60 °C for 30 s , and extension at 72 °C for 45 s . The PCR product was digested with respective restriction enzymes ( New England Biolabs , http://www . neb . com ) overnight and then separated on 3% agarose gels . TaqMan assays were obtained from the Assay-by-Demand service of Applied Biosystems ( http://www . appliedbiosystems . com ) . Genotyping primers and conditions were presented in Table S2 . Eight water controls were included on each plate to monitor the potential PCR contamination , and 10% of SC and HREU samples were genotyped twice . The genotypes obtained were free of water contamination or of inconsistencies between duplicates . To assess the difference in allele frequency distribution in two populations , we performed a contingency χ2 test for each marker to test the null hypothesis that the allele frequencies are the same in the two populations . Fst values were estimated by Wier and Cockerham's method [40] . Pairwise LD was quantified using the absolute value of D′ . Absolute values of D′ range from 0 for independence to 1 for complete LD between the pairs of loci . LD plots were generated utilizing Haploview ( http://www . broad . mit . edu/mpg/haploview ) [20] . A triangular matrix of D′ value was used to demonstrate LD patterns within AA and EA . Haplotype blocks were estimated using the solid spine of LD method [20] . Haplotype blocks were defined with a default algorithm based on confidence intervals of D′ [41] , or the solid spine of LD method , which creates blocks of SNPs that have contiguous pairwise D′ values of greater than 0 . 8 . With the latter method , the first and last SNPs in a block are in strong LD with all intermediate SNPs , but the intermediate SNPs are not necessarily in LD with each other [20] . Eight SNP haplotype frequencies were inferred separately for each population by means of an expectation-maximization algorithm [42] . Association analyses were conducted using the statistical packages SAS ( version 9 . 0 , SAS Institute , http://www . sas . com ) . EA and AA groups were analyzed separately because allele and haplotype frequencies were quite different between the two groups . Conformity to the genotype frequencies expected under Hardy-Weinberg equilibrium was examined for each SNP . The genetic effects of SNPs on HIV-1 infection susceptibility were assessed by comparing allelic and genotypic frequencies between HIV-1 HREU and HIV-1 SC participants using the chi square or Fisher's exact test . Regardless of the exposure route , persons in the HREU or SN groups were at risk to HIV-1 infection based on their inclusion in a HIV-1 risk group; therefore , we combined participants across cohorts to achieve a reasonable statistical power . Kaplan-Meier survival statistics and the Cox proportional hazards model ( Cox model ) were used to assess the effects of SNPs and haplotypes on the rate of progression to AIDS . Two separate endpoints reflecting advancing AIDS pathogenesis were considered for SCs: ( 1 ) HIV-1 infection plus a decline of CD4+ T-cell counts <200 cells/mm3 ( CD4 <200 ) ; ( 2 ) the 1987 Center for Disease Control definition of AIDS ( AIDS-87 ) : HIV-1 infection plus AIDS-defining illness [43] . The significance of genotypic associations and RH was determined by unadjusted and adjusted Cox model regression analyses . For each SNP , we compared the minor allele genotypes to the most common genotype as a reference group . All p-values were two-tailed . Genetic factors previously shown to affect progression to AIDS were pre-determined to be included as confounding covariates in the Cox model analysis: CCR5 Δ32 , CCR2-64I , CCR5-P1 , HLA-B*27 , HLA-B*57 , HLA-B*35Px group ( including HLA-B*3502 , B*3503 , B*3504 , and B*5301 ) , and HLA Class I homozygosity for EA ( reviewed in [26 , 27] ) ; HLA-B*57 and HLA Class I homozygosity for AA . CCR2-64I , HLA-B*27 , and HLA-B*35Px were not considered as covariates in AA due to no or weak effects in the AA participants , and CCR5 Δ32 was not considered due to its rarity in AA . Analyses were stratified by sex and by age at seroconversion: 0–20 , >20–40 , and >40 y [27] . Further stratification by cohort was also performed for the exploratory analyses . In the stratified Cox regression model , the overall log-likelihood of hazards obtained is the sum over strata of the stratum-specific hazards , as estimated by the method of partial likelihood . As the same criteria for determining startpoints ( seroconversion date ) and endpoints and the similar sampling strategy and follow-up settings were used across cohorts , we combined SCs from all cohorts for the survival analysis to increase the power . Although these cohorts differ in routes of HIV-1 transmission , no appreciable effect of mode of infection on AIDS progression has been found through re-analysis of more than ten thousands of SCs from 38 studies around the world ( including three used in this study ) [44] . To test the association of PPIA haplotypes and HIV-1 disease progression , we first performed a global test in the Cox regression model for each of two disease outcomes separately for AA and EA . The global null hypothesis is that the odds ratios of all haplotypes are equal between cases and controls . Likelihood ratio tests were used to compare a full model with all haplotypes and a base model with only covariates . When the significance of the global test exceeded a relaxed nominal level , p < 0 . 10 , the associations of individual haplotypes were further tested . To assess the level of correction factor for the number of SNPs , assuming this was a discovery study , we applied spectral decomposition analysis [45] . This multiple testing correction method assesses the equivalent level of independent SNPs taking account of the extensive LD across PPIA . Based on spectral decomposition analysis of SNPs in this study , a corrected p-value of 0 . 01 would be equivalent to p = 0 . 05 . As this study was a confirmation and extension study of markers with previous positive association , uncorrected p-values were reported . Cell culture and an electrophoretic mobility-shift assay were performed as described [46] . Freshly explanted human T lymphocytes were obtained from normal donors , purified by isocentrifugation , and activated for 72 h with 1 mg/ml PHA in RPMI 1640 medium containing 10% FCS ( Sigma , http://www . sigmaaldrich . com ) , 2 mM L-glutamine , and penicillin-streptomycin ( 50 IU/ml and 50 mg/ml , respectively ) . T lymphocytes were made quiescent by washing and incubating for 24 h in RPMI 1640 medium containing 1% FCS before exposure to cytokines . Cells were then stimulated with 100 nmol/L IL-4 ( PeproTech , http://www . peprotechec . com ) or 100 nmol/L human rIL-2 ( Hoffmann-La Roche , http://www . roche . com ) at 37 °C for 10 min . Cell pellets were frozen at −70 °C . The probe sequences were 5′-ttgcgggcggggCcgaacgtggtat-3′ for SNP3C and ttgcgggcggggGcgaacgtggtat-3′ for SNP3G; 5′- ggcgggaggcCaggctcgtgccgtt for SNP4C , and 5′- ggcgggaggcGaggctcgtgccgtt-3′ for SNP4G allele; 5′-aggaaaaccgtgtActattagccatggt-3′ for SNP5A and 5′-aggaaaaccgtgtGctattagccatggt-3′ for SNP5G ( SNP site is capped ) . In cold oligonucleotide competition assay , 100-fold excess of cold unlabeled probe was added as a competitor . The band density was measured by using the software ImageJ ( http://rsb . info . nih . gov/ij/index . html ) . The sequence in and around the SNP sites in the promoter region were searched for the presence of transcription factor binding sites using the program TESS: Transcription Element Search System ( http://www . cbil . upenn . edu/tess ) . Sequence alignment was performed using program ClustalW ( http://www . ebi . ac . uk/clustalw ) .
The Entrez Gene databank ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=gene ) accession numbers for the genes mentioned in this paper are PPIA ( 5478 ) and TRIM5 ( 85363 ) . The GenBank ( http://www . ncbi . nlm . nih . gov ) accession numbers for the human and chimpanzee PPIA genomic sequence are X52851 and NW_001237949 , respectively . The Transfac binding site ( http://www . cbil . upenn . edu/cgi-bin/tess/tess ) accession number is R04195 and R03868 for the SP1 factor . | Individual risk of acquiring HIV type 1 ( HIV-1 ) infection and developing AIDS is not equal; some people are more prone to HIV/AIDS than others . Susceptibility to HIV-1/AIDS is likely determined by a combination of environmental , viral , and host genetic factors . Genetic variations in host cellular factors involved in HIV-1 cell entry , replication , and host defense have been found to affect susceptibility to HIV-1/AIDS . In this report , we focused on the gene PPIA that encodes cyclophilin A , a human cellular protein that is incorporated into the HIV-1 virion and promotes viral replication . We studied genetic variation in the PPIA gene in persons with different susceptibility levels to HIV-1 infection or different rates of disease progression . We found that individuals who processed two functional variants in the promoter region of PPIA had higher risk of CD4+ T-cell loss or progression to AIDS-defining diseases . We also observed that an additional variant occurred more frequently in HIV-1-infected individuals compared to HIV-1-exposed , but uninfected , individuals . These results suggest that genetic variation in PPIA may influence host susceptibility to HIV-1 infection or disease progression and targeting PPIA might provide therapeutic benefit . | [
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| 2007 | Regulatory Polymorphisms in the Cyclophilin A Gene, PPIA, Accelerate Progression to AIDS |
Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution . However , these experiments deal with individual enzymes and explore a tiny part of the fitness landscape . We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding . This model mimics the essential features of the interactions between amino acids , is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins . We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes . Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones . The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus , these landscapes are substantially robust to mutation . The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness . We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding .
One of the most intriguing questions in evolutionary biology is: to what extent evolution is deterministic and to what extent it is stochastic and hence unpredictable ? In other words , what happens if “the tape of evolution is replayed:” are we going to see completely different outcomes or the constraints are so strong that history will be repeated [1]–[4] ? If evolution is envisaged as movement of a population across a fitness landscape , the question can be reworded more specifically: among the numerous trajectories connecting any two points on the landscape , what fraction is accessible to evolution ? Until recently , these remained purely theoretical questions as experimental study of fitness landscapes in the actual sequence space was impractical , due both to the technical difficulty of producing and assaying numerous expressed sequence variants and to the more fundamental problem of defining an adequate quantitative measure of fitness . However , recent experimental studies of fitness landscapes could potentially shed light on the problem of evolutionary path predictability . The most thoroughly characterized feature of empirical fitness landscapes is the structure near a peak . In experiments that examine the peak structure , a high fitness sequence is typically subjected to either random mutations or an exhaustive set of mutations at a small number of important sites . The resulting library of mutants is then assayed to measure a proxy of fitness [5]–[9] . Significant sign epistasis ( a situation in which the fitness effect of a particular mutation can be either positive or negative depending on the genetic context ) has been observed . Deviations from the additive fitness model have been found to be independent of the genetic context and purely random [10]–[13] . Because these studies characterize only a small region of the landscape , they cannot be used to address the question of path predictability . Another broad class of experiments probes the evolutionary trajectories from low to high fitness . Usually , in such experiments , a random peptide is subjected to repeated rounds of random mutagenesis and purifying selection [8] , [14]–[17] . During this process fitness grows with each generation and eventually stagnates at a suboptimal plateau . The characteristics of the fitness growth as well as the dependence of the plateau height on the library size can be used to classify landscapes [18] . A quantitative comparison to the model of random epistatic landscapes ( is the number of sites in an evolving sequence and is the number of sites that affect the fitness contribution of a particular site through epistatic interactions ) can even yield quantitative estimates of and [19] , [20] . The directed evolution studies explore the evolutionarily accessible portion of the landscape and could in principle be used to shed light on the question of path predictability . However , the inaccessible regions of the landscape remain unexplored and the volume of data at this point is insufficient to obtain quantitative conclusions regarding path predictability . A different type of landscapes has been explored in various microarray experiments where protein-DNA ( RNA ) binding affinity serves as the proxy for fitness [21] , [22] . These experiments produce vast , densely sampled landscapes . A comparison with a sophisticated Landscape State Machine model of a correlated fitness landscapes yields estimates of the model parameters [23] , [24] . The DNA binding landscapes , in principle , contain the information required for the analysis of path statistics , and could be a valuable resource for advancing the understanding of evolutionary path predictability . Empirical studies that exhaustively sample a region of the fitness landscape allow one to actually assess the accessibility of the entire set of theoretically possible evolutionary trajectories in a particular ( small ) area of the fitness landscape . For example , all mutational paths between two states of an enzyme , e . g . , the transition from an antibiotic-sensitive to an antibiotic resistant form of -lactamase [25]–[27] or the transition between different specificities of sesquiterpene synthase [28] have been explored . The results of these experiments , which out of necessity explore only short mutational paths of amino acid replacements , suggest that there is a substantial deterministic component to protein evolution: only a small fraction of the possible paths are accessible for evolution [25] , [29]–[31] . Recent analyses of fitness data have revealed dense networks of genetic and molecular interactions responsible for the substantial ruggedness and sign epistasis of empirical fitness landscapes [13] , [32] . The emerging quantitative analysis of fitness landscapes can shed light on some of the most fundamental aspects of evolution but the interpretation of the currently available experimental results requires utmost caution as only a minuscule part of the sequence space can be explored , and that only for a few more or less arbitrarily selected experimental systems . Here we focus on the question of the predictability of mutational paths which is intimately tied to the ruggedness/smoothness of the fitness landscapes . The study of random landscapes of low dimensionality revealed an intuitively plausible negative correlation between the roughness of a landscape and the availability of pathways of monotonic fitness [33] . In the same study , Carneiro and Hartl showed that experimentally characterized landscapes are significantly smoother than their permuted counterparts and exhibit greater peak accessibility [33] . To gain insights into the structure of the fitness landscapes of protein evolution and in particular the accessibility of mutational paths we used a previously developed simple model of protein folding and evolution [34] . The key assumption of this model , which is based on the concept of misfolding-driven evolution of proteins [35]–[37] , is that the fitness of model proteins is determined solely by the number of misfolded copies that are produced before the required abundance of the correctly folded protein is reached . We have previously shown that this model accurately reproduces the shape of the universal distribution of the evolutionary rates among orthologous protein-coding genes along with the dependencies of the evolutionary rate on protein abundance and effective population size [34] . These results appear to suggest that our folding model ( described in detail the Methods section ) is sufficiently rich to reproduce some of the salient aspects of evolution . The model is also simple enough to allow exhaustive exploration of the fitness landscapes , which prompted us to directly address the problem of evolutionary path predictability . We build on the efforts of Carneiro and Hartl [33] who examined the statistics of evolutionary trajectories . Although counting monotonic fitness paths reveals important features of the landscapes , we argue that reliable retrodiction of the evolutionary past is possible ( i . e . , evolution is quasi-deterministic ) only when the available monotonic paths are similar to each other in a quantifiable way . We therefore propose a measure of path divergence to quantify the difference between the available monotonic paths . Our aims are to investigate the structure of the fitness landscapes of protein evolution and to elucidate the connection between the roughness of landscapes and the predictability of mutational trajectories . We analyze three classes of fitness landscapes: landscapes in which fitness is derived from the folding robustness of model polymers; additive random landscapes perturbed by noise; and experimental landscapes derived from the combinatorial mutation analysis of drug resistance and enzymatic activity . We show that all three classes of landscapes are markedly smoother than their randomly permuted counterparts and all exhibit a similar qualitative connection between roughness and path predictability . However , at the same level of path predictability , the folding landscapes have substantially fewer fitness peaks . Equivalently , mutation paths are more predictable than one would expect based on the number of peaks if the landscapes were uncorrelated . Given that the statistical properties of the model landscapes can be directly traced to the constraints imposed by the energetics and kinetics of a folding heteropolymer , we hypothesize that the relative smoothness and the suppression of suboptimal peaks in fitness landscapes of protein evolution are fundamental consequences of protein folding physics .
Carneiro and Hartl compared small random landscapes to several empirical fitness landscapes using deviation from additivity as a measure of roughness [33] . They found that empirical landscapes were significantly smoother than their random counterparts and that the degree of smoothness was correlated with the number of monotonic paths to the main summit . Deviation from additivity of a landscape is computed by fitting an additive model in which the fitness of each sequence is different from the peak fitness by the sum of contributions of the substitutions that differentiate it from the peak sequence . The negative fitness contributions of the substitutions to the peak fitness are adjusted to minimize the sum of squares of the differences between the actual fitnesses in the landscape and the fitnesses predicted by the additive model . Deviation from additivity is defined as , where is the number of points in the landscape . Because roughness of a multidimensional landscape with variable degree connectivity is not an intuitive concept , we introduce three additional quantitative measures to probe alternative facets of the concept of roughness . First , local roughness is the root mean squared difference between the fitness of a point and its neighbors , averaged over the entire landscape . As defined , local roughness conflates the measures of roughness and “steepness . ” For example , a globally smooth landscape , in which fitness depends only on the distance from the peak , will have a non-zero local roughness . However , because there is a large number of directions that change the distance from the peak by one , the local roughness of a globally smooth landscape will be vanishingly small . In addition , our landscapes tend to be globally flat–so that the average decrease in fitness due to a single mutation step away from the main peak is much smaller than the local fitness variability–everywhere except a small region around the main peak ( see Fig . 1 ) . Therefore , the landscape-average local roughness in our case is a true measure of the local fitness variability . Second , the fraction of peaks is the number of points with no fitter neighbors divided by the total number of points in the landscape . A strictly additive landscape has a single peak [30] whereas the peak fraction in landscapes derived from the folding model as well as the corresponding randomized landscapes depends on the method of landscape construction , alphabet size and sequence length . Third , the roughness of a landscape can be assessed by identifying its tree component . The tree component is the set of all nodes with no more than one neighbor of higher fitness . Thus , the tree component includes peaks and plateaus . Monotonic fitness paths along the tree component form a single or several disjoint tree structures without loops . In the limit of high selection pressure , a mutational trajectory that finds itself on the tree component has a single path to the nearest peak or plateau , i . e . evolution on the tree component is completely deterministic . We use the mean distance to the tree component , i . e . the distance to the tree component averaged over the landscape , as a measure of roughness . In a fully additive landscape , only the peak sequence and its immediate neighbors belong to the tree component and therefore the mean distance to the tree component is a measure of the diameter of an additive landscape ( which , for example , could be defined as the maximum pairwise distance between points on the landscape ) . Kauffman and Levin have shown that in a large class of correlated random landscapes , the mean distance to the tree component grows only logarithmically with the number of points in the landscape [19] . We utilize two quantitative measures of the predictability of evolutionary trajectories . First is fraction of monotonic paths to the main peak which is computed by counting the number of simple ( without reverse substitutions or multiple substitutions at the same site ) monotonic paths to the main peak from each point on the landscape , dividing it by the total number of simple paths ( where is the Hamming distance from point to the peak ) , and averaging over the landscape via ( 1 ) where is the number of points in the landscape and the sum excludes the main peak . The monotonic path fraction measures the scarcity of accessible evolutionary paths when selection is strong . When the monotonic path fraction is small , evolution is more constrained . Second , the mean path divergence , is a fine-grained measure of evolutionary ( un ) predictability . We first define the divergence of a pair of paths and , as the average of the shortest Hamming distances from each point on one path to the other path . Suppose that we have a way of generating stochastic evolutionary paths . The outcome of a large number of evolutionary dynamics simulations is a collection of paths with their associated probabilities of occurrence . In general , the probability of occurrence of an evolutionary path is proportional to the product of fixation probabilities of its constituent mutation steps . Given a bundle of paths with the same starting and ending points , we define its mean path divergence to be ( 2 ) where is the probability of occurrence of path in the ensemble . In other words , if two paths were drawn from the bundle at random with probabilities proportional to , their expected divergence would be . Alternatively , if we were to fix one path to be the most likely path in the bundle and to select the second path at random with probability proportional to , the divergence would be proportional to as well . The six quantitative characteristics of fitness landscapes are summarized in Table 1 . In an additive landscape , the mutational trajectory is maximally ambiguous . As every substitution that brings the sequence closer to the peak increases fitness , substitutions can occur in any order and all shortest mutational trajectories to the peak–without reverse substitutions or multiple substitutions at the same site–are monotonic in fitness . In the strong selection limit of our model defined below , all monotonic trajectories have roughly the same probability of occurrence , so the mutational path cannot be predicted . The mean path divergence is a better measure of the predictability of evolutionary trajectories than the number or fraction of accessible paths . Even when only a small fraction of paths are monotonic in fitness , these paths could potentially be quite different , perhaps randomly scattered over the landscape . In such a case , prediction of the evolutionary trajectory would be inaccurate despite the scarcity of accessible paths which will be reflected in a high value of path divergence . Equation ( 2 ) introduces the mean path divergence of a bundle of paths with the same starting and ending points . The landscape-wide mean path divergence is measured by constructing representative path bundles with all possible [start , peak] pairs including suboptimal peaks as trajectory termination points . Path divergence is averaged over all bundles with the starting and ending points separated by the same Hamming distance . To construct the path bundles , we employed a low mutation rate model in which the attempted substitutions are either eliminated or fixed in the population before the next mutation attempt occurs . We invoke the misfolding-cost hypothesis to assign a fitness to a sequence that folds with probability to a particular structure . To produce an abundance of correctly folded copies , an average of of misfolded copies are produced . The “fitness” of a sequence should be a monotonically decreasing function of the cost incurred by the misfolded proteins . Previously we showed that qualitative conclusions drawn from the average population dynamics on the fitness landscape did not depend on the precise functional relationship between the number of misfolded copies and fitness [34] . We use simply the negative of the number of misfolded copies and assign a fitness , to a sequence whose probability of folding to the reference structure is . Because the exact population dynamics model is not important , we use diploid population dynamics in the low mutation rate limit . Therefore , the probability of fixation of a mutant in the background of is given by ( 3 ) where is the effective population size [38] which in all simulations was fixed at . The required abundance is a measure of the strength of selection . In the limit of large , the probability of fixation of a beneficial mutation is unity whereas deleterious mutations are never fixed . Since the effective population size is large in our simulations , neutral mutations are almost never fixed either . Because uphill steps in the fitness landscape are equally likely , all monotonic uphill trajectories have equal evolutionary significance . In the analysis that follows , we study the association between landscape roughness and path predictability for the folding landscapes and their randomized ( also referred to as permuted or scrambled ) versions . In the scrambled landscapes , the topology ( i . e . connectivity ) of the landscape is preserved but the fitness values are randomly shuffled . We also compare the roughness and path predictability characteristics of the model and the experimental landscapes for -lactamase [25] and sesquiterpene synthase [28] to those for noisy additive landscapes with a continuously tunable amount of roughness .
Here we examined the fraction of monotonic paths and introduced mean path divergence as quantitative measures of the degree to which the starting and ending points determine the path of evolution on fitness landscapes . The lower the mean path divergence value , the more deterministic ( and predictable ) evolution is . Global measures of landscape roughness correlate with path divergence in the three analyzed classes of fitness landscapes: additive landscapes perturbed by noise , landscapes derived from our protein folding model and two small empirical landscapes . The folding landscapes are substantially smoother than their permuted counterparts . As a result , although in all analyzed landscapes only a small fraction of the theoretically possible evolutionary trajectories is accessible , this fraction is much greater in the folding and experimental landscapes than it is in randomized landscapes . In addition , the mean path divergence in the randomized landscapes is significantly smaller than in the original landscapes . Thus , the model and empirical landscapes possess similar global architectures with many more diverged monotonic paths to the high peaks than uncorrelated landscapes with the same distribution of fitness values . Consequently , evolution in fitness landscapes is substantially more robust to random mutations and less deterministic ( less predictable ) than expected by chance . These findings are compatible with the concept that might appear counter-intuitive but is buttressed by results of population genetic modeling , namely , that robustness of evolving biological systems promotes their evolvability [39]–[41] . Additionally , the folding landscapes exhibit a substantial deficit of peaks compared to perturbed additive landscapes and experimental landscapes , a property that translates into a substantially greater fraction of paths leading to the main peak . When it comes to the interpretation of the properties of fitness landscapes described here , an inevitable and important question is whether the folding model employed here is sufficiently complex and realistic to yield biologically relevant information . In selecting the complexity of our folding model , we attempted to construct the simplest model which exhibits 1 ) a rich spectrum of low energy conformations across the sequence space , and 2 ) a non-trivial distribution of substitutions effects on the low energy conformations . An important choice is whether the location of monomers is confined to a lattice or can be varied continuously . When the configuration space is continuous , the distribution of energy barriers between energetically optimal conformations can extend to zero . Therefore , the subtlety of distinctions between conformations can lead to a richer structure of the fitness landscape . We chose not increase the complexity of the model further and treated monomers as point-like particles in a chain where the distance between nearest neighbors is fixed but the angle between successive links in the chain in unrestricted . Our level of abstraction is therefore somewhere between lattice models and all-atom descriptions of proteins [42]–[51] . Another important choice is the number of the model monomer types . Again , we opted for an intermediate level of abstraction and chose four types of monomers: hydrophobic , hydrophilic , and positively and negatively charged . This choice drastically reduces the size of the sequence space while retaining some of the substitution complexity whereby hydrophilic and charged monomers can be swapped under some conditions without radically altering the native state . The intermediate level of abstraction in our approach has its pros and cons . Although the model reproduces key features of protein folding such as the existence of the hydrophobic folding nucleus and two-stage folding kinetics [52] , [53] , compact conformations certainly do not represent proteins . Rather , we might think of our monomers as representing structurally grouped regions several ( perhaps up to a dozen ) amino-acids in length . Compact conformations in the model might therefore be analogous to tertiary structures of proteins . Representing sequence space with only four monomer types and treating mutations without reference to the underlying DNA or genetic code does not accurately reflect the natural mutation process . However , our goal was to isolate the features of fitness landscapes which could be traced directly to the constraints imposed by the heteropolymer folding kinetics and energetics . We therefore used a simple sequence space and a homogeneous mutation model to avoid compounding the fitness landscape structure by the complexity derived from the mutation process . Most importantly , our folding model has been shown to reproduce the observed universal distribution of the evolutionary rates of protein-coding genes as well as the dependencies of the evolutionary rate on protein abundance and effective population sizes [34] . Therefore , despite its simplicity , the behavior of this model might reflect important aspects of protein evolution . In particular , the conclusions drawn from the analysis of the model landscapes exhaustively explored here could also apply to the fitness landscapes of protein evolution . In the previous work , we concluded that the universal distribution of evolutionary rates and other features of protein evolution follow from the fundamental physics of protein folding [34] . The results presented here suggest that the ( relative ) smoothness and a substantial deficit of peaks in the fitness landscapes of protein evolution that lead to mutational robustness and the ensuing evolvability could similarly follow from the fact that proteins are heteropolymers that have to fold in three dimensions to perform their functions . The experimental landscapes considered here are decidedly incomplete . Due to experimental limitations , only the analysis of binary substitutions at a handful of sites is feasible at this time . The incompleteness of the empirical landscapes analyzed in this work could be the cause of the observed lack of peak suppression . This proposition will be put to test by the study of larger parts of experimental landscapes that are becoming increasingly available .
The goal of this study is to explore the relationship between roughness and path divergence in realistic fitness landscapes . Our polymer folding model provides a simple way of constructing such landscapes . The model has been described in detail previously [34] . In brief , the model polymer is a flexible chain of monomers in which the nearest neighbors interact via a stiff harmonic spring potential with rest length . The angles between the successive links in the chain are unrestricted . There are four types of monomers: hydrophobic H , hydrophilic P , and charged + and − . Next nearest neighbors and in the chain and beyond interact via a pairwise potential ( 4 ) where is the distance between monomers and , is the monomer's charge , is the Debye-Hückel screening length , and and depend on the pair in question . The interaction parameters are chosen to mimic the essential features of the amino-acid interactions . To emulate the effects of solvent , we assign a stronger attraction to the HH pair than to the PP , ++ , and −− pairs . There is also a long range repulsion between H and P and even stronger repulsion between H and the charged monomers . The values of the parameters are , Debye-Hückel screening length . The Lennard-Jones coefficients and are ( 5 ) Note that a can be substituted by a in the subscripts and the coefficients are symmetric with respect to the interchange of the indices . The energy of the chain is ( 6 ) where the first term is the sum of the pairwise energies given by Eq . ( 4 ) over non-nearest neighbor pairs , and the second term reflects the springs connecting nearest neighbors . The spring constant is proportional to temperature . The parameters are fixed for all simulation runs at , and the quench temperature . To mimic the observed tendency of the and termini to be in close proximity , we fixed the endpoint monomers of the model sequences to be of and types . Dynamics of folding are simulated via over-damped Brownian kinetics which are appropriate when inertial and hydrodynamic effects are not important . Units are chosen so that each component of the 'th monomer's coordinates is updated according to ( 7 ) where is the time step and is a random variable with zero mean , variance , uncorrelated with for other times , monomers and spatial directions . The “native structure” of a particular sequence is represented by an equilibrium ensemble of conformations . The ensemble is constructed by identifying the typical folded conformation and measuring the characteristic RMSD due to thermal fluctuations in the folded state . Three thousand quenches are then performed and the resulting folded conformations are accumulated . The equilibrium ensemble that represents the native structure is defined as the largest cluster of quenched conformations within RMSD distance from each other . Thus , each conformation in the ensemble differs from any other by an amount comparable to the differences introduced by thermal fluctuations alone . The concept of the native structure ensemble allows us to compute the probability that a sequence folds to a particular structure in a natural , physically plausible fashion . Given a native structure ensemble we assess its conformation space density by computing the distance between each member of the ensemble and its closest neighbor . Given the set of these shortest distances we compute the median and the median absolute deviation ( MAD ) . A new conformation is deemed to belong to the ensemble if the shortest distance from this conformation to the members of the ensemble is smaller than . Given a native structure ensemble of some sequence we compute the probability that sequence ( which could be itself ) folds to the this structure by accumulating equilibrated quenched conformations of and using the above criterion to determine the fraction that belong to the native structure ensemble of . Because sample conformations are computed , the smallest measurable is . The sample size used to measure dictated by the computational demands of the model , introduces a random component to the model fitness landscapes . As we report below , model landscapes turn out to be substantially smoother than random . Therefore the underlying global structure of the model landscapes appears to survive the modest amount of randomness introduced by the relatively small sample size used for measuring . Robust folders ( sequences with a high probability of correct folding ) tend to have large linear regions stretched by repulsive Coulomb interactions . Because the linear regions have no contacts with other monomers , we focused our attention on compact conformations with a high monomer contact density . Substitutions in these higher complexity conformations were more likely to exhibit non-trivial effects . To find compact robust folders in the vast available sequence space of -mers ( the sequences are of length but the endpoint monomer types are fixed ) with monomer types , we implemented a simulated annealing search which optimized the correct folding probability divided by the cube of the native conformation's radius of gyration . The search produced over 800 sequences with and at least two distinct regions of the polymer in mutual contact . We examined each single substitution mutant of a robustly folding sequence and computed the folding probability to the structure of the original sequence . All mutants with were added to the landscape and if their mutants were also examined . This process is repeated until all mutants of the last sequence under consideration have . From our study of complete landscapes we estimate that on average for each sequence with which is included into the landscape , roughly 6 others with need to be examined . Since each quench and equilibration takes about 2–4 seconds , landscape construction takes roughly 30 minutes to an hour per included sequence . Thus landscapes larger than 10 , 000 sequences take months to compile . At the time of submission , 39 complete landscapes have been constructed , the largest comprising 12969 sequences . The organization of the folding fitness landscapes and experimental landscapes were compared with perfectly additive landscapes perturbed by noise constructed as follows . Each substitution to the peak fitness sequence was assigned a negative fitness differential drawn at random from an exponential distribution with parameter . The sum over the fitness differentials of a particular set of substitution was modified by either additive of multiplicative noise [54] . Additive noise is drawn from a Gaussian distribution with zero mean and standard deviation which was varied between and The multiplicative perturbation is achieved by multiplying the fitness by a number drawn from a uniform distribution raised to a positive power varied between and When is small , multiplicative factors are close to unity and the perturbation is small as well . If the perturbed fitness was positive , the mutant was included into the landscape . The noise amplitude was varied to obtain a family of landscapes of continuously varying roughness . Only the data for the additive landscapes with multiplicative noise were included in this manuscript . Landscapes perturbed by other types of noise exhibited essentially the same qualitative behavior . The studies on experimental fitness landscapes typically involve constructing a library of all possible combinations of binary mutations at a small number of sites . The first study included in the present analysis measured the minimum inhibitory concentrations ( MIC ) of an antibiotic for a complete spectrum of mutants with modified TEM -lactamases; the transition from the antibiotic-sensitive to the antibiotic-resistant form requires five mutation , so the landscape encompassed 120 mutational trajectories between the most distant points on the landscape ( or 32 sequences ) [25] . The logarithm of MIC was used as the proxy for fitness . In the second study , catalytic activity of 419 sesquiterpene synthase mutants that differed by at most 9 substitutions was measured [28] . We used the catalytic specificity ( propensity for producing a particular reaction product rather than a broad spectrum of products ) of the mutant enzymes as the proxy for fitness . Before performing the analysis , the fitnesses in the experimental landscapes are mapped onto the interval to enable meaningful quantitative comparisons of the roughness measures . | Is evolution deterministic , hence predictable , or stochastic , that is unpredictable ? What would happen if one could “replay the tape of evolution”: will the outcomes of evolution be completely different or is evolution so constrained that history will be repeated ? Arguably , these questions are among the most intriguing and most difficult in evolutionary biology . In other words , the predictability of evolution depends on the fraction of the trajectories on fitness landscapes that are accessible for evolutionary exploration . Because direct experimental investigation of fitness landscapes is technically challenging , the available studies only explore a minuscule portion of the landscape for individual enzymes . We therefore sought to investigate the topography of fitness landscapes within the framework of a previously developed model of protein folding and evolution where fitness is equated with robustness to misfolding . We show that model-derived and experimental landscapes are significantly smoother than random landscapes and resemble moderately perturbed additive landscapes; thus , these landscapes are substantially robust to mutation . The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness . Thus , the smoothness and substantial deficit of peaks in fitness landscapes of protein evolution could be fundamental consequences of the physics of protein folding . | [
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| 2011 | Predictability of Evolutionary Trajectories in Fitness Landscapes |
Taenia solium is a neglected zoonotic parasite endemic throughout many low-income countries worldwide , including Zambia , where it causes human and pig diseases with high health and socioeconomic burdens . Lack of knowledge is a recognized risk factor , and consequently targeted health educational programs can decrease parasite transmission and disease occurrence in endemic areas . Preliminary assessment of the computer-based education program ‘The Vicious Worm’ in rural areas of eastern Zambia indicated that it was effective at increasing knowledge of T . solium in primary school students . The aim of this study was to evaluate the impact of ‘The Vicious Worm’ on knowledge retention by re-assessing the same primary school students one year after the initial education workshops . Follow-up questionnaires were administered in the original three primary schools in eastern Zambia in 2017 , 12 months after the original workshops . In total , 86 pupils participated in the follow-up sessions , representing 87% of the initial workshop respondents . Knowledge of T . solium at ‘follow-up’ was significantly higher than at the initial ‘pre’ questionnaire administered during the Vicious Worm workshop that took place one year earlier . While some specifics of the parasite’s life cycle were not completely understood , the key messages for disease prevention , such as the importance of hand washing and properly cooking pork , remained well understood by the students , even one year later . Results of this study indicate that ‘The Vicious Worm’ may be an effective tool for both short- and long-term T . solium education of rural primary school students in Zambia . Inclusion of educational workshops using ‘The Vicious Worm’ could be recommended for integrated cysticercosis control/elimination programs in sub-Saharan Africa , particularly if the content is simplified to focus on the key messages for prevention of disease transmission .
Taenia solium is a zoonotic parasite known as the pork tapeworm , which infects over 50 million people worldwide [1] . Invasion of the human brain by the larval stage of the parasite is known as neurocysticercosis ( NCC ) , which can cause neurological deficits including severe progressive headache , stroke and hydrocephalus , and is the world’s leading cause of preventable epilepsy [2] . Other impacts of human infection include treatment costs , productivity losses and social stigmatization of epilepsy sufferers [3] . Porcine infections ( porcine cysticercosis , PCC ) cause substantial economic losses from carcass condemnation , and reductions to farmer income and food safety that exacerbate the poverty cycle in many developing countries in which the parasite is endemic [4 , 5] . Despite global ‘tool readiness’ for control of T . solium [6] , high levels of active parasite transmission persist in many endemic countries throughout Latin America , Asia and sub-Saharan Africa , including Zambia . Transmission is to a large extent socially determined , with inadequate sanitation , poor hygiene practices , minimal access to medical or veterinary services , and low levels of health education enabling parasite transmission in areas where pigs are raised . A lack of knowledge of the parasite has been identified as one of the barriers for control , and targeted health education interventions have been shown to be an effective addition to other T . solium control measures [7–11] . Education is recognized by the World Health Organization as an important part of the multisectoral approach needed for control of zoonotic pathogens such as T . solium [12] . Computer-based tools have the advantages of providing standardized educational messages , reduce training costs , are able to be widely disseminated and can be updated more easily , compared to traditional paper-based learning systems [13] . ‘The Vicious Worm’ ( https://theviciousworm . sites . ku . dk ) is a freely-downloadable computer-based educational program designed to provide comprehensive information about T . solium in a fun and interactive way . It is set in a sub-Saharan African context and has different levels of detail to allow tailoring of the educational content to suit the needs of the target audience [13] . Studies with medical and agricultural professionals in Tanzania demonstrated significant knowledge uptake and retention , and reported behavioral changes and knowledge dissemination directly attributable to exposure to ‘The Vicious Worm’ [14 , 15] . The program had not previously been evaluated for use in school-going children , who have been shown to be effective ‘health change agents’ capable of effectively disseminating educational messages to family and community members [16 , 17] . A preliminary study conducted by the authors of this manuscript in three primary schools in the highly T . solium–endemic Eastern Province of Zambia in 2016 demonstrated significant uptake of T . solium-associated knowledge in adolescent primary school pupils in the short-term [18] . The study at hand revisited the same primary school pupils one year later , to evaluate the longer-term impact of ‘The Vicious Worm’ on T . solium–associated knowledge retention .
The study took place in the Nyembe ( Katete district ) , Chimvira and Herode ( Sinda district ) communities in the Eastern Province of Zambia . As discussed in [18] , the region is highly endemic for T . solium; prevalence of active human and pig infections are among the highest in the world , and over 57% of human epilepsy cases are attributable to NCC [19 , 20] . ‘CYSTISTOP’ is a prospective , large-scale community-based T . solium intervention study , which commenced in three study arms in the Katete and Sinda districts in the Eastern Province of Zambia in 2015 . The study has two intervention arms designed to compare integrated human- and pig-based interventions ( elimination study arm ) versus pig-only ( control study arm ) interventions , as compared to a negative control study arm . Health education was also conducted at four- ( elimination study arm ) and twelve-monthly ( control and negative control study arms ) intervals ( Fig 1 ) . Health educational methods included village-based educational sessions during sensitization , conducted in Chewa ( the local language ) by a trained bilingual CYSTISTOP program member . These sessions included descriptions of the parasite’s life cycle and ways to prevent its transmission in the villages , and utilized visual aids including a large canvas life cycle poster , a five-meter long ribbon to represent the adult tapeworm , and life-sized plasticine models of human stool demonstrating expelled tapeworm proglottids . Participation in village-based sensitization sessions was higher in the elimination study arm than in the control study arm ( 89% compared to 46% , [35] ) , and sessions were primarily attended by women , very young children , and few men ( personal observation . ) Large color posters of the parasite’s life cycle were permanently displayed at the rural health centers in each of the three study areas . Simplified A4-sized paper copies of the life cycle poster were also distributed to each household in the two intervention study areas ( elimination and control study arms ) during the baseline visits in October 2015 . The final component of CYSTISTOP’s health education intervention was workshops in primary schools using the ‘The Vicious Worm’ computer program . The educational workshops were conducted in Nyembe ( elimination study arm ) in July 2016 , and in the Kondwelani ( control study arm ) and Gunda ( negative control study arm ) primary schools in November 2016 as described in [18] . The initial workshops comprised a ‘pre’ questionnaire to assess baseline knowledge , an educational session using ‘The Vicious Worm’ , followed immediately by a ‘post’ questionnaire to evaluate knowledge uptake ( see Fig 2 ) . Follow-up sessions were scheduled in the same primary schools in July ( elimination study arm ) and early December ( control and negative control study arms ) 2017 , one year after the initial workshops . There were two questionnaires ( QS ) used in the sessions as per Hobbs et al [18]: the original questionnaire ( QS1 ) , modified from the original questionnaire [14] to include Zambian terminology , was used in the elimination study arm and had 24 questions grouped into eight categories . As the QS was deemed too long and complicated for primary school pupils , a simplified version ( QS2 ) containing 15 questions in three categories was subsequently used in the control and negative control study arms . Both QS were designed to test knowledge of human tapeworm infections , known as taeniosis ( TS ) ; human ( neuro ) cysticercosis ( NCC/CC ) ; and PCC , including the linkages between the disease states and methods of transmission , diagnosis , and prevention . ( The QS used in the sessions are provided in the data repository . ) All of the pupils who had attended the initial educational workshops in 2016 were invited to return for a follow-up session , conducted in the same primary schools in July ( elimination study arm ) and December ( control and negative control study arms ) 2017 . Follow-up sessions were conducted as per the ‘post’ QS used in the initial workshops , as described in [18] , and were conducted by one of the same two trained bilingual CYSTISTOP project members as in the original workshops . Briefly , QS were projected onto a classroom wall , and questions and answer options were read aloud in Chewa and repeated at least once for clarity . Using Bluetooth-connected TurningPoint clicker devices , all pupils had to individually submit their answer to each question before the group could proceed to the next question . At the conclusion of each session , the group was taken through the QS again to discuss the correct answers and address any remaining misconceptions . The sessions were between 30 ( QS2 ) and 45 ( QS1 ) minutes in duration . The differences in the two questionnaires prevented direct comparison of response data , so QS1 data ( elimination study arm ) were analyzed separately from QS2 ( control and negative control study arms ) . Each question was scored as either correct ( 1 ) or incorrect ( 0 ) , resulting in a maximum score of 24 for QS1 and 15 for QS2 . Some questions in QS1 had more than one correct answer; selection of any one of these answers resulted in a ‘correct’ outcome . Group ( QS1 and QS2 ) and individual ( QS2 only; a technical problem prevented the collection of individual QS1 data during the initial elimination study arm workshop ) responses to each session were exported into an Excel ( Microsoft Corporation , 2010 ) spreadsheet for descriptive statistics . Responses were assessed individually and by category . Grouped result data for QS1 were analyzed using a generalized linear model , using the number of positive and negative answers as binomial response variable , and study time point as categorical covariate . The absence of individual data did not allow taking the within-respondent correlations across study time points into account . Pairwise comparisons of mean scores by study time point were performed using Tukey’s all-pair comparisons method . Individual result data for QS2 collected at both baseline and follow-up allowed further analyses . The analysis of the correlated ‘pre’ , ‘post’ and ‘follow-up’ scores was carried out using a generalized linear mixed model using individual respondent as random effect , the number of positive and negative answers as binomial response variables , and study time point as categorical covariate . Pairwise comparisons of mean scores by study time point were performed using Tukey’s all-pair comparisons method . Additional multivariable analyses were performed adding the respondents’ age , gender , and school . This model was applied to the total scores and to each of the three categories . The analyses were performed using the lme4 and multcomp packages for R 3 . 5 . 1 [21–23] . This study was conducted as part of the ongoing CYSTISTOP project ( https://clinicaltrials . gov/ct2/show/NCT02612896 ) . Ethical clearance was obtained from the University of Zambia Biomedical Research Ethics Committee ( 004-09-15 ) and the Ethical Committee of the University of Antwerp , Belgium ( B300201628043 , EC UZA16/8/73 ) . The study was introduced and explained to all project participants , both in village group settings and within individual households , prior to each field visit . Written informed consent to participate in the workshops , voluntarily provided by a parent or guardian , was obtained for each pupil , and attendance at the educational sessions was voluntary . The sessions took place outside of normal school hours . There was no incentive for participation , but light refreshments were provided after the sessions .
This follow-up study indicates that educational workshops using ‘The Vicious Worm’ may have lasting positive effects on T . solium knowledge uptake and retention in rural adolescent primary school pupils in eastern Zambia . Knowledge levels at ‘follow-up’ were significantly higher than at baseline one year earlier , with increases of 14% and 10% compared to ‘pre’ levels in QS1 and QS2 , respectively . Compared to ‘post’ knowledge levels immediately following the educational component one year earlier , however , knowledge at ‘follow up’ was similar ( QS1 ) or significantly lower ( QS2 ) . The questions relating to general knowledge of TS and NCC , diagnosis of PCC , and prevention of PCC/TS/NCC were answered very well in both QS at ‘follow-up’ , with 63% of categories in QS1 and 66% of categories in QS2 answered correctly by at least 75% of the groups . The knowledge regarding prevention of the parasite’s transmission was both the best answered category , and showed the lowest decrease in knowledge from the ‘post’ round one year earlier . This indicates that although some aspects of the parasite’s life cycle remained imperfectly understood at ‘follow-up’ , the pupils generally retained the main aspects of T . solium and the key messages for disease prevention one year after ‘The Vicious Worm’ educational workshops . The parasite’s life cycle is complex , and certain aspects remained imperfectly understood by the pupils at ‘follow-up’ . Transmission of PCC was not well understood , nor was transmission of NCC/CC in humans . Many respondents from both QS selected the incorrect answer responses stating that NCC/CC is obtained via ingestion of raw or undercooked pork that is infected with PCC , which given the complexity of the T . solium life cycle is not surprising . Indeed , many other field studies have demonstrated similar results with adults , farmers and even veterinary and medical professionals showing imperfect understanding of the life cycle despite educational interventions [7 , 9 , 10 , 14 , 17 , 24] . However , what is of concern from these data is that some respondents apparently believed that people with NCC/CC or specifically epilepsy can transmit the disease to others ( 24% , QS2 ) . Epilepsy is often stigmatized in many low-income countries including Zambia , and the social and psychological effects of stigmatization can substantially decrease quality of life for epilepsy sufferers and their families [25 , 26] . While the majority of other respondents correctly indicated that NCC is not transmissible to others , this message should be particularly emphasized in future educational interventions . Many pupils again selected destruction of the pig and/or carcass as the most suitable method for management of live or slaughtered pigs with PCC , as was also seen in the initial workshops and discussed in [18] . While the ‘correct’ answers for the purposes of the QS scoring were treating pigs with oxfendazole or properly cooking pork , destruction of and proper disposal of heavily infected pork is in fact the recommended approach mandated by World Organization for Animal Health’s ( OIE ) Terrestrial Animal Health Code [27] and the Zambian Public Health Act [28] , and this should be reflected in the marking of these questions in future workshops . However , the OIE Terrestrial Animal Health Code also states that the meat of carcasses infected with less than 20 cysticerci can be consumed after treatment ( that is , freeze- or heat-treatment , with the latter reaching a core temperature of 80°C ) . As ‘backyard’ animal slaughter is frequently conducted in rural and remote communities in many developing countries including Zambia , meat inspection is often rudimentary or absent . Given the limited availability of nutrition and particularly protein in many rural and remote developing communities , insisting on strict measures pertaining to meat inspection and condemnation is not always realistic , and may foster resistance and/or resentment in some situations . We therefore feel it is important to also highlight the alternative options to carcass destruction , especially considering the nutritional needs of these and many other low-resource communities that are endemic for T . solium . Consequently , we would recommend that future educational messages and workshops should recommend destruction of heavily infected meat and carcasses wherever possible , while also promoting proper cooking of lightly infected meat and/or anthelmintic treatment of pigs as more realistic alternatives for some resource-poor endemic communities . The reason for the decreased knowledge regarding PCC transmission routes seen in students from the control and negative control study arms at ‘follow-up’ ( more students indicating that infection arises after pigs being mated with an infected pig , or after eating moldy feedstuff ) is unclear , but may be related to the decreased frequency of educational delivery in these study arms compared to in the elimination study arm . Adolescent primary school pupils were selected to participate in these educational workshops because studies have shown that school students can be ‘health change agents’ capable of effectively disseminating educational messages to family and community members [16 , 17] . A cluster-based education trial in northern Tanzania utilized leaflets and videos containing T . solium-specific health education in primary and secondary schools , and demonstrated generally increased knowledge and attitudes in pupils from intervention schools compared to control schools [17] . Using computer-based programs allows standardization of educational messages , while allowing flexibility and adaptation of the content to specific audiences . The recent release of ‘The Vicious Worm’ as a multiplatform smartphone app and the completed translation of the online version into Kiswahili [29] , will allow expansion of the program across the African continent . Other language translations are currently underway ( personal communication , C . Trevisan ) , and with adaptation of the illustrations and contexts for Latin American , Asian or other specific settings , this tool could be implemented worldwide . Other electronic educational media including short animated videos , talking books , songs and DVDs are increasingly used in public health campaigns around the world , with encouraging results [30] . In a Chinese study , a short animated cartoon called ‘The Magic Glasses’ was shown to halve infection rates of parasitic worms in school-aged children ( 8 . 4–4 . 1% , P<0 . 0001 ) , and observed occurrence of handwashing increased from 54% to 98 . 9% ( P<0 . 0001 ) in the intervention group compared to the control group [31] . Tablet-based educational interventions have also been successful at raising awareness and changing behaviors for prevention of other , non-parasitic diseases , including cervical cancer and human papilloma-virus infections [32] . It should be emphasized that increased knowledge and awareness of a topic does not necessarily translate into behavioral change , and there may be underlying sociocultural and/or economic factors contributing to parasite transmission in endemic communities that can override even known adverse health outcomes associated with certain behaviors [33 , 34] . Student responses given during these assessment situations may indicate what the students believed to be technically correct answers , rather than reflecting their actual behaviors and beliefs . Feedback from focus group discussions conducted in the elimination and control study arms indicated that behavioral changes have been initiated in the villages since the start of the CYSTISTOP project [35] , and follow-up observational visits to the study areas are planned for 2019 to corroborate these reports . The effectiveness of information transfer from educated individuals to others is difficult to quantify , and evaluation of such knowledge transfer was not within the scope of this study . A primary school-based health education trial in Tanzania demonstrated significant knowledge uptake in pupils from intervention schools compared to control schools , whereas evaluation of knowledge transfer to the community showed mixed results [36]: some parents reportedly implemented behavioral changes such as building toilets and boiling drinking water based on knowledge passed on from their children; others reportedly wished to do more but lacked resources to do so; and some parents found it improper for children to instruct their parents . Mwidunda et al [17] reported that secondary school students are often more respected in their families and communities than primary school pupils , and suggested that focusing health educational messages on secondary schools may increase effects of knowledge transfer to communities . No secondary schools are present in the study areas , as is typically the case for many remote and rural regions of Zambia , but conducting Vicious Worm workshops in secondary schools would be encouraged where possible . This study has limitations . The project activities including health education were conducted more frequently in the elimination study arm ( four-monthly ) than in the control and negative control study arms ( annually ) , which could have been at least partially responsible for the seemingly better knowledge retention at ‘follow-up’ demonstrated by the elimination study arm students ( QS1 ) . The use of two different QS prevented direct comparison of knowledge uptake and retention from individuals across all three study arms , which would have allowed even more robust analyses . In addition , because the technical error in the initial elimination study arm workshop prevented collection of individual response data , we only had grouped result data for QS1 , and were consequently not able to take the within-respondent correlation across study time points into account . This led to an underestimation of variances , and consequently an increased probability of ( falsely ) detecting significant associations . The comparisons across study time points for QS1 should therefore be interpreted with caution . The loss of twelve of the original students to follow-up in this study is another limitation , however statistical significance was nevertheless achieved . Evaluating the effects of knowledge uptake on behavioral change or the extent of knowledge transfer from students to others was outside the scope of this study , but would be useful to attempt in future studies . In future educational workshops using ‘The Vicious Worm’ it may be beneficial , as per the authors’ previous recommendations [18] , to modify the educational component to focus on the main methods for prevention of disease transmission , rather than detailing the T . solium life cycle . Tailoring educational materials to the specific sociocultural context , including use of non-textual media to include individuals with low literacy skills , may further enhance education uptake in endemic communities . The use of locally-broadcast radio programs or simple , illustrative printed material such as posters , leaflets and comic books may also add value to educational programs [7 , 8 , 11 , 37] , especially in areas where access to smartphones or computers is limited . Some standardized educational posters are available for T . solium education [38] , including several recently published online by the European Network on Taeniosis/Cysticercosis ( CYSTINET , COST Action TD1302 , http://www . cystinet . org/ ) ( see S1 File ) . The results from this follow-up study demonstrate that educational workshops using ‘The Vicious Worm’ can contribute to significantly increased T . solium knowledge in rural Zambian primary school students in both the short- and long-term . Despite some confusion regarding the precise relationships between TS , NCC/CC and PCC , in general the data indicate that the key messages for prevention of disease transmission , including the importance of hand washing and of proper cooking of pork , remained well understood by the students one year after the educational sessions . The flexible nature of ‘The Vicious Worm’ program , combined with recent and ongoing translations into languages other than English and the development of the app for smartphones , provides standardized educational content that can be tailored to the specific educational and sociocultural context of the target audience . For village-level educational interventions in rural endemic communities it may be advised to simplify or omit the more scientific aspects of ‘The Vicious Worm’ in favor of promoting key behavioral messages , to enhance knowledge uptake and retention . Focusing education on school-going children as key change agents may also increase community awareness and engagement . Tailored ‘Vicious Worm’-based educational interventions should be considered for incorporation with integrated T . solium control or elimination programs in future . | The zoonotic parasite Taenia solium , commonly known as the pork tapeworm , causes substantial public health and economic losses worldwide . It is commonly found in low-income countries where pigs are raised in areas of poor sanitation , including Zambia . The links between the parasite and its different disease forms in humans and pigs are not very well known , and ignorance of the parasite is a known risk factor for infection . Health education can significantly increase knowledge and awareness of the parasite and can inspire behavioral change that reduces disease transmission . ‘The Vicious Worm’ is a computer-based program designed to provide T . solium education in a fun and interactive way . We conducted educational workshops in three primary schools in rural areas of eastern Zambia , and preliminary assessment indicated that the ‘Vicious Worm’ educational content significantly improved students’ knowledge of T . solium . We also conducted follow-up studies in the same students one year later , and discovered that the students’ knowledge was still significantly higher than at baseline . We conclude that ‘The Vicious Worm’ may be a useful educational component to enable targeting of school students , and would recommend its inclusion in integrated T . solium control programs in future . | [
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| 2019 | Effects of ‘The Vicious Worm’ educational tool on Taenia solium knowledge retention in Zambian primary school students after one year |
The 2020 Sustainable Development goals call for 100% certified interruption or control of the three main forms of Chagas disease transmission in Latin America . However , how much will achieving these goals to varying degrees control Chagas disease; what is the potential impact of missing these goals and if they are achieved , what may be left ? We developed a compartmental simulation model that represents the triatomine , human host , and non-human host populations and vector-borne , congenital , and transfusional T . cruzi transmission between them in the domestic and peridomestic settings to evaluate the impact of limiting transmission in a 2 , 000 person virtual village in Yucatan , Mexico . Interruption of domestic vectorial transmission had the largest impact on T . cruzi transmission and prevalence in all populations . Most of the gains were achieved within the first few years . Controlling vectorial transmission resulted in a 46 . 1–83 . 0% relative reduction in the number of new acute Chagas cases for a 50–100% interruption in domestic vector-host contact . Only controlling congenital transmission led to a 2 . 4–8 . 1% ( 30–100% interruption ) relative reduction in the total number of new acute cases and reducing only transfusional transmission led to a 0 . 1–0 . 3% ( 30–100% reduction ) . Stopping all three forms of transmission resulted in 0 . 5 total transmission events over five years ( compared to 5 . 0 with no interruption ) ; interrupting all forms by 30% resulted in 3 . 4 events over five years per 2 , 000 persons . While reducing domestic vectorial , congenital , and transfusional transmission can successfully reduce transmission to humans ( up to 82% in one year ) , achieving the 2020 goals would still result in 0 . 5 new acute cases per 2 , 000 over five years . Even if the goals are missed , major gains can be achieved within the first few years . Interrupting transmission should be combined with other efforts such as a vaccine or improved access to care , especially for the population of already infected individuals .
While the World Health Organization’s ( WHO ) London Declaration on Neglected Tropical Diseases has proposed 2020 goals of “100% of countries certified with no intradomiciliary transmission” , “100% of countries with certification of transfusional transmission interrupted” , and “100% of countries with control of congenital transmission” regarding the three main forms of Chagas disease transmission in Latin America[1] , the question remains: what will be the impact of achieving these goals to varying degrees be on Chagas disease ? Interruption of domestic transmission ( often measured by infections in children under 5 years of age ) is thought to play a key role in controlling Chagas disease ( i . e . , reduction in Chagas disease burden ) , which is caused by the protozoan parasite Trypanosoma cruzi . [2–5] While previous studies have tried to elucidate the mechanisms of transmission or evaluate particular interventions[6–14] , none to our knowledge have specifically tried to evaluate the impact of achieving the 2020 goals . In fact , many existing studies preceded the formulation and announcement of the goals . Moreover , not all locations may be able to achieve the 2020 goal , which does not necessarily mean aspiring to them is not worthwhile . Some regions have yet to implement policies or mandate control programs[1] ( e . g . , Mexico has no national control program[4] ) , while other regions have programs that are not consistent from year-to-year and region-to-region ( e . g . , geographic variations in control activities in Ecuador[15] ) . Additionally , low attendance to perinatal care can hinder adequate diagnosis and treatment[16] of pregnant women and infants , and compliance with universal screening of blood donors is not always 100% . [17] Furthermore , Chagas policies may be thwarted by decentralization ( i . e . , movement of authority from a central to a local government ) . [1 , 18] Therefore , knowing the impact of partially achieving the goals to varying degrees would be helpful . Really assessing the potential impact of achieving the 2020 goals would need a computational model that incorporates all the complexities . For example , a model would need to incorporate all the other relevant routes of transmission ( e . g . , vectorial , tranfusional , and congenital[2 , 3 , 19] ) to help determine how much disease would persist if vectorial transmission were interrupted . It also should include various vector habitats ( e . g . , domestic , peridomestic , and sylvatic ) to help determine the impact of reinfestation . The Yucatan in Mexico can serve a good sample location as Mexico has not received certification , this region has some of the highest levels of Chagas in the country , and its main vector has more than one habitat ( i . e . , domestic , peridomestic , and sylvatic ) which allows us to capture re-infestation dynamics that may thwart the 2020 goals . Therefore , our team developed a dynamic model of T . cruzi transmission among vectors ( Triatoma dimidiata ) and human and non-human hosts in Yucatan , Mexico and evaluated different levels of achieving the three transmission related 2020 goals on Chagas disease prevalence and number of new acute human cases .
We developed a deterministic compartmental model ( Fig 1 ) using Python ( Python Software Foundation , Wilmington , DE ) to represent vector and host populations involved in T . cruzi transmission and included triatomines , human hosts , non-human hosts ( i . e . , dogs ) , and dead-end hosts ( i . e . , chickens ) to simulate vector-borne transmission between these populations in both domestic and peridomestic habitats , as well as congenital and transfusion/organ transplantation transmission . The S1 Text provides additional model details ( including equations representing transitions between compartments ) . The model ran in monthly time steps ( i . e . , t = 1 month or 30 days ) and was simulated across a 50-year period . During each time step , probabilities and rates determined the number of individuals in each compartment . Triatomine bugs could be susceptible ( not infected with T . cruzi and able to become infected ) or infectious ( infected with T . cruzi and able to transmit to vertebrate hosts upon biting ) . Upon feeding on an infectious host ( human and viable non-human ) , a susceptible bug had a probability of becoming infected with T . cruzi , conditional on the disease state of the host . The number of triatomine bugs ( NV ) in the model was determined from the carrying capacity , or the number of bugs sustainable in each habitat . The number of susceptible triatomines entering the domestic or peridomestic population was dependent on the vector birth rate , carrying capacity , and number of triatomines in each habitat ( S1 Text ) . Each member of the human population ( NH ) could be in any of the following mutually exclusive disease states ( Fig 1 ) : susceptible ( not infected with T . cruzi and able to become infected ) , acute Chagas disease ( infected with T . cruzi and able to transmit , exhibiting mild and nonspecific symptoms , but in some cases can show specific symptoms such as Romaña’s sign or can be serious and life-threatening , and having microscopically detectable parasitemia for 6 to 8 weeks[19] ) , indeterminate Chagas disease ( infected with T . cruzi , able to transmit , but showing no symptoms , i . e . , asymptomatic ) , and symptomatic chronic Chagas disease ( infected with T . cruzi , able to transmit , and showing symptoms of chronic disease such as cardiomyopathy and/or megaviscera ) . Upon a feeding contact by an infectious triatomine , a susceptible human had a probability of becoming infected with T . cruzi via contamination with bug feces during or immediately after the feeding . This is represented in the vector-borne force of infection ( S1 Text ) . Based on the clinical progression of disease in humans[2 , 19] , all new infections start in the acute state . Pregnant women had a probability of transmitting Chagas to their infants upon birth , with newborns becoming infected based on the congenital force of infection ( S1 Text ) . Additionally , a proportion of humans receiving a blood transfusion or organ transplant had a probability of becoming infected with T . cruzi , based on the transfusion force of infection ( S1 Text ) . We assumed that once infectious , persons were considered always infectious in the absence of treatment . Those in the acute and symptomatic chronic states of disease had probabilities of Chagas-related mortality . Dogs ( ND ) served as reservoir hosts for T . cruzi and could be either susceptible or infected , with a susceptible dog becoming infected upon the bite of an infected vector at a rate depending on the force of infection ( S1 Text ) . Dogs were considered competent transmitters of T . cruzi ( i . e . , susceptible triatomines could become infected upon biting an infected dog ) . Chickens ( NC ) served as dead end hosts and could not transmit T . cruzi back to vectors , as they are unable to become infected with T . cruzi . [20] Our model included transmission in both domestic and peridomestic habitats , which vary by vector-host contact rates , and allowed for the movement of triatomines between them ( e . g . , re-infestation ) . Vectorial transmission in our model was governed by the vectorial force of infection ( S1 Text ) . Consistent with other models of vector-borne diseases[21] , this is a function of: ( 1 ) the triatomine biting rate , ( 2 ) the triatomine feeding proportion for each host type in each habitat , ( 3 ) the probability of transmission from vector to susceptible host , ( 4 ) the probability of transmission from infected host to susceptible bug , ( 5 ) the proportion of hosts in each habitat , and ( 6 ) the number of hosts in each habitat . Transmission probabilities from vector to host varied with host species , while triatomine biting rates were assumed to be constant . Despite having one of the greatest burdens of Chagas disease worldwide , Mexico has not yet undertaken a national vector control program[4] and only started mandatory serological screening in 2012[17] . In 2010 , approximately an estimated 876 , 458 people were infected and 23 . 5 million were at risk for infection[22] , with 88% of the population potentially exposed to at least one competent vector species[23] . These cases result in an estimated $32 . 3 billion in societal costs over their lifetime . [24] Yucatan State has one of the highest Chagas burdens in Mexico . Chagas is endemic throughout the peninsula , with 12–25 cases reported per 100 , 000 population over the last several years . [25 , 26] Additionally , the Yucatan is home to only one main vector species , Triatoma dimidiata , which can be found in the domestic , peridomestic , and sylvatic environments , and typically infests houses on a seasonal basis with limited ability to colonize . [27 , 28] Thus , the domestic and peridomestic transmission cycle are fueled by the sylvatic transmission of T . cruzi and house invasion by infected bugs . Currently , there are no vector intervention or control strategies in place in the Yucatan . Thus , this endemic setting , with no programs currently in place and home to a vector that can reinfest homes , is an ideal location to fully estimate the impact of the 2020 goal . Our model was populated and calibrated to simulate T . cruzi transmission in a rural village ( NH = 2 , 000 ) in Yucatan , Mexico . Table 1 shows our key input parameter values and sources . The number of dogs ( ND = 617 ) was based on the ratio of dogs to humans[29 , 30] , while the number of chickens ( NC = 250 ) was based on the proportion of households with chickens and the number of persons per household[28] . The carrying capacity was set at 50 bugs per person ( consistent with previous work[9] ) , yielding a T . dimidiata population size of 99 , 885 . Our model was calibrated to assume a median T . cruzi prevalence value of 32 . 5% in T . dimidiata[27 , 31–38] , and seroprevalence estimates of 1 . 85% in humans[4 , 38–47] , and 14 . 58% in dogs[31 , 41 , 42 , 48–51] . As transmission probabilities ( i . e . , from vectors to humans and dogs , and from dogs and humans to triatomines ) and T . dimidiata feeding proportions across host species are highly variable and/or not well defined in the literature , these parameters were calibrated to available empirical data for the Yucatan ( Table 1 and S1 Text ) . We evaluated the impact of interrupting vector-borne transmission in the domestic setting , and congenital and transfusional transmission to varying degrees to achieve the 2020 goals . We modeled each as an attenuation of the force of infection ( S1 Text ) . For vector-borne transmission , we modeled this as a reduction in the contact rate between humans and triatomines only in the domestic setting; that is , we attenuated the force of infection by a specified amount and account for the proportion of transmission due to domestic vectors ( S1 Text ) . As the goal is to evaluate the 2020 goals and not the way in which these are achieved , these reductions served as a proxy to represent a variety of ways transmission could be interrupted in the domestic settings ( e . g . , housing improvements , indoor residual spraying , bed nets ) and for congenital transmission ( e . g . , screening and treatment ) . Sensitivity analyses evaluated the degree to which transmission was interrupted for the three types ( 0% to 100% ) . Additional sensitivity analyses further evaluated each calibrated parameter at their low and high calibrated values ( Table 1 ) . We also varied the movement of triatomines to and from the peridomestic and domestic settings ( ±50% ) , as this can vary with many factors . [13 , 52] Model outcomes are the number of new/acute human cases ( which reflects transmission ) and the overall prevalence of human cases ( which reflects the general disease burden ) .
With no interruption in any form of transmission , T . cruzi prevalence in humans remained stable at 1 . 8% , with 1 . 0 new acute case each year ( i . e . , transmission event ) , so that at any given point in time there were 1 . 5 acute cases , 30 . 6 indeterminate cases , and 4 . 6 chronic Chagas disease cases in the population of 2 , 000 persons ( Table 2 ) . T . cruzi prevalence in triatomines remained stable at 23 . 5% and 48 . 4% for those in the domestic and peridomestic habitat , respectively ( Fig 2B and 2C ) , while the prevalence of T . cruzi in dogs remained stable at 8 . 8% ( Fig 2D ) . Fig 3A shows the maginitude of impact of each of various parameters on the resulting number of new monthly acute infections . Movement of triatomines between habitats had the largest impact , resulting in 0 . 064 to 0 . 089 transmission events per month ( for -50% to +50% of the baseline value ) ; followed by transmission from vector to dog and vector to humans . Varying the three most impactful parameters ( the transmission from vector to dog , transmission from vector to human , and the proportion at which triatomine feed on humans ) to their extreme values resulted 0 . 72 to 1 . 18 new acute cases each year and a prevalence of 17 . 7% to 26 . 0% in domestic triatomines and of 35 . 9% to 52 . 6% in periodomestic triatomines . Fig 2 and Table 2 show the impact of only domestic vectorial interruption to varying degrees over time on T . cruzi transmission events , prevalence , and the number of acute , indeterminate , and chronic Chagas disease cases . The largest reductions in prevalence were seen within the first year of reducing vector-host contact with the impact becoming stable by year five , regardless of the degree of reduction . Fig 2A shows the reduction in total T . cruzi transmission events in humans . Over the course of one year , a 50% to 100% reduction in domestic vector-host contact resulted in a 42 . 8% to 82 . 5% relative reduction in the number of new acute Chagas cases; this increased to a relative reduction of 46 . 1% to 83 . 0% over five years ( Fig 2A and Table 2 ) . Even with a sharp reduction in the total number of new transmission events , the number of Chagas disease cases remained relatively stable over time , with a decrease in the number of indeterminate and chronic disease cases taking approximately 12 years to manifest ( Table 2 ) . Fig 3B shows the impact of varying parameters on new acute cases with a 100% reduction in domestic vector-host . As shown , the rank order of parameters change from the no interruption scenario so that transmission from vector to dog , congenital transmission , and transmission from vector to humans had the largest impact . Varying transmission from vector to dog , transmission from vector to human , and the proportion at which triatomines feed on humans to their extremes resulted 0 . 08 to 0 . 13 new acute cases each year . Among triatomines ( Fig 2B and 2C ) , T . cruzi prevalence among domestic triatomines experienced relative decreases of 16 . 2% , 21 . 3% , 25 . 8% , and 27 . 7% compared to no reduction for vector-host reductions of 50% , 70% , 90% , and 100% respectively , after five years , while peridomestic triatomines garnered relative reductions up to 3 . 9% . After 50 years , the prevalence among triatomines ranged from 10 . 9% to 17 . 4% in the domestic ( 15 . 8% for base assumptions , Fig 2B ) and from 30 . 4% to 48 . 6% in the peridomestic ( 44 . 2% for base assumptions , Fig 2C ) settings under all conditions tested with a 100% vector-host reduction . The prevalence of T . cruzi among dogs decreased to 8 . 7% at 5 years when attenuating domestic transmission by 50% to 100% ( Fig 2D ) . The differences between scenarios in Fig 2 and Table 2 show that gains can be achieved by increasing the degree of vector-host interruption at different points in time . For example , increasing from 50% to 100% in year 3 would result in 2 . 0 total transmission events by year 5 compared to 2 . 7 events per 2 , 000 persons . After five years , only controlling congenital transmission led to a 2 . 4% ( 30% reduction ) to 8 . 1% ( 100% reduction ) relative reduction in the total number of new acute cases . This resulted in 0 . 1 to 0 . 4 fewer total transmission events , respectively . However , controlling only congenital transmission had very little impact on T . cruzi prevalence in triatomines and dogs , with maximum relative reductions of 0 . 9% , 0 . 2% , and 0 . 1% in domestic triatomine , peridomestic triatomine , and dog seroprevalences , respectively , after five years . Reducing only transfusional transmission had minimal impact on the number of new acute cases and no impact on T . cruzi prevalence in any population . The relative reduction in the total number of new acute cases ranged from 0 . 1% to 0 . 3% ( 30% to 100% reduction ) over five years . Fig 4 and Table 3 show the impact of reducing all three transmission routes to varying degrees . After five years , there are two to five fewer Chagas cases per 2 , 000 persons , varying with the degree of interruption ( Table 3 ) ; however , differences increase over time , with 25 fewer cases given 100% interruption of all three transmission routes . Stopping all three forms of transmission resulted in 0 . 2 transmission events over the first year and 0 . 5 over five years ( compared to 1 . 0 and 5 . 0 with no interruption over one and five years , respectively ) ; interrupting all forms by 30% resulted in 3 . 4 total events over five years . This corresponds to a 32% to 90% relative reduction ( 30% to 100% interruption in all forms ) in new acute cases over five years . Interrupting all three transmission routes by 100% resulted in a human prevalence of 0 . 6% after 50 years . Transmission from vector to dog ( ranging from 0 . 006 to 0 . 008 transmission events per month ) , followed by triatomine movements between habitats , and transmission from vector to human had the largest impact on the number of new transmission events ( Fig 3C ) . Again , varying the three parameters most impactful with no transmission resulted in a range of 0 . 06 to 0 . 10 new acute cases per year ( compared to 0 . 08 per year when held at middle values ) . Relative reductions in domestic triatomine prevalence over five years ranged from 10 . 5% to 27 . 8% ( 30% reduction in all types to 100% reduction in all types ) . The differences between scenarios show the gains that can be achieved by increasing the degree of vector-host interruption at different points in time . Greater achievements could be made by increasing vector-host interruption alone than by increasing control of congenital and transfusional together ( i . e . , greater gains increasing vectorial from 30% to 70% than increasing congenital and transfusional from 30% to 90% ) . For example , if congenital and transfusional transmission were interrupted by 90% , further reducing vector-host contact from 70% to 90% in year two for two years would result in 1 . 0 total transmission event vs . 1 . 3 per 2 , 000 persons .
All models are simplifications of real life and as such cannot represent every possible event or outcome . Our current model is deterministic in nature and does not include the full heterogeneity possible for Chagas disease transitions between states . Our model inputs were fit to disparate data of varying quality yet can be refined as new data become available . As Chagas disease is underdiagnosed and underreported , our estimates for T . cruzi seroprevalence in the absence of control measures are subject to limitations; however , we used the best available data for these parameters . We assumed a robust interruption in transmission that did not wane over time and assumed a constant reduction . While our model allows for differential infectiousness of humans in the three disease states , we assumed the same value for both indeterminate and chronic patients , as evidence suggests these patients have comparable levels of low parasitemia . [61] We also did not consider oral Chagas disease transmission nor account for seasonal effects . For simplicity , our model also does not include other biological states , transmission types , and outcomes for dogs nor other synanthropic wildlife ( however , dogs and chickens serve as reservoir and dead-end populations , respectively ) . Given lack of data on transmission rates for parameterization , we did not further stratify the infection state in dogs to include acute and chronic disease , nor did we consider oral transmission . Likewise , we did not include the impact of predation rate on vectors by dogs , given we modeled a stable bug population . While there is a possibility that excluding these factors may affect results , most likely they would not as we calibrate the simulation to a certain prevalence in dogs and this would be maintained regardless of the number of dog disease states and transmission types . This prevalence is maintained by the vector to dog transmission rate , thus highlighting its importance to our model; however , we note that our resulting calibrated value for the infectiousness of dogs was lower than values reported in the literature . [62] Our future work can further incorporate these factors . Our results suggest that achieving the 2020 Sustainable Development Goals of 100% interruption and control of domestic sectorial , congenital , and transfusional transmission in the Yucatan and other regions with similar epidemiological conditions fall short of completely interrupting T . cruzi transmission , despite considerably reducing the number of new Chagas cases . Thus , complementary approaches and other prevention and control measures ( e . g . , peridomestic vector control , vaccines and increased healthcare utilization ) are needed to fully interrupt Chagas disease transmission . Even if these goals are missed , most gains are achieved within the first year of implementation , thus the goals should be actively pursued . | While World Health Organization’s ( WHO ) London Declaration on Neglected Tropical Diseases has proposed 2020 goals of 100% certified interruption or control of the three main forms of Chagas disease transmission ( vectorial , congenital , and transfusional ) in Latin America , the impact of achieving and/or missing these goals is not known . Policy makers need to know the potential impact of missing these goals on disease incidence and prevalence . If they are achieved , decision makers need to know what may be left to adequately inform policies and the future for controlling Chagas disease . Our compartmental simulation model suggests that achieving the 2020 goals would still result in 25 new acute cases per 100 , 000 over five years . However , substantial gains could still be garnered within one year by interrupting transmission to varying degrees , so the goals should still be pursued . | [
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| 2018 | Are the London Declaration’s 2020 goals sufficient to control Chagas disease?: Modeling scenarios for the Yucatan Peninsula |
The increasing incidence of osteoporosis worldwide requires anabolic treatments that are safe , effective , and , critically , inexpensive given the prevailing overburdened health care systems . While vigorous skeletal loading is anabolic and holds promise , deficits in mechanotransduction accrued with age markedly diminish the efficacy of readily complied , exercise-based strategies to combat osteoporosis in the elderly . Our approach to explore and counteract these age-related deficits was guided by cellular signaling patterns across hierarchical scales and by the insight that cell responses initiated during transient , rare events hold potential to exert high-fidelity control over temporally and spatially distant tissue adaptation . Here , we present an agent-based model of real-time Ca2+/NFAT signaling amongst bone cells that fully described periosteal bone formation induced by a wide variety of loading stimuli in young and aged animals . The model predicted age-related pathway alterations underlying the diminished bone formation at senescence , and hence identified critical deficits that were promising targets for therapy . Based upon model predictions , we implemented an in vivo intervention and show for the first time that supplementing mechanical stimuli with low-dose Cyclosporin A can completely rescue loading induced bone formation in the senescent skeleton . These pre-clinical data provide the rationale to consider this approved pharmaceutical alongside mild physical exercise as an inexpensive , yet potent therapy to augment bone mass in the elderly . Our analyses suggested that real-time cellular signaling strongly influences downstream bone adaptation to mechanical stimuli , and quantification of these otherwise inaccessible , transient events in silico yielded a novel intervention with clinical potential .
Mechanical stimuli are anabolic for bone and hold promise to counteract skeletal fragility associated with bone loss pathologies [1] , [2] . Exercise based strategies are especially attractive given the critical need for inexpensive options to treat osteoporosis worldwide [3] . However , readily complied and tolerated exercise has proved ineffective in enhancing bone mass in the elderly population most in need of such interventions [4] , [5] . Studies that have examined the apparent ineffectiveness of mild exercise in the elderly using cell culture systems and animal models have sometimes led to conflicting outcomes . For instance , specific aspects of cell signaling pathways may/may not be altered with age [6] , [7] . Furthermore , reports suggest that exercise training can elicit enhanced bone tissue responses in animals at advanced age [8] , [9] . However , in studies where in vivo deformations and strains induced by mechanical stimuli are equivalently calibrated , observations suggest that aging markedly blunts the osteogenic response to mechanical stimuli [10] , [11] , and renders adaptation into a low-level binary off-on state [12] . To begin to explore underlying potential for age-related deficits in mechanotransduction function , we first focused upon observations that a single bout of mechanical stimuli ( ∼100 s ) is sufficient to influence bone matrix secretion up to a week later [13] . Furthermore , brief stimuli ( ∼15 s ) repeated every 24 hrs robustly enhances bone formation and bone mass [14] . These observations suggest that cell signaling activated during brief stimuli are focal events that guide unique downstream bone adaptation . A variety of second messengers ( e . g . , Ca2+ , NO , PGE2 , cAMP , ATP ) are acutely activated by mechanical stimuli . Of these , the Ca2+ ion second messenger system may be unique in its specificity given that all important aspects of mechanical stimuli that influence bone formation in vivo ( e . g . , magnitude , strain rate , frequency , rest intervals ) have been observed to provoke highly specific real-time Ca2+ oscillations in bone cells in vitro [7] , [15]–[17] . Furthermore , while blockade of Ca2+ signaling disrupts mechano-responsive gene expression [18] , [19] , gap junctional communication integrates Ca2+ signaling within the bone cell network [20] and may be required to influence ‘effector’ osteoblast cell differentiation [21] , [22] . Downstream of signaling through the Ca2+ ion system , activation of a variety of transcription factors ( e . g . , NF-κB , JNK , NFAT ) provides putative links between cell responses on the order of seconds to cell function over successive days of the week . Of these , the nuclear factor of activated T-cell family of transcription factors ( NFAT c1–c4 ) may be unique . Specifically , NFAT activation dynamics ( within minutes ) is remarkably specific to Ca2+ amplitudes and frequencies known to influence distinct downstream cell functions , including proliferation , differentiation and apoptosis [23] , [24] . Taken together , and given recent evidence of NFAT's critical involvement in bone mechanotransduction [25] , [26] , characterization of Ca2+/NFAT signaling and age-related alterations in bone cells in vivo may prove useful . However , it is not currently possible to experimentally quantify real-time activation of Ca2+/NFAT signaling in situ within bone . Given this inaccessibility , we previously developed a model for Ca2+ signaling induced in simple networks of osteocytic cells subject to mechanical stimuli using a numerical/computational technique called agent-based modeling ( ABM ) [27] . ABMs have been used to explore bottom-up emergent phenomena in biological systems ranging from cell-cell interactions [28] to ecosystem dynamics [29] . In our prior study , we used the characteristic of the ABM technique to examine how local properties ( e . g . , cell response thresholds ) and dynamic interactions between local properties over time ( e . g . , cell-cell communication ) influenced the emergence of global properties ( e . g . , collective Ca2+ signaling in cell ensembles ) . Based in part upon our original framework [27] , here we present an ABM of Ca2+/NFAT signaling in situ within bone's cellular syncytium and its relation to osteoblastic relative mineral apposition rates induced by mechanical loading ( r . MAR; where r . MAR = MAR in loaded−MAR in contralateral bones; Fig 1 ) . We hypothesized that the Ca2+/NFAT ABM would accurately simulate bone adaptation , predict aspects of the modeled pathway that were altered by aging and identify aspects amenable to intervention . To test our hypothesis , we examined whether the model could be calibrated to accurately simulate relative periosteal bone formation rates ( rp . BFR ) induced in vivo by a variety of mechanical stimuli in young and senescent female C57BL/6 mice [12] , [30] . We subsequently used the model to predict age-related deficits within the pathway . Similar to the literature , the model predicted age-related deficits in the ability of Ca2+ oscillations to dephosphorylate NFAT [31] and in NFAT DNA binding capacity [32] , as factors underlying the muted adaptation observed in senescent animals [12] . We then surveyed the literature to examine pharmaceutical agents that could modulate these deficits and identified Cyclosporin A ( CsA ) as one possibility [33]–[35] . Lastly , to validate model predictions , we examined whether the use of low-dose CsA as a supplement could restore the bone formation response to loading when implemented in vivo in senescent animals .
The ABM of the Ca2+/NFAT pathway incorporated sufficient parametric complexity to simulate rp . BFR induced by a variety of loading protocols in young adult [30] and senescent animals [12] . We identified an optimal ABM parameter vector by maximizing likelihoods via an optimization procedure , called simulated annealing that explored the model parameter space ( Table 1 ) . At the young and aged maximum likelihood estimate ( MLE , Table 2 ) , simulation of Ca2+ oscillations in individual cells ( Fig 2 , 3 ) was qualitatively similar to that observed in in vitro systems [15] , [17] . Further , NFAT signaling downstream of Ca2+ oscillations conferred a high-fidelity memory , and the resulting relative mineral apposition rates in individual cells retained information regarding the distinct osteogenic potentials of the loading protocols ( Fig 2 , 3 ) . Importantly , the consequent tissue level rp . BFR simulated for animal specific strains was not significantly different from in vivo data in young adult and in senescent animals ( p = 0 . 67 , 0 . 57 respectively; Fig 2 , 3 ) . To test for redundant model parameters , we next examined whether the ABM would remain ‘true’ upon further simplification via parameter ‘knock-outs’ ( i . e . , constraining parameter values = 0 , one at a time ) . ‘Knock-out’ of parameters resulted in simulations that were significantly different from both the experimental rp . BFR data ( p≤0 . 02; Table S1 ) [12] , [30] and from rp . BFR simulated by the unconstrained ABM ( p≤0 . 01; Fig 4 ) . Of note , removing parameters ER0 or r . MARx always resulted in rp . BFRi = 0 ( regardless of the loading protocol ‘i’ and whether the remaining parameters were optimized ) . Post-hoc analysis of ABM simulations suggested that ‘knock-out’ of the remaining parameters de-couples Ca2+ oscillation from induced strains ( Tεx = 0 ) , inhibits secondary Ca2+ transients ( R0 = 0 ) , decreases Ca2+ induced dephosphorylation of NFAT ( α = 0 ) and renders mineral apposition rates into a binary ‘on/off’ state in osteoblasts ( NFATnx = 0; Fig 4 ) . As such , attempts to simplify the model via functional ‘knock-outs’ significantly degraded model ability to simulate the in vivo data ( Fig 4 ) . The ability of the unconstrained ABM to accurately simulate the in vivo data from both young [30] and senescent animals [12] uniquely permitted an examination of aging related alterations in the model's parameters . The null-hypothesis that aging does not alter the Ca2+/NFAT pathway ABM was rejected ( 6-df , p<0 . 0001 ) . Given this result and in the context of literature reports , analysis of null hypotheses that aging does not alter the parameters Tεx , ER0 , r . MARx were accepted both when considered individually ( p = 0 . 31 , 0 . 58 and 0 . 75 , respectively; 1-df ) and synchronously ( p = 0 . 85; 3-df ) . Subsequently , when it was assumed that aging does not alter Tεx , ER0 , and r . MARx , the null hypothesis that aging does not degrade components ( 1-df ) was rejected for the parameter α ( p = 0 . 004 ) and NFATnx ( p = 0 . 002 ) but accepted for the parameter R0 ( p = 0 . 27 ) . Lastly , we examined the influence of simulated interventions that restored ( the identified ) age-related deficits in components of the Ca2+/NFAT pathway . We found that synchronously restoring parameters with significant age-related deficits ( i . e . , α , NFATnx ) to their young optima values significantly increased rp . BFR induced by a variety of loading stimuli ( p<0 . 001; mean: +95% , range: +49% to +120% ) . We next performed in vivo experiments to validate model insights and to thereby evaluate the promise of this strategy ( i . e . , interventions that could restore age-related deficits in α , and NFATnx ) . Specifically , senescent female C57BL/6 mice ( 22 Mo ) were subjected to mechanical loading 3 d/wk for 3-wks with/without low-dose CsA supplements ( 0 . 3 or 3 . 0 mg/kg s . c . ) . Additionally , to examine the extent to which bone adaptation can be rescued at senescence , young female C57BL/6 mice underwent an identical loading protocol ( without CsA supplementation ) . We found that in contralateral bones ( not subject to exogenous loading ) , periosteal mineralizing surface ( p . MS/BS , p = 0 . 29 ) , mineral apposition rate ( p . MAR; p = 0 . 82 ) and bone formation rate ( p . BFR/BS; p = 0 . 56 ) were not significantly different between aged animals with or without CsA supplements or compared to young animals ( Fig 5 ) . In these experiments , loading induced periosteal strains were not significantly different between senescent and young mice ( p = 0 . 27 ) . While p . MS/BS was not significantly different in the loaded limbs across groups , we found that loading induced p . MAR ( p<0 . 01 ) and p . BFR/BS ( p = 0 . 04 ) were significantly lower in vehicle treated senescent mice compared with young mice ( Fig 5 ) . In contrast , loading supplemented with CsA at both 0 . 3 and 3 . 0 mg/kg significantly enhanced both p . MAR ( p<0 . 01 for both dosages ) and p . BFR/BS ( p<0 . 01 for both dosages ) compared to that in vehicle treated senescent mice and to levels not different from that in young animals ( Fig 5 , p>0 . 55 ) . Lastly , we examined whether the model could be used to predict rp . BFR induced in senescent animals supplemented with low-dose CsA . Specifically , to simulate the hypothesized mode of interaction between loading and low-dose CsA ( Fig 6 a ) , model parameters significantly degraded by age ( i . e . , α , NFATnx ) were synchronously restored to their young optima values ( Table 2 ) . Simulated restoration of age-related deficits in α and NFATnx ( that are downstream of Ca2+ signaling ) did not differentially influence Ca2+ oscillations in cells around the surface in response to the loading protocol ( 1700 με , 50 c/d; not shown here for brevity ) . Restoring α and NFATnx resulted in increases in NFAT dephosphorylation in surface precursors ( Fig 6 b , c ) and consequent r . MAR ( Fig 6 d ) in differentiated osteoblasts around the bone surface compared with the case with aged model parameters ( Table 2 ) . Validating model predictions , we found that simulation of rp . BFR was not significantly different from in vivo data ( Fig 5 f ) obtained from senescent mice subject to loading without or with CsA , and from young mice subject to loading ( p = 0 . 87; Fig 6 f ) .
We present the development of an ABM that describes activation of Ca2+/NFAT signaling within and between cells in bone . The model incorporated sufficient complexity ( i . e . , parameters and analytics ) to accurately simulate relative periosteal bone formation rates induced by a variety of mechanical stimuli in young adult and senescent animals . Subsequent in silico ‘knock-out’ of ABM parameters indicated that attempts to further simplify the model significantly degraded the ability to simulate the in vivo data . Model predictions of age-related alterations ( or lack thereof ) in 5 of 6 ABM parameters were similar in direction to that reported individually in the literature . Model simulations suggested that restoring deficits in NFAT dephosphorylation/translocation and DNA binding capacity could substantially enhance bone's responsiveness to loading at senescence . Finally , follow-up in vivo experiments validated the model predictions and confirmed that low-dose CsA , when used as an adjuvant to skeletal loading , can completely rescue loading induced bone formation in senescent mice . The broad assumption underlying our study is that activation of intracellular Ca2+ oscillations and subsequent activation of the NFAT pathway is a critical mechanism underlying bone mechanotransduction . As described in the introduction , the literature clearly supports this hypothesis [23]–[26] . Our model represents a first attempt at quantifying activation of the Ca2+/NFAT pathway in situ and its downstream consequences for bone tissue . However , given that a number of additional second messenger systems are also activated by brief mechanical stimuli ( e . g . , cAMP , NO ) , and that transcription factors in addition to NFAT also modulate and influence cell and tissue adaptation downstream [1] , [36] , [37] , we expect to explore these interacting pathways in future model refinements . Another limitation was associated with the mathematical formulations used to simulate loading induced activation of the Ca2+/NFAT pathway . The formulations were specified to efficiently ( computationally ) describe experimental observations and were similar to previous mathematical descriptions [17] , [21]–[23] , [27] , [37]–[45] . While the current model parameterization undoubtedly represents a simplified description , the ABM technique would readily enable finer mathematical specifications of more fundamental mechanisms and processes via which Ca2+/NFAT signaling emerges in individual cells . One direct limitation of the ABM formulation is that model time increments are restricted to 1 s and greater ( and hence , the current model cannot simulate adaptation induced by aspects of loading such as strain rates and frequencies ) [2] , [46] . While formulating the biological observations in a more refined manner ( e . g . , differential equation based ) would address this limitation , such an approach would also be expected to substantially increase the computational effort required . Also , the parameter optima for this ABM ( Table 2 ) are relevant only to this current model . As such , any further refinement of the model would be likely to result in new optima . In the context of these observations , the accuracy of our model predictions ( age-related pathway deficits , prediction of the effects of targeted interventions ) does lend initial validity for the implemented formulations . We next consider some of other simplifications and limitations of the model . For instance , while the current model accurately simulates adaptation at the periosteal surface , it does not accurately predict adaptation at the endocortical surface . This is not entirely surprising given that the cells responsible for endocortical adaptation may be unique from periosteal precursors , and their distinct milieu can induce differential responses to mechanical stimuli [47] . Further , cells present at the endocortical surface may receive stimulation secondary to loading in a very different manner ( e . g . , marrow pressurization [48] ) compared to periosteal cells - a difference that may instead contribute to the differential response at the bone surfaces . As such , planned model expansions to simulate adaptation at the endocortical surface will have to address some of these possibilities . Another limitation is that the current model is a 2-D description of a biological process that undoubtedly unfolds in 3-D . While there is no feature within the mathematical model per se that precludes extension to 3-D , a representation of the cell network in the tibia diaphysis via imaging is first needed to incorporate 3-D network topology and anatomy within the model , and will be a focus of future model refinements . A third limitation is that the model does not explicitly incorporate parameters that distinguish between the different loading protocols in previous young ( 3 days/wk for 3-wks ) vs senescent animal experiments ( 5 days/wk for 2-wks ) [12] , [30] from our group that were used in model calibration . The reasons for this omission were: 1 ) reports , including our preliminary data , which suggest that loading bone every 24 hrs vs 48 hrs does not differentially influence bone formation [49] , 2 ) the time between fluorochrome labeling were the same in the two studies ( 9 days ) [12] , [30] , 3 ) the current model can accurately simulate the previous data [12] , [30] without an additional parameter , and 4 ) bone formation rates induced in our current in vivo experiments and model predictions in aged mice ( 1700 με , 50 c/d , 3 days/wk , 3-wks; Fig 6 ) were per expectations given our previous data in senescent mice ( that underwent 50 cycles/d of loading for 5 days/wk over 2-wk protocols at both 1200 με and 2400 με ) . However , the lack of explicit model consideration of this loading bout scheduling aspect of protocols is a limitation that will need to be addressed if broader optimization questions are to be examined ( e . g . , when and how often is it best to load bone ? ) . Despite these and other limitations , analysis indicated that the model of the Ca2+/NFAT pathway was sufficient to simulate bone formation induced in both young adult and senescent animals in response to a wide variety of loading stimuli [12] , [30] . In the statistical realm , refinement of our idealized model for Ca2+/NFAT signaling , parametric inclusion of other second messengers ( e . g . , NO , ATP ) and interacting pathways ( e . g . , MAPK , Wnt signaling ) [33] , [50] , or alternate mathematical forms will not result in significantly more accurate simulations of the data at hand . As such , we believe that this result establishes the current model as a critical first step in our exploration of bone mechanotransduction in silico . Furthermore , given the modularity of the ABM technique , our approach readily permits future ABM expansions that address limitations inherent to the current model . We expect that such iterative expansions could lead to more comprehensive models that provide increasingly nuanced representations of mechanotransduction while conferring the ability to predict and optimize bone adaptation induced by a substantially wider variety of mechanical stimuli . On the other hand , attempts to simplify the current model of the Ca2+/NFAT pathway via parameter ‘knock-outs’ significantly degraded the accuracy of simulations . Similar to literature reports [40] , knock out of the parameter specifying the maximal capacity of ER Ca2+ stores ( ER0 = 0 ) abolished intracellular Ca2+ oscillations and eliminated all further downstream outcomes . Knock-out of the parameter specifying the maximal rate of osteoblastic mineral apposition ( r . MARx = 0 ) represented the trivial case where rp . BFR was always zero . As the ABM was unable to adapt to these knock-outs ( via optimization of the remaining parameters ) , the parameters ER0 and r . MARx were therefore critical components of our model of the Ca2+/NFAT pathway . While knock-outs of the remaining parameters ( Tεx , R0 , α and NFATnx ) were not synthetically lethal for bone formation ( i . e . , rp . BFR ≠ 0 ) , they nevertheless caused significant loading protocol specific declines in the accuracy of ABM simulations of the in vivo data ( Fig 4 ) . This was despite the flexibility of the model and the ability of the remaining intact parameters to adapt ( via optimization and location of alternate MLEs under the knockout constraint ) . Taken together , these results further suggest that while the 6-parameter baseline model was sufficient and accurate , further simplification of the model ( via parameter ‘knock-outs’ ) significantly degraded model ability to simulate the data at hand [12] , [30] . The highly specific adaptation that resulted from simulated knock-out of ‘sub-critical’ components of the pathway had an alternate utility as they further elucidated the functioning of the pathway ( as modeled ) . Specifically , knock-out of parameter Tεx decoupled strain amplitudes from the Ca2+ dynamics and caused highly unusual Ca2+ oscillations in cells ( Fig 4 a ) . While these unusual Ca2+ oscillations occurred directly as a consequence of compensatory adaptation of remaining ABM parameters , it is unclear whether they are representative of what might occur in vivo ( or in vitro ) should such a knock out prove feasible . Ultimately , knockout of parameter Tεx resulted in significant differences in simulated rp . BFR in loading protocols where increasing strains induce differential adaptive responses . Knock-out of the parameter R0 prevented re-filling of stores , eliminated secondary intracellular Ca2+ transients , and despite model compensation , significantly degraded rp . BFR simulated when bones were subject to the extended rest-inserted loading protocol ( that was most impacted by the lack of secondary Ca2+ transients ) . Knock-out of the parameter α decoupled the ‘memory’ conferred to NFAT dephosphorylation events from upstream Ca2+ oscillation histories , and impacted the simulated rp . BFR responses to cyclic loading stimuli ( the secondary Ca2+ transients induced by rest-inserted loading shielded this stimulus from the effects of knocking out parameter α ) . However , the effect of knocking out parameter NFATnx was more ubiquitous and significantly altered simulated rp . BFR for both cyclic and rest-inserted loading protocols . Of note , experimental exploration of some of these knock-outs is possible in vivo and could form the basis for additional , future model validation . For example , knock-out of model parameter R0 could be partially achieved via functional knock-out of proteins regulating SERCA pumps [51] . Additionally , knock-out of the parameter α could be achieved , for instance , by modulating expression of negative feedback elements of the Ca2+/NFAT pathway such as RCAN1 [52] . Each of these functional knockout experimental models could provide extremely valuable tools to further validate the ABM , and more importantly , to investigate bone mechanotransduction function in finer detail . The ability to accurately simulate data in both young adult and senescent animals ( without resorting to additional qualifiers ) enabled us to examine age-related alterations in our model for the Ca2+/NFAT pathway . Of note , parameter alterations were not specified a priori . Similar to literature , analysis suggested that aging does not significantly alter three components of the modeled pathway: 1 ) the induced strain magnitude that maximizes Ca2+ amplitudes ( Tεx ) [7] , 2 ) the Ca2+ sequestration capacity of the ER stores ( ER0 ) [53] , and 3 ) the maximal rates of mineral apposition by osteoblasts ( r . MARx ) [54] . Subsequent analysis predicted that aging significantly decreased Ca2+ induced dephosphorylation/translocation of NFAT ( α ) [31] , and decreased NFAT DNA binding capacity ( NFATnx ) [32] . However , contrary to literature [55] , predicted alterations in Ca2+ store refilling rates ( R0 ) did not attain statistical significance . It is possible that addressing model limitations ( e . g . , addressing our lack of consideration of cell viability at senescence [56] ) could resolve this one apparent inconsistency . However , it also remains to be determined whether the reported deficit in R0 occurs synchronously with correctly predicted age-related deficits ( i . e . , in α , NFATnx ) [31] , [32] and lack of deficits in other parameters ( i . e . , in Tεx , ER0 , r . MARx ) [7] , [53] , [54] , and in the specific context of bone response to mechanical stimuli . Taken together , we believe that the validity of the model is augmented by its ability to correctly predict the direction of age-related alterations in 5 of 6 components of the modeled Ca2+/NFAT pathway . By describing pathway alterations and their relation to bone adaptation within a consolidated framework , the ABM provided a unique tool to query the impact of targeted interventions . Interestingly , a hypothetical simulated intervention that fully and synchronously restored parameters significantly degraded by aging ( α , NFATnx ) to their young ‘states’ was found to nearly double bone formation response to loading at senescence . To explore and exploit this promising prediction , we considered the use of low-dose CsA supplements to mechanical stimuli . CsA is a currently approved immunosuppressant normally regarded as an inhibitor of NFAT signaling . There is substantial conflict regarding the influence of CsA upon bone adaptation , with reported effects dependent upon a number of cofactors , including experimental system and dosage [57] , [58] . A recent study clarifies these inconsistencies [35] , and suggests that CsA has bi-phasic effects in the trabecular compartment of bone in young adult mice ( anabolic at low dosages and inhibitory at high dosages ) . However , to our knowledge , no previous study has examined whether and how CsA and exogenous mechanical stimuli might interact and modulate bone adaptation . Here , we hypothesized that supplementation with low-dose CsA would mitigate predicted deficits in loading ( and Ca2+ ) induced dephosphorylation and nuclear translocation of NFAT ( α ) in part via CsA suppression of negative regulators such as p38 [34] and/or GSK-3β [33] ( Fig 6 a ) . We additionally speculated that CsA supplementation could also effectively counteract predicted deficits in NFAT-DNA binding ( NFATnx ) by enhancing cooperative binding interactions between loading induced translocation of NFAT and CsA enhanced activation of transcription factor families such as AP-1 [35] ( Fig 6 a ) . We found that supplementation with CsA ( at both 0 . 3 and 3 . 0 mg/kg ) significantly enhanced periosteal mineral apposition rates ( by over 50% ) and bone formation rates ( by over 80% ) compared to that induced by loading alone , and to levels not different from that observed in young animals . While the mechanisms underlying this interaction are not known at this time , we expect that a combination of RT-PCR , immunohistochemistry ( for NFAT proteins and targets of the pathway such as RCAN1 ) , and primary culture in vitro experiments might elucidate how CsA and loading interact in the aged skeleton . In this context , while the agreement between model simulations and experimental data in senescent mice supplemented with CsA validates model predictions ( Fig 6 ) , it also provides the rationale for initially investigating CsA-loading interactions as hypothesized ( Fig 6a ) . Importantly , the low-dose CsA supplements tested here were observed to completely rescue loading induced bone formation in senescent animals , a deficit that , to our knowledge , has not previously been overcome . These pre-clinical data suggest that CsA in combination with readily complied , mild physical exercise could represent an extremely low-cost anabolic option to augment bone mass . Specifically , we estimate that low-dose CsA could be utilized as an adjunct with mild physical activity for less than $1 US/month [59] . If optimized , and efficacy is borne out in clinical trials , this intervention would enable an inexpensive , anabolic treatment option that could be readily implemented in elderly populations at risk in developed and developing countries alike . In summary , we present an agent-based model that describes , for the first time , how bone tissue formation in response to brief loading might be controlled by real-time signaling interactions within the bone cell syncytium . Despite the relative simplicity of our approach compared to complexity of bone mechanotransduction , the model was sufficiently sophisticated to enable successful calibration and hence , was able to quantitatively simulate bone formation rates induced by a variety of mechanical stimuli in both young adult and senescent animals . Post calibration , the model was found to correctly predict the directions of age-related alterations in 5 of 6 modeled components of the Ca2+/NFAT pathway and identified deficits that were amenable to therapeutic manipulation . Validating model predictions , supplementing mild mechanical stimuli with low-dose CsA was observed to completely restore the bone formation response to loading at senescence . Our model-enabled discovery provides the direct rationale to consider this currently approved pharmaceutical alongside mild physical exercise as an inexpensive , yet potent therapy to augment bone mass in the elderly . These results also further emphasize that intracellular events initiated during transient/infrequent stimuli ( and deficits within ) may have profound and lasting influences upon cells , tissues , and organs . Given the inaccessibility of such real-time signaling pathways in biological systems and the success of an agent-based modeling approach in this context , we speculate that an analogous approach may prove successful in understanding and predicting the behavior of other primarily homeodynamic systems facing sudden perturbations ( e . g . , adaptation following musculoskeletal trauma ) .
The ABM was developed to examine signaling interactions between cells at the murine tibia mid-shaft , the site where experimental data had previously been measured ( Table S1 ) [12] , [30] . To determine cell network topology at the tibia mid-shaft ( Fig 1 ) , we obtained thin ( 5 µm ) decalcified sections from the tibia mid-shaft of young adult ( 4 Mo , n = 6 ) and senescent female C57BL/6 mice ( 22 Mo , n = 5 ) . Sections were Toluidine blue stained per standard protocols [60] , imaged under white light excitation ( 200 X ) , combined into composites and oriented anatomically . For each composite image , the cortical bone was sub-divided and osteocytic cell lacunae present within each sub-sector ( ∼100 µm2 , 384 sub-sectors ) were counted for each bone and averaged across bones ( young , senescent separately ) . This provided an estimate of the ‘average’ count of osteocytic cells present within each of 384 anatomically oriented sub-sectors . To define bone anatomy , we also imaged un-decalcified sections separately obtained from young adult ( 90 µm , 4 Mo , n = 5 ) and senescent female C57BL/6 mice ( 22 Mo , n = 5 ) . The cross-section chosen for ABM implementation was representative of the average anatomy ( i . e . , in cortical area , thickness , moments of inertia ) . Upon this cross-section , each of 384 sub-sectors was ‘seeded’ with the average number of cells previously determined within the sub-sector . Cell locations within each sub-sector were then specified via a non-overlapping , random walk process . With regard to the effector cells ( collectively termed ‘precursors’ in the model ) , per literature [61] , we assumed that a single layer of precursor cells were present around the cortex and were positioned at the endocortical and periosteal surfaces ( along 96 equal angle sectors ) . While immediately adjacent precursors were assumed to be functionally coupled , osteocyte - osteocyte coupling and osteocyte-precursor couplings were specified , per literature [62] , if cell-cell separation was within a canalicular length of 50 µm ( Fig 1; where ‘functional coupling’ simulates an ability to pass Ca2+ ions between coupled cells ) . Similar to literature [63] , in the idealized network for young animals , the mean ( ± S . D . ) number of functional connections per periosteal precursor was 4 . 0±1 . 6 and that for osteocytes was 7 . 9±2 . 3 , and the average canalicular length was 34±12 µm . In the network for senescent animals , the mean ( ± S . D . ) number of functional connections was 4 . 2±1 . 5 per periosteal precursor and 10 . 7±2 . 7 for the osteocyte ( p<0 . 001 vs young ) , and the average canalicular length was 34±12 µm . Of note , osteocytic lacunar density was significantly increased in these aged vs young female C57BL/6 mice ( 940±80 vs 626±90/mm2; p = 0 . 001 ) . However , this data is contrary to declines in lacunar density typically observed with age in healthy human bone [64] , but similar to increases observed in osteoporotic vs control bones [65] . Given that , to our knowledge , there is no comparable report on age-related alterations in lacunar density in mice , the increase in lacunar density observed here may be specific to the female C57BL/6 mice at this advanced age ( 22 Mo ) and remains to be further investigated . Given the cell network topology ( Fig 1 ) , we next determined localized tissue strains at the location of each cell induced by loading of the tibia . Using a combined strain gauging and finite element modeling approach , we have previously calibrated the force , moment and tissue level strain environment that is induced at the tibia mid-shaft by loading [12] , [30] . As such , for each independent loading protocol ( Table S1 ) [12] , [30] , we utilized beam theory along with known force and moment boundary conditions at the tibia mid-shaft to determine tissue level strains at the location of each mechanosensory osteocytic cell over the time duration of the loading protocol . The ABM is a biophysical model that relies on experimentally identified ‘building blocks’ to algorithmically describe how activation of the Ca2+/NFAT pathway by loading of bone influences mineral apposition rates induced in individual osteoblasts ( Fig 1 , Table S2 , Protocol S1 ) . In the model , intracellular Ca2+ oscillations induced in individual cells ( osteocytes and precursors ) at time ‘t’ during application of loading , [Ca2+]i ( t ) , emerged via an interplay between Ca2+ influx due to loading induced strains at the location of the osteocytic cell ( εi ( t ) ) , cell-cell gap junctional exchange of Ca2+ ions and efflux from ER Ca2+ stores as follows: ( 1a ) where , ( 1b ) ( 1c ) and , ( 1d ) where , Tεx is the parameterized threshold strain magnitude required to maximize the amplitude of Ca2+ oscillations in mechanosensory osteocytes ( normalized to 100% values ) , ni is the number of cells ( osteocytes and/or precursor cells ) functionally coupled to the ith cell , j is an element of the cell set ni , TCn represents threshold ‘incoming’ Ca2+ ion levels required to initiate oscillations in the recipient cells ( specified via preliminary simulations to be 2 . 5% ) , and ER0 and R0 are the parameterized maximal Ca2+ store capacity and maximal store recovery rates , respectively . The specific mathematical formulations for osteocyte sigmoidal dose-response relation to strain ( eqn 1b ) were based upon observations of Ca2+ oscillations induced in bone cells by mechanical stimuli [17] , [38] . Further , cell-cell communication ( eqn 1c ) is a simple description ( via averaging ) of Ca2+ ion propagation between cells based upon reports [38] , [39] and is similar to our previous descriptions [27] . The formulation for ER Ca2+ store depletion ( eqn 1d ) is similar to previously [27] . Lastly , the formulation for ER Ca2+ store recovery upon cellular quiescence ( eqn 1d ) , models the action of the SERCA pump working against a Ca2+ ion concentration gradient and is similar to literature reports [40]–[42] . Given observations that Ca2+ oscillations in osteocytic networks hold potential to regulate surface osteoblastic cells [21] , [22] , we next consider the downstream consequences of Ca2+ oscillations upon NFAT transcription factor dynamics . Specifically , downstream of increased intracellular Ca2+ oscillations , cytoplasmic NFAT dephosphorylation and nuclear translocation in surface precursor cells was modeled to be reflective of a ‘memory’ of Ca2+ oscillation characteristics such as amplitude , frequency and durations , and included an implicit consideration of negative feedback elements [23] , [37] as follows: ( 2a ) where , the parameter ‘α’ controls the amount of NFAT dephosphorylated based upon prior Ca2+ oscillations . The quantity of NFAT translocated into the ith cell's nucleus ( ) over the duration of an entire loading bout ( tbout ) was modeled simply as: ( 2b ) Finally , given observations that the activation of the Ca2+ signaling and the NFAT pathway code for highly specific cell proliferation , differentiation and apoptotic events [23] , [37] , [43] , we assume that nuclear NFAT induced by a given loading bout controls the differentiation of precursor cells and ultimate , the relative mineral apposition rates by differentiated osteoblasts ( ) as follows: ( 3 ) where , NFATnx is the maximal nuclear NFAT DNA binding capacity , and r . MARx , the maximal relative mineral apposition rate by osteoblasts . The mathematical form ( eqn 3 ) reflects considerations that transcriptional regulation is likely to be a threshold-driven process [44] , [45] . Simulations with the ABM involved solving equations ( 1–3 ) , given osteocyte level strains derived over each second of a loading bout in the context of the ABM's 6 independent parameters ( i . e . , specific values assigned to Tεx , ER0 , R0 , α , NFATnx , r . MARx prior to execution of the simulations ) . To initialize the ABM ( at t = 0 ) , strains induced by loading at osteocyte cell locations were 0 ( i . e . , εi ( t = 0 ) = 0 ) , and the stores of all cells were assumed to be filled to capacity ( i . e . , ERi ( t = 0 ) = ER0 ) . Based upon preliminary studies , we simulated intracellular Ca2+ oscillations for an extra 200 seconds beyond cessation of a given loading bout to permit the unfolding of cell-cell cross talk , and NFAT dephosphorylation for an extra 100 s beyond cessation of Ca2+ oscillations to more fully account for prior Ca2+ oscillation histories . We implemented standard dynamic histomorphometry methods to determine bone formation indices simulated by the model at the tissue level [66] . We first made the simplifying assumption that repeated loading of bone over days of the week does not adaptively alter cells ( i . e . , make them more/less sensitive to repeated bouts of loading ) . Therefore , r . MAR induced in individual osteoblasts over a loading protocol ( e . g . , loading for 9 bouts provided over 3 days/wk for 3 weeks ) was simply determined as the average of induced by each bout of loading . Second , surface referent relative mineralizing surface ( r . MS/BS ) at the tissue level was determined simply as the proportion of surface osteoblasts with non-zero . Tissue level r . MAR was then determined as the average of non-zero at each bone surface . Finally , tissue level relative bone formation rates ( surface referent ) was determined as r . BFR/BS = r . MS/BS×r . MAR at each of the surfaces . The ABM was implemented in C++ on an Apple Mac Pro ( 2×quad core processors , 3 . 0 GHz , 8 Gb RAM ) , and simulations were performed at 1-Hz ( i . e . , smallest time unit was 1 s ) . The ABM simulates activation of the Ca2+/NFAT pathway in cells and ultimately its relation to relative bone formation rates at the tissue level . However , given that the ABM is an idealized biophysical model , the parameter values must be estimated from observed rp . BFR induced by a variety of mechanical stimuli [12] , [30] . We modeled the observed rp . BFRi from the ith loading protocol as normally distributed with population mean μi and variance σi2 . The ABM with values specified for the 6 parameters θ = ( Tεx , ER0 , R0 , α , NFATnx , r . MARx ) at the initiation of the simulations then predicts mean values μi ( θ ) = rp . BFRi for each loading protocol ‘i’ . Under the normal error model , the log-likelihood function for the ABM is then [67]: ( 4 ) where ‘yij’ is the rp . BFR measured in the jth animal for the ith loading protocol , μ ( θ ) is the vector of means given by the ABM and σ2 is the vector of population variances for rp . BFR under the different loading protocols , ‘Nprotocol’ is the number of independent loading protocols , ‘nai’ is the number of animals that underwent the ith loading protocol , ‘na’ is the total number of experimental observations . The maximum likelihood estimate ( MLE ) , for the vector of means , μ ( θ ) and variances σ2 , is simply the set of model parameters that maximizes L ( μ ( θ ) , σ2 ) . For a given vector of mean parameters μ ( θ ) of σ2 , maximization with respect to the variances σ2 is achieved by setting: ( 5 ) However , maximization with respect to the ABM parameters θ is more challenging because the likelihood function contains local maxima . To address this , we applied the simulated annealing procedure ( SA ) [68] , with random restarts to find the MLE for the ABM parameters . Briefly , the SA algorithm is based on annealing in metallurgy and is a probabilistic heuristic applied to a stochastic process ( random walk ) that seeks to determine a material configuration where energy is minimized [68] . In the context of our ABM , the SA procedure explores the 6-D model parameter space seeking to identify parameter values that would maximize the log-likelihood function L ( μ ( θ ) , σ2 ) . Using this procedure , separate ABM models were calibrated to the rp . BFR data obtained from young animals subjected to 10 protocols and senescent animals subjected to 7 protocols ( Table S1 ) [12] , [30] . We denote the resulting maximum likelihood estimates of the parameter vector for each ABM by and respectively ( Table 2 ) . To analyze model simulations , we addressed the following questions ( described in more detail in supporting information , Text S1 ) : ( a ) Are the calibrated ABM models for young and senescent animals compatible with the observed data [12] , [30] ? ( b ) What are 95% confidence intervals for the parameters θyng and θaged ? c ) Is there evidence against the null hypothesis that each of the ABM parameters takes value zero ( i . e . , simulating parameter ‘knock-outs’ ) ? ( d ) Is there evidence in the observed data against the null hypothesis that the population values of the ABM parameters are the same for young and senescent animals ? ( e ) If null hypothesis in ( d ) is rejected , is there evidence against the null hypotheses that specific model parameters or combination of parameters are unchanged by age ? ( f ) Do the calibrated ABMs make predictions about the results of hypothetical interventions that would restore specific ABM parameters in senescent animals to their young values ? ( g ) Do ABM simulations of loading induced rp . BFR , upon complete restoration of parameters significantly degraded by aging , predict data from senescent animals subject to loading supplemented with CsA ? We examined these questions using a series of likelihood ratio tests ( within MATLAB ) and factorial ANOVAs ( within SPSS ) where appropriate; please see supporting information for additional details ( Text S1 ) . | Post-menopausal and age-related osteoporosis afflicts large segments of the population and can markedly increase skeletal fragility . Bone fractures that occur as a consequence substantially increase health care expenditures and raise levels of morbidity . While strategies that prevent further loss of bone exist , options that compensate for bone loss accrued over age are less numerous . Physical exercise holds promise in this realm . However , in part due to deficits in how cells within bone respond to skeletal loading , readily complied exercise has proved ineffective in enhancing bone mass in the elderly . In this study , we examined whether the ability of physical exercise to increase bone mass can be restored at advanced age . To this end , we developed a computational model describing how a specific aspect ( or pathway ) activated in bone cells by skeletal loading may be altered with age . Our model proved successful in describing age-related pathway alterations and identified specific deficits that were amenable to therapeutic manipulation . We subsequently discovered that when an extremely inexpensive , currently approved pharmaceutical is used as a supplement , bone response to skeletal loading was completely rescued in aged animals . We believe that this result provides the rationale to consider this approach as a means to increasing bone mass in the elderly . | [
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]
| 2010 | Rescuing Loading Induced Bone Formation at Senescence |
The small Rho-family GTPase Cdc42 is critical for cell polarization and polarizes spontaneously in absence of upstream spatial cues . Spontaneous polarization is thought to require dynamic Cdc42 recycling through Guanine nucleotide Dissociation Inhibitor ( GDI ) -mediated membrane extraction and vesicle trafficking . Here , we describe a functional fluorescent Cdc42 allele in fission yeast , which demonstrates Cdc42 dynamics and polarization independent of these pathways . Furthermore , an engineered Cdc42 allele targeted to the membrane independently of these recycling pathways by an amphipathic helix is viable and polarizes spontaneously to multiple sites in fission and budding yeasts . We show that Cdc42 is highly mobile at the membrane and accumulates at sites of activity , where it displays slower mobility . By contrast , a near-immobile transmembrane domain-containing Cdc42 allele supports viability and polarized activity , but does not accumulate at sites of activity . We propose that Cdc42 activation , enhanced by positive feedback , leads to its local accumulation by capture of fast-diffusing inactive molecules .
Cell polarization is an evolutionary , ancient cellular property that , in eukaryotes , centers around the Rho-family GTPase Cdc42 . Cdc42 , which cycles between active GTP-bound and inactive GDP-bound forms , is locally activated by Guanine nucleotide Exchange Factors ( GEFs ) and accumulates at presumptive sites of polarity . Active Cdc42 then promotes the activation of numerous effectors , including p21-activated kinases ( PAK ) , nucleators of actin cytoskeleton assembly , and the exocyst complex for polarized exocytosis [1–3] . Collectively , these pathways transduce the location of Cdc42 activity into effective cell polarization , which underlies essential processes such as proliferation , migration , and signal transduction . Consistent with its central role , the misregulation of Cdc42 and other Rho-GTPases has been implicated in multiple human conditions , such as congenital diseases or infection [4] . Thus , one critical question is , “What are the mechanisms that promote the local activation and accumulation of Cdc42 ? ” Rho-family GTPases are associated with cellular membranes . The vast majority , including Cdc42 , carries a C-terminal CAAX box , which serves as signal for prenylation on the cysteine residue and insertion in the endoplasmic reticulum membrane [5] . From there , the Rho proteins can be distributed through the trafficking system up to the plasma membrane , where Cdc42 localizes . Rho-family GTPases , including Cdc42 , can also be extracted from membranes by so-called Guanine nucleotide Dissociation Inhibitors ( GDIs ) , which shield the prenyl group and keep the Rho protein in a soluble cytosolic form [6] . While local activation of Cdc42 may depend on the trivial presence of a pre-localized activator , groundbreaking work in the budding yeast has shown that Cdc42 displays the ability to polarize spontaneously , whereby both its active form and the total protein pool become dynamically polarized , even in absence of pre-established landmarks . Spontaneous polarization—also known as symmetry breaking—is observed in many cell types when the spatial cues normally directing cell polarization , such as external chemo-attractants or internal landmarks , are absent [7 , 8] . It also occurs naturally , for instance , in germinating yeast spores , which establish polarization in absence of any known pre-localized landmarks [9] . Spontaneous polarization relies primarily on positive feedback mechanisms that amplify initial stochastic noise into robust polarization [7 , 8] . One such feedback mechanism involves the formation of a protein complex between a Cdc42-GTP-binding effector—a PAK—and a Cdc42 GEF , which propagates Cdc42 activation around clusters of Cdc42 activity [10–13] . However , this autocatalytic self-amplifying system does not by itself explain accumulation of Cdc42 at the site of activity , which is thought to arise from coupling to dynamic recycling of Cdc42 . Two modes of Cdc42 recycling from and to the plasma membrane , with distinct dynamic properties , have been proposed . The first slow mode relies on Cdc42 trafficking on vesicles through endo- and exocytosis [14–18] . As Cdc42 promotes the assembly of actin cables , which serve as tracks for the delivery of secretory vesicles , this , in principle , constitutes a mechanism by which to enrich Cdc42 at sites of Cdc42 activity [19 , 20] . However , the low concentration of Cdc42 on secretory vesicles has raised debate about whether this feedback would indeed reinforce Cdc42 polarization [18 , 21 , 22] . The second fast recycling mode depends on GDI-mediated extraction of Cdc42-GDP [23–25] . As the GDI interaction can be competed out by the GEF , this may also enhance Cdc42 delivery and membrane re-insertion at sites of GEF localization [23 , 24] . Because simultaneous block of vesicle secretion and GDI deletion leads to loss of GFP-Cdc42 polarization [23 , 25] , the current view is that spontaneous polarization of Cdc42 requires these two recycling routes . However , it is important to note that the N-terminally tagged GFP-Cdc42 fusion used in all dynamic studies to date is not fully functional ( see below; [18 , 23 , 26 , 27] . In addition to positive feedbacks , the existence of negative feedback and competition mechanisms has been revealed by the oscillatory behavior of polarity patch components [26 , 28] . These oscillations were observed in both budding and fission yeasts , with a distinct outcome: whereas these oscillations resolve into a single patch at the prospective bud site in the former , they persist throughout bipolar extension at cell poles in the latter [29] . In fission yeast , bipolar growth occurs after passage through S-phase and requires the Tea1-Tea4 landmark complex , which is deposited at cell poles by microtubules and feeds into the Cdc42 activation cycle [30 , 31] . The presence of oscillations in both organisms , as well as the existence of mutant conditions allowing the rare formation of two simultaneous buds in Saccharomyces cerevisiae , suggests that the transition from one to several polarization sites may be an intrinsic , modular property of the Cdc42 spontaneous polarization system [24 , 26 , 32–35] , though the specific mechanisms remain unclear . Here we describe a functional , fluorescently tagged Cdc42 allele in fission yeast , which reveals that Cdc42 is mobile at the plasma membrane independently of GDI and vesicle trafficking . Engineered Cdc42 alleles targeted to the plasma membrane in a prenylation-independent manner demonstrate that Cdc42 spontaneously polarizes , often to multiple sites , independently of these recycling pathways in both fission and budding yeast cells . Our work further reveals that inactive Cdc42 displays fast lateral diffusion and slows down and accumulates as a consequence of its local activation .
To better understand the mechanisms underlying Cdc42 dynamics in live cells , we set out to construct a functional fluorescent fusion protein in fission yeast . Existing fusions in various organisms fuse fluorescent proteins to Cdc42 N-terminus , as the C-terminus is subject to post-translational modification by prenylation ( Fig . 1A ) . However , when this has been tested by gene replacement , these N-terminal fusions compromise Cdc42’s functions , yielding temperature-sensitivity and failure to associate with post-Golgi vesicles in S . cerevisiae [18 , 23 , 26] or altered cell morphology in Schizosaccharomyces pombe [27] . Indeed , the GFP-cdc42 strain displayed slow growth and aberrant morphology at all tested temperatures ( see Fig . 1D–F ) . We chose the alternate approach of placing a fluorescent protein gene within the cdc42 reading frame , a strategy previously tried with success on other proteins [36 , 37] . Potentially permissive sites for fluorophore insertion were determined by examining Cdc42 crystal structure and looking for solvent-exposed poorly-conserved external loops distant from the switch regions and α2 helix that mediate the interface of most known interactors ( Fig . 1A ) [38] . The α3′ helix fit these criteria well and the linker-SGGSACSGPPG- was inserted following amino acid Q134 . Expression of Cdc42-Q134-linker from a plasmid complemented the cdc42-1625 temperature sensitive mutant at restrictive temperature ( Fig . 1B ) . Insertion of either GFP or mCherry at this site , generating sandwich fusions Cdc42-GFPSW or Cdc42-mCherrySW , likewise complemented the cdc42-1625 mutant ( Fig . 1B ) . We next engineered strains expressing the sandwich fusions as the sole source of Cdc42 from its native genomic promoter . Replacement of cdc42 in diploid cells followed by germination of haploid spores yielded colonies of equal size ( Fig . 1C ) . Proper integration in the genome was confirmed by diagnostic PCR and Southern blotting ( S1A–C Fig ) . Remarkably , cells expressing Cdc42-mCherrySW or Cdc42-sfGFPSW ( superfolder-GFP ) showed growth rate , cell width and length at division , and division plane positioning indistinguishable from wild type , even at temperatures up to 36°C ( Fig . 1D–F , S1D Fig ) . We note that Cdc42-GFPSW was deficient at elevated temperatures ( Fig . 1F ) , suggesting that the slow rate of GFP folding slightly impairs the functionality of the fusion protein [39 , 40] . We thus used Cdc42-mCherrySW or Cdc42-sfGFPSW in all subsequent experiments . Cdc42-mCherrySW was enriched at the cell tips and division sites ( Fig . 1G ) . Significant levels of Cdc42-mCherrySW were also found along the cell sides and on internal membranes including the nuclear and presumably vacuolar membranes . Cdc42-sfGFPSW showed similar localization ( S1E Fig ) . In cells depleted of the exocyst member Sec8 , in which exocytic vesicles accumulate but fail to fuse at the cell tip [41 , 42] , Cdc42-mCherrySW accumulated sub-apically , confirming that Cdc42 is trafficked on exocytic vesicles ( Fig . 1G ) . Finally , we found no synthetic interactions with any of the mutants used below . Thus , at least during mitotic growth , the mCherry and sfGFP sandwich constructs appear to be functional fusions to Cdc42 . To quantitatively define the distribution and activity of Cdc42 at the plasma membrane , we co-imaged Cdc42-mCherrySW with CRIB-3GFP , a probe that selectively binds active Cdc42-GTP ( CRIB stands for Cdc42- and Rac-interactive binding domain ) [43] . The distribution of CRIB closely mirrored that of Cdc42 enrichment at cell poles ( Fig . 2A ) . At cell poles with strong CRIB localization , Cdc42 was enriched 3-fold ( ± 0 . 7 , n = 40 ) over its levels at cell side ( Fig . 2B ) . At cell poles with low CRIB levels , these correlated with low Cdc42 enrichment ( S2 Fig ) . Normalization of the Cdc42 and CRIB distribution profiles to their maximum and minimum yielded overlapping curves with identical decay rates , suggesting a very tight correlation between enrichment of Cdc42 and the active form ( Fig . 2B right ) . Examination of Cdc42-mCherrySW and CRIB-3GFP in a panel of mutants shown or predicted to regulate Cdc42 activity further strengthened this correlation ( Fig . 2C ) . Indeed , Cdc42 tip enrichment strongly correlated with CRIB tip enrichment across all mutants examined ( linear regression r2 = 0 . 96; Fig . 2D–E ) . Two mutants—orb2-34 , a largely inactive allele of the PAK kinase Shk1/Pak1 proposed to act in a negative feedback to inhibit Cdc42 activity [28] , and deletion of the Tea4 landmark [44 , 45]—showed higher average Cdc42 activity and enrichment at their single growing cell tip . Deletion of the Cdc42 GEF Scd1 [46] showed dramatic loss of Cdc42 local activity and enrichment . By contrast , Cdc42 activity and enrichment were not as severely affected by deletion of the putative scaffold Scd2 , which forms a complex with Scd1 and Cdc42 [46] . Finally , deletion of the second Cdc42 GEF Gef1 [47 , 48] , or of the only predicted GDI Rdi1 [49] , had no or minor effect on Cdc42 local activity and enrichment . In summary , these data indicate that the accumulation of Cdc42 at growing cell poles represents the active form . We used fluorescence recovery after photobleaching ( FRAP ) experiments to measure the mobility of Cdc42 at the plasma membrane . Using an identical 0 . 9 μm bleach spot in all experiments , we found that Cdc42-mCherrySW fluorescence recovers significantly faster at cell sides than cell poles , with recovery halftimes of 1 . 0 ± 0 . 3 versus 4 . 6 ± 2 . 0 s−1 , respectively ( Fig . 3A , B ) . Thus , Cdc42 is highly mobile at the plasma membrane , but significantly slower at cell tips ( Student’s t test , p = 3 . 5 x 10−5 ) . We considered whether cell geometry may cause this difference by examining Cdc42 mobility at cell tips lacking Cdc42 activity or at sites of activity on cell sides . Cdc42 mobility at the non-growing cell tip of tea4Δ cells , which has vastly reduced Cdc42 activity and enrichment ( Fig . 3C , D ) , was significantly higher than at the other cell tip and more similar to the cell sides . Conversely , we generated zones of Cdc42 activity and enrichment at cell sides by treating cells with the actin depolymerizing drug Latrunculin A ( LatA ) for 30–40 min ( Fig . 3C , D , see below ) . This leads to progressive loss of CRIB from cell poles and formation of dynamic zones of CRIB on cell sides [42] . Cdc42 was enriched in these zones and displayed significantly slower mobility , whereas Cdc42 from depleted cell poles showed fast mobility . Thus , the geometry of the cell tip does not constrain Cdc42 mobility , and Cdc42 mobility can be slowed down also at cell sides . Measurement of Cdc42 FRAP halftimes in the panel of regulator mutants described above further established a correlation between the levels of active Cdc42 , as detected by CRIB , and Cdc42 slow mobility ( high FRAP halftimes ) at cell tips ( Fig . 3E , F; linear regression r2 = 0 . 82 ) . Cdc42 mobility was high ( low FRAP halftimes ) at the sides of all mutants , though some differences were noticed in comparison to wild type . We also tested the mobility of the Cdc42 GTP-locked allele , Cdc42Q61L-mCherrySW expressed from plasmids under control of an inducible promoter in a cdc42-sfGFPSW strain . Long-term induction led to cell rounding , as previously reported for untagged Cdc42Q61L ( S3A Fig ) [50] . FRAP experiments performed after short-term induction before cell shape change showed slow mobility of Cdc42Q61L-mCherrySW at both cell tips and cell sides , with halftimes similar or higher than those of wild-type Cdc42 at cell tips ( S3B , C Fig ) . We note that Cdc42Q61L-mCherrySW expression had no effect on the dynamics of Cdc42-sfGFPSW on cell sides , but led to reduction in its halftime at cell tips , suggesting titration of some factor for Cdc42 activation or stabilization . Cdc42 dynamics remained slow at cell tips and fast at cell sides in both channels when wild-type Cdc42-mCherrySW was co-expressed from plasmids in a cdc42-sfGFPSW strain . We conclude that Cdc42-GTP exhibits slower mobility than Cdc42-GDP at the plasma membrane . We were surprised to discover that deletion of rdi1 did not affect Cdc42 mobility at cell tips in the experiment above ( Fig . 3F , Fig . 4A ) . Though Rdi1 is the sole predicted GDI in S . pombe , its deletion yields only a minor morphological phenotype , with cells slightly shorter and wider than wild-type cells at division ( S4A–D Fig ) . Disruption of actin cables in formin for3Δ mutant [51] , interference with endocytosis in end4Δ mutant [52] , or disruption of all actin structures by treatment with 200 μM LatA for a short time ( 5–10 min ) , also had no or minor effect on Cdc42 mobility ( Fig . 4A ) . Remarkably , Cdc42 mobility at cell tips was even maintained in rdi1Δ mutant cells treated with LatA . Further collapse of the membrane trafficking system by treatment with Brefeldin A ( BFA ) also failed to slow down Cdc42 dynamics at cell tips ( Fig . 4A ) . We note , however , that rdi1 deletion slightly slowed down Cdc42 mobility at cell sides especially in combination with actin cytoskeleton disruption , though the absence of known actin structures or trafficking pathways at cell sides suggests the effect of actin disruption may be indirect . These data indicate that Cdc42 dynamics at the plasma membrane occurs largely independently of GDI-mediated membrane extraction and vesicle trafficking . Furthermore , long-term ( 30–40 min ) LatA treatment led to the formation of new zones of active Cdc42 polarization at cell sides , which were dynamic , forming and disappearing over time , even in rdi1Δ cells ( S4E Fig ) . Finally , zones of active Cdc42 formed spontaneously in spores [9] , even upon removal of both GDI and actin structures ( Fig . 4B ) . Thus , Cdc42 mobility and its ability to locally accumulate require neither GDI nor actin-dependent vesicle trafficking , though the actin cytoskeleton is required for maintenance of an active Cdc42 zone at a stable location . The FRAP measurements described above may reflect exchange of Cdc42 between the membrane and the cytosol or lateral diffusion along the membrane . In case of lateral diffusion , the rate of fluorescence recovery decreases with increasing size of the bleach zone . Photobleaching of Cdc42 over wider ( 4–6 μm ) zones at the sides of wild-type , rdi1Δ , and rdi1Δ cells treated with LatA yielded significantly slower recovery , suggesting significant contribution of lateral diffusion to Cdc42 dynamics ( Fig . 4C–E ) . The recovery on the cell sides was fitted with a model that accounts for 2-D membrane diffusion and uniform exchange with a fast-diffusing cytoplasmic pool ( Fig . 4D , E ) . Cytoplasmic exchange results in exponential recovery over time while membrane diffusion results in algebraic recovery of the intensity at the center of the bleached and marginally detectable broadening of the bleached region over time . The model predicts the evolution of the initial Gaussian-shaped bleach profile as a function of distance along the cell contour and time , with the diffusion coefficient and exchange time as fitting parameters . Fits to the diffusion-dominated recovery of narrow bleached regions give a diffusion coefficient ranging between 0 . 15 and 0 . 35 μm2/s in wild-type and rdi1Δ cells ( Fig . 4E ) . Use of this range of diffusion coefficient values provides good fits to the recovery of large bleached zones , with a cytoplasmic exchange time longer than 20 s in wild-type and rdi1Δ cells ( Fig . 4D and S5F Fig ) . These values for the exchange time suggest that membrane removal , with or without GDI , is a small contribution to kinetics over the diffusion time across the cell side . In rdi1Δ cells treated with LatA , diffusion coefficients of 0 . 1–0 . 2 μm2/s and no exchange component provided good fits . We conclude that lateral diffusion of inactive Cdc42 at cell sides is a major component of Cdc42 mobility . Photobleaching of half-cell tips also showed recovery from the sides of the bleached zone , with concomitant fluorescence loss in the adjacent tip region , indicating lateral movement along the tip membrane at a slower rate compared to cell sides ( S5A–E Fig ) . Diffusion at cell tips may reflect spatially dependent inter-conversion between fast diffusing Cdc42-GDP and less mobile Cdc42-GTP , which complicates a precise calculation of the Cdc42-GTP diffusion coefficient . However , measurements of the rate of bleached region broadening , as well as fits to a model of diffusion-dominated recovery over a small bleached tip area , suggest at least a 10-fold smaller lateral diffusion coefficient for Cdc42-GTP . We used fluorescence correlation spectroscopy ( FCS ) to investigate the mobility properties of Cdc42 further . At the cell sides , the FCS autocorrelation function showed two components with distinct diffusion regimes ( Fig . 4F , S5G Fig ) . The sub-ms component is attributed to cytosolic diffusion , while the slower component depends on the presence of prenylation and hence is associated to a membrane-bound species . A fit with a two-component model revealed a diffusion rate of 0 . 18 μm2/s for the membrane-associated species , in agreement with the FRAP fit above . At the cell tips , FCS did not detect a slower diffusing species , instead only revealing diffusion similar to that observed on cell sides ( Fig . 4F and S5G–I Fig ) . Our inability to detect a slower diffusing components stems from the fact that in standard FCS , to obtain statistical relevance the measuring time has to be 103–104-fold longer than the diffusion time to be determined [53] . This would require a recording time of 15–150 min with an accuracy of 100 nm , incompatible with cell and optical focus stability . As FRAP measures a large ensemble of fluorophores over a much larger area , it can detect much slower diffusing components , but without resolving multiple diffusing components , especially if one ( e . g . , the slow diffusing component ) is dominating . Therefore , by resolving the faster component , our FCS measurements are complementary to the FRAP experiments and suggest that Cdc42-GDP is able to diffuse into the cell tip region . In summary , Cdc42-GDP diffuses at rates of about 0 . 2 μm2/s , whereas Cdc42-GTP diffuses at least 10-fold slower . To strengthen our findings that GDI and vesicle trafficking only play a minor role on Cdc42 dynamics and further test the role of Cdc42 membrane attachment in cell polarization , we engineered Cdc42 alleles with alternative plasma membrane targeting mechanisms ( Fig . 5A ) . Membrane targeting is essential to Cdc42 function as shown by the fact that removal of the CAAX sequence yielded a diffuse , non-functional Cdc42 allele unable to complement the cdc42-1625 temperature-sensitive mutant ( S6A , B Fig ) . Remarkably , two distinct Cdc42 alleles in which the CAAX sequence is replaced by a trans-membrane sequence ( cdc42-psy1TM ) , or an amphipathic helix ( cdc42-ritC ) , were able to complement the cdc42-1625 mutant when expressed from plasmids ( Fig . 5B ) and were viable when integrated as single cdc42 copy at the native cdc42 genomic locus . All experiments presented below use strains with these alleles as single cdc42 copy . ( H ) Average profiles of fluorescence intensity along cortical traces with standard deviation . n = 40 . ( I ) Halftimes of Cdc42-mCherrySW-ritC FRAP recovery at cell tips and sides . n ≥ 18 . The two values are statistically significantly different ( Student’s t test p-value = 0 . 0056 ) . ( J ) Cdc42-mCherrySW-ritC localization following depletion of exocyst component Sec8 . Note that Cdc42-mCherrySW-ritC fusion does not accumulate sub-apically as wild type does ( see Fig . 1G , right ) . Bars = 5 μm . Cdc42-psy1TM , containing the trans-membrane domain of the t-SNARE syntaxin-like protein Psy1 [54] , localized to the plasma membrane , where it was almost immobile at cell sides and displayed slow turnover at cell tips ( FRAP halftime > 1 . 5 min ) , similar to endogenous Psy1 ( Fig . 5D , E ) . This allele did not accumulate at cell poles , where it was less abundant than along cell sides ( Fig . 5F ) . Remarkably , however , these cells localized CRIB-3GFP and polarized growth to both cell poles ( Fig . 5C ) , indicating Cdc42-psy1TM is active at cell poles . We hypothesize that the membrane insertion of Cdc42-psy1TM prevents its rapid recycling and accumulation at sites of activity . We conclude that accumulation of Cdc42 is not absolutely necessary for , and occurs as a consequence of , its local activation . Though viable , Cdc42-psy1TM cells displayed irregular shapes with variable cell width , with low amounts of CRIB-3GFP also detected on cell sides ( Fig . 5C inset; S6C Fig ) . We tested whether this allele supports the formation of new sites of polarization upon long-term LatA treatment . This led to increased occurrence of CRIB zones on cell sides , similar to our observations in wild-type cells , though the zones were often less well defined , and Cdc42-psy1TM was not enriched in these zones ( S6D Fig ) . While this result shows that Cdc42-psy1TM can support spontaneous polarization to some extent , it also suggests that this almost immobile Cdc42 allele is compromised in its ability to form a focal , well-defined growth zone . We were unable to examine whether this allele can support spontaneous polarization in spores , because of difficulty in obtaining homozygous cdc42-psy1TM mutant zygotes . The Cdc42-psy1TM allele also showed synthetic defects with deletion of the landmark Tea1 ( S6F Fig ) . Tea1 , which marks cell poles for growth , is required for bipolarity as well as for the maintenance of the rod shape , as tea1 deletion produces curved , occasionally T-shaped , monopolar cells [55] . Double cdc42-psy1TM tea1Δ mutants were slow-growing and displayed very aberrant shapes , suggesting that Cdc42-psy1TM activation at cell tips largely relies on upstream polarization cues . The Cdc42-ritC fusion contains the C-terminal amphipathic helix of a heterologous mammalian protein Rit , previously shown to efficiently localize to the plasma membrane but not to endomembrane systems [56 , 57] . Cdc42-ritC localized specifically to the plasma membrane and was not detected on endomembranes ( Fig . 5G ) . It also did not accumulate sub-apically in cells depleted of the exocyst member Sec8 , indicating that this allele is not trafficked on exocytic vesicles ( Fig . 5J ) . This allele is also predicted not to be a substrate for GDI , because the prenyl group is required to bind GDI [49 , 58 , 59] . Remarkably , cdc42-ritC mutant cells grew at near wild-type rates at 25°C and 30°C , though it was compromised at high temperatures , and showed only minor morphological defects , with slightly wider and shorter cells than wild type ( S6C , G Fig ) . Cdc42-ritC accumulated at cell poles , where it was active and enriched 3 . 1-fold ( ±1 . 0 , n = 40 ) over its levels at cell sides , similar to wild-type Cdc42 ( Fig . 5H ) . It also showed slower FRAP recovery at cell poles compared to cell sides , though the FRAP halftime at cell sides was about 4-fold slower than wild type ( Fig . 5I ) . The minor phenotype displayed by cdc42-ritC mutant cells is consistent with the minor role played by GDI and vesicle trafficking in Cdc42 dynamics . We used four distinct assays to test whether Cdc42-ritC was able to break symmetry in absence of upstream polarity cues . First , upon long-term LatA treatment , Cdc42-ritC , like wild-type Cdc42 , enriched to novel active zones on cell sides , which are likely devoid of landmarks ( S6E Fig ) . Second , after cell wall digestion generating round protoplasts , both wild-type Cdc42 and Cdc42-ritC enriched in dynamic peripheral active zones during protoplast recovery ( Fig . 6A ) . Third , cdc42-ritC mutant spores germinated and polarized growth as efficiently as wild-type spores ( Fig . 6B and S7A ) . Finally , in absence of the landmark Tea1 , cdc42-ritC mutants formed T-shapes upon re-feeding , indicating polarization at cell sides in absence of the landmark ( Fig . 6C ) . Unexpectedly , and in contrast to tea1Δ single mutants , cdc42-ritC tea1Δ double mutants grew in a bipolar manner in exponential phase ( Fig . 6D ) . This finding may explain the reduced efficiency in the formation of T-shapes , because of competition with growing cell poles [60] . We conclude that Cdc42-ritC is able to break symmetry in absence of upstream cues . Together with our dissection of Cdc42 dynamics , these data show that spontaneous polarization of Cdc42 activity and localization does not require GDI-mediated extraction and actin-based vesicle trafficking . We were intrigued by the observation that Cdc42-ritC confers bi-polarity in absence of Tea1 . In addition to the role of the Tea1/Tea4 landmark , bipolar growth normally is controlled by the cell cycle and occurs only after passage through S-phase , such that cdc10-v50 mutant cells blocked in G1 phase remain monopolar [31 , 61] . Remarkably , both Cdc42-ritC and Cdc42-psy1TM promoted bipolar growth in cdc10-v50 G1-arrested cells ( Fig . 6E ) . Cdc42-ritC and Cdc42-psy1TM mutant cells also displayed clear bipolarity when examined in time-lapse imaging ( S7B Fig ) . Thus , both Cdc42 alleles with altered plasma membrane targeting override the normal regulation to promote bipolar polarization and growth . Our observations that Cdc42 polarizes independently of GDI and actin-based trafficking conflict with data in S . cerevisiae in which simultaneous disruption of GDI and actin blocks GFP-Cdc42 recycling and polarization [23 , 25] . We tested whether targeting of Cdc42 to the plasma membrane by an amphipathic helix would permit cell polarization and viability also in S . cerevisiae ( Fig . 7A ) . We replaced the endogenous cdc42 gene in a diploid strain with a cdc42-ritC or a cdc42-ritC-GFP allele . Sporulation yielded four viable spores , of which two grew slowly and carried the mutant allele ( Fig . 7B ) . Thus a Cdc42 allele directly targeted from the cytosol to the plasma membrane independently of GDI also confers viability in the budding yeast . Cdc42-ritC-GFP efficiently polarized in haploid mutant cells , but often accumulated at two or more sites simultaneously ( Fig . 7C–G ) , even upon actin disruption ( Fig . 7F ) . A large fraction of these cells formed multiple buds or aberrant growth projections , which could grow concurrently ( Fig . 7C–G ) . Examination of simultaneous double bud formation on time-lapse movies showed 45 events in 250 cells . Finally , these cells formed an abnormal budding pattern , budding at random locations , suggesting override of the normal landmarks at the previous bud scar ( Fig . 7G–I ) [62] . These data are entirely consistent with our results in the fission yeast and suggest that Cdc42 can also polarize to naïve sites at the plasma membrane independently of GDI and vesicle-mediated transport in the budding yeast .
Living systems are able to spontaneously break symmetry and self-organize in ordered patterns . These patterns generally reflect the steady state of a dynamic protein flux . Thus , live fluorescently tagged alleles have become indispensable experimental tools . However , for small , highly conserved proteins that have multiple binding partners , it can be challenging to preserve functionality of the tagged molecule . The highly conserved polarity regulator Cdc42 GTPase is one such small protein . N-terminal GFP fusions previously used to derive much of our knowledge on Cdc42 localization and dynamics compromise Cdc42 functionality and localization in yeasts [18 , 23 , 27] . N-terminal GFP-Cdc42 fusions have also been abundantly used in more complex eukaryotic organisms to derive information about Cdc42 localization and dynamics [63–68] . We note , however , that in these systems , functionality is more difficult to test . We present here an improved internally tagged version of Cdc42 and its use in revealing new biology of this important polarity factor . We note that , besides the position of the fluorescent marker , its folding properties need to be taken into consideration , as use of mCherry or sfGFP , but not GFP , which folds considerably slower [39 , 40] , resulted in functional fusions . All functional tests conducted here indicate these sandwich fusions do not compromise Cdc42 function , though we cannot rule out that phenotypes may be revealed in other , more sensitive backgrounds . Our approach is , in principle , generally applicable for Cdc42 , or indeed for any small GTPase , in all organisms , though the specific site for fluorescent protein insertion will need to be carefully selected and tested . The ability of Cdc42 to spontaneously polarize—i . e . to display local zones of accumulation—relies on positive feedback mechanisms . It has been proposed in the budding yeast to depend on two Cdc42 recycling routes from and to the plasma membrane: GDI-mediated extraction and trafficking on vesicles . In fission yeast , these two recycling routes very likely exist: Cdc42 is a probable GDI substrate because GDI binds Cdc42 [49] , and GDI deletion causes modest reduction in Cdc42 dynamic turnover at cell sides . However , GDI deletion does not overtly affect the ability of Cdc42 to polarize in spores or vegetative cells . Cdc42 may also traffic on vesicles since it is detected on secretory vesicles upon block in exocytosis . Yet , short-term pharmacological treatment blocking vesicle trafficking causes no or very modest changes in Cdc42 dynamics . We note , however , that the actin cytoskeleton plays an important role in the longer-term stability of the polarized zone , which progressively diminishes at cell poles and spontaneously re-appears on cell sides upon sustained LatA treatment [42] . This may be similar to observations in the budding yeast , in which actin disruption causes flickering of the polarity patch and prolongs naturally observed oscillations [20 , 26] . We conclude that the actin cytoskeleton ( and thus polarized vesicle transport and endocytosis ) does not by itself significantly modify the dynamics of Cdc42 at the site of polarity , but plays a role in restraining the zone of Cdc42 activity to a stable location . Importantly , we now provide extensive evidence that Cdc42 polarizes spontaneously even when both recycling routes are impaired . First , FRAP experiments show only modest effect on Cdc42 turnover rates upon combined GDI deletion and pharmacological disruption of vesicle trafficking . Second , Cdc42 can spontaneously polarize in spores lacking GDI and F-actin . Third , Cdc42-ritC locally accumulates and supports polarized cell growth in multiple instances of spontaneous polarization in fission yeast: spore germination , protoplast polarization , or at cell sides in absence of the Tea1 landmark or upon long-term actin disruption . This allele localizes to the plasma membrane independently of GDI and vesicle trafficking: indeed , Cdc42-ritC is likely not a substrate for the GDI because Cdc42 requires prenylation to bind Rdi1 in fission yeast [49] , RitC is targeted to the plasma membrane directly from the cytosol [57] , and we also show that Cdc42-ritC does not accumulate on secretory vesicles . Finally , we show that this same allele , as sole Cdc42 copy , polarizes and supports polarized growth in S . cerevisiae . Here again , Cdc42-RitC likely breaks symmetry spontaneously , as the budding pattern is random , suggesting it polarized independently of the normal landmarks . We conclude that Cdc42 dynamics independent of GDI-extraction or vesicle trafficking play a key role in Cdc42 polarization . This conclusion conflicts with the previous observation in S . cerevisiae that Cdc42 polarization is lost upon simultaneous GDI deletion and disruption of vesicle trafficking [23 , 25] . One explanation may lie in the use of the partially functional GFP-Cdc42 allele in previous dissection of Cdc42 dynamics . Another explanation may be that , in contrast to our engineering of a Cdc42 allele with a distinct membrane-targeting mode , GDI deletion or disruption of vesicle trafficking are likely to affect many other proteins besides Cdc42 , and may thus have off-target effects . In any case , our results demonstrate that Cdc42 local accumulation can occur independently of proposed positive feedback on Cdc42 delivery and should incite re-examination of Cdc42 polarization models and of their dynamic parameters . Cdc42 polarization ( i . e . its local accumulation ) and the polarization of its activity , as detected by the localization of a Cdc42-GTP reporter , are coincident in all cells examined to date ( with the notable exception of the near-immobile Cdc42-psy1TM ) , suggesting that both events are intimately linked . Indeed , we provide very strong evidence that the local levels of Cdc42 robustly correlate with its local activity level both within wild-type cells and in a range of mutants . As the molecular role of some of the deleted proteins is to activate Cdc42 ( e . g . , Scd1 ) , we can infer that Cdc42 local activation causes its accumulation . More convincingly , we show that Cdc42 local activation , in the Cdc42-psy1TM allele , can occur without its local accumulation . This is likely due to the almost immobile behavior of this trans-membrane Cdc42 allele . Thus Cdc42 accumulation occurs as a result of its activation . So how does Cdc42 accumulate at sites of activity ? Our results provide strong evidence that active Cdc42-GTP moves more slowly at the membrane than inactive Cdc42-GDP . Indeed , Cdc42 turnover at cell sides , where absence of CRIB indicates that Cdc42 is in its GDP-bound form , is fast , with FRAP and FCS estimates of lateral diffusion constants of the order of 0 . 2 μm2/s . By contrast , zones of Cdc42 activity , either at cell tips or ectopically at cell sides , show significantly slower FRAP dynamics , and we observe a strong correlation between the levels of Cdc42 activity at the cell tip and Cdc42 FRAP halftime in a range of mutants . In addition , a GTP-locked Cdc42 allele also showed slow FRAP dynamics . Our interpretation is that Cdc42-GTP is significantly less mobile with estimates of diffusion rates at least 10-fold slower than Cdc42-GDP . This slower diffusion may be due to Cdc42-GTP forming large complexes with GEFs and effectors , multimerizing , or inducing the formation of membrane microdomains . In summary , Cdc42-GTP activation renders Cdc42 less mobile and leads to its accumulation . We note that non-uniform Cdc42 dynamics were also observed in the budding yeast [22] . However , use of GTP- and GDP-blocked GFP-Cdc42 alleles , both of which showed dramatically reduced turnover , led to the different conclusion that Cdc42 hydrolysis cycle is required for its dynamic turnover [20 , 22] . Though exchange of Cdc42 between plasma membrane and cytosol likely participates , our results suggest that differential lateral diffusion of Cdc42-GTP and Cdc42-GDP contributes to Cdc42 polarization . A positive feedback mechanism acting on Cdc42 activation cycle , where a GEF is recruited by active Cdc42 [10–12 , 69] , would lead to activation and thus “capture” of laterally diffusing Cdc42-GDP by Cdc42-GTP . This is , in principle , similar to previously proposed Turing-type mechanisms [13] , but with the fast diffusing Cdc42-GDP component residing on the membrane rather than in the cytoplasm . Thus , lateral diffusion may play a positive role by providing material for polarization . The relative depletion of Cdc42-psy1TM from zones of local activation at cell tips , where it is more dynamic than at cell sides , is consistent with the idea that material for the cell tip normally derives from the cell sides . More strikingly , this finding raises the question of whether Cdc42 accumulation matters for spontaneous polarization . The synthetic phenotype observed with deletion of the Tea1 landmark suggests that local activation in absence of Cdc42 accumulation may be largely driven by pre-localized activators . However , the dynamic zones of Cdc42 activity that form on cell sides upon long-term LatA treatment reveal some level of spontaneous polarization in absence of Cdc42 enrichment . One prediction from these data is that positive feedback mechanisms acting on Cdc42 activation cycle , coupled to mechanisms preventing the propagation of Cdc42 activation to the entire plasma membrane [35] , may be sufficient for symmetry breaking even without accumulation of Cdc42 itself . Future work should focus on the dynamic cycle of regulators of Cdc42 activity , in addition to Cdc42 itself . One important , yet unresolved , question is that of what underlies the ability of cells to polarize at single versus multiple zones . One general idea is that competition between polarized zones for a limiting factor allows one zone to win over the others , resulting in singularity [35] . This competition is enhanced by negative feedback mechanisms that destabilize the polarized zone and lead to oscillations [26] . Bipolarity may then result from saturation at the first polarized zone , allowing the initiation of a second one [28] . How may the alteration of Cdc42 membrane interaction in Cdc42-ritC and Cdc42-psy1TM alleles promote multi-site polarization ? These more stable Cdc42 alleles may lead to faster saturation of a limiting factor at the polarized zone , for instance , due to absence of Cdc42 accumulation in the case of Cdc42-psy1TM . These alleles could also alter the negative feedbacks that normally destabilize the polarization sites , for instance , by modifying the proposed dilution effect caused by incoming vesicles [21 , 35] . More generally , these more stable Cdc42 alleles may compromise the competition between polarization sites by slowing down Cdc42 recycling , in agreement with the role of the GDI in promoting singularity [23] . In summary , our dissection and re-engineering of Cdc42 interaction with the plasma membrane in two very distinct organisms demonstrate that proposed positive feedback mechanisms acting on Cdc42 delivery to the plasma membrane are not essential for spontaneous polarization , but may be critical for the emergence of a single site . The growth and division mode of S . cerevisiae makes it highly dependent on establishing a single bud [23] , whereas the requirements of S . pombe for mono- or bipolarity are less clear . It will be interesting to investigate whether evolutionary constraints on singularity may have shaped the molecular regulation of Cdc42 dynamic turnover .
Fission and budding yeast strains used in this study are listed in S1 Table . Expression plasmids were made by PCR amplification of cdc42 from genomic DNA and cDNA using primers osm1471 ( 5′-ccGTCGACatgcccaccattaagtgtgtcg ) and osm1483 ( 5′-tttCCCGGGttacagtaccaaacactttgac ) . Products were digested with SalI and XmaI and the resulting 1 , 189 bp ( gDNA ) and 596 bp ( cDNA ) products were ligated with similarly treated pREP41 ( leu1+ ) yielding pSM1135 ( pREP41-cdc42-cDNA ) and pSM1139 ( pREP41-cdc42-gDNA ) . The linker-SGGSACSGPPG- was inserted after codon for Q134 as follows . First , PCR was done on cDNA and gDNA with primer pairs to amplify the 5′ and 3′ end of cdc42 . The 5′ part was amplified with osm1471 and osm1481 ( 5′-ccagagcatgcGGATCCgccagactgatgctggcgagctag ) and the 3′ part with osm1482 ( 5′-tctggcggatccgcatgctctGGCGCGCCgggccatccccttacacatgagc ) and osm1483 . The two products were subjected to PCR stitching with addition of primers osm1471 and 1483 . The product was digested with SalI and XmaI and the resulting 618 bp ( cDNA ) and 1 , 211 bp ( gDNA ) product was ligated to similarly treated pREP41 yielding plasmids pSM1136 ( pREP41-cdc42-linker-cDNA ) and pSM1140 ( pREP41-cdc42-linker-gDNA ) . Fluorophore genes were then cloned into the linker site . meGFP and mCherry were amplified by PCR with primers pairs osm1394 ( 5′-ccGGATCCatggtgagtaaaggagaagaacttttcactgg ) and osm949 ( 5′-cccGGCGCGCCtttgtatagttcatccatgcc ) for meGFP , and osm1393 ( 5′-ccGGATCCatggtgagcaagggcgaggaggataac ) and osm947 ( 5′-cccGGCGCGCCcttgtacagctcgtccatgc ) for mCherry . The products were digested with BamHI and AscI and the resulting 724 bp and 718 bp products , respectively , were ligated to similarly treated pSM1136 yielding pSM1137 ( pREP41-cdc42-GFPSW ) , pSM1136 yielding pSM1142 ( pREP41-cdc42-mCherrySW ) and pSM1140 yielding pSM1138 ( pREP41-cdc42-cDNA-mCherrySW ) . Integrative plasmids were made as follows . First , the 3′UTR of cdc42 was amplified with primers osm1689 ( 5′-acgCATATGtttaacccctggttttctttcc ) and osm1749 ( 5′-acgGTCGACgaatctcaactgggtttcgg ) and the resulting 622 bp NdeI-SalI digested product was ligated to similarly treated pFA6a-kanMX yielding pSM1222 ( pFA6a-3′UTR ( cdc42 ) -kanMX ) . Next , the cdc42-GFPSW-terminator and cdc42-mCherrySW-terminator fragments from pSM1137 and pSM1138 , respectively , were amplified with primers osm1471 and osm1686 ( 5′-acgACGCGTcttctaattacacaaattccg ) . The products were digested with SalI and MluI and ligated to similarly treated pSM1222 yielding plasmids pSM1223 ( pFA6a-3′UTR ( cdc42 ) -cdc42-GFPSW-kanMX ) and pSM1224 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-kanMX ) . The super folder GFP gene was amplified from pMaM4 [70] with primers osm2217 ( 5′-ccGGATCCtccaagggtgaagagctatttactgggg ) and 2218 ( 5′-cccGGCGCGCCcttataaagctcgtccattccgtgag ) and the product was digested with BamHI and AscI and the resulting 718 bp product was ligated to similarly treated pSM1224 yielding pSM1438 ( pFA6a-3′UTR ( cdc42 ) -cdc42-sfGFPSW-kanMX ) . Plasmids encoding retargeted or mutant cdc42 were made as follows . For the prenylation defective mutant ( noCAAX ) PCR was done on template pSM1224 with primers osm1471 and osm2216 ( 5′-tttCCCGGGttactttgactttttcttgtgaggaacagg ) and the 1 , 907 bp SalI-XmaI digested product was ligated to similarly treated pREP41 yielding pSM1364 ( pREP41-cdc42-noCAAX ) . The integrative plasmid was made by subcloning the 891 bp BamHI-XmaI fragment from pSM1364 into pSM1438 yielding pSM1522 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-noCAAX-kanMX ) . For retargeted Cdc42 with the amphipathic helix ritc , PCR was done on template pSM1224 with primers osm1471 and 2219 ( 5′-aaaCCCGGGttaGCTAGCaggaacaggaggatcaagagcggctac ) and the resulting1 , 292 bp SalI-XmaI digested product was ligated to similarly treated pREP41 yielding pSM1439 ( pREP41-cdc42-mCherrySW-noK-noCAAX ) . Next , PCR was done with template pSM789 [57] and primers 2288 ( 5′-cctGCTAGCcacaagaaaaagtcaaagtgtcccttttttgagacatctgctgc ) and 2289 ( 5′-ttaCCCGGGtcaagttactgaatctttcttcttccgg ) and the resulting 213 bp NheI-XmaI digested product was ligated to the similarly treated pSM1439 yielding pSM1440 ( pREP41-cdc42-mCherySW-ritC ) . The integrative version was made by subcloning the 1 , 083 bp SalI-XmaI fragment from pSM1440 into similarly treated pSM1224 yielding pSM1442 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-ritC-kanMX ) . For the retargeted Cdc42 with transmembrane domain of tSNARE Psy1 , PCR of gDNA with primers 2283 ( 5′-cctGCTAGCagagcagctcgtaagaaaaagtgg ) and 2284 ( 5′-ttaCCCGGGtcaatgtctattgccaagaacagg ) was done and the resulting 120 bp NheI-XmaI digested product was ligated to similarly treated pSM1439 yielding pSM1441 ( pREP41-cdc42-mCherrySW-psy1TM ) . The integrative version was made by subcloning the 990 bp BamHI-XmaI fragment from pSM1441 into the similarly treated pSM1224 yielding pSM1443 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-psy1TM-kanMX ) . Plasmids pSM1223 ( pFA6a-3′UTR ( cdc42 ) -cdc42-GFPSW-kanMX ) , pSM1224 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-kanMX ) , pSM1438 ( pFA6a-3′UTR ( cdc42 ) -cdc42-sfGFPSW-kanMX ) , pSM1522 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-noCAAX-kanMX ) , pSM1442 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-ritC-kanMX ) and pSM1443 ( pFA6a-3′UTR ( cdc42 ) -cdc42-mCherrySW-psy1TM-kanMX ) were linearized with SalI and transformed into diploid S . pombe cells . Integration was confirmed as described in S1 Fig Southern analysis was performed as follows . A probe was prepared using the random DIG labeling kit ( Roche ) and the 1 , 028 bp PCR product of gDNA with osm1471 and osm1481 . Resolved genomic DNA digested with XbaI was probed along with controls SalI-XmaI and SalI digested pSM1139 ( pREP41-cdc42-gDNA ) . Binding was detected using anti-DIG antibody and detected by chemoliminescence ( Roche ) . Plasmid pSM1358 ( pREP41-cdc42-Q61L-mCherrySW ) was made by site directed mutagenesis using template pSM1142 and primers osm1983 ( 5′-cttggtttattcgataccgctggtctcgaggattatgatcgcttgcg ) and osm1984 ( cgcaagcgatcataatcctcgagaccagcggtatcgaataaaccaag ) ( codon change is underlined ) . Replacement of prenylation sequences with the amphipathic helix ritc in S . cerevisiae was done by a PCR-based targeting approach . First , the yeast codon-optimized ritC sequence was synthesized by Eurofins Genomics . The 195 bp SalI-XmaI digested ritC coding sequence was subcloned into similarly digested pFa6a-kanMX yielding pSM1534 ( pFA6a-ritC-kanMX ) . Then , the 210 bp SalI-Asc1 digested fragment from pSM1534 was subcloned into pFA6a-GFP-term-KanMX yielding pSM1571 ( pFA6a-ritC-term-kanMX ) . For tagging cdc42 with ritC-GFP , PCR was performed with template pSM1534 and primers osm2688 ( 5′-cagGTCGACtgtccattttttgagac ) and 2689 ( 5′-aaaCCCGGGagtaacagaatccttcttcttacgg ) and the resulting digested 192 bp SalI-XmaI product was ligated to similarly treated pFA6a-GFP-term-KanMX yielding pSM1572 ( pFA6a-ritC-GFP-term-kanMX ) . Plasmids pSM1571 and pSM1572 were used as templates for PCR with primers osm2625 ( 5′-caacgcggtttgaagaatgtattcgatgaagctatcgtggccgccttggagcctcctgttatcaagaaaagtaaaaaatgtccattttttgagacttctgc ) and osm2949 ( 5′-taataaaaggataggaaggtgtatatataagttaattttagatatagattaagaaaagatgggcatatactaatatgagaattcgagctcgtttaaac ) ( cdc42 homology underlined ) to amplify product and transformed into diploid S . cerevisiae strains . Integration was confirmed by diagnostic PCR and diploids were sporulated to isolate haploid progeny with the cdc42-ritC ( -GFP ) allele as sole copy of cdc42 . For cdc42 temperature sensitivity complementation analysis cells were grown in EMM media supplemented with thiamine but without leucine to log phase at 25°C , washed three times in EMM without leucine and incubated overnight at 25°C in the same media . Cells were then diluted 10-fold and spotted onto EMM plates without leucine and incubated at indicated temperatures . Tetrads were dissected onto YE5S ( S . pombe ) or YPD ( S . cerevisiae ) and germinated at 25°C . For imaging analysis , cells were grown to log phase for at least 36 h by consecutive dilution of cultures in EMM at 25°C . Imaging of calcofluor ( Fig . 1F and 6C–E ) and transmitted light ( Fig . 7C ) samples were done by placing cells on glass slides . All other cells were imaged on EMM , YE , or SD 2% agarose pads ( SD for S . cerevisiae ) and sealed with VALAP . LatA ( Enzo Life Sciences , T-119 ) in DMSO was used at final concentration of 200 μM , except for imaging of spore outgrowth and S . cerevisiae , where it was used at 100 μM . Cells treated with Brefeldin A ( Sigma , B7651 ) in ethanol were incubated in 100 μg/ml of drug for 1 h before imaging . Drugs were added directly to molten agarose before preparation of pads . Drug efficacy was monitored by mixing small amounts of control cells expressing the actin marker CDH-GFP ( LatA ) or the Arf GEF Sec71-GFP ( BfA ) directly into the imaging field and verifying marker mislocalization . Depletion of Sec8 from the nmt81-sec8 strain was done by diluting cells grown in EMM without thiamine into EMM supplemented with 0 . 25 μg/ml thiamine and grown for 20 h at 25°C . For calcofluor staining of S . cerevisiae bud scars , cells were diluted from a pre-culture and grown overnight before staining with calcofluor and imaging . For the re-feeding experiments of tea1Δ strains , cells were grown to starvation in YE5S liquid media for 4 d and diluted 1:50 into YE and stained with calcofuor after 4 h . Imaging of G1 arrested cdc10-v50 strains was done by diluting precultures in EMM at 25°C into EMM and grown at 36°C for 6 h before staining with calcofluor . For FRAP imaging of Cdc42-GTP in S3 Fig , cdc42-sfGFPSW-kanMX cells harboring plasmids pSM1139 ( pREP41-cdc42-mCherrySW ) or pSM1358 ( pREP41-cdc42-Q61L-mCherrySW ) were grown as follows . Cells were grown overnight in EMM-AU supplemented with thiamine and washed three times in EMM-AU lacking thiamine . Cells were diluted to OD600 = 0 . 05 and grown for 18 h before imaging . Spinning disk confocal images were acquired on a Leica DMI4000B or DMI6000SD inverted microscope equipped with an HCX PL APO 100X/1 . 46 numerical aperture ( NA ) oil objective and a PerkinElmer Confocal system . This system uses a Yokagawa CSU22 real-time confocal scanning head , solid-state laser lines and a cooled 14-bit frame transfer EMCCD C9100-50 camera ( Hamamatsu ) and is run by Volocity ( PerkinElmer ) . Time-lapse microscopy ( Figs . 6A , B , 7G and S6D–E ) was performed on a DeltaVision platform ( Applied Precision ) . This platform is composed of a customized Olympus IX-71 inverted microscope equipped with a UPlan Apo 100X/1 . 4 NA oil objective , a CoolSNAP HQ2 camera ( Photometrics ) , and an Insight SSI 7 color combined unit illuminator . Calcofluor and transmitted light microscopy was performed on either the DeltaVision system or a Leica DMI400B microscope equipped with a HCX PL Fl 63X/1 . 25 NA objective . The FRAP data acquired for analysis in Fig . 4C , D and S5A–F was acquired on a Zeiss Axio Observer . Z1 inverted microscope equipped with a LSM 710 scan head , a PL APO 100X/1 . 4 NA oil DIC objective and Argon multiline 458/488/514 nm ( 25 . 0 mW ) ( Lasos ) and 561 nm ( 20 . 0 mW ) laser . Quantification of Cdc42-mCherrySW and CRIB distribution shown in Fig . 2B–D , 5F , 5H , S2 was done by using the sum projection of five consecutive images . The intensity of a 3-pixel-wide segment was collected from at least 20 tips for both red and green channels . The tips were centered around the maximum pixel values for the CRIB channel and subsequently split into two half tips . Enrichment values were derived by dividing the maximum intensity value by the average intensity of the final 1 μm region . Spores were prepared from mating mixtures by overnight digestion in Glusulase at 25°C followed by three washes with water . The spores generated from cdc42-mCherrySW-ritC and the wild-type control crosses ( Fig . 6B ) the spores were enriched by addition to Percoll ( Sigma ) and centrifugation at 10K for 10 min . Dense spores were collected from the bottom fraction . For fluorescence imaging of germinating spores in Fig . 4B spores were incubated in YE media at 25°C for 3 h before placed onto YE 2% agarose pads and sealed with VALAP before imaging . FRAP data from Figs . 3A , B–E , 4A , E , 5E , I and S3C was gathered with a Photokinesis module on the spinning disk confocal system described above . A 0 . 9 μ m region was bleached following two pre-bleach acquisitions and recovery was followed at regular intervals except those for GFP-Psy1 and Cdc42-mCherrySW-psy1TM where intervals increased over time . Analysis of FRAP was done as previously described [71] . For S3 Fig the green and red channels were simultaneously bleached and the fluorescence in each channel was monitored . The significance of differences in FRAP values is depicted in figures with asterisks as follows; n . s . = p > 0 . 05 , * = p ≤ 0 . 05 , ** = p ≤ 0 . 01 , *** = p ≤ 0 . 001 , **** = p ≤ 0 . 0001 . The FRAP experiments described in Figs . 4C and S5A , C were performed by bleaching a cortical region at the cell side or a cortical region that included half of the cell tip and part of the cell side with the Zeiss laser scanning system described above . The contour of the cell membrane was measured with JFilament , an ImageJ plugin for segmentation and tracking of 2-D and 3-D filaments in fluorescence microscopy images ( S5A Fig ) [72] . This program was used to fit a closed smooth active contour to the cell boundary of an image of the cell prior to bleaching . We used settings that attract the contour to image regions with a local maximum intensity gradient . The membrane intensity was calculated as a function of distance along the contour and time by integrating the intensity within a band around the contour that included the cell membrane ( typically 3 pixels ) , in the direction normal to the contour , using Boundary Kymograph in JFilament . To correct for continuous photobleaching during image acquisition , the intensities at each time were normalized to the average intensity at the unbleached cell side , after subtracting the out-of-cell background . With this normalization method , the profiles at times of order 30 s after bleaching recovered close to the pre-bleach profile , even when continuous photobleaching caused the overall intensity to drop by 50% over that time . The normalized intensity profiles I ( x , t ) at the cell sides ( Fig . 4D , E ) were fitted to the following equation that represents recovery by diffusion with diffusion coefficient D and uniform exchange with the cytoplasm with time constant τ , along an approximately flat membrane: I ( x , t ) =I0−C e−t/τe− ( x−x0 ) 2/ ( 4Dt+2σ02 ) /[ ( 4πDt+2πσ02 ) 1/2 ( 4πDt+2πσz2 ) 1/2] Here x is distance along the cell contour , I0 is the value of the uniform intensity prior to bleaching , x0 is the position of the center of the bleached region , σ0 gives the standard deviation of the initial bleached region along the cell contour that has an approximately Gaussian shape , σz = 0 . 8 − 1 . 5 μm is the estimated standard deviation of the bleached region in the z direction , and C is a constant describing the magnitude of the intensity dip in the bleached region . The above expression is obtained by evolving an initial 2-D bleached distribution with a Gaussian shape in each direction with the 2-D free diffusion propagator , multiplied by an exponential representing uniform cytoplasmic exchange . To obtain estimates for D and τ , the recovery curves of narrow bleached regions were first fitted by assuming that they are dominated by diffusion and setting the exponential term dependent on τ to unity ( Fig . 4E ) . The obtained value of D was then used to fit recovery curves in wider bleached regions and obtain an estimate for τ ( Fig . 4D ) . The above procedure is self-consistent since the resulting value of τ is large enough to justify neglecting it for the recovery of narrow bleach regions . The error bar of the reported D and τ values is obtained by observing the range of values that allow fits going through the data , within the experimental noise and the range of values of σz . Larger ( smaller ) values of D and τ are obtained for larger ( smaller ) σz . To obtain an upper bound for the diffusion coefficient of Cdc42-GTP , the distance d between the maximum and half-maximum of the intensity profile within the tip bleached region was measured versus time ( S5D Fig ) . This value was used to calculate the standard deviation σ = 085 d of an equivalent 1-D Gausian diffusion propagator . A diffusion coefficient Dslow was then estimated by the setting the slope of the graph in S5E Fig to 2Dslow . Protoplasts were generated as described previously with modifications ( Flor-Parra et al . 2014 . Yeast ) . Cells grown in EMM were loaded into chambers of microfluidic plates from CellASIC ONIX ( Miilipore ) prewashed with EMM . Cells were then washed with E buffer ( 50mM NaCitrate , 100 mM NaPhosphate pH5 . 6 , 1 . 2 M Sorbitol ) for 10 min followed by cell wall digestion by flowing in 0 . 1 g/ml Lallzyme MMX ( Lallemand ) in E buffer for 15 min . The resulting protoplasts were allowed to recover by flowing in EMM containing 1 M Sorbitol and cells were imaged every 20 min . Fluorescence correlation spectroscopy experiments were performed on a LSM 510 Meta laser scanning microscope equipped with a ConfoCor 3 unit ( Carl Zeiss MicroImaging GmbH ) . A 40x/1 . 2NA water-immersion objective focused the 488 and 561 nm excitation laser beams to a diffraction-limited spot and collected the emitted fluorescence light . The location of the excitation/observation volume on different parts of the yeast was adjusted as close as possible to the plasma membrane with the help of a galvano mirror and an acousto-optical tunable filter ( AOTF ) , which fine-tuned the incident irradiation . Fluorescence time traces were recorded for 10x10 seconds and a built-in photon correlator analyzed the fluorescence fluctuations to generate autocorrelation curves: G ( τ ) =〈F ( t ) ⋅F ( t+τ ) 〉〈F ( t ) 〉2 where <…> denotes time average , F ( t ) the fluorescence signal and τ the delayed time , also called lag time . The autocorrelation curves were subsequently fitted with diffusion models using a Marquardt algorithm on Igor Pro software ( WaveMetrics ) to extract different parameters such as translational diffusion time τD and mean number of particles N in the observation volume as described in detail elsewhere [73] . Analysis of receptor diffusion on yeast membrane was performed by combining a three-dimensional diffusion of free proteins in cytosol and a two-dimensional diffusion of membrane receptors: G ( τ ) =1+1N ( Fmemb ( 1+ττDmemb ) −1+ ( 1−Fmemb ) ( 1+ττDcyt ) −1 ( 1+τS2τDcyt ) −1/2 ) where τmemb and τcyt are the diffusion times of the membrane receptor and free proteins , respectively , S is the structure parameter defined by the ratio of the axial and lateral axes of the observation volume and Fmemb is the fraction of receptor . To take into account triplet state , the second term might be multiplied by: ( 1+T1−Te−ττT ) where T is the triplet fraction and τT the triplet time . Finally , diffusion coefficients D were calculated according to: D=ωxy24⋅τD where the lateral beam waist radius ωxy was determined measuring the translational diffusion time of a calibration dye of known diffusion coefficient . | Cell polarization is a critical feature of most cells that underlies their functional organization . A central polarity factor called Cdc42 , a small GTPase targeted to the plasma membrane by prenylation , promotes cell polarization in its active GTP-bound form . Cdc42 is a key polarity factor because it accumulates at presumptive sites of polarity , which previous work suggested involves Cdc42 recycling on and off the plasma membrane . In addition , its activity can spontaneously polarize cells in a single location by self-enhancing positive feedback mechanisms , even in the absence of any pre-localized landmarks . In this study , we constructed the first functional fluorescently tagged allele of Cdc42 that replaces the endogenous genomic copy in Schizosaccharomyces pombe . This allowed measurements of Cdc42 dynamics at the plasma membrane by live microscopy . Unexpectedly , this approach revealed that Cdc42 primarily moves through lateral diffusion , rather than on and off the plasma membrane . Engineered Cdc42 alleles with alternative membrane-targeting mechanisms demonstrated that Cdc42 activity , indeed , polarizes in the absence of known pathways that recycle Cdc42 on and off the membrane . We further show that the active form , Cdc42-GTP , is less mobile than Cdc42-GDP . We thus propose that Cdc42 polarization occurs as a consequence of its local activation—either through self-enhanced feedback or in response to upstream cues—by a reduction in the active Cdc42 diffusion rate . | [
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| 2015 | Spontaneous Cdc42 Polarization Independent of GDI-Mediated Extraction and Actin-Based Trafficking |
Antigen B ( EgAgB ) is the most abundant and immunogenic antigen produced by the larval stage ( metacestode ) of Echinococcus granulosus . It is a lipoprotein , the structure and function of which have not been completely elucidated . EgAgB apolipoprotein components have been well characterised; they share homology with a group of hydrophobic ligand binding proteins ( HLBPs ) present exclusively in cestode organisms , and consist of different isoforms of 8-kDa proteins encoded by a polymorphic multigene family comprising five subfamilies ( EgAgB1 to EgAgB5 ) . In vitro studies have shown that EgAgB apolipoproteins are capable of binding fatty acids . However , the identity of the native lipid components of EgAgB remains unknown . The present work was aimed at characterising the lipid ligands bound to EgAgB in vivo . EgAgB was purified to homogeneity from hydatid cyst fluid and its lipid fraction was extracted using chloroform∶methanol mixtures . This fraction constituted approximately 40–50% of EgAgB total mass . High-performance thin layer chromatography revealed that the native lipid moiety of EgAgB consists of a variety of neutral ( mainly triacylglycerides , sterols and sterol esters ) and polar ( mainly phosphatidylcholine ) lipids . Gas-liquid chromatography analysis showed that 16∶0 , 18∶0 and 18∶1 ( n-9 ) are the most abundant fatty acids in EgAgB . Furthermore , size exclusion chromatography coupled to light scattering demonstrated that EgAgB comprises a population of particles heterogeneous in size , with an average molecular mass of 229 kDa . Our results provide the first direct evidence of the nature of the hydrophobic ligands bound to EgAgB in vivo and indicate that the structure and composition of EgAgB lipoprotein particles are more complex than previously thought , resembling high density plasma lipoproteins . Results are discussed considering what is known on lipid metabolism in cestodes , and taken into account the Echinococcus spp . genomic information regarding both lipid metabolism and the EgAgB gene family .
The larval stage of the cestode parasite Echinococcus granulosus is the causative agent of cystic echinococcosis ( hydatid disease ) in a range of mammalian species ( mainly domestic ungulates ) as well as in humans . It is a unilocular fluid-filled cyst , which steadily grows inside host visceras ( mostly liver and lung ) . One of the major molecules produced in large amounts by the cyst is a highly immunogenic lipoprotein named antigen B ( EgAgB ) [1] , [2] , which represents a major diagnostic antigen for human infection [3]–[5] . This antigen is present in various larval locations including the parasite cellular layer of the cyst wall ( germinal layer ) , the larval worms or protoscolex ( asexually produced towards inside the cyst ) and the hydatid cyst fluid ( HCF ) . HCF is a complex mixture of parasite excretory-secretory products and host-derived molecules that constitutes the liquid content of the cyst [6]–[8] . Evidence for EgAgB presence in host circulation is very limited [9] . The strong antibody response mounted by infected patients against this antigen indicates that it is likely released into the host-parasite interface . However , it is unknown whether it is released throughout the infection or just at a certain time point [10] , [11] . A lot of efforts have been made to understand the molecular composition/organization of EgAgB ( reviewed by [12] ) . The native antigen is a lipoprotein which exhibits an estimated molecular weight of 120 to 160 kDa according to sedimentation equilibrium and gel filtration studies , respectively [1] , [2] . The apolipoprotein components of EgAgB are encoded by a polymorphic multigene family that comprises five clades named EgAgB1 to EgAgB5 [13]–[17] . There is a long and yet unsettled controversy regarding EgAgB gene copy number . Based on the characterisation of E . granulosus isolates from different geographic origins , a recent study has proposed that there are at least 10 EgAgB distinct genes , including four and three different genes corresponding to the EgAgB3 and EgAgB4 clades [18] . However , a recent analysis of EgAgB loci in the current assembly of E . granulosus genome revealed the presence of seven EgAgB loci clustered on a discrete region of the genome , with one copy each of EgAgB1 , EgAgB2 , EgAgB4 and EgAgB5 , as well as three slightly differing copies of EgAgB3 [19] . Outside this cluster only an EgAgB pseudogene was detected . However , the authors did not rule out the possibility of additional EgAgB genes in extra-chromosomal DNA arrays that might have slipped the genome assembly process [19] . There is evidence that EgAgB genes are differentially expressed in single life-cycle parasite stages , and also within distinct tissues of a same parasite stage ( i . e . protoscolex and germinal layer ) [18] , suggesting that structural and/or functional differences between individual EgAgB lipoproteins may exist . The comparison of the amino acid sequences between members of EgAgB family showed that members of the EgAgB1 , EgAgB3 and EgAgB5 clades are more similar among each other than to members of the EgAgB2 and EgAgB4 clades and vice versa [18] . The polypeptides encoded by these genes are between 65 and 71 amino acids long , and have approximately 8 kDa in mass; reason by which these apolipoproteins have traditionally been called EgAgB8/1 to EgAgB8/5 . Some of them were found to be capable of self-associating into homo- or hetero-oligomers of 16 and 24 kDa [20] or even into higher order homo-oligomers [21] . Although EgAgB has been studied in some detail at the protein level , very little is known concerning its lipid moiety . EgAgB was originally described as a lipoprotein on the basis that lipids were non-covalently bound to the protein component since they could be mostly removed by extraction with alcohol/ether mixtures [2] . However , the characterisation of the lipid component has not been attempted; neither has the protein/lipid stoichiometry been determined nor the class lipid composition . More recently , it has been shown that EgAgB apolipoproteins belongs to a family of hydrophobic ligand binding proteins , referred to as HLBPs , found exclusively in cestode organisms . To date , members of this family include intracellular HLBP identified in Monienza expansa [22] , [23] and Hymenolepis diminuta [24] , [25] as well as extracellular HLBP identified as secreted components of Taenia solium and Echinococcus granulosus [26] . All these proteins have been found to be highly abundant and immunogenic , and exists as high-molecular-mass oligomers composed by α helix-rich subunits of about 7–11 kDa . Related immunogenic proteins were also described in Taenia crassiceps and Taenia hydatigena although their lipid-binding properties have not been analysed [26]–[29] . The ligand specificity of intracellular HLBPs has been characterised in vitro [22]–[25]; they bind saturated and unsaturated fatty acids ( but not their CoA-ester derivatives ) , retinoids , and some antihelminthic drugs , and the M . expansa protein can also bind cholesterol . The in vitro lipid binding properties of extracellular HLBPs has been partially examined by binding assays using fluorescent lipid analogues and shown to bind fatty acids only [26] , [30] , [31] . In the case of EgAgB , the delipidated native molecule and the recombinant EgAgB8/1 and EgAgB8/2 apolipoproteins showed ability to bind a palmitic acid fluorescent analogue with high affinity , but the possibility that these proteins could bind lipids other than fatty acids was not evaluated [30] . Cestodes have a very restricted lipid metabolism . On the one hand , lipids are not suitable substrates for energy metabolism because they cannot be oxidised due to the limited aerobic capacity of tissue-dwelling parasites ( reviewed by [32] and [33] ) . On the other hand , cestodes are unable to synthesise fatty acids , phospholipids and cholesterol de novo . Yet , lipids are required for biosynthetic purposes , and thus , parasite lipid-binding proteins play a key role in cestode metabolism , as they are likely involved in the uptake of lipids or their precursors from the host . In this scenario , it is generally thought that EgAgB and its secreted homolog could have an important role in the biology of cestodes , controlling the acquisition and distribution of lipids to specific tissues . Alternatively , it has been proposed that HLBPs could act as messenger molecules by carrying signalling lipids which would play a role in cell activation and/or differentiation processes involved in parasite adaptation to the host immune system . In the case of EgAgB , in vitro evidence suggests that this lipoprotein may modulate host defenses by down-regulating neutrophils and dendritic cell-mediated innate responses as well as T-cell dependent mechanisms , which globally influence the intensity and quality of the adaptive immune responses [34]–[37] . The present work was aimed at identifying the native lipid moiety of EgAgB in order to complete our knowledge on the EgAgB molecular composition; this information could simultaneously shed light into EgAgB structure and function . Of particular relevance was to determine whether EgAgB binds in vivo lipid classes other than fatty acids . For that purpose , we purified EgAgB to homogeneity , using a protocol based on ion exchange chromatography coupled to immunoaffinity with a monoclonal antibody ( Mo EB7 ) , and then purified the EgAgB lipid moiety by extraction with organic solvents . Characterisation of immunopurified EgAgB of bovine origin showed that lipoprotein particles are constituted mostly by EgAgB8/1 apolipoprotein and that the native lipid moiety of this antigen comprises neutral and polar lipids that have not been previously described as ligands of this HLBP family .
Inorganic salts , 3 , 5-di-tert-butyl-4-hydroxytoluene ( BHT ) , ethylenediaminetetraacetic acid ( EDTA ) and authentic lipid standards including cholesterol ( CH ) , fatty acids ( FA ) , triacylglycerols ( TAGs ) , phosphatidylethanolamine ( PE ) , cardiolipin ( CLP ) , phosphatidylinositol ( PI ) , phosphatidylserine ( PS ) and phosphatidylcholine ( PC ) were acquired from Sigma Chemicals ( USA ) . Solvents ( HPLC grade or better ) and α-naphthol were purchased from Merck ( Germany ) or Fisher Scientific ( USA ) . E . granulosus HCFs from cysts containing protoscoleces of bovine origin were obtained by aspiration of the content of cysts present in lungs and livers of naturally infected cattle . Cysts were collected during the routine work of local abattoirs in Montevideo ( Uruguay ) . E . granulosus HCFs of human origin , collected from surgically-removed hepatic hydatid cysts , were generously donated by Dr A . Leites and Dr E . Torterolo ( Hospital Militar , Montevideo , Uruguay ) . All HCF samples were preserved by addition of 5 mM EDTA and 20 µM BHT , and maintained at −20°C . For EgAgB purification , three batches of bovine HCF ( each one containing a pool of HCF from individual cysts ) and two samples of individual human HCF were used . Native EgAgB was purified from HCF following a previously described protocol [20] with slight modifications . HCF was centrifuged at 10000 g for 30 min at 4°C and the resulting supernatant filtered consecutively through 5 , 2 , 0 . 8 and 0 . 45 µm filter membranes ( Millipore ) . The clarified HCF was firstly fractioned by anion exchange chromatography on a Q-Sepharose column ( Pharmacia Biotech , Uppsala , Sweden ) previously equilibrated in 20 mM phosphate buffer , pH 7 . 2 containing 200 mM NaCl , 5 mM EDTA and 20 µM BHT . After washing in equilibration buffer , the retained material was eluted by changing ionic strength to 400 mM NaCl in a single step . The eluted fraction ( enriched in EgAgB and almost free of host albumin and immunoglobulins ) was used to purify the antigen to homogeneity by immunoaffinity chromatography based on the utilization of a monoclonal antibody ( MoAb ) -named EB7- that specifically recognises the native lipoprotein [20] . For this purpose , the Q-Sepharose eluted fraction was diluted in 20 mM phosphate buffer , pH 7 . 2 containing 5 mM EDTA and 20 µM BHT , to reach a final concentration of 200 mM NaCl and then applied to the EB7-Sepharose column . After washing , EgAgB was eluted with 100 mM glycine-HCl , pH 3 , immediately neutralised with 2 M Tris pH 9 . 6 and then equilibrated in 20 mM phosphate buffer , pH 7 . 2 containing 5 mM EDTA and 20 µM BHT ( PBS-EDTA-BHT ) using a PD-10 desalting column ( Amershan , Biosciences ) . The homogeneity of EB7-affinity purified EgAgB ( immunopurified EgAgB ) was monitored by SDS-PAGE on 15% polyacrylamide gels followed by silver stain . In addition , samples were analysed by two-dimensional gel electrophoresis as described below . First dimension was performed with commercially available IPG-strips ( 7 cm , linear 3–10 , GE Healthcare ) . Immunopurified EgAgB was prepared and concentrated by using the 2-D Clean-Up kit ( GE Healthcare ) and dissolved in rehydration solution ( 7 M urea , 2 M thiourea , 2% CHAPS , 0 . 5% IPG buffer 3–10 ( GE Healthcare ) , 0 . 002% bromophenol blue , DTT 17 mM ) . Samples in rehydration solution were loaded onto IPG-strips by passive rehydration during 12 h at room temperature . The isoelectric focusing was done in an IPGphor Unit ( Pharmacia Biotech ) employing the following voltage profile: constant phase of 300 V for 30 min; linear increase to 1000 V in 30 min; linear increase to 5000 V in 80 min and a final constant phase of 5000 V to reach total of 6 . 5 kVh . Prior running the second dimension , IPG-strips were reduced for 15 min in equilibration buffer ( 6 M urea , 75 mM Tris–HCl pH 8 . 8 , 29 . 3% glycerol , 2% SDS , 0 . 002% bromophenol blue ) supplemented with DTT ( 10 mg/ml ) and subsequently alkylated for 15 min in same equilibration buffer but supplemented with iodoacetamide ( 25 mg/ml ) . The second-dimensional separation ( SDS-PAGE ) was performed in 15% polyacrilamyde gels using a SE 260 mini-vertical gel electrophoresis unit ( GE Healthcare ) . The molecular size markers used were Amersham Low Molecular Weight Calibration Kit for SDS Electrophoresis ( GE Healthcare ) . The gels were silver stained according to [38] . Images were digitalised using a UMAX Power-Look 1120 scanner and LabScan 5 . 0 software ( GE Healthcare ) . Identification of protein spots was performed by mass spectrometry ( MS ) using a 4800 MALDI TOF/TOF™ ( AB Sciex ) . Briefly , peptide mass fingerprinting plus MS/MS ion search of selected spots were carried out by in-gel trypsin treatment ( sequencing-grade , Promega ) at 37°C , overnight . Peptides were extracted from gels using 60% acetonitrile in 0 . 1% TFA , concentrated by vacuum drying . Peptides were further concentrated and desalted using C18 reverse phase micro-columns ( OMIX Pippete tips , Varian ) . Peptide elution from micro-column was performed directly into the mass spectrometer sample plate with 2 µl of matrix solution ( α-cyano-4-hydroxycinnamic acid in 60% aqueous acetonitrile containing 0 . 1% TFA ) . Mass spectra of digestion mixtures were acquired in the MALDI-TOF/TOF mass spectrometer using the reflector mode . Spectra were externally calibrated using a mixture of peptide standards ( Mix 1 , AB Sciex ) . For increased confidence of identification , selected peptides were further fragmented by post-source decay ( PSD ) and collisional-induced dissociation ( CID ) . Proteins were identified by NCBI nr database searching using the MASCOT program ( Matrix Science http://www . matrixscience . com/search_form_select . html ) and using the following search parameters: monoisotopic mass tolerance , 0 . 05 Da; fragment mass tolerance , 0 . 2 Da; carbamidomethyl cysteine and methionine oxidation as variable modifications and up to one missed tryptic cleavage allowed . Total lipids were extracted according to the methodology described by [39] , with slight modifications . Briefly , HCF ( previously concentrated 10-times using a Savant SpeedVac System ) or immunopurified EgAgB ( 1 mg protein in approximately 2 mL of PBS-EDTA-BHT ) were mixed with 30 ml of a CHCl3∶CH3OH mixture ( 2∶1 ) and vigorously shaken for 2 minutes . Next , the homogenate was filtrated and the extract washed with NaCl solution to reach a final concentration of 0 . 73% and a CHCl3∶CH3OH∶H2O ratio of 2∶1∶0 . 2 in volume . After vigorous agitation for 1 minute , the separation of phases was achieved by centrifugation at 2400 rpm for 20 min and the upper phase was removed by aspiration and discarded . The lower phase containing lipids was recovered and taken to dryness by rotary evaporation at 40°C under vacuum; last traces of solvent were removed by a stream of N2 ( g ) . Finally , the lipid fraction was dissolved in CHCl3 to a concentration of 10 mg/mL . As a control of lipid contaminants in buffers and/or solvents , an equal volume of PBS-EDTA-BHT was used in parallel for extraction . Purified lipid extracts were stored at −20°C under N2 ( g ) until analysis . The protein content of immunopurified EgAgB preparations was determined using bicinchoninic acid in a microtitre plate assay ( BCA Protein Assay kit ) with BSA as standard ( Pierce , Rockford , Ill . ) . Total lipids in EgAgB were determined gravimetrically by weighting purified lipids immediately after extraction and solvent removal under a stream of N2 ( g ) . Separation of lipid class components was performed by high-performance thin layer chromatography ( HPTLC ) on Kieselgel 60 plates ( Merck ) . HPTLC plates ( 10×10 cm ) were pre-washed by migration in CHCl3/CH3OH ( 2∶1 v/v ) and then activated at 100°C for 30 min . Lipid samples ( fractions obtained from HCF and EgAgB and standards ) were spotted manually using a micro-syringe ( Hamilton ) . Double development was initially carried out as follows: plates were first half-developed using a mobile phase for resolving polar lipids ( PL ) , and after drying under a N2 ( g ) stream , were developed to completion in a mobile phase for neutral lipids ( NL ) . Single development for resolving PL or NL was also performed . The solvent systems used as mobile phase were: for PL resolution- methyl acetate/isopropanol/chloroform/methanol/0 . 25%KCl ( 25∶25∶25∶10∶9 , v/v/v/v/v ) and for NL resolution- hexane/diethyl-ether/acetic acid ( 80∶20∶1 , v/v/v ) . Lipid bands were visualised by spraying the plates with 8% ( m/v ) CuSO4 in a 10% ( v/v ) H3PO4 aqueous solution , and heating at 140°C; the identification of lipid classes was performed by comparison with primary and secondary standards run on the same HPTLC plate . The relative abundance of each lipid class respect to the total lipid content could not be estimated because not all lipids were resolved adequately in a single HPTLC using double development . We estimated the percentage of individual lipid classes in the total of NL or PL instead . 1-nonadecanol was used as internal standard for normalization . HPTLC plates were scanned and the intensity of the bands was determined using the ImageJ software ( http://rsb . info . nih . gov/ij/ ) . In addition , specific staining for sterol esters/sterols and glycolipids were carried out using FeCl3 . 6H2O and α-naphthol , respectively [40] . For the latter analysis a lipid fraction from murine macrophage-like J774 . A1 cells was prepared to use as a control . Briefly , J774 . A1 cells ( generously donated by Dr . M . Noel Alvarez , Departamento de Bioquímica , Facultad de Medicina , UdelaR ) were washed with PBS , lysed using a hypotonic solution ( 0 . 25 mM phosphate 3 . 75 mM NaCl ) and centrifuged ( 15000×g , 4°C for 30 min ) to obtain a cell membrane enriched fraction . Lipids were then extracted following the Folch method as described above . The fatty acid composition of EgAgB and HCF lipid fractions was analysed by gas-liquid chromatography of their methyl esters derivatives ( FAMEs ) ; for these studies material of both human and bovine origin were used . Lipid fractions were subjected to acid methanolysis with 1% H2SO4 in methanol at 50°C for 16 h . The purified FAMEs were dissolved in hexane and then subjected to GLC analysis on a Hewlett-Packard 5890 equipped with a Carbowax 20 capillary column and flame ionisation detector ( FID ) . The oven temperature was initially set at 180°C for 10 min , and then increased at 2 . 5°C/min to 212°C , level at which was held for 10 min . The individual FAMEs peaks were identified by comparison of their retention times with those of authentic FAMEs standards . Light scattering analysis was carried out using a Superset 200 HR 10/30 SEC column ( Amersham Biosciences , Piscataway , NJ ) , connected to an HPLC system ( Schimatzu ) at room temperature . Immunopurified EgAgB ( 200 µL of a 0 . 7 mg/mL solution in PBS ) was applied onto the column previously equilibrated in PBS , and elution was monitored with on-line detection using the following detectors: multiangle laser light scattering ( miniDAWN system , Wyatt Technology Corporation , Santa Barbara , CA ) , ultraviolet UV ( SPD-20A , Shimadzu ) and differential refractive index ( RID-10A , Shimadzu ) . In addition , plasma-derived high density lipoprotein ( HDL ) was analysed in the same conditions for comparison . For HDL preparation , human plasma was obtained from a healthy donor and HDL purification was carried out following conventional ultracentrifugation methods [41] . A written consent was obtained from the donor according to the Ethic Committee of the Faculty of Chemistry ( UdelaR ) and the Executive Decree N° 379/008 . Light scattering data were collected and processed with ASTRA software ( v4 . 73 . 04 , Wyatt Technology ) using the Debye fit method with a dn/dc ratio set to 0 . 186 mL/g [42] .
Most studies were performed with EgAgB of bovine origin since the availability of parasite material of human origin was very limited . EgAgB was purified to homogeneity from bovine HCF using a previously described procedure based on selective adsorption of this antigen on Q-Sepharose followed by immunoaffinity chromatography using immobilised MoAb EB7 [20] . This purification methodology was found to be suitable for the main objectives of this work , as it has been shown that affinity immunoadsorption permits isolation of lipoproteins under minimally perturbing conditions [43] . We characterised the apolipoprotein component of bovine EgAgB using 2-D gel electrophoresis and found that it contained two major spots electrofocused at pH 8 . 8 and 9 . 6 ( Figure 1A , arrows ) . These two spots were identified as EgAgB8/1 by mass spectrometry analysis . Identification was mainly due to the analysis of the data arising from two main peptide signals with m/z of 1275 . 68 and 1399 . 69 that matched with the ELEEVFQLLR and YFFERDPLGQK sequences , respectively . These peptides lie in conserved regions of the amino acid sequence of EgAgB8/1 . Therefore , we cannot discriminate which ones of the already described EgAgB8/1 isoforms is present in the samples . These isoforms include molecules having different theoretical isoelectric point ( Figure 1B ) . In the case of the most basic spot , focused at pH 9 . 6 ( Figure 1A , arrow ) , an additional peptide matching the MFGEVK sequence ( Figure 1B , dashed line box ) was observed . This sequence is present in at least four EgAgB8/1 isoforms . Interestingly , only one of them has a theoretical pI of 9 . 52 , matching the observed value ( Figure 1B , solid line box ) . A minor spot electrofocused at pH 7 . 9 was also detected ( Figure 1A , head arrow ) , and peptide mass spectrometry of this spot indicated that it also corresponded to EgAgB8/1 . When higher amounts of antigen ( 3-fold ) were analysed , EgAgB8/4 was detected; identification was mainly based on the presence of a signal with m/z = 1152 that matched with the LGEIRDFFR sequence , which is only present in EgAgB8/4 isoform ( data not shown ) . Previous work suggested that EgAgB8 apolipoproteins are capable of binding fatty acids [30] . However , the fact that HCF , the physiological milieu to which EgAgB is secreted , contains a wide range of neutral and polar lipids [44] , [45] , opens up the possibility that the putative physiological ligands of EgAgB8 apolipoproteins include a more diverse set of lipids . For identification of these ligands , we firstly purified the lipid fractions of both , EgAgB and the HCF from which this antigen was immunopurified , using parasite material of bovine origin via the Folch extraction method . This procedure is broadly applied to the analysis of lipoproteins and has the advantage of solubilising all major lipid classes using a single solvent mixture , even though it does not allow the protein to be recovered because this fraction irreversibly precipitates during the procedure . From the dry mass of total lipids extracted and the protein concentration of the starting sample we estimated the lipid∶protein ratio ( w∶w ) , finding that it was plainly higher for immunopurified EgAgB ( between 0 . 6∶1 and 1 . 1∶1 , n = 3 independent batches ) than HCF ( between 0 . 17∶1 and 0 . 19∶1 , n = 3 independent batches ) . This implies that the lipid fraction of EgAgB represented approximately 40–50% of its total mass . As an initial approach for examining the complexity of lipid classes , HCF and EgAgB lipids were analysed by HPTLC using double development . Under these conditions the majority of neutral and polar lipids are clearly separated , although the resolution of some lipid classes may not be optimal . By analysing in parallel a mixture of authentic lipid standards , we observed that the natively-bound lipid component of immunopurified EgAgB is highly heterogeneous , comprising several lipid classes , all of which are also found in the HCF . Indeed , the lipid fractions of both EgAgB and HCF showed to contain a wide variety of lipid classes from very hydrophobic ones ( compatible with sterol esters and TAGs ) to charged ones ( phospholipids ) ( Figure 2A ) ; this pattern was obtained for EgAgB and HCF samples derived from three independent batches ( data not shown ) . Thus , these results indicated that the lipid fraction of EgAgB particles carrying EgAgB8/1 apolipoproteins consists not solely of fatty acids , but also of a variety of polar and neutral lipids present in HCF . For comparative purposes , we analysed the lipid fraction of EgAgB immunopurified from HCF of human origin ( two independent batches ) , finding similar results in terms of lipid∶protein ratio as well as lipid class composition ( data not shown ) . In order to improve the identification of the lipid classes of EgAgB , the EgAgB lipid fraction was analysed by HPTLC using conditions for resolving separately neutral and polar lipids; in this analysis , 1-nonadecanol was added to all samples as an internal standard ( IS ) for normalization . This analysis was carried out only for HCF and EgAgB of bovine origin . As shown in Figures 2B and 2C , the lipid composition of EgAgB and HCF was very similar in terms of the variety of lipid classes . Among neutral lipids , three classes were assigned considering their mobility in comparison with standards: TAGs , free sterols and free fatty acids . In addition , three components having higher mobility than TAGs were observed in both EgAgB and HCF lipid fractions . Among these , there was a component that migrated slightly faster than FAMEs but slower than cholesteryl laureate , being compatible with dialkyl-monoacylglycerols and/or alkenyl-diacylaglycerols [40] . The other two components were not completely resolved and migrated as a wide smear in the assay conditions . The least mobile component of this smear could correspond to sterol esters according to the mobility of cholesteryl laurate . This was confirmed by using a sterol ester-specific staining method based on the formation of a purple complex with FeCl3 at acid pH ( Figure 3A ) . The most mobile component was also observed in the control , indicating that it corresponded to a very hydrophobic contaminant derived from the extraction procedure . A second contaminant having a very low mobility in the assayed conditions was also detected in the control . With respect to polar lipids , the analysis of EgAgB and HCF in parallel with phospholipid standards showed that phosphatidylcholine was the main phospholipid present in both samples . Phosphatidylethanolamine , phosphatidylinositol and phosphatidylserine were also detected in smaller amounts as well as traces of cardiolipin . Furthermore , minor components with higher mobility than phospholipids but lower than neutral lipids compatible with glycolipids were observed . The presence of glycolipids was confirmed by staining with α-naphthol , a dye that specifically reacts with sugar groups ( Figure 3B ) . The relative abundance of the major lipid classes found in immunopurified EgAgB and HCF within total neutral or polar lipids was estimated as shown in Figure 4 . This estimation was carried out by analysing the intensity of HPTLC bands by densitometry , for which errors due to unequal sample application or irregular staining across the plate were normalised using the internal standard . TAGs and phosphatidylcholine corresponded to the most abundant neutral and polar lipids of EgAgB , reaching around 30% and 60% , respectively; similar percentages of these lipids were found in HCF . Moreover , the relative abundance of any lipid class within neutral and polar lipids was almost identical for EgAgB and HCF , suggesting a non-selective binding of lipids by EgAgB8/1 apolipoproteins . It is worth to mention that the relative abundance values are likely affected by two factors inherent to the method: i ) the fact that the intensity of staining does not follow the same proportionality with the lipid mass for the different lipid classes , and ii ) the spreading of the band affects densitometry , which means that how each lipid is resolved/focused during the chromatography could influence its quantitation . We next analysed the fatty acid composition of total lipids extracted from human and bovine EgAgB and HCF ( Figure 5 ) . Globally , the results revealed that the fatty acid profiles of EgAgB obtained from both sources were very similar . The predominant fatty acids were 16∶0 , 18∶0 and 18∶1 ( n-9 ) , while 18∶2 ( n-6 ) and 20∶4 ( n-6 ) were present in a much lower proportion . Other fatty acids were present in minor quantities , representing less than 5% each . Furthermore , the content of fatty acids in EgAgB closely resembles that of the HCF used for its purification , suggesting that EgAgB8/1 apolipoproteins are capable of binding the most abundant fatty acids of HCF in a non-selective manner . Nevertheless , EgAgB8/1 apolipoproteins showed some degree of selectivity in fatty acid binding properties since the relative abundance of 13∶0 , 16∶0 , 18∶0 and 18∶1 ( n-9 ) were similar in bovine HCF , but not in bovine EgAgB , which contained lower percentage of 13∶0 and higher percentage of 18∶1 ( n-9 ) than bovine HCF . The comparison of the relative abundance of fatty acids in bovine vs . human HCF strongly suggests that the fatty acids are taken from the host , since 13∶0 , a more abundant fatty acid in ruminants , was found in higher levels in bovine than human HCF ( 14 vs . 3% of total fatty acids ) . Bovine EgAgB was analysed by SEC to study its physical state in aqueous solution . EgAgB eluted as a main peak centered at 12 . 5 mL , from which an apparent MW of 160 kDa was estimated by comparison with protein standards . This value is in agreement with previous results [1] , [2] . In order to determine the absolute MW independently of hydrodynamic assumptions , we also analysed the MW of bovine EgAgB by SEC-MALLS . The molecular mass curve obtained by SEC-MALLS displayed two phases ( Figure 6 ) : an initial sharp decrease of the size at the beginning of elution ( started at molecular-mass species of ∼400 kDa ) , rapidly followed by a plateau at a calculated mass of ∼229 kDa , which included the maximum and extended to the end of the peak . This behaviour suggests that EgAgB exists in solution as a heterogeneous population of lipoprotein particles , with most species showing an average molecular mass of 229±7 kDa . This heterogeneity could be , at least partially , associated to the capacity of EgAgB apolipoproteins to form particles accommodating variable amounts of lipids . Another possibility is that the lipoprotein could have some tendency to self-associate , leading to the formation of ( less abundant ) higher-order oligomers . On the other hand , the lipid/protein mass ratio , lipid composition and size of EgAgB suggested that , globally , it exhibits compositional and dimensional characteristics resembling those of plasma HDL . For comparison , human HDL was then analysed by SEC-MALLS under the same conditions . HDL showed a similar SEC-chromatographic profile to EgAgB , exhibiting molecular mass species from 150 to 400 kDa , with a mean MW of 209±4 kDa ( Figure 6 , inset ) .
This work aimed at characterising the native lipid moiety of EgAgB . As we have already mentioned , studies were mainly performed with EgAgB of bovine origin , yielding data that improve our knowledge on EgAgB lipoprotein composition . In addition , the characterisation of EgAgB protein∶lipid ratio and EgAgB lipid composition ( major lipid classes and total fatty acids ) was carried out with limited amounts of human EgAgB , obtaining similar results that confirm our findings . EgAgB was purified from HCF using a previously described method based on anion exchange followed by immunoaffinity on MoAb EB7-Sepharose [20] . For a complete description of the lipoprotein composition we firstly characterised the apoliprotein component of immunopurified EgAgB of bovine origin . In addition , the identification of the apolipoprotein subunits forming EgAgB is relevant because the lipid-binding properties of EgAgB8 isoforms may differ . Native as well as recombinant EgAgB8/1 and EgAgB8/2 did not show differences in their capacity to bind fatty acid analogues in vitro [30] . Nevertheless , the physiologic lipid ligands of EgAgB8 isoforms could depend not only on the lipid-binding properties of individual isoforms , but also on the cell/tissue compartment where they are synthesised , assembled and/or transported ( e . g . germinal layer vs . protoscolex ) . We found that EgAgB8/1 was the major apolipoprotein component of immunopurified EgAgB ( Figure 1 ) , while EgAgB8/4 was detected in much smaller amounts . In the original article describing this purification method , peptides matching sequences of EgAgB8/1 , EgAgB8/2 and EgAgB8/4 were found , although those corresponding to EgAgB8/4 ( SDPLGQK and LGEIR ) were not initially assigned to this molecule , because its sequence was unknown . The purification of lipoproteins carrying EgAgB8/1 agrees with the fact that EgAgB8/1 is expected to be present in HCF of bovine origin [46] and MoAb EB7 strongly reacts with this isoform [20] . In contrast , MoAb EB7 does not bind to EgAgB8/2 [20] and it is unlikely that it binds to EgAgB8/4 since this isoform has much higher similarity to EgAgB8/2 than to EgAgB8/1 [18] . Therefore , purification by this method of particles carrying EgAgB8/2 and/or EgAgb8/4 apolipoproteins may result from lipoprotein particles carrying EgAgB8/1 along with EgAgB8/4 and/or EgAgB8/2 , and/or associative interactions between lipoprotein particles carrying different EgAgB8 isoforms . In this regard , it is worthy to note that association of EgAgB particles could occur according to the EgAgB analysis by SEC-MALLS ( Figure 6 ) and previous observations made by studying EgAgB sedimentation equilibrium [2] . The fact that we did not detect EgAgB8/2 in immunopurified EgAgB of bovine origin may be due to the sensitivity of the technique or to variations in the composition of distinct HCF pools . Indeed , the presence of EgAgB8/2 was detected in bovine HCF by Chemale et al . [30] but not by Aziz et al . [46] and it seems to be more variable than that of EgAgB8/1 and EgAgB8/4 among cysts of different origin/fertility [46] . Furthermore , EgAgB8/2 would not be one of the most abundant EgAgB isoforms in the HCF according to the expression levels of EgAgB family in E . granulosus metacestode [18] . Therefore , the characterisation of the apolipoprotein composition of EgAgB highlights that HCF-derived EgAgB preparations may differ depending on parasite material and these differences may be relevant when analysing EgAgB structural and/or functional properties . In any case , since EgAgB8/1 is highly expressed in the E . granulosus metacestode [18] , [19] , the EgAgB8/1-enriched lipoprotein that we used for lipid characterisation likely represents one of the most abundant lipoproteins of the EgAgB family present in HCF . Analysis of the lipid composition of immunopurified EgAgB of both bovine and human origin , revealed a high diversity in terms of lipid classes ranging from highly hydrophobic lipids ( mainly TAG , but also sterol esters ) to a variety of phospholipids ( mainly phosphatidylcholine ) . These findings indicate that EgAgB is a more complex lipoprotein than previously suggested from lipid-binding studies in vitro , in which fatty acids were described as the main lipid ligands of EgAgB8/1 and EgAgB8/2 [30] . Also , these results set EgAgB apart from the fatty acid binding protein family , whose members bind mainly fatty acids [47] . On the other hand , the fact that the lipid moiety of immunopurified EgAgB represented between 40 and 50% of the total mass reveals that EgAgB require to adopt a very well organised structure to accommodate a high proportion of lipid molecules in a single particle , suggesting similarities with animal lipoproteins found in both invertebrate hemolymph and vertebrate plasma [48] . The structural organization of these animal lipoproteins is well established; the most hydrophobic lipids ( triacylglycerols , cholesteryl esters and other lipid-soluble components ) are sequestrated in a central core , surrounded by an external hydrophilic shell that contains the apolipoproteins and amphipathic lipids ( mostly phospholipids and unesterified cholesterol ) ( review by [49] ) . A structure like this could explain the heterogeneity of molecular mass species observed during analysis of immunopurified EgAgB by SEC-MALLS . Among vertebrate plasma lipoproteins , EgAgB would be more similar to the smallest HDL particles , referred to as HDL3 [48] , [50] , which exhibits a lipid∶protein mass ratio ( w∶w ) between 0 . 67∶1 and 1 . 2∶1 ( 0 , 6∶1–1 . 1∶1 for EgAgB ) and an average molecular mass of 200 kDa ( 229 kDa for EgAgB ) ; the comparative analysis of HDL and EgAgB by SEC-MALLS supports this hypothesis ( Figure 6 , inset ) . However , the content of TAG in EgAgB is much higher than in HDL3 , which likely reflects differences in the lipid transport function of these lipoproteins . In addition , in the context of this structural organisation share by all plasma lipoproteins , and taken into account its size and chemical composition , each EgAgB particle would contain between 11 and 15 EgAgB8 apolipoprotein molecules inserted into the outer phospholipid monolayer . This new scenario is relevant when considering the biological effects of EgAgB through parasite's as well as host's receptors . The exposure of more than a dozen of apolipoproteins on the surface of the lipoprotein particle would facilitate the establishment of multiple interactions with receptors , increasing the avidity of the interaction and the signals derived from it . The formation of multiple EgAgB-B cell interactions likely contributes to the immunogenicity of this lipoprotein . On the other hand , it cannot be ruled out that lipids could participate , at least partially , in some of the EgAgB biological activities ( for example those anti-inflammatory described in vitro ) . Surface charge and/or hydrophobic distribution resulting from a lipoprotein ensemble ( as opposed to lipid or protein fractions alone ) may alter the type of receptors involved , affecting its physiological effects . The identification of the native lipid ligands of EgAgB provides relevant information for studying the function of this HLBP family member . Evidence for HLBP-mediated fatty acid binding and transportation across parasite membrane has been obtained in Taenia solium [26] . Our results suggest that EgAgB apolipoproteins are likely involved in solubilising and stabilising a variety of insoluble lipids in a lipoprotein particle , and that this may have an important role to deliver lipids from the tissues/sites where are synthesised/sequestered , to those that utilise or storage them . The fact that the lipids present in EgAgB are not only fatty acids , but also other essential building blocks such as sterols , highlights that EgAgB could serve a role for the E . granulosus metabolic demand of lipids . In this context is important to highlight that no enzymes for either fatty acid anabolism or squalene synthesis ( the precursor of the whole family of animal sterols ) have been found in the E . granulosus transcriptome ( data base http://www . compsysbio . org/partigene/ ) or the Echinococcus multilocularis genome ( unpublished observations , http://www . genedb . org/Homepage/Emultilocularis ) . In addition , metabolic studies have demonstrated that sterol synthesis in E . granulosus seems to stop at the level of farnesyl or nerolidol pyrophosphate and that the content of cholesterol in HCF derives from the cholesterol pool of the host [51] , [52] . Thus , E . granulosus needs to take these lipids from its host . Whether EgAgB apolipoproteins are directly involved in the uptake of fatty acids and sterols from host tissues remains to be elucidated , but most likely they contribute to transport these lipids within metacestode tissues . Delivery of sterols to metacestode target cells may be crucial for biosynthetic purposes ( i . e . cholesterol for biological membranes ) , but also for triggering signaling pathways associated with parasite development and growth . In fact , signaling pathways involving sterol-responsive nuclear receptors are conserved from simple invertebrates to mammals and regulate metabolism and development [53] . For signalling actions host sterols may be modified by the parasite; indeed , a couple of putative steroid modifying enzymes have been found in the E . granulosus trasncriptome ( our unpublished observations ) . Interestingly , about twenty nuclear receptors have been recently identified in E . multilocularis and E . granulosus and one of them displayed structural similarities to the DAF-12 subfamily , which binds cholesterol modified compounds and regulates cholesterol homeostasis and longevity in metazoans [54] . In addition , TAG are also a major component of EgAgB . TAG are not synthesised de novo by E . granulosus , but may be synthesised from building blocks obtained from the host as it occurs in other cestodes [33] . Metabolic studies have not been performed in Echinococcus , but evidence of TAG synthesis via α-glycerophosphate–phosphatidic acid-diglyceride pathway exists for the cestode Hymenolepis diminuta [55] . TAG are the major reserve of energy in animals , but no evidence on the operation of fatty acid oxidation pathways has been obtained in flatworms [33] including Echinococcus ( our unpublished observations from the E . granulosus transcriptome ) . This scenario suggests that TAG mainly provide a source of fatty acids and glycerol for synthetic purposes via an enzymatic hydrolysis . Consistent with this view is the identification of two ESTs clusters ( EGC01304 and EGC 03444 ) that encode putative lipases in the E . granulosus transcriptome data base . With respect to the high content of phospholipids in EgAgB ( mainly phosphatidylcholine ) , these molecules likely play a structural role in the lipoprotein by exposing a polar outer surface required for lipoprotein solubilization in the aqueous milieu . Phospholipids are not synthesised de novo by flatworms , but they can be synthesised from building blocks obtained from the host ( fatty acids and the head group ) [33] . The uptake and delivery of lipids by lipoproteins require the existence of lipoprotein receptors in target cells . The existence of parasite EgAgB receptors as well as the use of host receptors by EgAgB would be needed for EgAgB-mediated lipid traffic . In this sense it is worth to mention that in E . multilocularis and E . granulosus genomes , antigen B gene cluster is flanked by EmLDLR or EgLDLR genes , which encode proteins that display significant sequence similarities to low density lipoprotein ( LDL ) receptors from other species , and contain one single class A LDL receptor domain [19] . The N-terminal end of the LDL receptor contains seven successive class A domains ( a cysteine-rich repeat of about 40 amino acids ) , which are involved in the binding of LDL as well as very low density lipoprotein ( VLDL ) [56] . Furthermore , domains with homology to class A LDL receptor occur in related lipoprotein receptors such as VLDL receptor as well as LDL receptor-related protein/alpha 2-macroglobulin receptor [57] , [58] . Overall , a new picture for EgAgB structure and function has emerged from this work . EgAgB is a complex 229 kDa lipoprotein capable of transporting a variety of lipid classes including essential lipids that are not synthesised by the parasite , such as fatty acids and sterols . EgAgB uptake and delivery of these lipids may not only contribute to biosynthetic purposes , but also to signalling events associated with parasite metabolism and development . Whether the hydrophobic ligand binding properties of EgAgB , reflected by its lipid class composition , are an intrinsic feature of cestode HLBP family remains to be determined . | The larva of the cestode parasite Echinococcus granulosus affects a wide range of livestock mammals and humans , causing cystic echinococcosis ( hydatid disease ) , a zoonosis with significant economic and public health impact . The disease is characterised by the growth of a fluid-filled cyst in the host's viscera ( mainly liver and lung ) . The most relevant antigen for hydatid disease diagnosis is antigen B ( EgAgB ) , a highly abundant lipoprotein present in the cyst fluid . There is overwhelming literature regarding EgAgB antigenicity and molecular characterisation at the protein and gene levels , but the knowledge of the lipids physiologically bound to EgAgB protein subunits is very scarce . Indeed , there is only one report showing that delipidated EgAgB binds fatty acids in vitro . This work describes the physiological lipids of EgAgB , an important piece of information to complete our knowledge on EgAgB molecular composition . In contrast to what was thought , EgAgB consists of a variety of neutral and polar lipid classes , associated to protein subunits , forming a plasma lipoprotein-like particle of 229 kDa in size . Taken into account that E . granulosus cannot synthesise fatty acids and sterols , these data suggest that EgAgB plays a role in the uptake and transportation of these essential lipids across parasite structures . | [
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| 2012 | Characterisation of the Native Lipid Moiety of Echinococcus granulosus Antigen B |
Visuospatial working memory enables us to maintain access to visual information for processing even when a stimulus is no longer present , due to occlusion , our own movements , or transience of the stimulus . Here we show that , when localizing remembered stimuli , the precision of spatial recall does not rely solely on memory for individual stimuli , but additionally depends on the relative distances between stimuli and visual landmarks in the surroundings . Across three separate experiments , we consistently observed a spatially selective improvement in the precision of recall for items located near a persistent landmark . While the results did not require that the landmark be visible throughout the memory delay period , it was essential that it was visible both during encoding and response . We present a simple model that can accurately capture human performance by considering relative ( allocentric ) spatial information as an independent localization estimate which degrades with distance and is optimally integrated with egocentric spatial information . Critically , allocentric information was encoded without cost to egocentric estimation , demonstrating independent storage of the two sources of information . Finally , when egocentric and allocentric estimates were put in conflict , the model successfully predicted the resulting localization errors . We suggest that the relative distance between stimuli represents an additional , independent spatial cue for memory recall . This cue information is likely to be critical for spatial localization in natural settings which contain an abundance of visual landmarks .
Imagine trying to locate your friends while watching a crowded street parade . If you catch only a glimpse of them in the crowd before they are obscured by others , remembering how far they were from a nearby building ( a stable landmark in the external world ) may provide a useful cue to help you localize them later . Indeed , this relative ( or allocentric ) information may prove more valuable than memory of their location within your visual field ( egocentric information ) . However , the nature of storage of allocentric information , and its interaction with other forms of visual memory , have not been clearly established . Interruptions in sensory input represent a frequent challenge to the visual system , whether due to our own actions , such as an eye-movement or blink , or changes in the external world , such as object occlusions or the disappearance of a transient stimulus . Visuospatial working memory ( VSWM ) helps bridge these discontinuities , by allowing us to retain sensory information about visual objects even when they are no longer visible . However , the capacity of VSWM to store information is limited . Even when explicitly instructed to remember specific stimuli—in anticipation of an interruption—individuals make substantial errors in both their ability to detect the occurrence of a change [1–3] and to reproduce remembered features [4 , 5] . Error increases monotonically as the number of items increases , and this holds true for recall of object locations as well as features [6] . This is consistent with models in which objects compete for allocation of a limited representational resource [4 , 7–9] . Representations of visual information in early visual cortex are inherently egocentric , emerging directly from the projection of the external world onto the retina . Consequently , the spatial information associated with visual processing is at least initially gaze-centered , encoding locations relative to the observer , and decreasing in resolution as the distance from the fovea increases [10] . This retinotopic spatial encoding appears to be preserved throughout much of the brain , particularly in dorsal brain regions that support the execution of actions towards remembered locations [11 , 12] ( but see [13 , 14] with respect to ventral areas ) . Indeed , it is actively debated whether spatial information is ever encoded in non-retinotopic reference frames [15–18] . The point of contention is whether separable representations of stimuli are encoded—within distinct neural populations and potentially within different neural pathways—or if the apparent use of other representations merely reflects timely manipulations of egocentric information [16] . Important evidence has come from studies of motor action , which have shown that movement errors are reduced in the presence of visual landmarks , and suggested motor programming reflects the combination of egocentric ( retinotopically encoded relative to current gaze ) and allocentric ( relative to external landmarks ) spatial cues [19–21] . In this paper we investigate how egocentric and allocentric VSWM representations interact . Specifically , in view of the limited capacity of VSWM , we examine the impact of encoding additional allocentric spatial information in the form of distance from a visual landmark . We show that the behavioral data is consistent with an optimal integration of an egocentric signal , independent of the landmark , with an allocentric signal that degrades with distance from the landmark . We further show that allocentric information does not compete with egocentric information for storage , indicating that the two sources of information rely on independent memory resources .
In Experiment 1 we investigated the influence of a visual landmark on spatial working memory for different numbers of remembered objects ( set size: 1 , 2 or 4 ) . Participants used a computer mouse to report the remembered location of one item from a memory array , identified by color ( Fig 1A ) . Examining spatial recall precision in the absence ( LM-ABSENT ) and presence ( LM-PRESENT ) of a stable visual landmark , we observed a substantial reduction in the variability of memory reproduction for stimuli located near the landmark , at all set sizes ( Fig 1B–1D ) . These changes occurred in the absence of systematic shifts in bias ( S3 Fig ) and indicate that the presence of the landmark gave participants access to additional information to facilitate recall . We implemented a simple cue-combination model to investigate whether this spatially selective improvement in precision could be captured by optimal integration of independent egocentric and allocentric spatial encodings ( Fig 2; see Methods ) . In the model , the precision of the allocentric signal diminishes with distance to the landmark from a peak Amax at rate Ascale , while precision of the egocentric signal is independent of distance . The model also includes a lapse rate to capture random responding and “swap” errors [4 , 9] . The fit of the optimal integration model is shown as solid lines in Fig 1B–1D . This model provided a substantially better fit to data than a reduced model with allocentric encoding omitted ( ΔAICc = 662 ) . Consistent with previous studies [4 , 5 , 22] , the precision of the egocentric signal declined with increasing set size ( Fig 1E; comparison to model with fixed precision: ΔAICc = 234; linear regression slope = –18 . 4 ± 2 . 7 ( M ± SE ) , t ( 11 ) = 6 . 89 , p < 0 . 001 ) . Similarly , model comparison indicated a decrease in peak precision of the allocentric signal with set size ( Fig 1F; ΔAICc = 45 . 94; linear regression slope = -461 ± 78; t ( 11 ) = 5 . 88; p < 0 . 001 ) . There were no changes across set size in the rate with which precision of the allocentric signal scaled with distance ( Fig 1G; ΔAICc = 18 . 45 ) . The lapse rate increased with set size but accounted for only a very small fraction of trials ( Fig 1H; ΔAICc = 139; slope = 0 . 014 ± 0 . 003; t ( 11 ) = 4 . 48; p < 0 . 001 ) . The presence of a visual landmark substantially improved the localization of memory stimuli in the landmark’s vicinity , implying that participants remembered the allocentric distance between the landmark and each memory stimulus , in addition to the egocentric location of each stimulus . In previous studies , increasing the amount of information stored in working memory has consistently been shown to decrease the precision of recall , consistent with distribution of a limited memory resource between items to be remembered [7 , 9] . If egocentric and allocentric encodings of location similarly share memory resources , the additional inclusion of relative information should convey a cost in the form of decreased precision of egocentric information . However , we found no evidence for such a cost , as can be seen qualitatively in Fig 1A–1C by comparing LM-PRESENT performance at 180° separation to LM-ABSENT performance . Were there a cost in the fidelity of egocentric information associated with encoding allocentric information , then localization of targets far from the landmark ( where allocentric information should make a negligible contribution to response precision ) would be noticeably more variable than in the absence of a landmark . A model in which egocentric precision decreased in the presence of a landmark ( see Methods ) provided a substantially worse description of the data ( ΔAICc = 53 . 65 ) , confirming that memory resources for egocentric and allocentric information are independent . The distribution of individual parameter values obtained for the rejected model ( in this and subsequent experiments ) was also inconsistent with a cost to egocentric precision ( see S2 Text and S2 Fig ) . A plausible alternative account of the landmark effect is that the presence of the salient landmark in the initial array biased encoding towards memoranda in its vicinity . In Experiment 2 we tested a condition ( LM-ENCODE ) in which the landmark was visible only during the presentation of the memory array ( set size 4 ) . This condition was interleaved with other conditions such that participants did not know during encoding whether the landmark would disappear . We observed no landmark-related improvement of precision in this condition ( Fig 3A ) and a reduced model with no allocentric signal provided a better fit to data than the optimal integration model ( ΔAICc = 16 . 56 ) . This confirms that the landmark benefit is a result of the use of allocentric spatial information and not due to encoding bias . If items presented in the vicinity of a landmark were preferentially encoded , or encoded with enhanced precision , we would have seen a benefit for those items even when the landmark was absent during the response phase . Does the use of the landmark depend on its continuous presence during the memory delay ? We tested a condition ( LM-GAP ) in which the landmark disappeared at the offset of the sample array and only reappeared at the time of the probe . We found a robust landmark effect in this condition ( ΔAICc = 135 compared to reduced model; Fig 3B ) . Comparing the LM-GAP condition to one in which the landmark was continuously present ( LM-PRESENT , as in Exp 1 ) revealed no difference in peak precision of the allocentric signal ( ΔAICc = 9 . 34 favoring a model with shared Amax parameter between conditions ) but some evidence for a difference in the rate of change of precision with distance ( ΔAICc = 13 . 19 favoring a model in which Ascale differed between conditions; median Ascale 20 . 9% lower in LM-GAP condition ) . Exp 2 also replicated the finding from Exp 1 that the presence of a landmark incurred no cost to the precision of egocentric memory ( model with cost performed worse , ΔAICc = 23 . 31; parameter estimates for Exp 2 are shown in S4 Fig ) . A final possibility is that the benefits observed in the LM-GAP and LM-PRESENT conditions arose from enhanced retrieval of items whose previous locations were close to the landmark’s location at the time of the probe , perhaps due to internal attention being drawn to that location in memory . We therefore carried out an additional control experiment ( see S1 Text and S1 Fig ) which included an LM-RETRIEVE condition , in which the landmark was visible only at the time of response , and not during the presentation of the memory stimuli . In this condition , allocentric information about the items’ locations relative to the landmark could not be encoded from the memory array , but any effect of the landmark on internal attention at the time of retrieval should still be present . We found no evidence for a landmark-related improvement of precision in this condition , and a reduced model with no allocentric signal for the LM-RETRIEVE condition provided a better fit to data than the optimal integration model ( ΔAICc = 39 . 86 ) . Considering in combination the results of LM-ENCODE ( no benefit if the landmark is present only during encoding ) , LM-RETRIEVE ( no benefit if the landmark is present only during retrieval ) and LM-GAP ( clear benefit if the landmark is present during both encoding and retrieval ) , our results strongly indicate that landmark-related benefits are due to encoding and subsequent retrieval of allocentric ( relative position ) information present in the memory array . To provide a strong test of the optimal integration model , in Experiment 3 we implemented a variant of the LM-GAP condition in which the landmark reappeared at a location displaced through a small distance ( 6° on the circle ) from its original position ( LM-SHIFT; Fig 4A ) . According to the model , this manipulation should introduce a conflict between egocentric and allocentric spatial information , with the allocentric estimate shifting with the visual landmark . As a result , we predicted that participants would show systematic biases in their localization responses in the direction of the shift , with the strength of the bias determined by the relative reliability of each cue . We found a clear landmark effect in the LM-SHIFT condition ( ΔAICc = 179 compared to reduced model ) , and the recalled locations of items presented close to the landmark were strongly shifted in the direction of landmark displacement ( Fig 4B ) . The optimal integration model accurately predicted changes in both bias and variability with landmark distance ( Fig 4B&4C ) . The additional fitting of bias required no extra parameters , relying on the same reliability estimates used to calculate variability . Parameter estimates for Exp 3 are shown in S5 Fig . Examining the effect of shifting the landmark on precision of the allocentric signal ( by contrasting LM-GAP and LM-SHIFT conditions ) , revealed a reduction in both the peak precision of allocentric information ( ΔAICc = 19 . 03 favoring a model in which Amax differed between conditions; median Amax 74 . 0% lower in LM-SHIFT condition ) and the rate at which it decayed with distance ( ΔAICc = 21 . 6 favoring a model in which Ascale differed between conditions; median Ascale 35 . 1% lower in LM-SHIFT condition ) . Finally , as in previous experiments , we examined whether there was evidence for a precision cost on egocentric encoding . We found a ΔAICc of 28 . 11 favoring the model without cost , further confirming that allocentric and egocentric information are independently stored .
Natural scenes rarely contain only a single item , and are instead frequently populated by multiple stable objects , any of which could act as a visual landmark for locations we need to remember . However , how the brain stores and uses this information is only partially understood . Here , using simple experimental displays , we have demonstrated a spatially specific enhancement of localization precision in the vicinity of a landmark , consistent with observers using not only memory of the egocentric spatial locations of stimuli , but also memory of their locations relative to other objects in the environment ( allocentric information ) . We further investigated the consequences of encoding this additional information into VSWM , in light of established limitations on working memory resources [4 , 7 , 9 , 22] . It is now well established that increasing the number of items to be remembered increases variability in recall of their features and locations [4–6 , 22 , 23] . If egocentric and allocentric information compete for access to the same limited memory resource , then the introduction of a landmark ( with the consequent encoding of additional allocentric information ) should reduce egocentric memory fidelity . While we observed a spatially specific increase in recall precision for items near the landmark , memory items located far from the landmark were recalled just as precisely as when the landmark was absent . Thus , our results demonstrate that the presence of the landmark had no influence on the fidelity of egocentric memory representation . Instead , the presence of a landmark appeared to grant access to an additional allocentric source of spatial information . To confirm this finding , we incorporated a cost parameter into a cue-combination model , which allowed the reliability of egocentric information to be degraded in the presence of a landmark . In three separate experiments , we consistently found a model with no cost provided the best description of the data , a result further supported by a meta-analysis of cost estimates pooled across experiments ( S2 Text and S2 Fig ) . Thus , rather than directly competing , our results suggest that egocentric and allocentric locations are encoded independently and draw upon separate memory resources . We also examined competition within each representation as the number of items encoded increased . For egocentric spatial information , this competition led to a gradual decrease in the reliability of spatial estimates ( Pego ) as set size increased , consistent with previous results [6 , 7] . Similarly , we found that increasing set size led to a decrease in the maximum reliability of allocentric spatial information ( Amax ) , indicating that the recollection of multiple relative locations also reflects a distribution of limited memory resources . In contrast , set size had no influence on the rate at which allocentric precision diminished with distance ( Ascale ) . So , while the number of items in memory determined the overall reliability of allocentric information , the relationship between landmark distance and reliability appears to be fixed . We observed a substantial , spatially specific improvement in recall precision even when the landmark was hidden during the memory delay ( LM-GAP ) . While this manipulation did not change the maximum precision of allocentric information ( Amax ) , there was a decrease in the spatial scale over which the precision enhancement was observed ( Ascale ) . The interruption in landmark persistence may have reduced the perceived stability of the visual landmark , introducing uncertainty as to whether the returning landmark had reappeared at the same location or if it should be considered the same object [24–27] . This is consistent with a study of reach programming in which landmark locations were jittered [19] , which demonstrated that participants are sensitive to perceived landmark stability and adjust reliance on allocentric information as a result . Importantly , when the landmark was only present during encoding ( LM-ENCODE ) —and not during recall—there was no advantage in localization compared to conditions without a landmark ( LM-ABSENT ) . This means the landmark benefit cannot be explained simply by enhanced encoding of items in its vicinity , as this would predict improved localization irrespective of the landmark’s presence at recall . Furthermore , because the interleaved LM-GAP and LM-ENCODE conditions were indistinguishable until the time of recall , we can be certain that the same amount of allocentric information was encoded in both conditions . Therefore , the absence of a benefit in LM-ENCODE must arise from an inability to use this information . A landmark only appears to improve localization performance when at the time of testing the recalled allocentric distance can be anchored to the visible location of the landmark itself . We also saw no benefit when the landmark was present only at the time of retrieval ( LM-RETRIEVE ) , a condition in which allocentric information relating memory items to the landmark could not have been stored . This also demonstrates that the landmark benefit is not due to an enhancement of retrieval for items whose location in memory falls close to the probe , as such an enhancement would be observed regardless of whether the landmark was visible during encoding . The lack of difference in recall precision between LM-ENCODE and LM-ABSENT conditions enables two additional observations to be made . First , participants apparently did not encode the egocentric location of the visual landmark itself , as its presence had no influence on precision ( i . e . there was no set-size effect diminishing precision in the LM-ENCODE condition ) . This is consistent with both the task instructions and our conclusion that , to be useful for localization , the landmark had to be present at test . Second , given that we know allocentric information was encoded ( but not used ) in the LM-ENCODE condition , any competition between allocentric and egocentric information would be readily apparent as a decrease in precision compared to LM-ABSENT . The absence of such a difference is itself strong additional evidence for the independence of egocentric and allocentric spatial representations . Across a variety of different conditions , an optimal integration model accurately described how allocentric and egocentric information were combined to generate estimates of location . Based on exponential decay of allocentric precision with distance from the landmark , this model captured not only how recall variability changed as a function of distance , but also the distance-dependent recall biases that emerged when egocentric and allocentric cues were put in conflict ( Exp 3 ) . Specifically , when we covertly changed the location at which the landmark reappeared ( LM-SHIFT ) , we observed systematic shifts in recall position based on the distance of the recalled item from the landmark , with memoranda near to the landmark biased substantially in the direction of the displacement . Critically , these biases were consistent with a displacement in localization ( i . e . relying more strongly on the allocentric information ) , not with an attractive bias to the landmark’s location . This result adds considerable support for our model , demonstrating that the integration of egocentric and allocentric information was close to optimal , and reinforces the conclusion that allocentric and egocentric estimates are encoded separately and as such associated with independent noise . Such integration models have proved invaluable in the study of multisensory integration ( e . g . [28 , 29] ) , and several studies have used similar methods to describe the integration of allocentric and egocentric information in reaching and eye-movements to a single target [19–21 , 30 , 31] . However , to our knowledge , no previous study has quantitatively examined the consequences of encoding both egocentric and allocentric information on memory fidelity , determined how the precision of allocentric spatial information varies with set size , nor quantified the relationship between distance and the reliability of allocentric information . The results of the LM-SHIFT condition also provide evidence against any alternative account of our findings based on local changes in the encoding or retrieval of items in the vicinity of the landmark . The observed biases in recall could not be the result of a difference in how items near to the landmark were encoded , because the biases were specifically in the direction of the landmark displacement , which was entirely unpredictable at the time of encoding . Equally , the biases could not be a consequence of proximity of items in memory to the location of the landmark at the time of retrieval , because this location was also randomized with respect to displacement direction . In contrast , biases in the direction of displacement are fully compatible with an account in which observers remember the relative deviation of items from the landmark , and a model in which this allocentric memory provides an additional , independent source of information for item localization provided an excellent quantitative account of both the biases and the enhancements in precision associated with proximity to the landmark ( Fig 4B&4C ) . Other than the systematic localization shift in the conflict condition ( LM-SHIFT ) —which was well characterized by our optimal integration model—we observed no consistent biases in localization due to the presence of the landmark in any of our tasks . However , several previous papers have reported biases , both attractive and repulsive , linked to visual landmarks , as well as fixation and attended , non-fixated locations [32–41] . For example , in a task in which participants were required to make a pointing movement to the location of a single flashed target in the presence of a continuous visual landmark , Diedrichsen and colleagues [32] found that movement endpoints were both repulsed from the location of the landmark and less variable in its vicinity . However , in a similar condition to our LM-ENCODE , in which the landmark was only present during the encoding stage , they observed the presence of the same systematic biases without the improvement in precision . This suggests that the systematic biases they observed are independent of the spatially-specific improvements in precision that occur for items near a landmark . The absence of consistent landmark-related biases in the present experiments may be a consequence of preventing eye movements , ensuring both landmarks and stimuli were equally eccentric , and confining responses to the stimulus circle , all of which would tend to minimize the impact of attentional spatial distortions . Some dynamical models of working memory predict attraction or repulsion between items in memory depending on their separation [42–44] , but we would not expect the same principles to apply to the landmark , which as discussed above does not appear to itself be stored in memory . Our experimental manipulations compared recall in the presence and absence of a landmark object . This allowed us to quantify the performance changes resulting from adding a new source of allocentric information to the scene , irrespective of whether allocentric information was also encoded in the LM-ABSENT condition . One possibility is that participants encoded item locations relative to other elements that remained visible throughout the trial , i . e . the screen edges or the fixation spot . Although we elected not to obscure these elements ( removing the fixation spot would have made it impractical to control eye movements ) , we think a contribution of this relative information to our egocentric estimate is unlikely . In our task , the reliability of allocentric information diminished rapidly with distance from the landmark: indeed , the localization of memoranda more than 4 . 7° of visual angle ( 46° on the circle ) from the landmark received negligible benefit from allocentric information ( < 5% change in precision from no-landmark performance ) . This renders the distance from the memoranda to the screen edges ( min 6 . 5° ) or the fixation dot ( 6° ) too far to exert any meaningful influence on localization . Previous studies have attempted to estimate a distance threshold beyond which allocentric information no longer has a significant influence , based on qualitative comparisons of conditions with different spatial separations [30 , 32 , 45] . Our approach enabled us to identify and quantify a continuous change in the reliability of relative cues that occurs as the distance from the landmark increases . The format in which allocentric information is extracted from the array and stored in memory cannot be unambiguously determined from our experiments . The simplest account of our results would posit an internal representation of the vector connecting each memory item with the landmark . However , it is possible that other static elements in the participant’s surroundings , or overarching geometric principles such as the fact all stimuli were displayed in the vertical plane of the monitor ( defining an observer-independent coordinate frame ) , influence the representation format also . These issues have been explored primarily in the context of navigation and large-scale spatial cognition [46–49] . The present design could in future be extended to examine corresponding principles in VSWM , for example by presenting two or more landmarks in a single memory array . Competition in encoding information within a feature dimension has been linked to the normalization of neural population activity [50] , and this model has been successful in accounting for set size effects [51] . While this neural account of resource limitations has been extended to incorporate multiple feature dimensions , including spatial location [52] , no attempt has been made to distinguish between different spatial reference frames . This work has , however , both confirmed and provided new evidence for a privileged role of spatial information in binding object features [53 , 54] . Evidence for a specific contribution of allocentric information to object binding has been revealed in change detection tasks in which individual item locations ( egocentric ) or global spatial layout ( allocentric ) are separately manipulated . Here , even when explicitly informed that location information was irrelevant , performance was compromised by individual changes in spatial position unless allocentric information remained veridical [1 , 55–58] . While our observation of set size effects on the precision of allocentric information suggests a commonality in neural representation with other feature dimensions , relative location information may be unique in that it spans objects rather than being associated with a single object . For this reason it is unclear how object file [53] or slot-based models of VSWM [2 , 9] would be able to incorporate such spatial information . Our model does not attempt to capture transformations between egocentric and allocentric reference frames ( e . g . [59] ) and this will be an important direction for future investigation , particularly with respect to the effects of self-motion . Classically , the division between egocentric and allocentric information has been associated with the neuropsychological distinction between the dorsal and ventral visual processing streams [60 , 61] . While spatial information is encoded in egocentric coordinates throughout the dorsal pathway , the ventral projections into the inferior temporal cortex represent progressively more complex information about object properties , encoded by neurons with decreasing sensitivity to spatial location [61–64] and little retinotopic organization [13 , 14] . Contemporary research suggests that , rather than being lost , spatial information along the ventral path is instead increasingly represented in terms of the relations within and between objects in the environment [65–67] . Indeed , neuroimaging studies looking for correlates of allocentric coding have frequently identified higher areas in the ventral stream [68–73] as components of a broader distributed network contributing to allocentric representation [65 , 71] . Hippocampal structures are also implicated in relative spatial encoding , most clearly in relation to navigation , but with growing evidence for a role in coding visual space [59 , 71 , 74–78] . Despite these recent findings , the neural coding of allocentric space remains far more poorly understood than egocentric space . We believe the present work provides a computational and experimental framework within which future studies can explore the neural bases of these spatial memory mechanisms .
39 participants took part in the study in total . All participants gave informed consent , in accordance with the Declaration of Helsinki . The study was approved by the Cambridge Psychology Research Ethics Committee . All participants had normal or corrected-to-normal color vision . Each experiment recruited new participants , ensuring all were naïve to the aims of the experiment . Three subjects failed to understand the task and were excluded from analysis ( one in Exp 2; two in Exp 3 ) . This left 12 participants in Experiment 1 ( age range: 18–28; mean: 24±3; 4 male , 8 female ) , 12 in Experiment 2 ( age range: 20–34; mean: 26±4; 5 male , 7 female ) , and 12 in Experiment 3 ( age range: 19–30; mean: 25±4; 1 male , 11 female ) . Sample sizes were preselected based on pilot experiments and reports of previous studies examining spatial recall [6 , 52 , 79] . Recruiting new participants for each experiment had the advantage of providing multiple internal replications of our key results . We calculated the median angular deviation ( a measure of response bias ) and the median absolute angular deviation ( a measure of response variability ) between the response and the target for each condition and , in conditions with a landmark , for different landmark-target distances . For display purposes , we summarized data into 24 partially overlapping bins , separated by 15° and encompassing data from ±15° . | Human capacity to maintain spatial information over brief interruptions is strongly limited . However , while studies of visual working memory typically examine recall in sparse displays , consisting only of the stimuli to remember , natural scenes are commonly filled with other objects that—although not required to be remembered—may nevertheless influence subsequent localization . We demonstrate that memory for spatial location depends on independent stores for egocentric ( relative to the observer ) and allocentric ( relative to other stimuli ) information about object position . Both types of spatial representation become increasingly imprecise as the number of objects in memory increases . However , even when visual landmarks are present—and allocentric information encoded—there is no change in egocentric precision . This suggests that the encoding of additional allocentric spatial information does not compete for working memory resources with egocentric spatial information . Additionally , the fidelity of allocentric position information diminished rapidly with distance , resulting in a spatially specific advantage for recall of objects in the vicinity of stable landmarks . The effect of a landmark on recall matches that of an ideal observer who optimally combines egocentric and allocentric cues . This work provides a new experimental and theoretical framework for the investigation of spatial memory mechanisms . | [
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| 2019 | Independent working memory resources for egocentric and allocentric spatial information |
Mammalian bile acids ( BAs ) are oxidized metabolites of cholesterol whose amphiphilic properties serve in lipid and cholesterol uptake . BAs also act as hormone-like substances that regulate metabolism . The Caenorhabditis elegans clk-1 mutants sustain elevated mitochondrial oxidative stress and display a slow defecation phenotype that is sensitive to the level of dietary cholesterol . We found that: 1 ) The defecation phenotype of clk-1 mutants is suppressed by mutations in tat-2 identified in a previous unbiased screen for suppressors of clk-1 . TAT-2 is homologous to ATP8B1 , a flippase required for normal BA secretion in mammals . 2 ) The phenotype is suppressed by cholestyramine , a resin that binds BAs . 3 ) The phenotype is suppressed by the knock-down of C . elegans homologues of BA–biosynthetic enzymes . 4 ) The phenotype is enhanced by treatment with BAs . 5 ) Lipid extracts from C . elegans contain an activity that mimics the effect of BAs on clk-1 , and the activity is more abundant in clk-1 extracts . 6 ) clk-1 and clk-1;tat-2 double mutants show altered cholesterol content . 7 ) The clk-1 phenotype is enhanced by high dietary cholesterol and this requires TAT-2 . 8 ) Suppression of clk-1 by tat-2 is rescued by BAs , and this requires dietary cholesterol . 9 ) The clk-1 phenotype , including the level of activity in lipid extracts , is suppressed by antioxidants and enhanced by depletion of mitochondrial superoxide dismutases . These observations suggest that C . elegans synthesizes and secretes molecules with properties and functions resembling those of BAs . These molecules act in cholesterol uptake , and their level of synthesis is up-regulated by mitochondrial oxidative stress . Future investigations should reveal whether these molecules are in fact BAs , which would suggest the unexplored possibility that the elevated oxidative stress that characterizes the metabolic syndrome might participate in disease processes by affecting the regulation of metabolism by BAs .
In mammals , cholesterol is necessary for the structure and function of membranes , and is the substrate for the biosynthesis of signalling molecules such as sexual steroids , bioactive compounds such as vitamin D , and bile acids ( BAs ) [1] . Cholesterol is converted into BAs through a series of oxidation reactions , as well as a shortening of the side chain in mammals ( Figure S1 ) . The enzymes that catalyze the individual biosynthetic steps of BA synthesis are localized in different cellular compartments , including the endoplasmic reticulum , cytosol , mitochondria , and peroxisomes . For example , the oxidation of the side-chain takes place in the mitochondria , but side-chain shortening takes place in the peroxisomes . In vertebrates , these reactions occur predominantly in hepatocytes . BAs regulate cholesterol and lipid metabolism in a variety of ways . They participate in cholesterol , lipid and hydrophobic vitamin uptake through their properties as detergents . They also participate in cholesterol elimination as they are secreted into the gut from where a fraction is lost every day in the feces . However , most of the secreted BAs are taken up again through the gut epithelium and can be re-circulated to the liver and re-secreted into bile , a process that is called the entero-hepatic circulation of BAs . In addition , BAs are signalling molecules that integrate several aspects of metabolism , including fat , glucose , and energy metabolism by regulating gene expression through nuclear hormone receptors such as the farnesoid X receptor ( FXR ) , the pregnane X receptor ( PXR ) , and the vitamin D receptor ( VDR ) ( BA biology is reviewed in detail in [2] , [3] ) . In mammals , BA excretion and recirculation depend on a number of membrane transporters such as ATP8B1 and ABCB11 . ATP8B1 , a type 4 P-type ATPase is a predicted phospholipid flippase [4] . Flippases transfer lipids from one leaflet of the membrane to the other thus changing the composition of both leaflets and the properties of the membranes . Several studies in mice suggest that ATP8B1 deficiency causes loss of canalicular membrane phospholipid asymmetry and as a result the resistance of the canalicular membrane to hydrophobic BAs is decreased , which impairs the activity of ABCB11 , the BA export pump , and causes cholestasis , a pathological retention of bile [5] . Mutation of ATP8B1 in humans leads to progressive familial intrahepatic cholestasis type 1 ( PFIC1 ) [6] . ATP8B1 shares 56% sequence identity with C . elegans TAT-2 ( for Transbilayer Amphipath Transporters ) [4] , [7] , [8] . A tat-2 mutant was found to exhibit hypersensitivity to low dietary cholesterol with decreased reproductive growth [8] . tat-2 mutation also suppresses the conditional growth arrest phenotypes resulting from mutation of elo-5 , a gene encoding a very long chain fatty acid ( VLCFA ) elongase , which is required for the production of two monomethyl branched-chain fatty acids ( mmBCFAs ) in C . elegans [7] . As tat-2 also partially suppresses developmental defects caused by reduction of the expression of sptl-1 , which disrupts sphingolipid biosynthesis , the authors proposed that TAT-2 acts by affecting the localization of mmBCFA-containing sphingolipids . Like vertebrates , C . elegans need sterols ( reviewed in [9] ) . However , as C . elegans is capable only of modifying sterols and not of synthesising them de novo , worms are auxotrophic for sterols , which have to be added to the culture media ( generally at 5 µg/ml cholesterol ) . A reduction in sterol supplementation leads to a complex phenotype that includes abnormal moulting , and inappropriate dauer formation . A complete lack of sterol supplementation leads to lethality . As sterols appear to be required only in very small amounts for normal physiology in worms , the deficit resulting from the absence of dietary cholesterol might result from deficits in the synthesis of signalling molecules derived from cholesterol . Indeed BA-like molecules derived from cholesterol have been identified in C . elegans and shown to have roles in signalling [10] . Dafachronic acid , which is required for bypassing dauer formation , has some characteristics of BAs , with oxidation of the steroid ring and of the side-chain , but its oxidation is not extensive and the side-chain is not shortened [10] . Yet , like vertebrate steroids and BAs , it acts via a nuclear hormone receptor , encoded by daf-12 [10] . In mammals , after BA-mediated absorption , ingested lipids , cholesterol , and lipid-soluble vitamins , are transported from the gut to the tissues that need them via circulating lipoproteins such as chylomicrons . Other lipoproteins such as low density lipoproteins ( LDL ) distribute lipids and cholesterol from the liver to peripheral tissues , and high density lipoproteins ( HDL ) transport cholesterol from peripheral tissues back to the liver in a process termed reverse cholesterol transport . The best known lipoproteins in C . elegans are the yolk particles . The protein moieties of yolk particles are vitellogenins , distant homologues of ApoB , which is the apolipoprotein in chylomicrons and LDL [11] . In C . elegans , cholesterol , fatty acids , and possibly other nutrients are transported from the gut to developing oocytes through the pseudocoelomic cavity by means of yolk particles [12] , [13] . However , several observations suggest that there is another lipid transport system in worms [14] . For example , hermaphrodites are capable of transporting cholesterol before the vitellogenins are expressed and males do not express vitellogenins yet accumulate cholesterol in developing sperm [13] . Furthermore , a mutation in dsc-4 , which encodes the worm homologue of the microsomal triglyceride transfer protein ( MTP ) [15] , which is required in mammals for the synthesis of LDL in the ER , produces multiple phenotypic effects without affecting yolk production . CLK-1 is a conserved mitochondrial enzyme that is necessary for the biosynthesis of the antioxidant and redox cofactor ubiquinone ( co-enzyme Q; CoQ ) . Mutations in C . elegans clk-1 or its mouse orthologue affect mitochondrial function [16] , [17] , in particular they increase mitochondrial oxidative stress in both organisms [18] , [19] . In worms , this results in a number of phenotypes , in particular slow development , slow aging , and slow rhythmic behaviours such as defecation [20] . The defecation cycle of C . elegans generates rhythmic body muscle contractions . This is a well-studied , highly regulated behaviour that is readily quantifiable [21] . dsc-4/mtp was originally identified as a mutation that suppresses the slow defecation of clk-1 mutants [22] . Given the known function of MTP it was concluded that a type of MTP-dependent , LDL-like lipoprotein , distinct from yolk , affects the rate of defecation [14] . Reducing the level of dietary cholesterol mimics the effects of dsc-4 on the defecation cycle length of clk-1 mutants [15] , [23] . These observations suggest that clk-1 mutants have slow defecation because they have high levels of LDL-like lipoproteins biosynthesis and secretion . Furthermore , the MTP-dependent lipid transport system appears to be so well conserved between mammals and C . elegans that drugs that have been developed to lower lipid levels in humans can act as suppressors of the slow defecation rate of clk-1 [23] . In particular , the slow defecation is suppressed by drugs that antagonize high LDL levels by increasing HDL levels ( e . g . an inhibitor of the HDL receptor SR-BI [24] ) , or that reverse cholesterol transport by stimulating gene expression through nuclear hormone receptors ( e . g . gemfibrozil [25] ) . Thus , although it is not yet known how elevated lipoprotein biosynthesis slows down the defecation cycle , the clk-1 mutants provide a tractable genetic model for characterizing the mechanisms of lipids and sterol uptake and the biosynthesis and secretion of LDL-like lipoproteins . Here , using genetic and pharmacological approaches , we show that sterol uptake in C . elegans depends on molecules that are functionally similar to BAs and might be structurally similar as well . These molecules are distinct from dafachronic acids and are synthesized and secreted through a pathway that appears to be molecularly very similar to that of BA synthesis and secretion in mammals . We also show that this pathway is altered by the high mitochondrial oxidative stress of clk-1 mutants . A link of oxidative stress and aging with dyslipidemia and with the other cardiovascular risk factors that constitute the metabolic syndrome has repeatedly been evidenced in mammals , but its mechanistic basis has not yet been elucidated . Our findings suggest that the link could be a perturbation of BA biosynthesis , a possibility that has not yet been explored in mammals .
We previously carried out a genetic screen to find suppressors of the slow defecation phenotype of clk-1 mutants [22] . In this screen we identified the dsc-4/mtp mutation ( described in the Introduction ) as well as another mutation , dsc-3 ( qm179 ) , which produced a very similar phenotype [22] . As the effects of dsc-4/mtp and dsc-3 ( qm179 ) are not additive ( Table S1 ) , they may act in a common pathway or affect a common process . We mapped dsc-3 ( qm179 ) between dpy-13 and unc-5 on LG IV [22] . Using the hypotheses that dsc-3 is involved in lipoprotein metabolism ( based on the identity of dsc-4/mtp ) we identified tat-2 as a candidate gene in that chromosomal region . We determined that qm179 is allelic to tat-2 ( tm1634 ) based on the following experiments , whose results are shown in full in Table S1 ( Table S1 lists all numerical values , samples sizes and statistical analyses for all defecation data shown in figures or mentioned in the text ) . Firstly , both RNAi against tat-2 and the deletion mutation of tat-2 ( tm1634 ) were phenotypically similar to qm179 in both the wild type and clk-1 backgrounds . Secondly , the tat-2 ( tm1634 ) deletion mutation fails to complement qm179 ( Figure 1A ) . Thirdly , transgenic expression of tat-2 rescues the suppression of clk-1 by qm179 ( Figure 1A ) . Finally , a G-to-A point mutation that results in an amino acid change from Alanine to Threonine at residue 665 of the protein was found by sequencing the coding region of tat-2 in qm179 mutants ( Figure 1B ) . We name the gene tat-2 from this point on . The allele analyzed is always qm179 except when otherwise specified . The high sequence conservation between TAT-2 and ATP8B1 suggests that their functions could be conserved as well . To test this directly we introduced a cDNA coding for mouse ATP8B1 in clk-1;tat-2 mutants under the C . elegans tat-2 promoter ( Figure 1C ) . This could partially rescue the suppression of the defecation phenotype , and was abolished by RNAi against the mouse gene sequence ( Figure 1C ) . Moreover , rescue by the mouse Atp8b1 gene was also prevented by introduction of mutations corresponding to either the tat-2 ( qm179 ) mutation or the human G308V mutation ( Table S1 ) , strongly indicating a functional conservation . In order to determine the focus of action of tat-2 , we constructed a reporter gene in which the tat-2 gene with 3 . 4 kb of upstream promoter sequence was fused in frame to gfp . This construct was capable of rescuing the defecation phenotype of tat-2 ( qm179 ) in the clk-1 background ( Table S1 ) . The fusion protein was expressed in the gut , spermatheca , proximal gonad , vulva , excretory cell , excretory gland cell , pharyngeal procorpus , the pharyngeal-intestinal valve and the rectal gland cell ( Figure S2 ) , which is consistent with what was previously found by others [7] , [8] . We also constructed three other reporters in which 3 . 4 kb of the tat-2 promoter were replaced by the promoters from the intestinal specific ges-1 , spermatheca-specific sth-1 , or excretory canal-specific pgp-12 , genes . Only the Pges-1::tat-2::gfp construct could rescue the phenotype ( Table S1 ) . Given the known function of ATP8B1 in bile acid secretion in mammals ( see Introduction ) , and the fact that eliminating the function of tat-2 suppresses clk-1 , we wondered whether a pharmacological agent that targets BAs could also suppress clk-1 . Cholestyramine is a BA-binding resin that is taken orally by people to lower the availability and thus the re-absorption of BA in the gut , which ultimately results in lowering in the level of circulating LDL [26] . We found that addition of 0 . 025% cholestyramine to worm plates partially suppresses the slow defecation cycle of clk-1 mutants ( Figure 2A ) . Cholestyramine had no effect on the wild type or on isp-1 mutants , which , like clk-1 mutants , have mitochondrial defects and a slow defecation cycle [27] . There was also no effect on tat-2 or dsc-4/mtp mutants ( Table S1 ) . Cholestyramine can bind organic molecules of intermediate to low polarity that bear an acidic group . This supports the hypothesis that C . elegans secretes molecules that have chemical properties resembling those of BAs and that the altered defecation cycle of clk-1 mutants is due to enhanced secretion of such molecules . We reasoned that if there are mammalian-like BAs in worms they might be synthesized by enzymes that are similar to those in mammals . Reducing BA synthesis by depleting such enzymes by RNAi knock-down should suppress clk-1 , similar to the effect of tat-2 mutations and cholestyramine treatment . The biosynthesis of BAs in mammals is complex and involves a variety of enzymatic steps carried out in diverse cellular compartments [1] . In order to determine if BA-like molecules are synthesised in a similar manner in worms , we examined 17 of the most common of these steps by identifying the best C . elegans homologues of the mammalian enzymes , and testing their impact on the defecation cycle of clk-1 mutants by RNAi ( Table 1 ) . Six of these enzymes are part of the same class of proteins , the P450 oxidases . As all the C . elegans proteins of this class are more or less equally similar to each of the vertebrate proteins , we tested all those we found by homology searching ( 78 genes ) . For other classes we tested several of the most homologous proteins ( Table 1 ) . Some classes of homologues did not have any effect on the defecation phenotype of clk-1 mutants , e . g . 3β-hydroxy-Δ5-C27 steroid oxidoreductase , 2-methylacyl-CoA racemase , and bile acid CoA: amino acid N-acyltransferase . However , thirteen P450 enzymes as well as worm genes encoding proteins that are highly similar to mammalian branched-chain acyl-CoA oxidase and 3α-hydroxysteroid dehydrogenase , cholesterol 25-hydroxylase , bile acid CoA ligase , the D-bifunctional protein , and the two genes ( daf-22 and nlt-1 ) that separately encode the two activities of mammalian peroxisomal thiolase , were effective in affecting the defecation cycle of clk-1 mutants ( Table 1 ) , suggesting that they may participate in the biosynthesis of BA-like molecules . Note that RNAi against daf-9/cyp-22A1 and hsd-1 , which encode activities that are known to participate in the synthesis of dafachronic acids , did not affect clk-1 defecation ( Table 1 ) . daf-12 , the nuclear receptor target of dafachronic acids , was also knocked down by RNAi under the same conditions as the enzymes: it produced only a very small , not significant , suppression ( −2 . 8±3 . 9 seconds ( P = 0 . 4795 ) ; n = 27 for the control , n = 38 for daf-12 ( RNAi ) ) . Interestingly , in addition to suppressors of the phenotype , we also obtained a few enhancers , mostly among the P450s ( Table 1 ) . P450s in mammals have numerous functions besides BA synthesis , and thus have the potential to affect the rate of defecation in ways unrelated to the synthesis of BA-like molecules . This is consistent with the observation that most genetic changes that affect defecation tend to slow it down [21] . DAF-36 is a Rieske oxygenase that acts as a cholesterol 7-desaturase that converts cholesterol to 7-dehydrocholesterol [28] , [29] . DAF-36 is necessary for dafachronic acid biosynthesis , which is why mutation of daf-36 leads to a dauer constitutive phenotype . We found that daf-36 ( k114 ) also suppresses the slow defecation cycle of clk-1 ( by 19 . 4 seconds ) , with only a very small effect ( 1 . 5 seconds ) on the wild type ( Table S1 ) . This is consistent with the hypothesis that the active molecules that are transported by TAT-2 and are bound by cholestyramine could be oxidized cholesterol derivatives , although they are clearly distinct from dafachronic acids ( see above ) . Lowering the level of the hypothetical BA-like molecules by reducing their secretion via mutation of tat-2 , reducing their biosynthesis by RNAi or mutations against potential biosynthetic enzymes , or by sequestration through cholestyramine suppresses the clk-1 phenotype . We reasoned that the phenotype might therefore be enhanced by BA supplementation . We treated clk-1 mutants with mixed mammalian BAs and found that their phenotype was indeed enhanced while the wild type was completely insensitive ( Figure 2B ) . These findings show that externally applied BAs can act on C . elegans . It also suggests that in the wild type the processes that are affected by BAs and that ultimately determine the defecation cycle , such as cholesterol handling ( see below ) and lipoprotein metabolism ( see Introduction ) , are better regulated than in clk-1 mutants . In mammals , BAs of different structures have been found to interact differently with nuclear hormone receptors thus affecting differently the regulation of BA synthesis and secretion , and also to be more or less efficient in cholesterol uptake [2] . In particular , more hydrophobic BAs appear to result in greater cholesterol uptake [30] . To test whether the structures of the BAs are important for their effects on clk-1 mutants we treated the wild type and clk-1 mutants with three concentrations of cholic acid ( CA ) , one of the main relatively hydrophilic mammalian BA , and chenodeoxycholic acid ( CDCA ) , one of the main relatively hydrophobic mammalian BA . No treatment had any effect on the wild type ( Table S1 ) . However , at two concentrations ( 0 . 15 mM and 0 . 6 mM ) , CA suppressed the defecation cycle of clk-1 mutants , although it enhanced the phenotype at 2 . 5 mM , while CDCA enhanced the phenotype in a dose-dependent manner at all concentrations tested ( Figure 2C ) . One possibility to explain the ability of CA to suppress clk-1 suggests that it might be more hydrophilic than the average BA-like molecules secreted by worms , thus effectively diluting their strength in taking up cholesterol . This notion is also supported by the observation that CA was a better suppressor at lower ( 0 . 15 mM ) than at the higher ( 0 . 6 mM ) concentration , and enhanced the phenotype at the highest concentration ( 2 . 5 mM ) . This suggests that at the higher concentrations the greater amount of BA ( here CA ) provided by the treatment in part compensates for the fact that CA is a more hydrophilic BA . CDCA had no effect at the lowest concentration but enhanced the phenotype at higher concentrations ( Figure 2C ) . The results presented above suggest that C . elegans produces and secretes molecules with BA-like properties , and possibly structures , and that this process is deregulated in clk-1 mutants . We reasoned that the hypothetical endogenous BA-like molecules should have the same effect on the wild type and clk-1 mutants as exogenous BAs . To test this we made lipid extracts [31] from both the wild type and clk-1 mutants and assayed them on the defecation cycle of both genotypes . The lipid extracts were applied to plates in the same way as BAs in previous experiments . Extracts from both genotypes had no effect on the defecation of the wild type . However , extract from clk-1 mutants at 0 . 02 and 0 . 1 mg significantly enhanced the phenotype of clk-1 mutants ( Figure 2D ) . At these concentrations wild type extracts had no significant effect on the mutants . Thus to establish that the wild type also contains the activity , and to measure how much higher the activity was in clk-1 mutants , we produced a large quantity of extract from the wild type , which allowed to test 0 . 4 mg of activity on the wild type and clk-1 . The high concentration of wild type extract was again ineffective on wild type animals but enhanced the phenotype of clk-1 as much as 0 . 1 mg of extract from clk-1 ( Figure 2D ) . We conclude that both the wild type and clk-1 mutants contain the activity but that clk-1 mutants contain approximately 4× time higher steady-state levels of the activity . One of the functions of BAs is to regulate cholesterol uptake and handling . We therefore measured the level of cholesterol in the wild type and in clk-1 mutants grown under low ( 2 µg/ml ) , standard ( 5 µg/ml ) and high ( 50 µg/ml ) levels of cholesterol supplementation . Both the wild type and clk-1 mutants grown on high cholesterol contained significantly more cholesterol than when grown under standard conditions ( Figure 3A ) . However the increase was significantly greater in clk-1 mutants . There was no significant difference between 2 µg and 5 µg/ml of supplementation for either genotype . We also assayed the cholesterol content of tat-2 and clk-1;tat-2 mutants . The cholesterol content of tat-2 was similar to that of the wild type at all levels of cholesterol supplementation . However the increase of cholesterol content observed in clk-1 mutants under high cholesterol supplementation was fully abolished in clk-1;tat-2 double mutants ( Figure 3A and Table S2 ) . Furthermore , cholesterol content in the double mutants was elevated at low and standard level of supplementation and thus similar at all levels of cholesterol supplementation , indicating that clk-1 and tat-2 interact in determining the level of cholesterol uptake and content . We have previously shown that the defecation cycle of clk-1 mutants , but not that of the wild type , is suppressed by lowering the levels of dietary cholesterol from 5 µg/ml to 2 µg/ml [23] . We have now extended this observation to the effect of high cholesterol ( 50 µg/ml ) , which has no effect on the wild type but further slows down the defecation of clk-1 ( qm30 ) mutants ( Figure 3B ) . We had observed ( Figure 3A ) that clk-1 and tat-2 interact in determining the level of cholesterol uptake . We therefore wondered if the metabolism of the BA-like molecules was involved in these effects of the level of dietary cholesterol on the defecation cycle . We found that low cholesterol shortened the defecation cycle of clk-1;tat-2 , but that the effect of high cholesterol on clk-1 mutants was fully suppressed in clk-1;tat-2 mutants ( Figure 3B ) . The observation that altering the level of media cholesterol can affect the defecation phenotype of clk-1 mutants in both directions suggests that uptake or subsequent handling of cholesterol can change the phenotype caused by the deregulated metabolism of the BA-like molecules in clk-1 mutants . The results described above suggest that the suppression produced by the tat-2 mutation might be due to lower secretion of the BA-like molecules . To test this directly we treated tat-2 and clk-1;tat-2 mutants with a small amount ( 0 . 015% ) of mixed mammalian BAs ( Figure 2E and Table S1 ) . These exogenous BAs had no effect on the wild type or dsc-4 mutants but rescued the tat-2 phenotype in both the wild-type and clk-1 backgrounds . Furthermore , these effects of the exogenous BAs were abolished in the absence of cholesterol supplementation ( Figure 2E ) . We also found that the effects of BAs we have previously observed , such as suppression and enhancement of clk-1 by pure CA or CDCA at various concentrations require cholesterol supplementation ( Figure 2C ) . These results indicate: 1 ) that the effect of tat-2 on clk-1 mutants is mediated by a reduction in the secretion of BA-like molecules; and 2 ) that the effects of BAs and tat-2 on clk-1 mutants implicate changes in cholesterol uptake . We have shown above that the phenotypes of clk-1 mutants include deregulated metabolism of BA-like molecules , which results in altered cholesterol content and abnormal sensitivity to the level of cholesterol supplementation . Previous studies of clk-1 indicated that the principal cellular defect of these mutants is an elevated level of mitochondrial oxidative stress , characterized by elevated mitochondrial ROS production [18] , elevated oxidative damage [32] , [33] , and increased sensitivity to pro-oxidant drugs [34] . In addition , several of the clk-1 phenotypes are strongly enhanced when the expression of the main mitochondrial superoxide dismutase ( SOD-2 ) is reduced by RNAi [32] or mutation [33] . In fact defecation was among the phenotypes that were found to be enhanced in the clk-1;sod-2 double mutants [33] . To further explore the link between ROS and the clk-1 defecation phenotype we first determined whether RNAi against the other C . elegans sod genes had any effect . We found that in addition to sod-2 , RNAi knockdown of sod-3 , the gene encoding the other mitochondrial superoxide dismutase , enhanced the defecation phenotype of clk-1 ( Figure 4A ) . However , RNAi against the three non-mitochondrial sod genes ( sod-1 , sod-4 , and sod-5 ) did not affect the phenotype ( Figure 4A ) , indicating that the enhancement of the phenotype is specific to alterations in mitochondrial ROS levels . Consistent with previous findings , this suggests that the slow defecation phenotype of clk-1 mutants might be due to their elevated mitochondrial ROS production . In order to test this further we treated clk-1 mutants with the antioxidant N-acetyl-cysteine ( NAC ) a commonly used hydrophilic antioxidant , which can reduce mitochondrial ROS production [18] . We found that NAC treatment could partially suppress the slow defecation cycle in a dose-dependent manner ( Figure 4B ) . Complete suppression could not be obtained because higher levels of the compound was toxic , possibly because of inhibition of normal ROS levels in other compartments . Finally , to test whether the increased mitochondrial oxidative stress is the cause of the deregulated metabolism of the BA-like molecules we tested whether the tat-2 ( qm179 ) mutation could suppress the effect of antioxidant treatment . We found that treatment with 10 mM NAC was without effect on tat-2; clk-1 ( Figure 4C and Table S1 ) , indicating that tat-2 ( qm179 ) is epistatic to antioxidant treatment . This is consistent with the elevated mitochondrial oxidative stress being the primary cause of the deregulation of the metabolism of the BA-like molecules observed in clk-1 mutants . The hypothesis suggested by the results described so far is that the clk-1 defecation phenotype is the result of increased mitochondrial oxidative stress in these mutants , which increases the level of activity of BA-like molecules . We tested this hypothesis directly by producing and testing lipid extracts from clk-1 mutants treated with NAC and from clk-1 ( qm30 ) ;sod-2 ( ok1030 ) double mutants ( Figure 5 ) . NAC treatment reduced the level of the activity found in the extract , and the extract from clk-1;sod-2 double mutants contained substantially higher level of activity than the clk-1 extract . For an unknown reason the clk-1;sod-2 extract was the most variable in terms of its activity on individual worms ( Figure 5 and Table S1 ) .
Here we have shown that: 1 ) clk-1 mutants are suppressed by mutations of TAT-2 , the worm orthologue of an ATPase that is necessary for BA secretion in mammals , 2 ) the suppression by tat-2 can be rescued by exogenous BAs , 3 ) RNAi knockdown of several C . elegans enzymes homologous to those that are implicated in BA synthesis in mammals suppress the clk-1 phenotype , but not the knockdown of some of the enzymes known to be necessary for dafachronic acid synthesis , 4 ) clk-1 mutants display a cholesterol-dependent sensitivity to exogenous BAs , as well as a sensitivity to cholestyramine , a drug that sequesters BAs , 5 ) clk-1 mutants but not the wild type are sensitive to an activity contained in lipid extracts from worms , 6 ) the clk-1 defecation phenotype is suppressed by a mutation in daf-36 , which encodes a cholesterol 7-desaturase , suggesting that the activity is a cholesterol derivative , 7 ) clk-1 mutants contain more of this activity , 8 ) the level of the activity is altered by mitochondrial oxidative stress , 9 ) clk-1 mutants have a deregulated cholesterol metabolism , as indicated by the fact that their phenotype can be affected by reducing or increasing the level of dietary cholesterol and that they accumulate more cholesterol than the wild type when supplied with high levels of dietary cholesterol , 10 ) clk-1 and tat-2 interact in determining cholesterol content as , in contrast to what is observed in the wild type , the cholesterol content of clk-1;tat-2 is similar at all levels of dietary cholesterol supplementation . This last observation suggests that the abnormal cholesterol metabolism is caused by the deregulated metabolism of the BA-like molecules that are affected by clk-1 and tat-2 . Together all these observations imply that there are BA-like molecules involved in cholesterol uptake in C . elegans , but also that these molecules are likely to be structurally similar to BAs , as their biosynthesis and secretion are affected by activities that are known to affect BAs in mammals . The results summarized in the previous paragraph lead to a model of regulatory relationships between cholesterol availability , cholesterol uptake , the synthesis and secretion of BA-like molecules , and LDL-like lipoprotein synthesis and secretion in C . elegans ( Figure 6 ) . All our findings appear to be remarkably consistent with what is known about the synthesis and regulation of BAs and LDL in vertebrates . Thus we propose that secreted BA-like molecules participate in cholesterol uptake and that the function of TAT-2 is required for their secretion . Cholesterol is used in the synthesis of the BA-like molecules and , as in mammals , the BA-like molecules act directly on cholesterol uptake but also as signalling molecules that positively regulate the synthesis of LDL-like lipoproteins . The core of our model is that CLK-1 , via its effect on limiting mitochondrial ROS generation , is required for a negative feedback mechanism that down-regulates the synthesis of the BA-like molecules as a function of cholesterol uptake . In the absence of CLK-1 more BA-like molecules are synthesized ( Figure 2D ) and more cholesterol can be taken up ( Figure 3A ) . The increased synthesis of the BA-like molecules up-regulates the level of LDL-like lipoprotein synthesis and secretion , which in turn determines the length of the defecation cycle . Our data show that availability of BA-like molecules and the rate of defecation are tightly linked as shown by the sensitivity of the mutant defecation cycle to BA supplementation , sequestration of the BA-like molecules , and the inhibition of the synthesis of the BA-like molecules . The hypothesis that CLK-1 is necessary for a feed-back from cholesterol uptake to the synthesis of the BA-like molecules provides the link between the level of cholesterol supplementation and the level of the BA-like molecules ( and thus between the level of cholesterol supplementation and defecation ) ( Figure 6 ) . However , the model cannot accurately predict the effect of mutations on the level of whole-animal cholesterol . Indeed , the level of cholesterol likely depends on cholesterol flux through the entire organism . This is determined by a number of factors that we cannot precisely quantify at this stage , including the exact quantitative relationship between the level of cholesterol uptake and the level of synthesis of the BA-like molecules via the CLK-1-dependent mechanism , the level of cholesterol loss through the synthesis of BA-like molecules if these are cholesterol-derived , and the loss of the BA-like molecules through secretion , the level of cholesterol loss through LDL-like lipoprotein secretion ( whose target in the organism is unknown ) , the level of cholesterol loss through yolk synthesis and egg-laying , and in fact any other form of cholesterol elimination or storage , whether or not regulated by the BA-like molecules . Suppression of the clk-1 defecation phenotype can be obtained by knocking down the enzymes necessary for peroxisomal β-oxidation that in mammals are necessary for shortening the side-chain of cholesterol ( Table 1 ) . This suggests that if the C . elegans BA-like molecules are cholesterol derived they might have a shortened side-chain . This is in contrast to dafachronic acid ( Figure S1 ) , which is a steroid that acts as a hormone that regulates development in C . elegans [35] . We have not yet tested if the BA-like molecules can affect other clk-1 phenotypes in addition to defecation , such as slow aging . More detailed structural information on the C . elegans BA-like molecules , and possibly the availability of synthetic molecules , might be necessary to test rigorously their effect on phenotypes that are harder to quantify than defecation . The suppressive effect of cholic acid ( CA ) at very low concentrations is difficult to explain unless the BA-like molecules are indeed structurally similar to BAs . However , if this is the case the observed effect might result from the dilution by CA of the native and potentially more hydrophobic BA secreted by worms . However , as CA serves as negative feedback for BA synthesis and secretion in mice [36] , it is possibly that it could carry out a similar role in C . elegans , which would provide an alternative explanation for its paradoxical action at low concentration . If this is the case , further study of this phenomenon might help in identifying the nuclear hormone receptors ( NHRs ) through which the C . elegans BAs might regulate metabolism and their own synthesis . We have already identified a number of nuclear hormone receptor loci whose down-regulation suppresses clk-1 mutants ( not shown ) . One or several of these could be the receptors for the BA-like molecules . The metabolic syndrome is a collection of age-associated disease risk factors that includes obesity , insulin resistance , hypertension and dyslipidemia . Oxidative stress , which is well known to increase with age and in obese individuals [37] , has been implicated in most of the components of the metabolic syndrome and might be the common link between them [38] , [39] , [40] . Our findings with C . elegans , where there appears to be BA-like molecules whose synthesis , secretion and activity shares strong similarities with BAs in mammals , suggest that mitochondrial oxidative stress can lead to deregulation of BA synthesis . Abnormal BA levels in turn could lead to metabolic disease processes via the action of BAs on sterol , lipid and glucose metabolism by signalling through BA receptors . Interestingly , the possibility of an involvement of oxidative stress on the regulation of BA synthesis and thus on the consequences of a deregulation of this process has not yet been explored in mammals .
Fourth larval stage ( L4 ) animals were transferred to the test plates and grown at 20°C . The effects of the different cholesterol concentrations or compounds were scored after raising the worms on the test plates for one generation . Defecation cycle rates were measured as previously described [22] , at 20°C for all experiments except for the RNAi and antioxidant treatments for which 25°C was used . Compounds ( cholestyramine , mixed bile acids , cholic acid , and chenodeoxycholic acid were tested by spreading them on plates , except that N-acetyl-L-cysteine was added to the nematode growth media ( NGM ) prior to pouring it into plates . See also Text S1 . dsc-3 had previously been mapped to LG IV , between unc-33 and dpy-4 [22] . By using 2-point and 3-point mapping strategies , the genetic position of qm179 was refined to a position between the two cloned gene dpy-13 and unc-5 . Due to the incomplete cosmid coverage of the tat-2 gene , no cosmid that spans this region can rescue the qm179 mutants . Therefore qm179 mutants were rescued by injecting two partially overlapping PCR fragments of tat-2 genomic DNA ( from −3123 to +7277 and from +7252 to +13567 , which includes the UTRs ) for in vivo recombination . Two other mutations allelic to qm179 had been originally identified , qm180 and qm184 [22] . The lesion in qm184 was identical to the qm179 lesion , and the lesion in qm180 was not found in the tat-2 exonic sequences . The tat-2 ( tm1634 ) allele was obtained from the National Bioresource Project and outcrossed three times . See also Text S1 . The tat-2 transcriptional reporter , Ptat-2::gfp ( pCDB898 ) was used as backbone to build the Ptat-2::mAtp8b1 , Ptat-2::mAtp8b1 A705T , Ptat-2::mAtp8b1 G308V clones . The PCR product of 3 . 4 kb upstream of the initiating ATG of tat-2 was cloned into the PstI and SmaI sites of the pPD95_77 vector . The full length of mouse Atp8b1 cDNA was amplified from the RIKEN clone F830210O18 . To construct Ptat-2::tat-2::gfp ( pCDB902 ) a 3945 bp long wild type tat-2 cDNA containing 22 exons was inserted into the SmaI site of pCDB898 . To construct Pges-1::tat-2::gfp , Psth-1::tat-2::gfp , and Ppgp-12::tat-2::gfp , ( pCDB906 , pCDB905 and pCDB904 , respectively ) the tat-2 promoter of pCDB902 was replaced by PCR products of 2 kb upstream of the ges-1 initiation codon , 1 . 6 kb upstream of the sth-1 initiation codon or 2 . 7 kb upstream of the pgp-12 initiation codon . These constructs were injected into clk-1; tat-2 ( qm179 ) mutants at a concentration of 0 . 1 ng/µl along with the transformation marker ttx-3::gfp at a concentration of 200 ng/µl . See also Text S1 . Lipids were extracted following [41] , and the cholesterol content was determined with a kit ( 10007640 ) from Cayman Chemical . The final concentration of Triton X-100 in each sample was 0 . 5% . We also measured the volumes of young adults for all genotypes as previously described [42] , and no difference from the wild type was found ( data not shown ) . See also Text S1 . The lipid extracts were prepared as previously described [31] and re-suspended in DMSO . To assay the activity of extracts from the wild type , clk-1 ( qm30 ) , clk-1 ( qm30 ) ; sod-2 ( ok1030 ) or clk-1 mutants treated with NAC , 36 µl of DMSO-dissolved extract ( or 36 µl of DMSO as control ) was spread onto 5 cm plates . Phenotypes of adult progeny were measured after raising L4 animals on the test plates for one generation . Due to the sensitivity of clk-1 mutants to dietary cholesterol level , we measured and calculated that the final concentrations of extracts applied to the plates contained less than 0 . 1 µg/ml of cholesterol , which cannot therefore be responsible for any of the effects observed ( Figure 2D ) . See also Text S1 . 5–10 clk-1 ( qm30 ) hermaphrodites L4 larvae were picked to RNAi plates . For the following 3 days , worms were transferred to new RNAi plates to rid of contaminating OP50 bacteria . Progeny worms were grown to the L4 stage and were then picked to new RNAi plates for scoring . 18 hours later , they were transferred to 25°C . After two hours of acclimation , their defecation phenotype was scored . We used 25°C for all RNAi experiments , except those shown in Figure 1C , because the responses tend to be more robust [22] . For each RNAi clone , five worms were scored for one defecation cycle . Clones that had a significant effect on defecation rate were re-screened 2–3 times . | Cholesterol metabolism , in particular the transport of cholesterol in the blood by lipoproteins , is an important determinant of human cardiovascular health . Bile acids are breakdown products of cholesterol that have detergent properties and are secreted into the gut by the liver . Bile acids carry out three distinct roles in cholesterol metabolism: 1 ) Their synthesis from cholesterol participates in cholesterol elimination . 2 ) They act as detergents in the uptake of dietary cholesterol from the gut . 3 ) They regulate many aspects of metabolism , including cholesterol metabolism , by molecular mechanisms similar to that of steroid hormones . We have found that cholesterol uptake and lipoprotein metabolism in the nematode Caenorhabditis elegans are regulated by molecules whose activities , biosynthesis , and secretion strongly resemble that of bile acids and which might be bile acids . Most importantly we have found that oxidative stress upsets the regulation of the synthesis of these molecules . The metabolic syndrome is a set of cardiovascular risk factors that include obesity , high blood cholesterol , hypertension , and insulin resistance . Given the function of bile acids as metabolic regulators , our findings with C . elegans suggest the unexplored possibility that the elevated oxidative stress that characterizes the metabolic syndrome may participate in mammalian disease processes by affecting the regulation of bile acid synthesis . | [
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| 2012 | Mitochondrial Oxidative Stress Alters a Pathway in Caenorhabditis elegans Strongly Resembling That of Bile Acid Biosynthesis and Secretion in Vertebrates |
The extent to which epigenetic variation affects complex traits in natural populations is not known . We addressed this question using transcriptome and DNA methylation data from a sample of 135 sequenced A . thaliana accessions . Across individuals , expression was significantly associated with cis-methylation for hundreds of genes , and many of these associations remained significant after taking SNP effects into account . The pattern of correlations differed markedly between gene body methylation and transposable element methylation . The former was usually positively correlated with expression , and the latter usually negatively correlated , although exceptions were found in both cases . Finally , we developed graphical models of causality that adapt to a sample with heavy population structure , and used them to show that while methylation appears to affect gene expression more often than expression affects methylation , there is also strong support for both being independently controlled . In conclusion , although we find clear evidence for epigenetic regulation , both the number of loci affected and the magnitude of the effects appear to be small compared to the effect of SNPs .
It has been long speculated that epigenetic modifications , in particular DNA methylation , contribute to heritable phenotypic variation [1 , 2] . That the potential exists is not in doubt , especially in plants . Modern sequencing technology allows us to investigate DNA methylation on a genomewide scale , and has revealed that spontaneous changes in DNA methylation , or epimutations , can be inherited without accompanying DNA changes [3 , 4] , and that induced DNA methylation changes in genetically homogeneous lines can bring about heritable phenotypic changes [5] . However , these studies tell us nothing about the importance of epigenetic inheritance relative to actual genetic variation , which is typically substantial in natural populations . Recent population studies in A . thaliana have suggested a role for DNA methylation [6 , 7] , but did not explicitly investigate DNA methylation effects on top of SNP effects . To further address this question , we utilized an existing data set comprising genome- , epigenome- , and transcriptome-sequencing data for a population of 135 Swedish A . thaliana accessions [7] . We consider two types of DNA methylation: C methylation ( or TE-like methylation ) and CG-only methylation ( or gene body methylation ) , defined as in previous work [7] . The former is characterized by heavy methylation in all contexts ( CG as well as non-CG ) , involves the pathways dependent on RNA-directed DNA methylation ( RdDM ) or CMT2 [8 , 9] , and is associated with heterochromatin and the silencing of mobile elements [10] . The latter involves sparse CG methylation of a subset of “housekeeping” genes; its presence and level is evolutionarily conserved [11] and it is generally positively correlated with transcription . Based on type distinction and DNA context of the methylated cytosine , we divided DNA methylation variants into four non-overlapping sets: CG where no non-CG methylation is present; CG where there is non-CG methylation present; CHG , and , finally; CHH methylation . These variants are quantified by averaging methylation level of cytosines over all eligible cytosines in 200 bp windows ( see Methods ) . As the four types have different baseline levels and involve different pathways , we normalized their levels and performed most analysis separately . Our study faces statistical challenges in terms of strong population structure , which not only leads to the usual difficulties for genome-wide association studies [12] , but also means that DNA methylation variation will be strongly correlated with DNA variation due to linkage disequilibrium as well as direct causation [7] . In what follows we present several novel mixed-model methods that aim to solve these problems .
Several studies have investigated correlation between gene expression and local DNA methylation across genes within a single or a small number of genetic backgrounds [13 , 14] . Here , we investigate correlations between gene expression and local DNA methylation across many individuals ( with distinct genetic backgrounds ) for each gene instead . An immediate conclusion is that the relationship between DNA methylation and expression is not simple , but generally agrees with previously published results [15–20] . While CG-only methylation typically shows a weak positive correlation with expression , it can also be negatively correlated , and while C methylation generally shows a strong negative correlation with expression , it can also be positively correlated ( Fig 1 ) . Similar variation is found if we consider the pattern of correlations along genes . For genes with CG-only methylation , there is a clear tendency towards positive correlations in the middle of genes , whereas for genes with C methylation , strong negative correlations are found at the transcription start and termination sites . If phenotypic variation is due to many polymorphisms of small effect , we expect a linear relationship between genetic relatedness and phenotypic covariance [21] . While relatedness was historically estimated from pedigrees , genome-wide SNP data make it possible to estimate it directly , and this fact has recently been exploited to estimate the fraction of phenotypic variation that is attributable to genetic relatedness , i . e . , is due to genetic variation [22] . The same approach can also be used to control for the genetic background in GWAS [12 , 23 , 24] , and to further attribute genetic contributions to specific chromosomes [25] , annotation units [26] , or even loci [27] . We applied the same technique to epigenetic markers , and asked the question whether genome-wide similarity in DNA methylation helps explains expression variation . Formally , we seek to compare a genome-wide small effects model that includes only SNPs to models that also includes methylation ( see Methods ) . We considered CG-only and C methylation separately and together , but the results were unaffected by this . When comparing models that include methylation as well as SNPs to a model that does not , only 261 genes show marginally significant effects , and none are significant after taking multiple testing into account . Thus , including genome-wide methylation as a background effect did not explain any additional variation in gene expression . This does not mean that background methylation has no effect , because methylation variation is highly correlated with SNP variation ( either due to linkage , or direct causation [7] ) , and identifying a separate , orthogonal effect statistically may be very difficult . It does mean that there is no reason to include methylation as well as SNPs when correcting for background effects . Out of curiosity , we can also performed the reverse analysis: do we need SNPs if we have methylation ? The answer is similar ( 455 genes showed marginally significant effects of SNPs once methylation was taken into account ) , again emphasizing the very strong correlation between genetic variation and DNA methylation . Although genome-wide methylation relatedness does not help explain phenotypic variation , individual methylation variants may . We performed marginal [24 , 28] and stepwise [29] GWAS using methylation variants as fixed effects instead of SNPs . The results were then compared to those obtained using SNPs as fixed effects . Per the results above , we used only SNP-based kinship estimates to control for population structure confounding ( which it does well , see S1 Fig ) . A global view of significant methylation associations ( Fig 2 ) shows an abundance of cis-associations with scattered instances of trans-associations , similar to what is observed for SNP-based associations ( S2 Fig ) . A striking “hotspot” of putative trans-regulation was found near the center of chromosome 2 , and corresponds to AGO4 , a member of the Argonaute family involved in siRNA-mediated gene silencing [30 , 31] . CG gene body methylation of AGO4 ( pattern in S3 Fig ) is positively correlated with its expression , and expression of AGO4 is strongly correlated with that of close to 70 other genes ( seemingly unrelated; see S3 Table ) . Interestingly , no significantly associated SNPs were found , making this group of covarying genes detectable only using the methylation marker on AGO4 . While direct involvement of AGO4 in transcriptional or post-transcriptional regulation is plausible [30–33] , an alternative explanation is that all these genes are co-regulated , and that it is pure chance that methylation of AGO4 is associated with its own expression , and therefore with the rest of the genes . Experiments to distinguish between these explanations are planned . In support of the latter explanation , there is very little little correspondence between SNP and methylation associations in trans ( cf . S2 Fig and Fig 2 ) , as would be expected if a large fraction of these associations were false positives . For the rest of the paper , we instead focus on cis effects , which are demonstrably real . Based on the over-representation of local ( i . e . , cis ) vs . global ( i . e . , trans ) effects , cis-methylation associations have a false-positive rate of less than 0 . 5% ( Methods ) , and they also strongly overlap with SNP-associations . They are not nearly as common , however . As shown in Fig 3 , there is at least an order of magnitude more SNP associations than there are methylation associations , and 114 of the 177 ( 64 . 4% ) genes that have a significant methylation association also have a significant SNP association ( S2 Table ) . This leaves 63 significant methylation associations without an accompanying SNP association . Most of these are not associated with any SNP even at less stringent significance thresholds ( Fig 3 ) , and the corresponding genes are thus candidates for being regulated epigenetically . It is worth that 55 of the 63 have C-methylation , suggesting the presence of transposable element . An alternative explanation is that the methylation variation captures extensive allelic heterogeneity that is difficult to map [34 , 35] . Allelic heterogeneity could also help explain another interesting finding , namely that methylation associations are typically closer to the gene of interest than are SNP associations when both are found ( Fig 4 ) . Such behavior is expected if the most significant SNP is a “tag” SNP that serves as a proxy for multiple underlying causal variants [34 , 35] . In order to capture additional effects of cis methylation more accurately , we used a nested model in which we first estimate genetic effects with a combination of random effect terms ( based on local as well as global genetic similarity matrices [27] ) and stepwise fixed-effect cofactors for remaining large effect SNPs , then capture any remaining methylation effects as stepwise fixed effects ( See Methods for details ) . Across genes , almost all heritable expression variation is due to genetic effects , with cis-methylation explaining only a small additional fraction of the variance ( Fig 5 ) . Nonetheless , the contribution is significant in a small number of cases . Using a Bonferroni-threshold based solely on methylation bins , we detected 212 significant associations between expression and DNA methylation . Of these , 64 remain significant after taking cis-SNP effects into account , 46 of which were already identified as having only cis-methylation in the previous section ( S2 Table ) . Using an expanded data set that includes more genes for which a high proportion of individuals had no detectable expression ( potentially due to epigenetic silencing ) , the corresponding counts are 397 and 148 , respectively . Among the genes identified in this extended data set is QQS , a gene involved with starch metabolism which has been shown to be epigenetic regulated ( albeit it in a different population ) [36] . The genes with methylation associations span very diverse biological processes , but we find a significant enrichment for defense genes ( p = 1 . 2e-06 , FDR = 0 . 001; see Methods ) . That methylation is correlated with expression is clear , but whether there is a causal relationship , and , if so , in which direction it goes , is not . Transposon methylation is generally considered causally repressive in normal tissues , because disrupting methylation experimentally indeed often leads to transposon reactivation . However , little is known about gene body methylation , which is sometimes considered a consequence of transcriptional activity rather than a cause [37 , 38] . Because non-disruptive methods to change DNA methylation experimentally are not available , this has been a difficult question to answer directly , but several attempts have been made using statistical causal models [16 , 20] , indirect inference with positional information [18] , or stress induced changes [39] . We took the first approach , using a Bayesian network model-selection framework . A major challenge in our setting is the strong contribution of polygenic factors , even for relatively simple traits like expression . We explicitly included these factors in our models using a novel Bayes’ factor approach that expands upon existing methods [40–42] . We consider a total of four possible causal relationships between genetic variation , methylation variation , and expression variation ( Fig 6 ) . We are most interested in comparing the case where methylation regulates expression by mediating all genetic effects ( Model I ) to the case where the opposite is true ( Model II ) , but we also consider the possibility that genetic variation affects both methylation and expression independently ( Model III ) , and a “full model” where genetic variation affects both methylation and expression , which are also allowed to affect each other . For the 297 genes with significant associations among methylation , expression and SNPs , we calculated the likelihood for each of the four models and compared them using the Bayesian Information Criterion ( BIC ) . For most genes , Model I is a better fit than Model II , although the difference is often not significant ( Fig 7 ) . This suggests that DNA methylation is affecting expression rather than the other way around , for CG-only as well as C methylation . However , whereas the inverse-normal transformation used in this paper seemed to produce more reliable GWAS results ( see Methods ) , it may dampen effects in our causal model and cause bias . We therefore repeated the causality tests using untransformed versions of expression and DNA methylation data . The likelihood for all models increased , as expected given the removal of the dampening effect , be we also found a much stronger support for Model III ( S7 Fig ) . Thus , while the relationship between Model I and II remained , suggesting that methylation is more likely to affect transcription directly than the other way around , the best-fitting model with untransformed data is one in which both methylation and transcription are caused by genetic variation without necessarily affecting each other . Statistics alone is unlikely to resolve this issue .
There is currently great excitement about the potential role of epigenetics in complex trait variation , both as a regulatory and as an inheritance mechanism . That an important role is in principle possible is not in doubt [2 , 5] , but there is almost no information on whether it actually matters in practice . This is a clearly a quantitative question and the answer will not be the same for all traits and populations . In this paper , we focus on whether knowing the epigenome ( in the form of DNA methylation variation ) improves our understanding of expression variation in A . thaliana leaves . The general answer is: Only marginally ( Fig 5 ) . In terms of overall heritability , the genome-wide pattern of methylation polymorphism does not explain anything beyond the genome-wide pattern of SNP polymorphism , and while over a thousand expression traits have significant SNP associations , only about a hundred have associations with methylation variation , and most of these are also associated with SNPs . Indeed , no more than about sixty show evidence of a significant methylation association once SNP effects have been taken into account . Thus , although there are numerous caveats to our results ( limited sample size , limited technology for measuring both expression and methylation , uncertainty about how to quantify methylation variation , etc . ) , our overall conclusion is that the effects of methylation variation are marginal relative to those of genetic variation . However , this does not mean that knowing the methylation variation is pointless . One interesting finding is that , for expression traits with both methylation and SNP associations , the former are often physically closer to the gene being expressed than the latter ( Fig 4 ) . This could be because the most highly associated SNPs are in fact just tagging multiple underlying causal SNPs [34 , 43] , and suggests methylation polymorphism could help with fine-mapping especially in a study with larger sample size . Equally importantly , we do find a small number of genes with clear evidence for epigenetic regulation , including several with no significant cis-SNP associations . These merit further investigation . The same is true for the minority of genes in which promoter methylation is positively rather than negatively correlated with expression ( Fig 1 ) . It should again be emphasized that our definition of methylation variation ( average methylation in windows for different methylation contexts ) is rather crude , and that it may be possible to define more biologically relevant statistics . Finally , we address the issue of causality . In particular for gene body methylation , is debated whether the observed correlation is between methylation and expression is cause , effect , both , or neither [16 , 20] . While we find support for methylation variation being a direct cause of expression variation , and perhaps even stronger support for both types of variation being influenced by genetics independently , our main conclusion is that is a question that will require direct experimental evidence to answer .
We used previously published polymorphism [44] and transcriptome/methylome data [7] , which are available via the NHI Gene Expression Omnibus ( GSE54292 , GSE54680 , GSE65685 , GSE66017 ) and from the 1001 Genomes Project website . For the transcriptome/methylome data , only the data from the 10°C sexperiment were used . More details about growing conditions , tissues used and sequencing pipelines can be found in the relevant papers . In order to reduce the number of false associations while maintaining reasonable sensitivity , the three dataset were processed as follows . We extended the SNP-based heritability models to include DNA methylation variants , which are similarly considered to follow independent and identically distributed Gaussian distribution , but with scale parameter σ m 2 . The structure of such effect is dictated by the “epigenetic similarity matrix” KM , calculated analogously to the SNP based genetic relatedness/similarity matrices [22] . We then perform likelihood ratio test between a model that include this epigenetic term and one that does not: Y∼ N ( μ , K S σ S 2 + K M σ M 2 + σ ϵ 2 ) , ( 1 ) Y ∼ N ( μ , K S σ S 2 + σ ϵ 2 ) . ( 2 ) GWAS using both genotype data and DNA methylation bins was performed with linear mixed models ( as implemented in mixmogam: https://github . com/bvilhjal/mixmogam ) to correct for population structure . The model used was Y ∼ N ( μ + X β , K s σ s 2 + I σ e 2 ) , ( 3 ) where Y is the vector of phenotypes , X is a single vector of SNP or methylation bins , and the β’s correspond to allelic effect sizes . Ks and σ s 2 are again the genetic related matrix and its corresponding random effect size , while σ e 2 is the residual variance due to unexplained environment or noise . Marginal F-statistics were calculated as in ordinary linear model after rotating the phenotype Y and X by ( Λ δ + I ) - 1 2 Q T , where QΛQT is the spectral decomposition of the symmetric relatedness matrix K and δ is the ratio between σs and σe . To simplify calculations , we used the same approximation as in EMMAX [24] , i . e . , we only calculate the ratio δ once for the null model without fixed effects . The significance level ( p-value ) is then obtained by F-tests for SNPs and methylation bins of all contexts . A direct extension of the marginal model is to include large effects as cofactors . This is accomplished in the forward stepwise mixed model [29] which result in a final model as Y ∼ N ( μ + ∑ i n S X S , i β S , i + ∑ j n M X M , j β M , j , K S σ S 2 + I σ ϵ 2 ) , ( 4 ) where Xs and βs are SNP vectors and their respective effects , whiel XM and βM are methylation bin vectors and effects . At each step , the top marginal variant is added to XS until no variants remains significant at Bonferroni threshold . We can derive a conservative upper bound for the false discovery rate for our cis associations , defined for each expression trait as everything less than 20 kb away from either end of the gene , by considering the over-representation of associations in cis compared to in trans , and assuming that all the latter are false . This is similar to what was previously done for candidate gene lists [34] . We perform a association study that explicitly compare variance explained by DNA alone versus DNA methylation and DNA together . We try to capture large effect loci , effects due to allelic heterogeneity as well as background trans effects by using a linear mixed models that include an additional variance component for cis SNPs . In particular , the local equivalent of the global relatedness term is included which would capture most of cis effects from one or more ( heterogeneous ) loci . The full models are: genetics: Y ∼ N ( μ + X S β S , K S σ S 2 + K l σ l 2 + I σ ϵ 2 ) , ( 5 ) genetics+methylation: Y ∼ N ( μ + X S β S + X M β M , K S σ S 2 + K l σ l 2 + I σ ϵ 2 ) , ( 6 ) where Kl and σ l 2 are the cis SNP kinship and its effect . We do not include a global methylation kinship since that has been found to exert no influence in most cases . We used the web tool AgriGo ( http://bioinfo . cau . edu . cn/agriGO/ ) [45] to find functional categories that is significantly enriched in the subset of cis-methylation associated genes . We prepared the following set of data for use in causal structure analysis , with the goal being to identify pairs of associated expression/methylation that also shows evidence of being associated with the same genetic factors . We first correlated expression level with all cis methylation bins that is within the gene or within 2000 bp of the transcription start site . If any bin is correlated with a r2 greater than 0 . 2 , the pair of expression and the highest correlated bin is added to a testing pool . From this pool , mixed model GWAS is performed on each pair of expression/methylation , and any pair that does not: is filtered out . This results in a final set of data from 297 genes . We build upon earlier statistical framework [40–42] for causal analysis . Our methods try to infer causal relationship between three variables: genetic factors ( G ) , or more precisely DNA sequence; DNA methylation ( M ) ; and phenotypic trait , in this context mostly referring to expression traits ( E ) . Among these , it is assumed that genetic factors ( G ) are not subject to influences from the other factors . This is not true in general due to effects of selection and mutation rates on DNA sequences , but these effects are negligible for data collected in this study that are at most several generations apart . We thus reduce to the four possible scenarios in Fig 6 . Here our goal is to distinguish between the four potential models considered . We base our selection on Bayesian information criteria that are calculated from maximum likelihood of the respective models . These likelihoods are calculated as: Model I: L ( M1|g , m , e ) =p ( e|m ) p ( m|g ) p ( g ) Model II: L ( M2|g , m , e ) =p ( m|e ) p ( e|g ) p ( g ) Model III: L ( M3|g , m , e ) =p ( e|g ) p ( m|g ) p ( g ) Model IV: L ( M4|g , m , e ) =p ( e|m , g ) p ( m|g ) p ( g ) =p ( m|e , g ) p ( e|g ) p ( g ) ( 7 ) In cases where M is confined to one observation per individual like expression levels , the relationship between E and M are considered linear with Gaussian noise: p ( e | m ) ∼ N ( μ E + m β M , I σ ϵ E 2 ) | m p ( m | e ) ∼ N ( μ M + e β E , I σ ϵ M 2 ) | e ( 8 ) Whereas the distribution involving genotype would contain both fixed terms for large effects as well as random terms for genetic background: p ( e|g ) ∼N ( μE+XβX , M , KSσS , E2+IσϵE2 ) |gp ( m|g ) ∼N ( μM+XβX , E , KSσS , M2+IσϵM2 ) |gp ( e|m , g ) ∼N ( μE+XβX , M+mβM , KSσS , E2+IσϵE2 ) |m , gp ( m|e , g ) ∼N ( μM+XβX , E+eβE , KSσS , M2+IσϵM2 ) |e , g ( 9 ) The maximum likelihood of Eq 8 is calculated by least square estimate of βs , while those of Eq 9 are found by numerical method implemented in mixmogam . Since we are interested in the likelihood rather than estimates of the variance parameters , the ‘ML’ criteria ( instead of restricted ML ) is chosen as the optimization criteria for the latter . After we obtain the maximum log likelihood for each component , we sum them to obtain the overall log likelihood of the models minus a constant . Bayesian information criteria is chosen as our model selection criteria , corresponding to the fact that we already have all potential models chosen . It is calculated as: BIC = - 2 ln L i + k i ln 135 , ( 10 ) where L i are the likelihood for models I-IV and ki the corresponding number of free parameters . To investigate performance of our causal model , we performed a simulation study by generating pairs of traits using the our A . thaliana SNP dataset . Three sets of simulations are run: These effects are scaled to achieve various levels of heritability . Based on the results , when the underlying model is I ( II ) or III , we can deduce the correct model most of the time . However , when the real model is IV , it is very hard to capture . These results are summarized in S9 Fig . | It has been demonstrated experimentally that epigenetic variation , in particular DNA methylation , can transmit information across generations . However , it is difficult to evaluate the importance of such effects in natural populations due to complex genetic background effects , making experimental the separation of genetic and epigenetic effects challenging . Here we use quantitative genetic models to test whether epigenetic variation plays a significant role in gene expression variation once genetic variation has been taken into account . In addition , we devise and apply methods that go beyond a simple association framework in order to infer causal relationships . Our results suggest a significant but small epigenetic contribution to expression regulation . | [
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| 2016 | Limited Contribution of DNA Methylation Variation to Expression Regulation in Arabidopsis thaliana |
HIV-1 arose as the result of spillover of simian immunodeficiency viruses ( SIVs ) from great apes in Africa , namely from chimpanzees and gorillas . Chimpanzees and gorillas were , themselves , infected with SIV after virus spillover from African monkeys . During spillover events , SIV is thought to require adaptation to the new host species . The host barriers that drive viral adaptation have predominantly been attributed to restriction factors , rather than cofactors ( host proteins exploited to promote viral replication ) . Here , we consider the role of one cofactor , RanBP2 , in providing a barrier that drove viral genome evolution during SIV spillover events . RanBP2 ( also known as Nup358 ) is a component of the nuclear pore complex known to facilitate nuclear entry of HIV-1 . Our data suggest that transmission of SIV from monkeys to chimpanzees , and then from chimpanzees to gorillas , both coincided with changes in the viral capsid that allowed interaction with RanBP2 of the new host species . However , human RanBP2 subsequently provided no barrier to the zoonotic transmission of SIV from chimpanzees or gorillas , indicating that chimpanzee- and gorilla-adapted SIVs are pre-adapted to humans in this regard . Our observations are in agreement with RanBP2 driving virus evolution during cross-species transmissions of SIV , particularly in the transmissions to and between great ape species .
RanBP2 ( also known as Nup358 ) is the major constituent of the cytoplasmic filaments extruding from the mammalian nuclear pore complex , where it mediates cargo import and export [1 , 2] . Depletion of RanBP2 negatively affects HIV-1 and HIV-2 infection and nuclear import [3–9] . Although HIV-1 can use other redundant pathways for import , pathways not involving RanBP2 lead to suboptimal chromosomal integration sites for the HIV-1 genome [4 , 7] . The interaction between RanBP2 and HIV-1 may not occur strictly at the nuclear pore . It was recently reported that the Kinesin-1 motor , KIF5B , relocalizes RanBP2 to the cytoplasm during infection [10] . As summarized in Table 1 , RanBP2 seems to be generally important for the replication of HIV-1 and HIV-2 , but not as important for the replication of simian immunodeficiency viruses ( SIVs ) from monkeys . RanBP2 depletion does not alter infection with SIVmac ( macaques ) [4 , 11] , SIVmus ( mustached monkeys ) [8] , SIVmon ( mona monkeys ) [8] , or SIVcol ( colobus guereza monkeys ) [8] . One study reported mildly deleterious effects on SIVmac , SIVgsn ( greater spot-nosed monkeys ) and SIVmnd ( mandrill ) after RanBP2 depletion [8] . HIV-1 and HIV-2 are highly diverged from one another , and each share ancestry with separate lineages of monkey SIV , yet both require RanBP2 for optimal nuclear entry and genome integration [3 , 4 , 8 , 9 , 11–13] . This led us to hypothesize that evolution to bind and utilize RanBP2 is a necessary step in the successful transmission of SIVs to humans . RanBP2 has a C-terminal cyclophilin domain that protrudes into the cytoplasm ( herein , this domain is referred to as “RanCyp , ” Fig 1A ) [1 , 2 , 12] . The interaction with HIV-1 and HIV-2 is primarily mediated by this RanCyp domain [3 , 4 , 12] , although other regions of RanBP2 may also be involved because RanBP2 lacking the RanCyp domain still retains residual interactions with capsid [3 , 11] . One study shows that , when expressed in mouse cells , human RanBP2 lacking the RanCyp domain can function equally to full-length RanBP2 in most measures of virus entry into the nucleus [11] . It’s possible that the RanCyp domain is not important in the context of infection in mouse cells . On the virus side , the cyclophilin-binding loop of capsid is primarily responsible for the interaction with RanBP2 [13] . Both the cyclophilin-binding loop ( virus ) and RanCyp ( host ) are highly variable in sequence [4 , 14 , 15] . One serious limitation in our understanding of RanBP2 as a host cofactor for viral replication is that all of the data summarized in Table 1 are derived from the study of human RanBP2 , even though many of these viruses are nonhuman viruses . For instance , theoretically it is possible that monkey SIVs are reliant on RanBP2 for nuclear import in monkey cells , but because they don’t interact with human RanBP2 , no alteration in SIV infection is observed when RanBP2 is eliminated in human cells . In other words , SIVs may be adapted to the RanBP2 of their own species , but may have variable physical or genetic interactions with human RanBP2 that may or may not be biologically meaningful . This problem gets even worse when extrapolating the activities of human RanBP2 to non-primate retroviruses such as MLV of mice and FIV of cats . Here , we test the interaction of primate lentiviruses with the RanBP2 of their known host species . In the current study we show that , indeed , not all primate RanBP2 orthologs are functionally equivalent . Following the theme of observations made with restriction factors , species-specific patterns of RanBP2 interaction exist , and these patterns depend on the specific virus that is being tested . We show that , in at least two instances involving African apes ( chimpanzees and gorillas ) , the natural cross-species transmission of SIV to these species coincided with viral adaptation to interact with their RanCyp . Adaptation to bind RanCyp seems to have been an important evolutionary event that allowed SIV to jump into and then between great ape species , or that refined interactions with apes after these jumps occurred . While the importance of the RanCyp-capsid interaction has been debated , its restoration after two important cross-species transmission events lends credibility to its importance , particularly in the lineage of SIV that entered apes and ultimately humans to become HIV-1 .
We first employed a TRIM5 fusion assay to test for equivalence between different primate RanCyps . This assay has been used extensively to study the RanCyp-capsid interaction , and has been validated against many other types of biological assays ( summarized in Table 1 ) . The TRIM5 fusion assay exploits the architecture of the naturally-occurring owl monkey restriction factor TRIM-CypA [16 , 17] . In this assay , the cyclophilin domain ( CypA ) of this restriction factor is replaced with the cyclophilin domain of RanBP2 ( RanCyp , Fig 1A and 1B ) . In cases where RanCyp interacts with capsid , the restriction activity of the TRIM portion of the molecule blocks virus replication , providing a quantitative readout of RanCyp–capsid interaction . First , we used retroviral transduction to generate CRFK ( feline kidney ) cell lines stably expressing the owl monkey TRIM-CypA restriction factor ( positive control ) , or the hybrid TRIM-RanCyp ( from human RanBP2 , also a positive control ) , each fused to an HA tag . HIV-1 entry assays were then performed in these cell lines ( see methods ) . Both TRIM-CypA and TRIM-RanCyp proteins drastically restricted HIV-1 infection ( top graph , lanes 1 and 8 in Fig 1C ) compared to a negative control cell line transduced with an empty vector ( top graph , lane 9 in Fig 1C ) . Restriction of HIV-1 in this assay indicates that these cyclophilin domains are successfully interacting with the viral capsid . Additionally , both proteins equally restricted a second lentivirus , feline immunodeficiency virus ( FIV ) , consistent with the observation that RanCyp is known to interact with the FIV capsid ( middle graph , lanes 1 and 8 in Fig 1C ) [11 , 13] . Neither restricted MLV , consistent with the observation that RanCyp does not interact with this virus ( bottom graph , lanes 1 and 8 in Fig 1C ) [4 , 7 , 11] . Collectively , these controls confirm previous findings that RanCyp mediates interaction with HIV-1 capsid , and that the TRIM-RanCyp assay detects this interaction . We next tested the interaction between HIV-1 and the RanCyp of various nonhuman primate species . TRIM-RanCyps were constructed that represent the RanCyp domains of RanBP2 from chimpanzee ( Pan troglodytes troglodytes ) , gorilla ( Gorilla gorilla ) , orangutan ( Pongo pygmaeus ) , red-capped mangabey ( Cercocebus torquatus ) , marmoset ( Callithrix jacchus ) , and titi monkey ( Callicebus cupreus ) ( Fig 1D; see S1 Fig for numbering scheme ) . Stable cell lines were constructed to express each of these HA-tagged chimeric proteins ( Fig 1C , bottom ) , and then infected with HIV-1 ( Fig 1C ) . Most primate TRIM-RanCyp proteins restricted HIV-1 by approximately 100-fold compared to the negative control , similar to the restriction by human TRIM-RanCyp . The one exception was gorilla TRIM-RanCyp , which restricted HIV-1 about 2-fold compared to the negative control ( top graph lane 3 , Fig 1C ) . Gorilla TRIM-RanCyp efficiently restricted feline immunodeficiency virus ( FIV ) suggesting that the hybrid protein is functional and that the loss of interaction with HIV-1 capsid is specific to HIV-1 ( middle graph lane 3 , Fig 1C ) . Thus , gorilla RanCyp does not interact with the HIV-1 capsid to the same extent as the other primate RanCyp domains tested . Human and gorilla RanCyp are highly similar , differing by only five amino acids ( Fig 2A ) . We made reciprocal substitutions of amino acids at these sites between human and gorilla RanCyp . Mutant TRIM-RanCyps were stably expressed in CRFK cells and then these cells were infected with HIV-1 ( Fig 2B ) . We found that a mutation at site 75 plays a dominant role in reversing the phenotype of these two RanCyp domains . For instance , introduction of the gorilla residues 75R into the human RanCyp domain resulted in a 70-fold reduction in the ability to interact with HIV-1 capsid ( Fig 2B ) . Reciprocally , introduction of the human RanCyp residue 75G increased the ability of gorilla RanCyp to interact with the HIV-1 capsid by 35-fold ( Fig 2B ) . Mutations at the other four sites ( 82 , 103 , 113 , and 149 ) that vary between human and gorilla RanCyp only resulted in a 1–3 fold change . Collectively , these data suggest that a single amino acid change at site 75 in the RanCyp domain can alter interactions with HIV-1 capsid . While some codons in this region of RanBP2 have experienced recurrent positive selection [4 , 14] , residue 75 is not under positive selection and is conserved in all of the primates tested , except gorilla ( Fig 2C ) . Because HIV-1 does not interact with gorilla RanCyp , we wondered if this was also true for gorilla SIV ( SIVgor ) . We generated mutants of HIV-1 group M where the 10 amino acid long cyclophilin-binding loop of capsid was replaced by the corresponding cyclophilin-binding loop from SIVgor ( specifically , from the gorilla SIV isolate BQ664 ) . We took this strategy in order to better isolate RanBP2-relevant differences between HIV-1 and SIVgor . An alignment of this cyclophilin-binding loop is shown in Fig 3A , where this specific SIVgor isolate is designated as SIVgor “BQ . ” We also replaced the HIV-1 cyclophilin-binding loop with the corresponding cyclophilin-binding loop of SIVcpz ( specifically from the chimpanzee subspecies Pan troglodytes schweinfurthii; denoted SIVcpzPts in Fig 3A ) or SIVrcm ( specifically from a Nigerian isolate of SIV isolated from red-capped mangabey; denoted SIVrcm “NG” ) . As a negative control , we replaced the cyclophilin-binding loop of HIV-1 with that found in SIVmac ( representing macaque SIV isolate 239; denoted SIVmac 239 ) . To ensure that HIV-1 containing each of these cyclophilin-binding loop substitutions was not compromised in function , we performed a series of controls . First , VSV-G pseudotyped virions of each hybrid virus were produced in 293T cells and titrated on CRFK cells . All of the yields were similar , with HIV-1 bearing the cyclophilin-binding loop of SIVmac being somewhat reduced compared to the HIV-1 control ( Fig 3B ) . We also checked proper Gag ( containing capsid ) maturation in each cyclophilin-binding loop mutant virus by probing protein fractions isolated from 293T producer cells with an anti-p24 antibody . Equal expression and cleavage of Gag was observed for all virus mutants , both in cell lysates and in virions purified from cell supernatants ( Fig 3C ) . SIVmac has previously been shown not to interact with human RanCyp ( see Table 1 ) . As expected , TRIM-RanCyp ( from human RanBP2 ) did not interact with HIV-1 bearing the cyclophilin-binding loop of SIVmac ( denoted “SIVmac” on the figure ) ( Fig 3D ) . This result , where the SIVmac cyclophilin-binding loop is isolated within the context of HIV-1 , supports the conclusion that the cyclophilin-binding loop of HIV-1 , but not SIVmac , engages RanBP2 . It also supports the conclusion that the cyclophilin-binding loop of HIV-1 is required for interaction with RanBP2 . We conclude that the assay used here can recapitulate known interactions between RanCyp and lentiviral capsids . This proof of principle allowed us to ask whether or not SIVgor interacts with gorilla RanBP2 , given that HIV-1 does not . Using the system described above , we first tested the ability of the cyclophilin-binding loop of SIVgor to interact with gorilla RanCyp . We found that the cyclophilin-binding loop of SIVgor isolate BQ664 does interact with gorilla RanCyp , whereas the cyclophilin-binding loop of an HIV-1 group M virus does not ( Fig 4 ) . To confirm this result , we engineered HIV-1 to encode the cyclophilin-binding loop of another SIVgor isolate , CP684 , and found that this virus could also interact with gorilla RanCyp . These SIVgor variants ( SIVgorBQ and SIVgorCP ) represent the clade of gorilla viruses from which HIV-1 groups P and O arose after zoonotic transmission from gorillas to humans [18] . ( S2 Fig shows an alignment of all cyclophilin-binding loops used in this study . ) Finally , we also engineered HIV-1 to encode the cyclophilin-binding loop from HIV-1 group P . The logic in this experiment is that SIVgor isolates BQ and CP , as well as HIV-1 group P , can all be thought of as gorilla-derived viruses . We find that the cyclophilin-binding loop of HIV-1 group P can also interact with gorilla RanCyp ( Fig 4; right ) . This suggested to us that gorilla-derived viruses may have adapted to interact with the RanCyp domain of gorilla RanBP2 . Further , this was an initial hint that there is a highly dynamic relationship between host RanBP2 and lentiviruses in African apes . We next asked whether the ability of gorilla-derived SIVs to interact with gorilla RanBP2 was a trait that was gained as SIV transmitted into gorillas . Gorillas acquired SIV from chimpanzees . Specifically , SIVcpz was transmitted to gorilla populations from the Pan troglodytes troglodytes subspecies of chimpanzees ( this version of SIVcpz is called SIVcpzPtt; Fig 5A ) [19 , 20] . We next performed an experiment to determine if SIVcpz could already interact with gorilla RanBP2 before this transmission , or whether the virus had to adapt to do so during the spillover event from chimpanzees to gorillas . We cloned into HIV-1 group M the cyclophilin-binding loops of two chimpanzee-derived strains ( SIVcpzPtt , SIVcpzPts ) ( see S2 Fig for alignments ) . Further , HIV-1 group M is derived from chimpanzees , and therefore can be thought of as another chimpanzee-derived virus . The cyclophilin-binding loops of all three chimpanzee-derived viruses are unable to interact with the RanCyp of gorilla RanBP2 ( Fig 5B , columns 1–3 ) . Further , the cyclophilin-binding loop of the three gorilla-derived viruses gained the ability to interact with gorilla RanCyp , but in the process lost interaction with chimpanzee RanCyp ( Fig 5B , columns 4–6 ) . Therefore , it appears that this cross-species transmission was accompanied by changes to capsid that resulted in RanCyp binding in the new host species , and loss of RanCyp interaction in the old host species . Backing up one step further , SIVcpz emerged as the result of recombination between SIV strains from multiple African monkey species [21 , 22] . The gag coding sequence ( encoding capsid ) of SIVcpz was thought to have been contributed by SIVrcm from red-capped mangabeys ( Fig 6A ) [21] . To determine if viral adaptation to chimpanzee RanBP2 might have occurred during this spillover event , we next cloned the cyclophilin-binding loops of three red capped mangabey-derived isolates ( SIVrcmCAM , SIVrcmGAB , SIVrcmNG; see S2 Fig for alignments ) into the HIV-1 reporter virus . The cyclophilin-binding loop of all three mangabey SIVs tested were able to interact with the RanCyp of mangabey but not chimpanzee ( Fig 6A , columns 1–3 ) . However , the cyclophilin-binding loop of all three chimpanzee-derived SIVs assayed had gained the ability to interact with chimpanzee RanCyp , and also retained interaction with mangabey RanCyp ( Fig 6A , columns 4–6 ) . This suggests that the SIV capsid evolved the ability to bind chimpanzee RanBP2 during the transmission from African monkeys to chimpanzees , a key transmission event that first introduced SIV into apes . Recently , the SIVrcm origins of SIVcpz gag has been called into question [22] . If and when other sources for this portion of SIVcpz are proposed , viral capsids and RanCyp from those species can be tested as well . Finally , we wanted to test if viral adaptation to RanBP2 might have occurred during the spillover events that led to the emergence of HIV-1 in human populations . HIV-1 emerged in human populations following multiple independent zoonotic transmissions of SIVcpzPtt from chimpanzees and SIVgor from gorillas ( Fig 6B ) [18 , 23] . We find that the cyclophilin-binding loops of all chimp- and gorilla-derived viruses can interact with human RanCyp ( Fig 6B , columns 1–6 ) . Therefore , adaptation to utilize human RanBP2 was probably not a barrier to the zoonotic events that led to the emergence of HIV-1 . In this way , it appears that chimp and gorilla RanBP2 could have provided an evolutionary stepping stone to infecting humans . As summarized in Table 1 , RanBP2 seems to be generally important for nuclear import of HIV-1 and HIV-2 , but so far there is less evidence that it is important for SIVs from monkeys . We next tested whether great ape SIVs are dependent on RanBP2 for optimal entry . We used a lentiviral shRNA system to knockdown RanBP2 in 293T cells ( Fig 7A and 7B ) . The knockdown of RanBP2 is known to be toxic to cells [4 , 11] , so we tested three different RanBP2 shRNA constructs for their general effect on cell proliferation using an MTT assay ( see methods; Fig 7A ) . Compared to a non-targeting shRNA control , the RanBP2-targeting shRNA-2 construct had minimal toxicity ( Fig 7A ) and conferred approximately 85% knockdown of RanBP2 ( Fig 7B ) . 293T cells transduced with either the non-targeting shRNA control or RanBP2-targeting shRNA-2 were then infected with our panel of VSV-G pseudotyped HIV-1 bearing various cyclophilin-binding loops . Cell populations were first gated for live cells , and then the percent infection was calculated by scoring for GFP-positive cells ( Fig 7C ) . RanBP2 depletion caused an approximate 50% reduction in infection for most of the ape-derived viruses ( HIV-1 , SIVcpz , SIVgor ) . As expected , we did not observe a substantial effect of human RanBP2 knockdown on the monkey-derived viruses ( all three SIVrcm strains and SIVmac ) , probably because these viruses do not interact with human RanBP2 . Thus , it seems that ape-derived SIVs are dependent on RanBP2 for nuclear entry , to the same degree that HIV-1 is dependent on RanBP2 . This raises the speculation that evolution to utilize RanBP2-dependent entry pathways was a novel adaptation acquired as SIVs entered apes from the monkey reservoir . To definitively prove this , knockdown of monkey RanBP2 would need to be conducted in monkey cells , and then the impact on monkey-derived SIV infection assessed .
Our data suggest that gained physical interactions between SIV capsids and the RanCyp domain of RanBP2 correlate with known spillover events into and between apes . Because RanBP2 varies from one primate species to the next , SIVrcm and SIVcpz had to adapt to bind the RanBP2 of chimpanzees and gorillas , respectively . Our results are consistent with a scenario where some cross-species transmissions of primate lentiviruses , particularly to and between African apes , required the adaptation of capsid to interact with the RanCyp domain of RanBP2 encoded in the new host species . One could argue that the work of Meehan and colleagues [11] , performed with human RanBP2 in mouse cells , suggests that the RanCyp domain may not be important to HIV-1 at all . This conclusion is called into question by the otherwise unlikely observation that every chimpanzee and gorilla virus we have studied demonstrates a gain-of-interaction with the RanCyp domain of its new host species . Our findings do not rule out the possibility that other regions of RanBP2 or capsid are involved in their mutual interaction . Indeed , two important studies have shown that interactions between capsid and RanBP2 are maintained even in the absence of the RanCyp domain [3 , 11] . One possibility is that the interaction that we have isolated and studied in this work provides a level of specificity to an interaction that actually involves a more elaborate interaction surface . The cyclophilin-binding loop and surrounding regions of capsid interact with several proteins in the host cell , including MxB , CPSF6 , TRIM5α , and cyclophilin A ( CypA ) [24–28] . During a spillover event into a new host species , any of these host interactions ( or all of them ) that don’t function correctly in a new host would be predicted to drive virus evolution . At the most basic level , our study has shown that RanBP2 is not functionally equivalent between species , and therefore has the potential to act as a selective force driving evolution of the SIV capsid . This is also likely to be true for TRIM5α , which is highly host species-specific in its interactions with lentiviruses [29 , 30] . However , MxB , CPSF6 , and CypA are not likely to drive capsid evolution as lentiviruses move from one primate species to the next . This is because these three host proteins are predicted to function identically between primate species with respect to lentiviral interactions , for the following reasons . First , the capsid binding domain in CPSF6 is perfectly conserved between primate species [26] . Second , the CypA protein sequence has remained unchanged during ~30 million years of primate evolution ( S3 Fig ) . Third , MxB is under positive selection , but there is no overlap between these variable residue sites and those mapped to dictate lentiviral restriction [31] . Therefore , interactions between lentiviruses and the CPSF6 , MxB , and CypA proteins are predicted to be equal in all primate species . It seems reasonable to conclude that the evolution of the cyclophilin-binding loop in capsid during spillover is primarily driven by TRIM5α and RanBP2 , but not the other factors known to interact with this loop . The transmission of SIV from monkeys to apes was a key event that set the stage for zoonosis to humans . In the case of RanBP2 , we show that SIV capsid evolved to interact with the RanCyp domain of RanBP2 upon transmission to both chimps and gorillas , but once SIVgor and SIVcpz had become established , these viruses subsequently did not need to evolve further to utilize human RanBP2 . This pattern of viral adaptation to RanBP2 directly mirrors what has also been reported about viral adaptation to APOBEC3G . Similarly , APOBEC3G was previously shown to have acted as a barrier in the monkey-to-chimpanzee , and chimpanzee-to-gorilla , transmissions of SIV , but not subsequently in the transmission of chimpanzee viruses to humans [18 , 32] . In other ways , the RanBP2 and APOBEC3G findings differ . Because the usage of the RanBP2-mediated import pathway may not be as important for the biology of SIV in African monkeys , it is interesting to speculate that this entire viral import pathway was a novel adaptation of SIV as it entered ape species . However , this speculation is not yet well supported and a more in-depth study of interactions between monkey SIVs and their host RanBP2 molecules in monkey cells will be necessary in order to gain additional insight into the nuclear import of these viruses . APOBEC3G restriction , on the other hand , is relevant to all HIV and SIVs studied . Our current study is the first to provide functional evidence that divergence in post-entry cofactors for viral replication , provided by the host , can drive changes in SIV/HIV genomes during spillover . The nuclear pore and the cellular process of nuclear trafficking are both common targets of antagonism and utilization by viruses [33] . One can imagine intense selective pressure at play to keep viruses out of cells altogether via receptor evolution [34–40] or , when that fails , out of the nucleus via evolution of the nuclear transport machinery . It’s possible that nuclear pore components , like cellular entry receptors , are another class of host cofactor proteins that often create critical barriers to viral host switching , thus driving viral evolution in the process . RanBP2 adds to a growing list of dynamic cellular host proteins that exert selective pressure during the spillover of viruses in nature [34–38 , 41–48] .
HEK293T cells ( ATCC #CRL-3216 ) and CRFK cells ( feline kidney , ATCC #CCL-94 ) were cultured in Dulbecco’s modified Eagle media ( Sigma , #D6429 ) supplemented with 10% fetal bovine serum ( Sigma , #F2442 ) , 2 mM L-glutamine ( Invitrogen , #25030–081 ) , and 1% antibiotics ( Corning , #30–002 ) . Cells were cultured at 37°C and in 5% CO2 . Primary and immortalized cell lines from primate species ( see S4 Fig ) were grown in Dulbecco’s modified Eagle media ( fibroblasts ) or RPMI-1640 ( B-Lymphocytes; Sigma , #R0883 ) supplemented with 15% fetal bovine serum , 2 mM L-glutamine , and 1% antibiotics . Cells were cultured at 37°C and in 5% CO2 . The method of acquisition for all primate gene sequences is summarized in S4 Fig . Human RefSeq sequence was obtained from the NCBI nucleotide database . Chimpanzee , Sumatran orangutan , and rhesus macaque gene sequences were obtained from the UCSC genome database ( http://genome . ucsc . edu/ ) using the BLAT tool . Additional RanCyp and CypA sequences were sequenced from cDNA generated using Superscript III First-Strand Synthesis System ( Thermo , #18080051 ) with oligo ( dT ) primers . PCR was performed with Phusion High Fidelity PCR Master Mix ( NEB , #F-531S ) . Details of the PCR and sequencing strategy , along with primer sequences , are given in S1 Table . Primate gene sequences generated in this study have been deposited in GenBank and are listed in S4 Fig . HA-tagged owl monkey TRIM-CypA in the pLPCX expression vector was a kind gift from Michael Emerman . This TRIM-CypA construct is representative of the naturally occurring fusion protein from Aotus trivirgatus ( GenBank accession #AAT73777 ) [16 , 17] . TRIM-RanCyps were constructed by generating 20–25 base pair overlapping regions of owl monkey TRIM-CypA and RanCyp . Nucleic acids from primate species shown in Fig 1 were used as templates in PCR reactions to generate RanCyp . Details of the primate cell lines and PCR strategy and be found in S4 Fig and S1 Table , respectively . Overlapping fragments were spliced together in a PCR reaction using each fragment as a template and outside flanking primers . Constructs were TA-cloned into pCR4 ( Invitrogen , #K4575-01 ) . An N-terminal HA tag was added in a PCR reaction and these tagged constructs were TA-cloned into the gateway entry plasmid pCR8 ( Invitrogen , #K2500-20 ) . An LR Clonase II reaction ( Invitrogen , #11791–100 ) was used to move these constructs into a Gateway-converted pLPCX retroviral vector ( Clontech , # 631511 ) via a recombination reaction . TRIM-RanCyp mutants were generated using PfuTurbo DNA polymerase ( Stratagene , #600250 ) . Parental pLPCX plasmids were used as a template along with primers ( S1 Table ) containing the mutations of interest . In order to make cell lines that stably express owl monkey TRIM-CypA and TRIM-RanCyps , retroviral vectors were used to transduce CRFK cells . To generate the retroviral vectors , 293T cells were seeded at a concentration of 1x106 cells/well in a 6-well dish . 24 hours later each well was transfected with 2 μg pLPCX construct ( empty or encoding the gene fragment of interest ) , 1 μg pCS2-mGP encoding MLV gag-pol [49] , and 0 . 2 μg pC-VSV-G at a final 1:3 ratio of DNA to TransIT-293 ( μg DNA: μl TransIT-293 ) . Supernatants were collected after 48 hours , passed through a 0 . 2 μm filter , and used to infect CRFK cells . After 24 hours , media containing 8 μg/ml puromycin was added to select for transduced cells . Cell lines were expanded and grown in puromycin for at least two weeks before expression of TRIM-CypA and TRIM-RanCyp constructs was detected by western blot . Stable CRFK cell lines or 293T cells transduced with shRNA lentivirus were grown to confluency in a 6-well dish , collected using a cell scraper , and lysed in either a buffer containing 150 mM NaCl , 50 mM Tris-HCl ( pH 7 . 4 ) , 1% NP-40 , and Complete protease inhibitor ( Roche , #11836170001 ) ( stable CRFK cell lines ) or RIPA buffer supplemented with Complete protease inhibitor ( 293T cells ) . CRFK cells were rotated in lysis buffer at 4°C for 45 minutes and whole cell extracts were cleared in a microcentrifuge by spinning at maximum speed for 15 minutes . 293T cells were sonicated on ice in RIPA buffer using the Qsonica Q500 ( sonication settings: microtip , 40% amplitude , 7 second pulse ) . After quantitation of protein concentration using a Bradford assay , 20 μg of whole cell extract was resolved using a 10% ( HA-TRIM-RanCyp and Gag ) or 7 . 5% ( endogenous RanBP2 ) polyacrylamide gel and transferred to a nitrocellulose membrane . To detect endogenous RanBP2 , transfer was done at 400 mA for 2 hours in buffer containing 0 . 02% sodium dodecyl sulfate . HA-tagged constructs were detected using a 1:5000 dilution of mouse anti-HA antibody conjugated to horseradish peroxidase ( Roche , #12013819001 ) . Maturation of Gag protein was monitored using a 1:1000 dilution of mouse anti-p24 ( AIDS reagent database , #183-H12-5C ) . Endogenous RanBP2 was detected using a 1:1000 dilution of rabbit anti-RanBP2 ( Thermo Fisher Scientific , #PA1-082 ) . Endogenous β-actin was detected as a loading control using a 1:1000 dilution of mouse anti-β-actin ( Santa Cruz , #sc-47778 ) . Endogenous Nup153 was detected as a loading control using a 1:1000 dilution of mouse anti-Nup153 ( Covance , #MMS-102P ) . A 1:10 , 000 dilution of goat anti-mouse horseradish peroxidase-conjugated antibody ( Thermo Fisher Scientific , #32430 ) or goat anti-rabbit horseradish peroxidase-conjugated antibody ( Thermo Fisher Scientific , #32460 ) was used as a secondary probe . Blots were developed using the ECL Plus detection reagent ( GE Healthcare , #RPN2132 ) . Viruses for single-cycle infection assays were packaged in 293T cells by co-transfection of plasmids encoding viral proteins and VSV-G , along with a transfer vector , as follows: HIV-1 and cyclophilin-binding loop mutants ( pMDLg/pRRE , pRSV-Rev , pMD2 . G , pRRLSIN . cPPT . PGK-GFP . WPRE; all available on Addgene ) , FIV ( pFP93 [50] , pC-VSV-G , pGIN-SIN:GFP [50] ) , NB-MLV ( pCS2-mGP [49] , pC-VSV-G , pLXCG ) . After 48 hours , supernatant containing viruses was harvested , filtered , and frozen . Viruses were titered on CRFK cells by measuring percent GFP-positive cells along a volume gradient of virus supernatant . For infection assays , CRFK stable cells lines were plated at a concentration of 7 . 5x104 cells/well in a 24-well plate and infected with HIV-1 , cyclophilin-binding loop mutants , FIV , or NB-MLV such that 30–50% of the control cell line was infected . Two days post-infection , cells were fixed in 2% paraformaldehyde for 15 minutes , washed three times with 2 mL FACS buffer ( DPBS supplemented with 2% FBS and 1 mM EDTA ) , resuspended in 500 μl FACS buffer , and analyzed by flow cytometry for expression of GFP using the BD Bioscience Fortessa cell analyzer . All infections were performed in triplicate using a single virus stock , and all results were confirmed using at least two experimental replicates . Lentivirus harboring Sigma-Aldrich MISSION shRNA constructs ( #TRCN0000272800 , labeled ‘shRNA-1’ in Fig 7; #TRCN0000272801 , labeled ‘shRNA-2’ in Fig 7; #TRCN0000003453; labeled ‘shRNA-3’ in Fig 7; #SHC002 , labeled ‘control’ in Fig 7; all shRNA constructs were obtained from the Functional Genomics facility at University of Colorado Denver ) were packaged in 293T cells by co-transfection of pLKO . 1 ( shRNA packaging plasmid ) , pMDLg/pRRE , pRSV-Rev , and pMD2 . G ( the latter three plasmids are described above ) . After 48 hours , supernatant containing viruses was harvested , filtered , and frozen . Viruses were titered on 293T cells by measuring RanBP2 knockdown efficiency via western blot 72 hours post infection . Images were quantified using ImageJ and samples were normalized using a Nup153 loading control ( Fig 7B ) . For infection assays , 293T cells were plated at a concentration of 3 . 0x105 cells/well in a 12-well plate and infected with shRNA lentivirus . Two days post-infection , cells were re-seeded at a concentration of 7 . 5x104 cells/well in a 48-well dish . After an overnight incubation single-cycle infection assays using VSV-G pseudotyped HIV-1 and cyclophilin-binding loops mutants were carried out as described above . Simultaneously , a proliferation assay using MTT ( Thermo Fisher , #M6494 ) was performed to determine relative toxicity of shRNA constructs . Cells were incubated with MTT solution ( 1:1 mixture of serum-free media and 5 mg/mL MTT in PBS ) at 37°C for 3 hours and reactions were then solubilized with MTT solvent ( 4 mM HCl and 0 . 1% NP-40 in isopropanol ) by shaking at room temperature for 15 minutes . Absorbance at 590 nm was measured for each sample . pMDLg/pRRE expressing HIV-1 gag-pol was used as a template for site-directed mutagenesis using PfuTurbo DNA polymerase ( Stratagene , #600250 ) . Cyclophilin-binding loop sequences can be found in S2 Fig and primers used in site-directed mutagenesis PCR are listed in S1 Table . Purification of virions to probe Gag maturation of capsid mutants was performed by layering 500 μl of filtered supernatant from virus-producing 293T cells onto 1 mL of 20% sucrose in PBS followed by centrifugation at 20 , 000xg for 90 minutes at 4°C . Virus-containing pellets were resuspended in 40 μl 1X Laemmeli buffer and 10 μl was used for western blot analysis . Whole-cell extracts were also prepared at the same time to analyze intracellular Gag maturation . | Multiple times , HIV-1 has entered the human population after emerging from a viral reservoir that exists in African primates . First , simian immunodeficiency virus ( SIV ) made the jump from monkeys into African great apes , and then from apes ( namely , chimpanzees and gorillas ) into humans . It is well appreciated that restriction factors , which are specialized proteins of the innate immune system , acted as host-specific barriers that drove virus adaptation during these spillover events . Here , we present data showing that a major constituent of the nuclear pore complex , RanBP2 , was also a barrier to the spillover of SIVs , particularly in great ape species . Spillover of SIV into chimpanzee and gorilla populations required that the SIV capsid mutate to establish interaction with RanBP2 in the new host species . Our study highlights how essential housekeeping proteins , despite being generally more evolutionarily conserved than restriction factors , can also drive virus evolution during spillover events . | [
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| 2018 | Species-specific vulnerability of RanBP2 shaped the evolution of SIV as it transmitted in African apes |
In the ciliate Paramecium tetraurelia , differentiation of the somatic nucleus from the zygotic nucleus is characterized by massive and reproducible deletion of transposable elements and of 45 , 000 short , dispersed , single-copy sequences . A specific class of small RNAs produced by the germline during meiosis , the scnRNAs , are involved in the epigenetic regulation of DNA deletion but the underlying mechanisms are poorly understood . Here , we show that trimethylation of histone H3 ( H3K27me3 and H3K9me3 ) displays a dynamic nuclear localization that is altered when the endonuclease required for DNA elimination is depleted . We identified the putative histone methyltransferase Ezl1 necessary for H3K27me3 and H3K9me3 establishment and show that it is required for correct genome rearrangements . Genome-wide analyses show that scnRNA-mediated H3 trimethylation is necessary for the elimination of long , repeated germline DNA , while single copy sequences display differential sensitivity to depletion of proteins involved in the scnRNA pathway , Ezl1- a putative histone methyltransferase and Dcl5- a protein required for iesRNA biogenesis . Our study reveals cis-acting determinants , such as DNA length , also contribute to the definition of germline sequences to delete . We further show that precise excision of single copy DNA elements , as short as 26 bp , requires Ezl1 , suggesting that development specific H3K27me3 and H3K9me3 ensure specific demarcation of very short germline sequences from the adjacent somatic sequences .
Ciliates provide extraordinary model organisms with which to gain understanding into the organization of eukaryotic genomes . The differentiation of the somatic nucleus from the zygotic nucleus is characterized by massive and reproducible rearrangements at the DNA level [1] . In Paramecium tetraurelia , as in all ciliates , germline and somatic functions are separated between two distinct nuclei that coexist in the same cytoplasm . During vegetative growth , the diploid germline micronucleus ( MIC ) remains transcriptionally silent , while the highly polyploid somatic macronucleus ( MAC ) supports gene expression . During sexual events , the MAC is fragmented and eventually lost , whereas the MIC undergoes meiosis and transmits the germline genome to the new MIC and MAC of the next generation . During the differentiation of the zygotic MAC , germline-specific regions up to several kbp in length , often containing repetitive sequences , are imprecisely eliminated leading to germline chromosome fragmentation or intra-chromosomal deletions . Moreover , and this is a critical point for the present study , 45 , 000 single-copy , short and non-coding Internal Eliminated Sequences ( IES ) are excised precisely from intergenic and genic regions . These « DNA introns » are found throughout the germline genome , appear to be remnants of ancestral insertions of transposable elements ( TEs ) [2] , and are invariably flanked by two 5′- TA -3′ dinucleotide repeats , one of which is left after excision . All DNA elimination events rely on the domesticated piggyBac transposase , PiggyMac ( Pgm ) , which is essential to introduce DNA cleavages at each IES boundary [2] , [3] . Precision of IES excision is critical for the assembly of functional genes in the somatic genome and the survival of sexual progeny . Yet the weak consensus found at IES ends is not sufficient to determine the excision pattern across the whole genome [2] , [4] . The molecular mechanisms underlying the specific recognition of such a large number of different germline sequences remain poorly understood . A class of small RNAs that resemble the metazoan piRNAs , called the scnRNAs , is produced by the meiosis-specific Dicer-like proteins Dcl2 and Dcl3 [5] , [6] . scnRNAs are required to promote IES excision [7] . In the current “genome scanning” model , scnRNAs are produced from most of the germline genome during MIC meiosis and are then filtered by pairing interactions with nascent transcripts in the maternal MAC , resulting in the selective inactivation of those able to find a perfect match , and thus in the selection of MIC-specific scnRNAs [8] . Once selected by this “scanning” procedure , MIC-specific scnRNAs would be exported to the developing zygotic MAC to target homologous sequences , thereby recruiting the excision machinery [9] . This RNA-mediated genomic subtraction can explain the epigenetic inheritance of alternative rearrangement patterns , such as retention of a given IES in the MAC [10] , [11] , deletion of a given gene [12] or mating type determination [13] across sexual generations . The scnRNA pathway is conserved in the distantly related ciliate Tetrahymena thermophila , where scnRNA-mediated tri-methylation of histone H3 on lysine 9 and lysine 27 ( H3K9me3 and H3K27me3 ) [14]–[17] is thought to guide the recruitment of an endonuclease [18] , [19] initiating the deletion of germline sequences . As observed in small RNA-guided heterochromatin formation in other organisms [20] , [21] , the data obtained so far support the idea that heterochromatin formation occurs downstream of the scnRNA pathway and leads to the imprecise elimination of long , repetitive germline sequences , which are nearly all found in intergenic regions in Tetrahymena [22] . Recently , a class of 26-30 nt long , IES-specific Paramecium sRNAs , called iesRNAs , was reported [6] . iesRNAs accumulate during late MAC development and require the Dicer-like protein Dcl5 for their biogenesis . Dcl5 depletion leads to partial impairment of excision of a small fraction of IESs . The precise role of iesRNAs in IES excision remains to be elucidated . The chromatin modifications that may guide the Pgm endonuclease to specific germline sequences are not yet characterized in Paramecium . The vast majority of IESs are shorter than 150 bp and some are as short as 26 bp; they are thus not even as long as the DNA wrapped around a single nucleosome . Excision of these 45 , 000 DNA segments must require a marking mechanism of considerable precision , allowing the demarcation of these very short , numerous , interspersed germline sequences from adjacent retained somatic sequences . The present study was designed to test the involvement of histone H3 methylation in the DNA elimination process , with a special interest for its role on IES excision . We show here that the putative histone methyltransferase ( HMT ) Ezl1 is required for the accumulation of H3K27me3 and H3K9me3 in the developing somatic macronucleus . Re-sequencing the genome following Ezl1 depletion showed that EZL1 is required for correct genome rearrangements . We found that scnRNA-mediated H3K27me3 and H3K9me3 is necessary for the elimination of a fraction of germline DNA , including transposable elements and long IESs . Strikingly , the putative HMT Ezl1 is also required for the precise excision of about 70% of the 45 , 000 short , unique copy IESs , providing evidence that it may contribute to the precise demarcation of short germline sequences . Our genome wide study shows that IESs display differential sensitivity to depletion of the scnRNA pathway , Dcl5 or Ezl1 proteins and identifies cis acting determinants , such as DNA length that might act in concert with epigenetic signals to define germline specific sequences .
Indirect immunostaining experiments were performed to determine the in situ localization of H3K27me3 and H3K9me3 during various stages of the Paramecium life cycle ( Fig . S1-S2-S3 ) . No H3K27me3 ( Fig . S2 ) and H3K9me3 ( Fig . S3 ) could be detected in the transcriptionally active MAC or in the transcriptionally inactive MIC during vegetative growth . The sexual process of autogamy ( self-fertilization ) , which is induced by starvation , starts with meiosis of the MIC and proceeds through the development of new zygotic MACs . H3K27me3 was transiently found in the MIC during the first meiotic division and detected in the fragments of the maternal MAC by the end of meiosis ( Fig . S2 ) , whereas no H3K9me3 signal was observed at these stages ( Fig . S3 ) . After karyogamy , the diploid zygotic nucleus divides twice and two of the products differentiate into new MICs and the other two into new MACs ( Fig . S4 ) . H3K27me3 and H3K9me3 were detected at early stages of MAC development and the signals persisted throughout the course of MAC development ( Fig . 1A , Fig . S2-S3-S4 ) . The enrichment of H3K27me3 in the developing MAC compared to vegetative MAC was confirmed by Western blot analysis on purified nuclei ( Fig . S2B ) . A Pgm-GFP fusion protein was detected together with H3K27me3 and H3K9me3 in the developing MAC , indicating that both histone marks are present when genome rearrangements occur ( Fig . S2C and S3B ) . The staining in the developing MAC , initially diffuse and evenly distributed ( Fig . 1A a-b , Fig . S2-S3 ) , gradually condensed into a punctuate pattern ( Fig . 1A c-d , Fig . S2-S3 ) . This is reminiscent of the heterochromatin bodies detected in Tetrahymena , which comprise H3K9me3 , H3K27me3 [15] , [16] and the chromodomain protein Pdd1p [23] . Yet the H3K27me3 and H3K9me3 foci we observed are located inside the nucleus and are not preferentially found at the periphery of the developing MAC as observed in Tetrahymena . As development proceeds , the number of these intensely labeled foci diminishes and the single remaining spot found in a DNA-poor region of the macronucleus eventually disappeared ( Fig . 1B , Fig . S2-S3-S4 ) . To obtain further insight into the possible role of H3K27me3 and H3K9me3 in genome rearrangements , we knocked-down by RNA interference ( RNAi ) the domesticated transposase Pgm that is required for the introduction of DNA-double strand breaks at the boundaries of germline-limited segments [2] , [3] . Immunofluorescence experiments revealed that the H3K27me3 and H3K9me3 signals progressively increased in the developing MACs and are detected in all Pgm-depleted and control cells ( Fig . 2A and Fig . S4 ) . Western blot analysis showed that the amount of H3K27me3 is not altered in Pgm-depleted cells as compared to control cells ( Fig . S2B ) . Since depletion of Pgm does not affect the biogenesis and accumulation of H3K27me3 and H3K9me3 in the developing MACs , it suggests that the endonuclease Pgm must act downstream of H3K27me3 and H3K9me3 , in agreement with the scanning model . We noticed that the H3K27me3 and H3K9me3 signals remained diffuse as development proceeds in Pgm-depleted cells and no foci could be detected ( Fig . 2A and Fig . S4 ) . The endonuclease Pgm is thus required for H3K27me3 and H3K9me3 foci formation , even though it is not yet clear whether these foci are a prerequisite for or the consequence of DNA double strand break formation . The scanning model posits that MIC-specific scnRNAs guide the loading of histone marks specifically on DNA segments that are eliminated in the developing MAC . We therefore expected that the co-silencing of the two Dicer-like genes , DCL2 and DCL3 , that results in failure to generate scnRNAs [5] , [6] , would also abolish the establishment of H3K27me3 and H3K9me3 chromatin in the developing MAC . We examined the effects of DCL2/3 co-silencing on H3K27me3 and H3K9me3 by immunofluorescence staining . No detectable H3K27me3 or H3K9me3 signal was observed in Dcl2/3 depleted cells at an early stage when the developing MAC of control cells stained intensely ( Fig . 2B , Fig . S4 ) . As development proceeds , H3K27me3 and H3K9me3 signals in developing MACs start to be observed in Dcl2/3-depleted cells ( Fig . S4 ) but Western blot analysis showed that the total amount of H3K27me3 is greatly reduced in DCL2/3-knockdowns ( KD ) relative to control ( Fig . S2B ) . We then investigated the effects of silencing DCL5 , a gene required for iesRNA biogenesis , on H3K27me3 and H3K9me3 . In contrast to what is observed in DCL2/3 KD , H3K27me3 and H3K9me3 signals were not altered in DCL5 KD , as assessed by immunofluorescence staining ( Fig . 2C ) and this was further confirmed by Western blot analysis for H3K27me3 ( Fig . S2B ) . We conclude that the generation of scnRNAs , but not iesRNAs , is required for establishment and accumulation of these chromatin modifications in the developing MAC . These results suggest that scnRNAs and K9 and K27 methylation participate in the same pathway leading to genome rearrangements . To support this hypothesis , it is necessary to demonstrate that K27 and K9 methylation is required for DNA elimination . To eliminate K9 and K27 methylation , we sought to identify the gene ( s ) responsible for these modifications [24] . We searched for SET domain containing proteins encoded in the P . tetraurelia MAC genome [25] . Among 34 putative HMTs ( Fig . S5 , Table S1 ) , we identified putative H3K27-specific HMTs of the Enhancer of zeste family , named EZL1 to EZL4 ( Fig . 3A ) but no member of the H3K9-specific HMTs of the Suvar39/EHMT/SETDB8/SETMAR group could be identified in ciliate genomes . Alignment of the predicted Ezl proteins revealed conservation of key residues implicated in binding the methyl donor , the target lysine , and catalysis ( Fig . S6 ) . The expression patterns of EZL genes during the life cycle were examined using microarray data [26] and confirmed by RT–PCR analysis ( Fig . 3B-C ) . Little or no expression is observed during vegetative growth but the genes are specifically expressed during the sexual phase of the life cycle , although they show markedly different patterns . EZL2 and EZL4 are silent during vegetative growth but EZL4 is specifically expressed after meiosis , whereas EZL2 becomes expressed at the onset of MAC development . EZL1 is turned on to high levels immediately upon meiosis , and this is true also , to a lesser extent , for EZL3a and EZL3b . Expression of the EZL1 gene is very transient , preceding PGM and DCL5 expression , and detection of IES excision products ( Fig . 3D ) . This expression pattern is similar to that seen for the Dicer-like genes DCL2 and DCL3 [5] ( Fig . 3B ) . To test the function of EZL genes , we knocked down their expression by RNAi during autogamy . After EZL1 KD , 97% of post-autogamous progeny were unable to resume vegetative growth , whereas no lethality was observed after KD of any other EZL gene ( Fig . 3E ) . The transcription of EZL1 is induced during meiosis , largely before programmed genome rearrangements take place in developing new MACs . This pattern led us to consider the possibility that this protein may be involved in a meiotic function . We checked the progression of meiosis by Hoechst staining during autogamy of EZL1 KD cells . We observed that meiotic divisions I and II occur normally , since there were cells with 4 then 8 haploid nuclei in the population ( Fig . S4 ) . There was no arrest until new MACs differentiate from mitotic copies of the zygotic nucleus . To control for possible off-target silencing artifacts , two non-overlapping fragments of EZL1 were used independently to induce RNAi , and similar results were obtained with both constructs ( Fig . 3E ) . For one construct , the efficiency of EZL1 KD was checked by semi-quantitative RT-PCR of total RNA extracted throughout autogamy from control and EZL1 KD cells ( Fig . S7 ) : a significant decrease of EZL1 mRNA accumulation was observed at early time points in EZL1 KD cells , without affecting the onset of induction of other EZL genes . Therefore , EZL1 gene expression is essential during development for the production of viable sexual progeny . In an EZL1 KD , the transcription of the PGM gene and of all EZL genes is switched on normally during autogamy , indicating that these genes are not induced in response to EZL1 induction but more likely as part of a general transcription program during MAC development . In contrast to control cells , the levels of these mRNAs do not decrease at later time-points in an EZL1 silencing experiment ( Fig . S7 ) , suggesting that the completion of MAC development is a signal for transcriptional switch-off . Alternatively , EZL1 histone methylation could be required for silencing transcription of these genes . To gain further insight into the role of EZL1 , we examined the subcellular localization of Ezl1 . A GFP fusion was constructed by inserting the GFP coding sequence into the EZL1 gene , downstream of the start codon . Expression of the fusion gene was under the control of the natural EZL1 up- and downstream sequences . After microinjection of the construct into the MAC , no fluorescence could be detected during vegetative growth of transformed clones ( Fig . S8A ) . When autogamy was induced , GFP fluorescence first appeared transiently in the MIC during meiosis I and in the MAC before it became fragmented . When fragmentation of the maternal MAC was complete , GFP fluorescence started to decrease and progressively relocalized to the new MACs as they developed ( Fig . 4A and Fig . S8 ) . Eventually all of the fusion protein was concentrated to the new MACs . The localization pattern of GFP-Ezl1 fusion is very similar to that observed for H3K27me3 and H3K9me3 ( Fig . 1 and Fig . S2-S3 ) . Hence , the GFP-Ezl1 fusion colocalized with H3K27me3 and H3K9me3 foci in the new developing MACs ( Fig . 4B ) . Moreover , although the GFP-Ezl1 fusion protein properly localized in the new developing MACs in PGM and DCL2/3 KD cells , foci formation was prevented in the former and strongly reduced in the latter ( Fig . S8B ) . We therefore investigated the effects of EZL1 KD on H3K27me3 and H3K9me3 . Immunofluorescence staining with H3K27me3- and H3K9me3- specific antibodies showed little or no signal in Ezl1-depleted cells , whereas in control cells H3K27me3 and H3K9me3 increased as development proceeded and completely disappeared at the latest time point ( Fig . 4C and Fig . S4 ) . For more accuracy , we quantified the fluorescence intensities throughout the volume of the developing new MACs in control and EZL1 KD cells at different developmental time points ( see Materials and Methods ) . The quantification indicated that H3K27 and H3K9 methylation was nearly abolished in the developing new MACs of EZL1 KD cells ( Fig . 4D-E ) . This was further confirmed for H3K27me3 by Western blot analysis ( Fig . S2B ) . Together these data show that EZL1 encodes a development specific putative HMT necessary for H3K27me3 and H3K9me3 in the developing zygotic MAC . EZL1 KD led to phenotypes consistent with an essential function for Ezl1 during MAC development since: ( 1 ) no viable sexual progeny were isolated from Ezl1-depleted cells , a phenotype described in KDs defective in DNA elimination [3] , [5] , [27]–[29]; ( 2 ) no H3K27me3 and H3K9me3 were detected in developing new MACs . Different assays were used to monitor genome rearrangements in autogamy time course experiments , after EZL1 KD . We first tested the role of the EZL1 gene in the imprecise DNA elimination mechanism that is responsible for the deletion of MIC transposable elements during MAC development . We analyzed by Southern blot hybridization the germline region located downstream of the surface antigen G gene , which contains a Sardine transposon that is eliminated imprecisely during MAC development , leading to chromosome fragmentation [2] . At this locus , in control RNAi experiments , only the rearranged forms originating from both the maternal and new MACs could be detected ( Fig . 5A ) . In contrast , after EZL1 KD , non-rearranged forms accumulated throughout autogamy in the new MACs , relative to the rearranged forms present in the fragments of the maternal MAC ( Fig . 5A ) . EZL1 KD thus led to retention of the MIC sequences and impaired germline chromosome fragmentation . We further tested the role of Ezl1p in the imprecise elimination mechanism also responsible for maternally inherited deletions of non-essential cellular genes , which can be induced experimentally [12] . The variant cell line 51ΔND7 has a wild type MIC but carries a maternally inherited MAC deletion of the ND7 gene . We therefore used this strain to monitor the effect of EZL1 KD on maternal inheritance of MAC deletions . Phenotypic testing was used to assess reversion of the ND7 MAC deletion in post-autogamous cell populations . We observed the occurrence of trichocyst discharge indicating that the ND7 gene was at least partially maintained in the new MAC after EZL1 KD but not in the controls . ND7 was transiently amplified before deletion from the new developing MACs and , at later time points , only the rearranged forms , originating from both the old and new MACs , could be detected in control silencing ( Fig . 5B ) . In contrast , full-length ND7 gene product accumulated at late time points after EZL1 KD . Thus , like imprecise deletion of MIC specific regions , maternally inherited elimination of the ND7 gene is blocked in EZL1 KD cells , and the non-rearranged germline locus is retained in the developing new MACs . To expand these results genome wide , we sequenced DNA isolated from newly developed MACs following EZL1 silencing . DNA isolated from newly developed MACs at the same developmental stage from a cell culture grown without RNAi was also sequenced as a control ( Table S2 ) . We compared the sequence complexity of different datasets by mapping the reads in each dataset to contigs previously assembled from new MACs after Pgm depletion [2] , which is currently the best representation of the un-rearranged germline genome . As shown in Table S3 , EZL1 reads have the same sequence complexity as the PGM reads , while the control dataset has about 13 Mb less sequence complexity . Of note , the total sequence complexity in the MIC is expected to be larger than the 89 Mb that we analyzed since our analysis only used PGM contigs larger than 1 kb . If we compare the complexity of regions not covered by the control sample , which correspond to the part of the MIC genome that is not collinear with MAC chromosomes , PGM and EZL1 datasets again show a similar complexity . We also performed a qualitative evaluation of Sardine retention by mapping reads from each dataset to the known cloned copies of this transposable element [2] ( Fig . S9 ) . Consistent with our Southern blot analysis ( Fig . 5A ) , we found that all characterized Sardine copies are retained after EZL1 silencing . This global analysis supports the conclusions made at the molecular level for two individual loci: EZL1 , like PGM , is required for the imprecise elimination of germline-limited sequences . We then investigated the role of the EZL1 gene in IES excision . Excision was first analyzed by PCR on genomic DNA , extracted after EZL1 or control silencing at a time when IES excision is normally finished . In control RNAi experiments , the 10 IESs analyzed were completely excised from the new developing MACs as expected ( Fig . S10 ) . In contrast , IES-retaining forms accumulated in the new MACs of PGM or EZL1 KD cells . We observed that EZL1 KD impaired the excision of 7 out of 10 tested IESs , whereas PGM KD impaired the excision of all IESs ( Fig . S10 ) . Consistent with the lack of excision for affected IESs , we could not detect the formation of excised IES circles by PCR upon EZL1 KD ( Fig . S7 ) [30] . Altogether these data indicate that the EZL1 gene is required for IES excision and , most likely , EZL1p acts upstream of the introduction of DNA double-strand breaks . Based on our PCR analyses , not all IESs are affected following EZL1 KD . To observe the effects of Ezl1p depletion genome-wide , we used the EZL1 DNA-seq dataset . A retention score ( RS ) was calculated for each IES in the reference set [2]: reads that map to IES ends were classified as IES-containing or as MAC junction-containing reads , representing retained and excised IESs respectively . The RS is the ratio of IES-containing to total classified reads , and RS values vary from 0 for no IES retention to 1 for complete IES retention . As expected ( Fig . 6A ) , the RS distribution of the control dataset is close to 0 ( mean 0 . 008 ) , whereas a Gaussian distribution was observed for the PGM dataset [2] with a mean RS of 0 . 77 . Even if Pgm is responsible for complete excision of all IESs [2] , [3] , the mean RS never reaches 1 owing to the presence of rearranged DNA in the PGM sample coming from the fragments of the maternal MAC still present in the cytoplasm . Consistent with previous work [2] , excision of all IESs appears to be affected in a similar manner following PGM KD . In the EZL1 dataset however , the mean RS is 0 . 32 and the distribution is bimodal with 8 , 085 IESs that have an RS close to 0 , the rest of the IESs displaying a wide distribution of retention scores ( mean 0 . 39 ) with a mode of 0 . 5 ( Fig . 6A ) . We used a statistical test ( see Materials and Methods ) to compare the retention scores in the EZL1 or PGM datasets to the retention scores in the control dataset in order to identify significantly retained IESs . In the PGM dataset , 44 , 028 IESs ( 97 . 9% ) are significantly retained compared to control and in the EZL1 dataset , 31 , 481 IESs ( 70 . 1% ) are significantly retained with a mean RS of 0 . 42 . A biological replicate from an independent EZL1 silencing experiment showed good correlation of retention scores ( Spearman correlation coefficient: 0 . 887 , p< 2 . 2 10−16 ) . Based on these data , we can define two classes of IESs: those that are significantly retained after EZL1 KD and those that are not . Importantly , our PCR analyses are completely coherent with our genome-wide analysis ( Table S4 ) . We then wondered what distinguishes the two classes of IESs . Our PCR analyses indicated that long IESs were retained in the new developing MAC following EZL1 KD ( Fig . S10 and Table S4 ) . To confirm this observation genome-wide , IESs were grouped according to their size [2] and the distribution of retention scores for each group represented in a box plot ( Fig . 6B ) . The IES were grouped as previously described to follow the periodic distribution of IES size with peaks every ten base pairs [2] . The first 5 groups ( 26-72 bp ) have retention score distributions that are significantly different from each other: the larger the IES size , the higher the retention score . Starting with the 5th group ( >72 bp ) , the median does not change much , which indicates that IESs of these sizes are similarly affected by Ezl1p depletion . For the largest IESs ( >1 kb ) , the retention score distribution is significantly shifted to higher values . Among those , there is one group composed of 28 IESs , which have been shown to derive from Tc1/mariner TEs named Anchois [2] . All of them are retained after EZL1 inactivation ( Table S5 ) . Roughly 50% of IESs are over 52 bp in length and among them , 89 . 9% are significantly retained after EZL1 KD , while only 40% of the IESs shorter than 52 bp in length are significantly retained . This robust correlation between IES size and retention score is not observed for the PGM dataset ( Fig . S11 ) , indicating that it is a property specific to Ezl1p depletion . We searched for features other than size that could be associated with EZL1 retained IESs . We compared IESs of the same size ( 26-32 bp ) and did not find any meaningful correlation for a large number of criteria , including the consensus present at IES ends and the scnRNA density on IESs . We did observe that EZL1-retained IESs have: ( i ) a slightly higher GC content , ( ii ) a more frequent location within gene coding sequences ( Fig . S12 ) . It is intriguing that these two properties can also be important determinants in nucleosome positioning [31]–[33] . Since DCL2 and DCL3 genes , like EZL1 , are required for establishing H3K27me3 and H3K9me3 in the developing MAC ( Fig . 2 and 4 ) , we hypothesized that depletion of Dcl2 and Dcl3 proteins would impair DNA elimination in a similar manner to that of Ezl1 depletion . To address this question , we sequenced DNA isolated from newly developed MACs following DCL2/3 co-silencing . When compared to the PGM and EZL1 datasets , the total sequence complexity was similar in the DCL2/3 dataset ( Table S3 ) and analysis of Sardine retention showed that all characterized Sardine copies are retained following DCL2/3 co-silencing ( Fig . S9 ) . This global analysis confirmed that the Dcl2 and Dcl3 proteins are required for the imprecise elimination of germline-limited sequences [5] . Analysis of the effects of DCL2/3 KD on IES excision led to a surprising finding . Compared to PGM or EZL1 silencing , most IESs are weakly or not at all retained after DCL2/3 co-silencing ( Fig . 6A ) . Only 3 , 272 IESs ( 7 . 3% ) are significantly retained in the DCL2/3 dataset with a mean RS of 0 . 24 . The small number of significantly retained IESs and their low RS might be explained in part by incomplete silencing . Yet , the possibility that there are still low amounts of Dcl2 and Dcl3 proteins that would provide sufficient scnRNAs for IES excision is unlikely because very little if any scnRNAs can be detected in typical DCL2/3 KDs [5] , [6] . Moreover , we found 3 , 160 IESs significantly retained for a biological replicate [6] and a good correlation of RS was observed for the two biological replicates ( Spearman correlation coefficient 0 . 616 , p< 2 . 2 10−16 ) despite use of different silencing constructs . Furthermore , the RS measured for the DCL2/3 dataset are in agreement with our PCR analyses ( Fig . S10 and Table S4 ) . Based on our PCR analyses , we noticed that all mcIESs are significantly retained in the DCL2/3 dataset and that all IESs retained after DCL2/3 KD are retained in the EZL1 dataset . The latter was confirmed genome-wide: almost all significantly retained IESs in the DCL2/3 dataset are significantly retained in the EZL1 dataset ( 3 , 269/3 , 272 ) ( Fig . 6C ) . Furthermore , IESs retained upon DCL2/3 KD are among the most retained IESs in the EZL1 dataset ( Fig . S13 ) . Only the largest IESs are retained in the DCL2/3 dataset; 50% of the IESs larger than 1 kb are significantly retained ( Fig . S11 ) . Among those , 19/28 Anchois IESs are significantly retained after DCL2/3 KD ( Table S5 ) . Altogether our data indicate that IESs retained upon DCL2/3 KD correspond to a small subset of EZL1 retained IESs ( Fig . 6C ) . The Dicer-like protein Dcl5 was reported to be required for efficient excision of at least a fraction of IESs [6] . We therefore compared the effects of Dcl5 depletion on DNA elimination , using the previously published DCL5 dataset [6] ( Table S2 ) , to those observed after Ezl1 depletion . Compared to the PGM and EZL1 datasets , the total sequence complexity was much lower in the DCL5 dataset ( Table S3 ) and analysis of Sardine transposon retention showed that none of the characterized Sardine copies are retained following DCL5 silencing ( Fig . S9 ) . This global analysis indicates that , in contrast to Pgm , Ezl1 or Dcl2/3 proteins , the Dcl5 protein is unlikely to play a major role in the imprecise elimination of germline-limited sequences . We then measured the retention score for each IES using our criteria for statistical significance ( see Materials and Methods ) and , consistent with previous work [6] , most IESs are weakly or not at all retained after DCL5 silencing ( Fig . 6A ) . Only 3 , 024 IESs ( 6 . 7% ) are significantly retained in the DCL5 dataset with a mean RS of 0 . 21 . Almost all significantly retained IESs in the DCL5 dataset are strongly retained in the EZL1 dataset ( Fig . 6C ) . IESs retained upon DCL5 KD correspond to a small subset of EZL1 retained IESs , which is furthermore different than the subset of IESs retained upon DCL2/3 KD .
The work presented here demonstrates that the putative HMT Ezl1 is required for the elimination of transposable elements , of their more recent relics in the form of long IESs and of germline DNA regions that encompass several kb in length , which might altogether represent at least 25% of the germline genome . We have shown that H3K27me3 and H3K9me3 signals are abolished after Ezl1 depletion and that scnRNAs are necessary for the deposition of these histone marks in the developing somatic MAC . We also provide evidence that the Dcl5 protein necessary for iesRNA biogenesis does not play a major role in the elimination of transposable elements . Therefore , our results support the idea that scnRNAs guide the putative HMT Ezl1 to specific germline sequences in the developing somatic macronucleus . Consistent with the idea that the Ezl1 protein acts downstream of scnRNAs , analysis of small RNA sequencing datasets showed that scnRNA biogenesis is not affected upon Ezl1 depletion as compared to control RNAi or wild type ( A . de Vanssay and O . Arnaiz , personal communication ) . Deposition of H3K27me3 and H3K9me3 would allow the recruitment , or activation of the excision machinery , followed by elimination of marked DNA segments ( Fig . 7A ) , consistent with our observations that the Ezl1 protein acts upstream of the Pgm endonuclease . Our study provides evidence that RNAi-mediated heterochromatin formation is necessary for elimination of germline DNA in Paramecium , as is the case in Tetrahymena [1] . Ciliates use a similar , sRNA-dependent mechanisms for heterochromatin formation as other eukaryotes [20] , [21] , except that it goes a step further with the physical elimination of the targeted sequences during development of the somatic nucleus . Very much like metazoan piRNAs , the scnRNA pathway controls the silencing of ‘genomic parasites’ such as TEs , thereby ensuring the integrity of the genome [34] , [35] . While all IESs are ultimately excised by the Pgm endonuclease [2] , IESs appear to differ in their recognition mechanism . Only about a third of IESs ( 5 out of 13 tested ) , called mcIESs , are sensitive to the presence of homologous sequences in the maternal MAC [11] . Interestingly , genome-wide analyses of the effects of depletion of Dicer-like 2 and 3 proteins showed that they are both required for excision of Tc1/mariner TEs and of mcIES , but not of non-mcIESs ( [6] and this study ) . The evidence obtained so far is consistent with the idea that Dcl2/3 retained IESs correspond to mcIESs , but unfortunately , it is not possible to experimentally determine the genome-wide set of mcIESs . Surprisingly however , only a small fraction of IESs ( less than 10% ) are retained after depletion of the Dicer-like 2 and 3 proteins . Even though it remains possible that we underestimate the total number of IESs retained in DCL2/3 KD either for technical reasons or because the ablation of the scnRNA pathway is compensated by another unidentified small RNA pathway , our data indicate that the fraction of mcIESs in the genome might be smaller than initially thought . More importantly , our data indicate that most IESs are correctly excised in the absence of scnRNAs . IESs , even those that are not under maternal control , normally do produce scnRNAs during MIC meiosis [5] , [6] , [13] and , when introduced into the maternal MAC , give rise to non-coding transcripts like any other sequence [7] , suggesting that the genome scanning process should inactivate their scnRNAs . Our conclusion is thus that excision of non-mcIESs simply does not depend on scnRNAs . None of the shortest ( ∼28 bp ) IESs tested , which are also the oldest [2] , was found to be a mcIES , raising the possibility that non-mcIES represent the endpoint of IES evolution . In support of this view , our data indicate that recognizable TEs and young ( longer ) IESs display higher retention scores after depletion of Dicer-like 2 and 3 proteins , indicating that they indeed depend on their own scnRNAs for recognition and elimination . In addition to these two classes of IESs , genome wide analysis of the effects of Ezl1 depletion provided evidence for additional classes of IESs , showing differential sensitivity to the different factors studied here . In order to group IESs into functionally similar classes , we have quantitated the requirement of each of the 45 , 000 IESs for each of the factors analyzed ( Fig . 6C ) . Our data show that a large fraction of IESs are retained after Ezl1 depletion . One surprising finding is that the set of EZL1 retained IESs is not the same as the set of IESs retained upon DCL2/3 KD . We also showed that IESs retained upon DCL5 KD correspond to a small subset of EZL1-retained IESs , which does not correspond to IESs retained upon DCL2/3 KD . Even though IESs retained after DCL2/3 KD or after DCL5 KD are all included in EZL1 retained IESs , our results argue that EZL1 is necessary for correct excision of most IESs , without the need of scnRNAs or iesRNAs . Because the excision of IESs , whether they are maternally controlled or not , EZL1 sensitive or not , is still dependent on the Ptiwi01/09 proteins [27] , it remains possible that these proteins may be alternatively loaded with a different type of small RNA . EZL1 encodes a putative histone methyltransferase necessary for H3K27me3 and H3K9me3 and for excision of about 70% of IESs , suggesting that H3K27me3 and H3K9me3 are required for their excision , as discussed below . DCL2/3 KD also leads to diminution of H3K27me3 and H3K9me3 signals at early stages of MAC development ( Fig . 2 ) and yet , only approximately 10% of IESs are retained after DCL2/3 KD . The excision of IESs in DCL2/3 KD might be explained by H3K27me3 and H3K9me3 detected at late stages of MAC development in DCL2/3 KD , but not in EZL1 KD ( Fig . S4 ) . The low amount of H3K27me3 detected by Western blot appears to be compatible with the fact that IESs cover 3 . 2 Mb and thus represent about 3% of the sequence complexity of the MIC genome [2] . Yet we cannot formally exclude the possibility that the Ezl1 protein has an additional role in DNA elimination independently of histone H3 methylation . One can imagine , for instance , that the Ezl1 protein is also necessary for methylation of lysine residues within proteins involved in DNA elimination . We now understand that IES excision involves partially overlapping pathways given our observations of different classes of IESs . This led us rethink the simple model according to which scnRNAs -produced by the Dcl2/3 proteins- lead to the loading of chromatin modifications- H3K27me3/HK9me3 through the action of the putative histone methyltransferase Ezl1- and recruitment of the Pgm endonuclease . Indeed , a small subset of IESs require DCL2/3 and EZL1 ( 7% ) , while the majority of IESs require only EZL1 ( 63% ) , and some IESs require neither EZL1 nor DCL2/3 for complete excision ( 30% ) ( Fig . 7B ) . The relative position of the Dcl5 protein in this process is not yet clear . Whether the existence of overlapping pathways reflects distinct protein complexes , complexes containing some different components , nucleosome positioning and/or unidentified determinants remains to be investigated . Future studies combining genetic and biochemical approaches will be necessary to first describe and then determine the functional significance of the amazing level of complexity that is beginning to emerge . In Drosophila and in mammals , Enhancer of zeste proteins are catalytic subunits of Polycomb complexes , which target H3K27me3 and maintain repression of numerous developmental genes . Domains enriched in H3K27me3 cover large regions of the genome , usually exceeding 10 kb [36] . One unexpected finding of our study is the putative HMT Ezl1p is required for the excision of very short DNA segments , as Ezl1 depletion leads to retention of 31 , 481 IESs ( 70 , 1% ) . Since the vast majority of IESs are shorter than 150 bp in length , the Ezl1 protein might not necessarily trigger the formation of heterochromatin on eliminated sequences . Instead , we imagine that Ezl1 acts locally and is responsible for trimethylation on lysines 27 and 9 on one or a few nucleosomes that overlap with the IES . The quantitative genome-wide analysis of IES retention showed that all IESs are not equal: IESs are retained to a different extent after Ezl1 depletion and we could not identify any features in the IES sequences that distinguish IESs that are significantly retained from those that are not . However , our analysis revealed a strong correlation between IES size and retention score , as 90% of IESs longer than 52 bp are retained after Ezl1 depletion . We propose that the excision process is regulated by the presence of methylated nucleosomes and depends on the relative positions of IES ends with respect to the methylated nucleosomes . As illustrated in Figure 7A , the positioning of nucleosomes might play a major role in IES excision . We reasoned that longer sequences have a higher probability to be associated with modified nucleosomes and would thus be more sensitive to Ezl1 depletion and loss of methylated H3 . Any IES over 52 bp in length would be either entirely or partially covered by one nucleosome . This might reflect the length of the linker DNA in the developing somatic MAC , which is not known , but would be consistent with linker ranging from 20 to 90 bp in general [37] . Strikingly , small IESs between 26 and 52 bp in length have a wide range of retention scores . 34% of the smallest IESs ( 26-32 bp ) required Ezl1 to be excised and we imagine those small IESs are either within , or partially covered by , one modified nucleosome . IESs that are not retained after Ezl1 depletion might be located in the linker region between nucleosome core particles or in nucleosome-free regions and alternative mechanisms would ensure their correct and precise excision . In S . cerevisiae , the chromatin remodeler SWR1 binds in vitro long nucleosome-free DNA and the adjoining nucleosome core particle , allowing discrimination of gene promoters over gene bodies . SWR1 binding is enhanced on acetylated nucleosomes , but recognition of nucleosome-free and nucleosomal DNA is dominant over interaction with acetylated histones [38] . Such hierarchical cooperation between DNA and posttranslational histone modifications might participate in guiding the excision machinery . Precise mapping of nucleosomes and of histone marks along the genome will be needed to explore this possibility . An exciting challenge for the future is to understand the mechanisms by which histone modifications position the excision machinery for precise DNA cleavage .
Unless otherwise stated , all experiments were carried out with the entirely homozygous strain 51 of P . tetraurelia . Cells were grown in wheat grass powder ( WGP ) ( Pines International ) infusion medium bacterized the day before use with Klebsiella pneumoniae , unless otherwise stated , and supplemented with 0 . 8 mg/mL β-sitosterol ( Merck ) . Cultivation and autogamy were carried out at 27°C as described [39] , [40] . SET domain proteins were retrieved using Pfam [41] and BLAST [42] . Multiple alignments were performed with MUSCLE 3 . 8 [43] and were subsequently manually improved . Maximum likelihood ( ML ) analyses were performed with PHYML [44] using the PHYML web server [45] hosted at the Montpellier bioinformatics platform ( http://www . atgc-montpellier . fr/phyml/ ) . PHYML analyses were performed using the Le and Gascuel ( LG ) amino-acid substitution model [46] , using two rate categories ( one constant and four γ rates ) . Statistical support for the different internal branches was assessed by approximate Likelihood-ratio test ( aLRT; [47] ) . Plasmids used for T7Pol-driven dsRNA production in silencing experiments were obtained by cloning PCR products from each gene using plasmid L4440 and Escherichia coli strain HT115 DE3 , as previously described [48] . Sequences used for silencing of EZL2 , EZL3a , EZL3b , EZL4 , DCL5 were segments 955-1519; 1402–1980; 1404–1982; 1398-1976 and 4-1998 of GSPATG00032888001; GSPATG00012695001; GSPATG00013305001; PTETG1700020001 , GSPATG00003051001 [49] , respectively . For EZL1 silencing , two non-overlapping gene fragments covering positions 991-1500 ( EZL1-1 ) and 332-754 ( EZL1-2 ) of GSPATG00037872001 were used . The fragments used for ND7 [12] , ICL7a [27] , DCL2 , DCL3 [5] and PGM-1 [3] are those previously published . Preparation of silencing medium and RNAi during autogamy were performed as described in [3] . Lethality of post-autogamous cells after double silencing of DCL2 and DCL3 or silencing of PGM was 90-100% ( 30–60 cells were checked in each silencing experiment ) . As expected , Pgm depletion led to retention of all tested germline-limited elements in the developing MAC genome , while Dcl2/3 depletion led to retention of well-characterized IESs ( Fig . S10 ) and Dcl5 depletion led to partial impairment of excision for IESs retained in the DCL5 dataset . For the construction of in-frame GFP-EZL1 fusion , a GFP-coding fragment adapted to Paramecium codon usage [28] was added by PCR fusion to the 5′ end of the EZL1 gene . As a result , the GFP is fused to the N-terminus of EZL1 and the fusion protein is expressed under the control of the EZL1 transcription signals ( promoter and 3′UTR ) . It contains the 830-bp genomic region upstream of the EZL1 open reading frame , the 304-bp genomic region downstream . Plasmids carrying the GFP-EZL1 or PGM-GFP [3] fusion transgenes were linearized by XmnI or AflIII , respectively , and microinjected into the MAC of vegetative 51 cells . No lethality was observed in the post-autogamous progeny of injected cells , indicating that the GFP-EZL1 and PGM-GFP fusions did not interfere with normal progression of autogamy . DNA samples were typically extracted from 200-400-ml cultures of exponentially growing cells at <1 , 000 cells/ml or of autogamous cells at 2 , 000–4 , 000 cells/ml as previously described [40] . Small-scale DNA samples were prepared from ≤1 , 000 cells using the NucleoSpin Tissue kit ( Macherey-Nagel ) . Electrophoresis and blotting were carried out according to standard procedures . RNA samples were typically extracted from 200–400-ml cultures of exponentially growing cells at <1 , 000 cells/ml or of autogamous cells at 2 , 000–4 , 000 cells/ml as previously described [40] . RNA samples were reverse-transcribed with RevertAid H Minus Reverse Transcriptase ( Thermo Scientific ) using polydT primers ( Thermo Scientific ) according to the manufacturer's instructions . It was then followed by PCR amplifications in a final volume of 25 µL , with 10 pmol of each primer , 10 nmol of each dNTP and 2 U of DyNAzyme II DNA polymerase ( Thermo Scientific ) . PCR amplifications were performed in a final volume of 25 µL , with 10 pmol of each primer , 10 nmol of each dNTP and 1 . 9 U of Expand Long Template Enzyme mix ( Expand Long Template PCR system , Roche ) . PCR products were analyzed on 0 . 8% agarose gels ( Fig . 5 ) . For PCR analysis of IES excision ( Fig . 3 , S7 and 10 ) , PCR amplifications were performed with 1 . 9 U of Expand Long Template Enzyme mix ( Expand Long Template PCR system , Roche ) . Oligonucleotides were purchased from Eurofins MWG Operon ( see Table S6 ) . Cell pellets were mechanically lysed in three volumes of lysis solution ( 0 . 25 M sucrose , 10 mM MgCl2 , 10 mM Tris pH 6 . 8 , 0 . 2% Nonidet P-40 ) with a Potter-Elvehjem homogenizer . Following the addition of 2 . 5 volumes of washing solution ( 0 . 25 M sucrose , 10 mM MgCl2 , 10 mM Tris pH7 . 4 ) , the nuclei-containing pellet was collected by centrifugation at 1000 g for 1 min and acid extraction of histones was performed as previously described [50] . 10 µg of histone extracts were used for Western blot . Electrophoresis and blotting were carried out according to standard procedures . The H3K27me3 ( 1∶500; Millipore , 07-449 ) and H3 ( 1∶10 000; Millipore , 07-690 ) primary antibodies were used . Secondary horseradish peroxidase-conjugated donkey anti-rabbit IgG antibody ( Promega ) was used at 1∶10 000 dilution followed by detection by ECL ( SuperSignal West Pico Chemiluminescent Substrate , Thermo Scientific ) . For normalization , the membranes probed with H3K27me3 antibody were stripped in mild stripping buffer ( glycine 200 mM , SDS 0 . 1% , Tween-20 1% , pH 2 . 2 ) and probed again with H3 antibody . Cells were fixed for 30 minutes in solution I ( 10 mM EGTA , 25 mM HEPES , 2 mM MgCl2 , 60m M PIPES pH 6 . 9 ( PHEM 1X ) ; paraformaldehyde 1% , Triton X-100 2 . 5% , Sucrose 4% ) and for 10 minutes in solution II ( PHEM 1X , paraformaldehyde 6 . 5% , Triton X-100 1 . 2% , Sucrose 4% ) . The primary antibodies used were rabbit polyclonal α-H3K27me3 ( 07-449 , Millipore ) and α-H3K9me3 ( 07-442 , Millipore ) at 1∶500 . After incubation with the primary antibodies , cells were washed in 1X phosphate-buffered saline ( PBS ) , incubated with the secondary antibodies ( Alexa Fluor 568-conjugated goat anti-rabbit IgG , A-11036 , Invitrogen ) at 1∶500 for 1h , stained with 1 µg/mL Hoechst , washed in 1X PBS , centrifuged on microscope slides with the CytoSpin™ 4 Cytocentrifuge ( Thermo Scientific ) and finally mounted in Citifluor AF2 glycerol solution ( Citifluor Ltd , London ) . Images were acquired using a Zeiss LSM 710 laser-scanning confocal microscope and a Plan-Apochromat 63x/1 . 40 oil DIC M27 objective . Z-series were performed with Z-steps of 0 . 5 µm . To quantify the H3K27me3 and H3K9me3 signals , the Imaris 3D visualization software ( Bitplane ) was used . For each time point , the fluorescence intensities of H3K27me3/H3K9me3 in the developing MACs ( signal ) and in the corresponding volume of the cytoplasm ( noise ) were measured . The mean value and standard deviation of the signal to noise ratios were calculated using ten individual cells at each time point . DNA for deep-sequencing was isolated from post-autogamous cells as previously described [2] and sequenced by a paired-end strategy using Illumina GA-IIx and Hi-Seq next-generation sequencers ( Table S2 ) . The following reference genomes [2] were used in the IES analyses and for read mapping . MAC reference ( strain 51 ) : http://paramecium . cgm . cnrs-gif . fr/download/fasta/ptetraurelia_mac_51 . fa MAC+IES reference ( strain 51 ) : http://paramecium . cgm . cnrs-gif . fr/download/fasta/ptetraurelia_mac_51_with_ies . fa PGM contigs: http://paramecium . cgm . cnrs-gif . fr/download/fasta/assemblies/ptetraurelia_PGM_k51_ctg . fa Macronuclear DNA reads for PiggyMac [2] and DCL5 depleted cells and for a biological replicate of the DCL2/3 co-silencing experiment [6] were obtained from the European Nucleotide Archive ( Accession number ERA137420 ) ( PGM ) and the GenBank Sequence Read Archive ( Accession numbers: SRX387766 ( DCL2/3 ) ; SRX387766 ( DCL5 ) ) . After quality filtering and removal of adapters , Illumina reads were aligned to the reference genomes ( P . tetraurelia MAC reference genome and MAC+IES reference genome ) using BWA [51] with default parameters . Alignments were indexed with Samtools [52] . For each sample , IES retention scores were determined as follows , for each IES in the genome previously identified in [2] . The number of reads that contain the IES sequence ( symbolised IES+ ) and the number of reads that contain only the macronuclear IES junction consisting of a TA dinucleotide ( IES− ) were determined . Only reads with unambiguous alignments were counted . Each read was counted only once to avoid over-counting owing to paralogous matches . Reads were only counted at IES ends , to avoid length biases resulting from IES length variation . The retention score ( RS ) of an IES is then given by the following equation: RS = ( IES+ ) / ( IES+ + IES− ) Since RS are based on read counts , appropriate statistical tests allowed us to discriminate IES retention as a result of gene silencing from IES retention as a result of biases in Illumina sequencing or errors in the IES identification pipeline ( estimated false positive rate ≤ 4% , [2] ) . First , we calculated the confidence interval ( alpha = 0 . 95 ) of the control retention score value , using the Pearson-Klopper exact method as implemented by the R binom package version 1 . 0–5 [53] . Then we tested for higher retention in the experiment , thanks to a frequency comparison test ( based on a binomial law of probability ) between the experimental retention score and the upper bound of the confidence interval in the control . Resulting p-values were adjusted for multiple testing using the Benjamini & Hochberg method [54] . IESs with adjusted p-value <0 . 05 are considered significantly retained in the sample . The EZL1 KD , DCL2/3 KD and control DNA-seq datasets have been deposited in the European Nucleotide Archive ( Accession number ERA309409 ) . All IES retention scores may be obtained via ParameciumDB ( http://paramecium . cgm . cnrs-gif . fr/ ) . | The unicellular eukaryote Paramecium tetraurelia provides an extraordinary model for studying the mechanisms involved in zygotic genome rearrangements . At each sexual cycle , differentiation of the somatic nucleus from the zygotic nucleus is characterized by extensive remodeling of the entire somatic genome , which includes the precise excision of 45 , 000 short noncoding germline DNA segments to reconstitute functional open reading frames . Exploiting the unique properties of the Paramecium genome , we show that the enhancer of zeste like protein Ezl1 is necessary for histone H3 trimethylation on lysines 27 and 9 and is required for the precise excision of 31 , 000 of these single copy , dispersed germline DNA segments that can be as short as 26 bp in length . This implies that histone marks usually associated with heterochromatin may contribute to the precise demarcation of segments that are even shorter than the length of DNA wrapped around a single nucleosome . A quantitative analysis of high throughput sequencing datasets further shows that the underlying genetic properties of the germline DNA segments might act in concert with epigenetic signals to define germline specific sequences . | [
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| 2014 | Local Effect of Enhancer of Zeste-Like Reveals Cooperation of Epigenetic and cis-Acting Determinants for Zygotic Genome Rearrangements |
In the Drosophila brain , the neuropeptide PIGMENT DISPERSING FACTOR ( PDF ) is expressed in the small and large Lateral ventral neurons ( LNvs ) and regulates circadian locomotor behavior . Interestingly , PDF immunoreactivity at the dorsal terminals changes across the day as synaptic contacts do as a result of a remarkable remodeling of sLNv projections . Despite the relevance of this phenomenon to circuit plasticity and behavior , the underlying mechanisms remain poorly understood . In this work we provide evidence that PDF along with matrix metalloproteinases ( Mmp1 and 2 ) are key in the control of circadian structural remodeling . Adult-specific downregulation of PDF levels per se hampers circadian axonal remodeling , as it does altering Mmp1 or Mmp2 levels within PDF neurons post-developmentally . However , only Mmp1 affects PDF immunoreactivity at the dorsal terminals and exerts a clear effect on overt behavior . In vitro analysis demonstrated that PDF is hydrolyzed by Mmp1 , thereby suggesting that Mmp1 could directly terminate its biological activity . These data demonstrate that Mmp1 modulates PDF processing , which leads to daily structural remodeling and circadian behavior .
The rotation of the earth around its own axis imposes cyclic changes on environmental conditions , primarily through variations on luminosity and temperature . The existence of an endogenous , self-sustained and entrainable circadian clock in almost every living organism allows them to anticipate those daily changes and concomitantly adapt their physiology and behavior to a changing environment [1] . Although the biological processes that present circadian modulation may differ depending on the ecological niche that each species occupies , the molecular basis of the circadian clock shows an intriguing similarity through evolution . Briefly , circadian clocks depend on the coordinated activity of transcriptional/translational feedback loops of clock genes running within specific pacemaker cells [2] . In Drosophila melanogaster this molecular clock is allocated in a circadian network of approximately 150 neurons in the adult brain , and the coordinated activity of the whole circuit is necessary for plastic responses to different environmental stimuli ( revised in [3] ) . However , under constant conditions , circadian locomotor activity strongly depends on the activity of 8 neurons located on the accessory medulla on each side of the adult brain [4] , [5] , which are known as the small and large lateral ventral neurons ( sLNvs and lLNvs , respectively ) ; all of them express the PIGMENT DISPERSING FACTOR neuropeptide and are therefore also known as PDF neurons . Several experiments have determined that the sLNvs are in fact in charge of determining the endogenous period of locomotion under constant conditions [6] , [7] while the lLNvs appear to be involved in sleep and arousal [8]–[10] . How the circadian network transmits time of day information is still under debate but the activity of the PDF neuropeptide [5] , [11] and , more specifically , daily changes on immunoreactivity of the PDF-containing dense core vesicles at the axonal terminals [12] as well as circadian changes on electrical activity [13] have been proposed as putative mechanisms . In addition , we have demonstrated that the sLNvs axonal terminals exhibit a higher degree of complexity during the day and a reduced complexity during the night accompanying the daily changes in PDF levels [14] . Interestingly , this circadian structural plasticity may result in a change in synaptic partners at different times of the day and might offer another relevant mechanism to transmit time of day information [15] . Axonal structural plasticity related to circuit assembly during development has extensively been studied but only recently its occurrence during adulthood in the absence of physical lesions has been reported [16] , [17] . Axonal remodeling during adulthood is recruited to adjust biological processes such as axonal injury , adult neurogenesis , sensory experience , learning and memory [18] and as a response to homeostatic regulation followed by sleep deprivation [19] , [20] . In addition to such homeostatic changes , endogenous mechanisms determine circadian axonal remodeling of peripheral circuits [21] , [22] and , also , of central neurons relevant to circadian rhythms [14] , [19] , [23] . The molecular and cellular processes underlying such axonal plasticity during adulthood are not clear , but different mechanisms might be engaged in the remodeling of specific neurons [18] . In the case of circadian structural plasticity , it is expected that at least part of the molecules responsible for orchestrating changes in axonal terminals show circadian modulation of gene expression , protein stability and/or activity . In this regard , we found matrix metalloproteinases ( Mmps ) to be attractive candidates to modulate circadian axonal remodeling of PDF neurons . In Drosophila there are only two Mmps , Mmp1 and Mmp2 , and their action is involved in several processes ranging from tissue remodeling [24] , tumor invasiveness [25] , axon guidance , axonal fasciculation [26] and dendritic remodeling [27] . Interestingly , cell-type specific gene-expression profiling revealed enrichment of Mmp1 and 2 expression in sLNv neurons at the beginning of the night [28] . Moreover , Mmp1 appears to be a direct target of CLOCK , a central component of the molecular clock [29] . In this study we investigated the molecular mechanisms underlying circadian structural remodeling of PDF axonal terminals . We demonstrated that both Mmps are key players in the remodeling of PDF neurons , promoting a reduction of the complexity of the axonal arborizations . In concert with the action of Mmps , fine tuning of the dorsal arborizations also depends on the PDF neuropeptide . Furthermore , we found that cell-type autonomous modulation of Mmp1 levels , unlike Mmp2 , regulates the levels of the PDF neuropeptide , highlighting the relevance of Mmp1 in the determination of the neuronal output of the central pacemaker cells .
To examine a possible contribution of Mmps to the circadian structural plasticity of the sLNvs axonal terminals we altered Mmp1 or Mmp2 expression specifically in PDF neurons and analyzed the degree of arborization at the dorsal protocerebrum at two time points during the subjective day , at Circadian Time 2 ( CT2 , 2 hours after the lights should have been on ) and CT14 ( 2 hours after lights should have been off ) ( Figure 1A ) . We restricted our treatment to the adult stage by using the pdf-GS RU486-inducible GeneSwitch strain recently described [30] to bypass any potential developmental effect . As previously described [14] , control flies displayed a more complex arborization pattern during the early subjective day ( CT2 ) and less arborized display during the early subjective night ( CT14 ) . On the contrary , adult-specific Mmp1 or Mmp2 overexpression in PDF neurons abolished any remodeling of dorsal projections , leading to a non-oscillating and less complex circuit that shows even fewer axonal crosses than the nighttime control neurons ( Figure 1B ) . Overexpression with independent transgenic lines rendered similar results ( Figure S1 A ) . A more detailed analysis of structural complexity indicated that Mmp1 does not affect its total axonal length while it does reduce the complexity of the arborizations all along the axonal projections . In contrast , Mmp2 has a significant effect on total axonal length indicating that the changes trigged by Mmp2 overexpression involve modification of the length of axonal terminals ( Figure S1 B–D ) . Thus , although both Mmps impact the circadian remodeling of PDF neurons , the underlying mechanisms are not necessarily the same . To corroborate Mmp1 presence in PDF neurons , immunohistochemistry on whole mount brains was carried out during both transitions , dark to light and light to dark . Despite its overall low levels ( that precluded reliable detection in the sLNvs ) , Mmp1 was more frequently detected in the large LNvs somas at dusk rather than at dawn ( Figure S1F ) , which is in agreement with the transcription profile reported for this gene ( roughly undetectable at ZT0 and detectable at ZT12 , [31] ) . We extended our analysis on the role of Mmps in circadian plasticity through RNAi-mediated downregulation of Mmp1 or Mmp2 expression . Co-expression of Dicer2 ensured a drastic reduction of Mmp1 and Mmp2 levels since expression on the whole animal through the constitutive promoter actin-GAL4 caused larval or pupal lethality as it is the case for null mutants ( [24] ) . The acute activation of a component of the silencing machinery did not affect circadian remodeling per se , since control flies overexpressing Dicer2 showed changes in the degree of complexity reminiscent of wild type animals ( Compare “+” in Figures 1B and 1C ) . Adult specific downregulation of Mmp1 or Mmp2 disrupts the daily changes in the complexity , although the structure is fixed on a daytime configuration comparable to the one of control animals ( Figure 1C ) . Importantly , independent RNAi lines triggered similar effects ( Figure S1G ) . Downregulation of Mmp2 but not Mmp1 significantly increased the length of the main axonal branches , underscoring that they affect the structure of PDF neurons through different mechanisms ( Figure S1 E ) . In conclusion both Mmps are key players in the circadian modulation of the fine structure of the sLNvs , where high Mmp levels promote a less complex arborization , as the one observed during the early night , while low Mmp levels lead to the opposite effect . To examine if structural plasticity of PDF neurons is necessary for the control of behavioral rhythmicity we sought to determine if flies that do not present cyclic axonal remodeling show any disruption on circadian locomotor activity . Control flies and those overexpressing either Mmp1 , Mmp2 or specific RNAi constructs directed to Mmp1 or Mmp2 were recorded for their locomotor activity during 4 days in the presence of external cues ( cycles of 12 hours of lights and 12 hours of darkness , LD ) and then released to constant darkness ( DD ) to evidence the circadian control of behavior . Wild type flies present a clear rhythm in their locomotor activity both in the presence of synchronizing cues ( LD ) and in constant conditions ( DD ) . In DD , this rhythm has a period of approximately 24 h and flies consolidate their activity along the subjective day . In this experiment , genetic as well as the non-induced controls ( flies including all transgenes kept in the absence of the chemical inducer ) behave as wild type animals with largely rhythmic individuals with an endogenous period close to 24 h ( Figure 2 and Table S1 ) . Overexpression of Mmp1 or Mmp2 with a single copy of the transgenes did not cause any significant effect on locomotor rhythmicity ( Figure 2A ) although increasing Mmp1 levels through the addition of a second UAS-transgene produced a significant reduction of behavioral rhythmicity ( Figure S2 ) . Interestingly , downregulation of Mmp1 but not Mmp2 led to a severe deconsolidation of locomotor activity that resulted in a clear reduction in the rhythmicity of the population ( Figure 2B ) . Those that remained rhythmic displayed an endogenous period indistinguishable from control flies ( Table S1 ) , highlighting a specific effect of Mmp1 on the consolidation of rhythmic locomotor activity as opposed to period determination . Given the complexity and extent of daily reorganization we reasoned that other molecules might be implicated in fine tuning the structure of PDF neurons along the day . The analysis of structural changes in the same brain over time indicates that axonal projections of sLNvs endure changes in pruning and neuritogenesis as well as changes in the degree of fasciculation [15] . Consistent with such contribution , it has recently been shown that Fasciclin 2 ( Fas2 ) , the ortholog of mammalian NCAMs in Drosophila , plays a role in the structural remodeling of sLNv axonal projections [32] . In addition , Mmps act in concert with Fas2 promoting the fasciculation of axonal bundles during the development of neuronal circuits [26] but also interact with the Ecdysone pathway assisting dendritic pruning [27] . Taking this information into account , we sought to examine whether these two programs were also recruited in PDF neurons to accomplish their circadian structural remodeling . To shed light on this possibility , we tested if modulating Fas2 or Ecdysone Receptor ( EcR ) levels could modulate the structural defects caused by high Mmp1 levels . RNAi-mediated downregulation of Fas2 levels in the context of Mmp1 overexpression partially restored the complexity of axonal arborizations ( Figure S3 A ) . Noteworthy , this rescue was not a byproduct of the inclusion of additional UAS constructs since additional transgenes in the context of Mmp1 overexpression did not alter its phenotype ( see below ) . On the other hand , expression of a RNAi line directed to EcR in PDF neurons rescued the structural plasticity to wild type levels , antagonizing the effects caused by Mmp1 overexpression . Along these lines , downregulation of EcR affected PDF neurons per se , clamping the structure in the more complex , highly arborized , configuration ( Figure S3 B and [33] ) . Together these results demonstrate that the daily axonal remodeling of PDF neurons is a complex and highly regulated process that depends on the concerted activity of Mmps , Fasciclin 2 and the Ecdysone Receptor . PDF is crucial for the proper control of circadian locomotor activity since pdfo1 and pdf Receptor ( pdfR/han ) mutants largely become arrhythmic under DD conditions [5] , [34] , [35] . Therefore , we wondered if the behavioral phenotypes described for flies with Mmp1 missexpression were reflecting an alteration of PDF signaling . To address this possibility we measured the levels of the neuropeptide at the dorsal protocerebrum by immunohistochemistry during the early subjective day ( CT2 ) and night ( CT14 ) . In control animals , PDF immunoreactivity changes at the dorsal terminals , with high levels at CT2 and low levels at CT14 ( Figure 3 A–B , upper panels ) . Overexpression of Mmp1 or Mmp2 affected PDF immunoreactivity and disrupted its circadian oscillation . Mmp1 effect was far more severe , resulting in reduced PDF levels at both timepoints to an extent that reached statistical significance; on the contrary , Mmp2 affected PDF levels rather subtly and led to intermediate levels that did not significantly differ from any timepoint in control flies ( Figure 3A ) . Overexpression with independent transgenic lines retrieved similar results ( Figure S4 A ) . RNAi analysis showed that reduced Mmp1 but not Mmp2 levels abolished the circadian oscillation in PDF immunoreactivity , resulting in levels reminiscent of the daytime configuration ( Figure 3B ) . Interestingly , the fact that downregulation of Mmp1 but not Mmp2 affects PDF immunoreactivity correlates with the specific effect of silencing Mmp1 on locomotor activity , suggesting that clamping PDF at high levels might be the cause of the behavioral phenotypes observed . Recently , we have demonstrated that the PDF neuropeptide operates during development to determine the fine structure of the dorsal axonal projections of sLNv neurons [33] . As we demonstrated here , Mmp1 affects the circadian remodeling of PDF projections in the adult , concomitantly altering the levels of the neuropeptide . We reasoned that if PDF was responsible for the daily axonal remodeling of sLNvs , rescuing PDF levels in the context of Mmp1 overexpression should reestablish circadian structural plasticity . Indeed , PDF overexpression in the context of Mmp1 overexpression restored circadian structural plasticity of PDF neurons to wild type levels ( Figure 4A ) . To directly test a role of the neuropeptide on the plasticity of sLNv neurons , we expressed a specific RNAi to downregulate PDF levels in an adult-specific fashion and analyzed its effect on circadian axonal remodeling . PDF knockdown caused a severe abrogation of the daily remodeling of axonal terminals that rendered the structure in a configuration reminiscent of the one observed in animals overexpressing Mmp1 ( Figure 4B ) . In conclusion , these experiments clearly demonstrate the relevance of the PDF neuropeptide in the daily remodeling of PDF terminals . Moreover , these results led us to propose that daily changes in PDF levels at the dorsal terminals could be responsible for the circadian structural remodeling of the axonal arbor . One particularly intriguing observation made on the course of this work was that Mmp1 deregulation led to altered PDF immunoreactivity . In principle , Mmp1 could be altering PDF levels at the axonal terminals by affecting any step from transcription to neuropeptide processing , release or even degradation either directly or indirectly . To analyze if Mmp1 reduces pdf transcription or mRNA stability we measured the steady state levels of pdf mRNA by quantitative Real Time PCR ( qRT-PCR ) in head extracts of control and flies overexpressing Mmp1 or a RNAi against Mmp1 during the early morning ( ZT2 ) . No significant differences were observed between control and mutant flies ( Figure S4B ) , suggesting that neither pdf transcriptional levels nor mRNA stability were grossly affected upon Mmp1 deregulation . An alternative explanation to the observation that Mmp1 dramatically alters PDF levels at the dorsal protocerebrum is that it could affect neuropeptide release from the dorsal terminals . We tested this hypothesis expressing a GFP fusion to the atrial natriuretic peptide ( ANF-GFP ) in PDF neurons . When expressed in secretory cells , ANF-GFP was reported to be processed , localized and released in response to physiological signals as an endogenous neuropeptide [36] , [37] . Overexpression of Mmp1 reduced ANF-GFP levels , which could be taken as an indication of increased peptide release at all timepoints , suggesting that Mmp1 could promote PDF release from sLNv axonal terminals ( Figure S4C ) . To further investigate the ability of Mmp1 to process or degrade PDF , the Mmp1 catalytic domain was expressed in E . coli as a His fusion protein . After fast protein liquid chromatography ( FPLC ) purification and refolding , Mmp1 activity on a previously characterized substrate was confirmed ( Figure S5 A and [38] ) . Next , we incubated purified recombinant Mmp1 with PDF for 5 to 60 minutes at 37°C . The reaction products were purified by reverse-phase HPLC [39] . In contrast to recombinant Mmp1 ( Figure 5A ) and PDF alone ( Figure 5B ) , co-incubation of PDF with Mmp1 gave rise to four novel peaks consistent with PDF fragments ( Figure 5C and Figures S6 A–B ) . Moreover , preincubation of Mmp1 with Batimastat , a well-characterized inhibitor of mammalian metalloproteinases [40] , prevented PDF cleavage , underscoring that Mmp1 ( as opposed to any contaminant potentially present in the original purified fraction ) specifically hydrolyzes the neuropeptide ( Figure 5D ) . To identify Mmp1 cleavage sites , the four degradation peaks were analyzed by MALDI-TOF-TOF . In the fast eluting fraction , peptides containing the C-terminal sequence of PDF ( corresponding to the fragment LSLPKNMNDA of the reported sequence [41] ) and to the fragment LLSLPKNMNDA were identified ( Table 1 ) . Additional fractions included peptides containing the N-terminal PDF sequence ( corresponding to amino-acids YNSELINSL ) , thereby identifying the P1' L-L and P1' L-S as primary sites of Mmp1 cleavage ( Figure 5E ) . We also tested whether Mmp2 could degrade PDF in vitro . Surprisingly , no novel peaks were detected upon incubation under the same conditions that resulted in Mmp1-directed degradation , even though Mmp2 was able to degrade a previously reported fluorogenic substrate for Mmp2 [42] , thus confirming that recombinant Mmp2 displays proteolytic activity ( Figure S5 B–C ) . Taken together these results suggest that Mmp1 could modulate PDF levels at the dorsal terminals , thus contributing to the cyclical changes in PDF immunoreactivity relevant in the control of rhythmic locomotor behavior .
Throughout this work we extensively showed that deregulation of specific molecules within PDF neurons abrogates circadian structural remodeling of sLNv dorsal terminals , underscoring that PDF neurons can modulate the complexity of the arborization of their own axonal projections . In addition , cell-type specific downregulation and overexpression of Mmp1 or Mmp2 led to increased and reduced axonal complexity reminiscent of the wild type daytime or nighttime configuration , respectively . Interestingly , mRNA steady state levels of both Mmps are enriched in the sLNvs during the beginning of the night [31] , which was further confirmed for Mmp1 by immunohistochemistry in the somas of the lLNvs ( Figure S1F ) , suggesting that circadian expression of matrix metalloproteinases within PDF neurons contributes to the daily axonal remodeling . The fact that pacemaker neurons regulate their own structural plasticity allows this cellular phenomenon to be under tight temporal control . In fact , PDF neurons respond to the neuropeptide PDF [47] , [48] and this signal is necessary to coordinate molecular oscillations within sLNvs [11] , [49] , demonstrating that PDF neurons control diverse aspects of their physiology , in part , cell-autonomously . Extrinsic signals derived from other neurons or even from the glia might add modulation to this autonomous control of structural plasticity . Herein we demonstrate that both Mmps are key players in the control of circadian structural plasticity and their action promotes a reduction in the complexity of axonal arborizations . Matrix metalloproteinases have extensively been implicated in neuronal remodeling during development [26] , [27] , [50] , [51] but , to our knowledge , this is the first evidence of a direct role in adult structural plasticity . Interestingly , minocycline treatment alleviates structural defects in the sLNv axonal terminals of dfmr1 flies ( a fly model of Fragile X syndrome ) and this effect appears to be mediated by inhibition of Mmp activity [52] . Although both Mmps are involved in the active remodeling of PDF dorsal arborization , only Mmp2 significantly reduces the total length of axonal terminals . On the other hand , Mmp1 but not Mmp2 significantly reduces PDF levels at the dorsal terminals and in doing so it affects the consolidation of rhythmic locomotor activity . That said we cannot rule out a PDF-independent effect of Mmp1 on locomotor activity . Sequence analysis revealed that Drosophila Mmp1 and Mmp2 are more related to different human Mmps than they are to each other [24]; also , Mmp1 seems to be secreted while Mmp2 is retained in the cell membrane [24] , [42] therefore different substrates are anticipated for both Mmps . In sum , Mmps modulate relevant aspects of circadian physiology acting at different levels through non-redundant activities . Mmp1 effect on PDF levels and on the structural remodeling of the dorsal terminals correlates with behavioral arrhythmicity . This observation gives rise to interesting interpretations . On one hand , altering PDF levels or even PDF cycling at the axonal terminals through Mmp1 deregulation leads to arrhythmicity in the locomotor activity paradigm , highlighting once again the relevance of this neuropeptide in the control of circadian behavior [12] . On the other hand , altering Mmp2 expression abolished structural plasticity but did not affect locomotor rhythmicity suggesting that daily axonal remodeling of PDF terminals is not essential for consolidation of rest-activity cycles , in turn opening the attractive possibility that other outputs could depend on such cyclical structural changes [15] . Thus , we propose that pacemaker neurons employ PDF and other classical neurotransmitters to convey time-of-day information to other clock neurons relevant in the control of locomotor activity patterns , and in addition , they communicate via synaptic outputs that are modulated by the daily remodeling of PDF arborizations to regulate other aspects of circadian physiology . In agreement with this possibility , the mammalian suprachiasmatic nucleus uses diffusible signals , like neuropeptides , to daily adjust locomotor activity while depends on synaptic connections to control circadian release of hormones [53]–[55] . Mmp1 overexpression leads to a strong reduction of PDF levels in the sLNv axonal terminals while silencing Mmp1 expression clamps PDF levels high , comparable to the daytime configuration . In vitro analysis demonstrated that Mmp1 can cleave PDF at specific peptide bonds between the first serine-leucine and between two consecutive leucines; the latter a preferred position for several mammalian MMPs [56] ) , strongly suggesting that Mmp1 could terminate PDF biological activity . In favor of this possibility , it was reported that similar fragments ( PDF1-7 and PDF8-18 , targeting the peptide bond between S-L ) generated by a different ( human neprilysin ) peptidase do not activate the PDF receptor [39] . Noteworthy , Mmp1 has been shown to be a direct target of the CLOCK transcription factor [57] , enriched in PDF neurons particularly at the beginning of the night ( [28] , [31] , [58] and Figure S1F ) , which correlates with low PDF immunoreactivity . This time-of-day dependent expression profile , together with the in vitro and in vivo demonstration of a link between both molecules included here , strongly supports the possibility that endogenous Mmp1 could actively control PDF levels at the dorsal terminals . Interestingly , it has been reported that while most mammalian MMPs are secreted in an inactive form , a few of them contain a RXK/RR motif recognized by furin , which would enable them to be activated by intracellular serin proteinases before they are exported ( reviewed in [59] ) . Furthermore , Mmp1 contains a similar furin consensus sequence ( RXKR ) that could mediate its intracellular activation [38] . Thus , in principle , MMP1 could be activated within PDF terminals and thus modulate PDF levels at the protocerebrum . Alternatively , MMP1 could degrade PDF in the extracellular space . Lower PDF levels available would give rise to a reduced PDF signaling onto the sLNvs ( mediated by PDFR ) , and in doing so they could alter excitability ( Seluzicki et al . 2014 ) and in turn affect PDF release ( Figure S4C and [60] ) . Genetic interaction experiments suggest that Mmp1 is involved in the active ( and daily ) pruning of PDF axonal arborizations through modulation of the activity of EcR and axonal fasciculation; these observations are in line with a recent report showing that MEF2 mediates the activity-dependent remodeling taking place at the PDF dorsal terminals through the regulation of Fasciclin2 [32] . In addition , activation of B1-EcR triggers dendrite remodeling through the action of Mmps during metamorphosis [27] . Interestingly , several proteins induced by EcR ( for example , the ABC transporter E23 ) are enriched at the beginning of the night in PDF neurons [31] . Strikingly , PDF overexpression rescued the decreased axonal complexity triggered by Mmp1 overexpression . Moreover , adult-specific PDF downregulation reduced axonal complexity and rendered the structure in the nighttime configuration , similar to the effect of Mmp1 overexpression . Thus , as it has been reported during development [33] , PDF neurons modulate the structure of their own axonal projections via the action of the PDF neuropeptide . Taking these results into account we propose that PDF changes , acting directly via receptors in the sLNvs or indirectly through retrograde signals released by other PDFR immunoreactive neurons [61] , could provide relevant feedback information to pacemaker neurons and thereby adjust their connectivity . In addition to the role of the molecules identified throughout this work and elsewhere [32] , we previously demonstrated that adult-specific electrical silencing of PDF neurons reduces axonal complexity without abolishing circadian oscillations in their complexity , while it clamps PDF levels to the nighttime configuration [30] , underscoring that although electrical activity is relevant for structural plasticity , other activity-independent mechanisms underlie axonal remodeling of the sLNv arborizations . During the early morning lLNvs show higher action potential ( AP ) firing rate compared to the early night [13] , [62] and the limited data available on the electrophysiological properties of the sLNv neurons points in the same direction [13]; these changes in electrical properties are accompanied by high and low PDF immunoreactivity in the terminals during day and night respectively [12] . Since activity of a subset of mammalian MMPs can be modulated by electrical stimuli [63] , [64] , circadian changes in the electrical activity of sLNv neurons could modulate the activity of endogenous Mmps . This modulation would act in concert with the proposed clock- controlled transcriptional regulation of Mmp1 expression [29] . In this context , we propose that during the day , higher sLNv electrical activity along with low Mmp1 levels determine high PDF immunoreactivity in the axonal terminals; peptide signaling in turn promotes a more complex axonal arborization . In contrast , at night , reduced electrical activity and high Mmp1 levels result in decreased PDF immunoreactivity at the axonal terminals and this , along with the action of Mmp2 , Fas2 and EcR , reduces the complexity of axonal projections ( Figure 6 ) . In sum , the results presented here demonstrate that pacemaker neurons adjust their axonal arbors in a cell-type autonomous manner by recruiting complex mechanisms involving matrix metalloproteinases , modulation of the Ecdysone Receptor , changes in fasciculation and signaling through the PDF neuropeptide .
Flies were grown and maintained at 25°C in vials containing standard cornmeal/agar medium supplemented with yeast under 12∶12 h light∶dark cycles . GeneSwitch expression was induced by transferring 1–4 day old adult males to food containing RU486 ( mifepristone , Sigma , USA ) in 80% ethanol to a final concentration of 200 µg/ml ( or 500 µg/ml in the case of UAS-pdf rescue experiments ) or with the same amount of ethanol ( vehicle ) in control treatments . All stocks used in this study were described previously: pdf-GeneSwitch ( pdf-GS ) was generated in our laboratory [30] , UAS-Mmp1 ( chromosomes II and III ) and UAS-Mmp2 ( chromosomes II and III ) were gently provided by A . Page-McCaw [24] , UAS-Mmp1RNAiB and UAS-Mmp2RNAiB by D . Bohmann [65] and UAS-pdf by P . Taghert [5] . w1118 ( #40015 ) , UAS-CD8GFP ( #5137 ) , UAS-CD8RFP ( #27398 ) , UAS-ANFGFP ( #7001 ) and UAS-myrRFP ( #7119 ) were obtained from the Bloomington Stock Center . UAS-Mmp1RNAi ( #101505 ) , UAS-Mmp2RNAi ( #107888 ) , UAS-Dicer2 ( #60008 and 60009 ) , UAS-pdfRNAi ( #4380 ) , UAS-EcRRNAi ( #37059 ) and UAS-Fas2RNAi ( #36351 ) were obtained from the Vienna RNAi Stock Center . Experiments shown in Figure S1 G were carried out with the RNAi lines generated by the Bohmann laboratory [65] . Male adult flies ( 2–4 days old ) were placed in glass tubes containing standard food ( supplemented with 200 µg/ml RU 486 or vehicle , as indicated in each experiment ) and monitored for activity with infrared detectors and a computerized data collection system ( TriKinetics , Waltham , MA ) . Activity was monitored in LD conditions for 4 days , followed by constant darkness for 9–10 more days ( DD ) . Period and rhythmicity in DD were estimated using ClockLab software ( Actimetrics , Evanston , IL ) as previously described [66] . Adult heads were fixed with 4% formaldehyde in 100 mM phosphate buffer pH 7 . 5 for 30 min at room temperature ( RT ) . Brains were dissected and rinsed three times in PBS with 0 . 1% Triton X-100 ( PT ) for 15 min , with the exception of immunohistochemistry against Mmp1 were PBS with 0 . 6% Triton X-100 was used in all the incubations . Samples were blocked in 7% normal goat serum for 1 h in PT , and incubated with primary antibody at 4°C overnight . The primary antibodies employed were rabbit anti-GFP 1∶500 ( Invitrogen , USA ) , chicken anti-GFP 1∶500 ( Upstate , USA ) , rabbit anti-RFP 1∶500 ( Rockland , USA ) , a cocktail of mouse anti-Mmp1 antibodies 1∶10 ( 3A6B4 , 3B8D12 and 5H7B11 from DSHB ) and homemade rat anti-Drosophila-PDF 1∶500 [30] . Samples were washed 4×15 min in PT , and incubated with secondary antibody at 1∶250 for 2 h at RT; secondary antibodies were washed 4×15 min in PT and mounted in 80% glycerol in PT . The secondary antibodies used were Cy2-conjugated donkey anti-rabbit , Cy2-conjugated donkey anti-chicken , Cy3-conjugated donkey anti-rat , Cy3-conjugated-donkey anti-rabbit , Cy3-conjugated donkey anti-mouse , Cy5-conjugated donkey anti-mouse IgG1 ( Jackson InmunoResearch , USA ) . Images were taken either on a Zeiss Pascal LSM or a Zeiss LSM 510 Meta Confocal microscope . Images were taken with a 40× objective and an optical zoom of 2× . For the analysis of PDF immunoreactivity all pictures were taken employing the same confocal settings and quantification was performed using Image J software ( downloaded from http://rsbweb . nih . gov/ij/ ) . Briefly , mCD8GFP signal was adjusted to threshold levels generating a selection that delimit the area of sLNv axonal terminals . This selection was then applied to the PDF channel and mean intensity was measured . A rectangle of the same or a higher area was located outside of PDF neurons and used to subtract background signal . The same protocol was applied to measure GFP levels in ANF-GFP experiments with the exception that mCD8RFP was used to delimit the circuitry . Structural plasticity was analyzed as reported [14] . Total axonal length was measured with the LSM Image Browser Software by following the principal axonal branch of the dorsal projections ( illustrated in Figure S1C ) . In all cases the analysis was performed blind . Total RNA isolation from fly head extracts was performed using Trizol ( Invitrogen , Carlsbad , CA ) , and FastStart Universal SYBR Green Master ( Roche ) was used for reverse transcription following the manufacturer's instructions . The real-time assays were conducted in the Stratagene Mx3000P QPCR System ( La Jolla , CA ) using SYBR green as the detection system and ROX as reference dye . The primers were designed using Primer3 ( available online at http://frodo . wi . mit . edu/primer3/ ) . mRNA levels were assessed from four independent RNA extractions and two technical replicates were performed on each sample . Only primer pairs with efficiency between 90% and 110% were used . For pdf the following primers were used: Forward ‘GCCACTCTCTGTCGCTATCC’ and Reverse ‘CAGTGGTGGGTCGTCCTAAT’ . RpL49 was used for normalization and the following primers were used: Forward ‘GAACAAGAAGGCCCATCGTA’ and Reverse ‘AGTAACAGGCTTGGCTTGC’ . The catalytic domains of Drosophila Mmp1 ( 735 bp ) and Mmp2 ( 483 bp ) were expressed in E . Coli as a His fusion protein . Catalytic domains were cloned after PCR amplification using the following plasmids as templates ( Drosophila Genome Resource Center , RE19818 and SD03462 for Mmp1 and Mmp2 , respectively ) and then transformed into E . coli BL21 AI ( Invitrogen ) . The following primers were used: Forward ‘CAATCGGCACCCGTTTCCACC’ and Reverse ’CTAATACAGTGACTGGATGGCCGC’ for Mmp1 and Forward ‘CAGGGACCCAAGTGGTCCAGAA’ and Reverse ‘AACCTAGTACAACTGCTGAATGCC’ for Mmp2 . Expression was induced by addition of 0 . 2% L-arabinose ( Calbiochem ) , followed by incubation for 2 hours at 37°C . Recombinant Mmp1 was solubilized using 50 mM Tris-HCl ( pH 8 . 5 ) containing 500 mM NaCl , 2 M urea , 1 mM β-mercaptoethanol , 1 mM PMSF , and 1% Triton x-100 . Recombinant Mmp2 was solubilized using 20 mM Tris-HCl , pH 7 . 6 , containing 6 M GdnHCl and 5 mM DTT . Both recombinant proteins were purified by FPLC with a His Trap Ni2+-chelating column ( GE Healthcare ) with a 0–250 mM Imidazol gradient at 0 . 5 ml/min flow during 50 minutes . After sodium dodecyl sulfate–polyacrylamide gel electrophoresis ( SDS-PAGE ) analysis , fractions with the recombinant protein were pooled . Refolding of Mmp1 was achieved by dialysis against a 50 mM Tris buffer ( pH 7 . 6 ) containing 5 mM CaCl2 , 200 mM NaCL , 50 µM ZnSO4 , 0 . 05% Brij 35 , 20% glycerol and 2 mM DTT , O . N at 4°C . Refolding of Mmp2 was obtained by a 2-step dialysis , first against a 50 mM Tris buffer ( pH 7 . 6 ) containing 5 mM CaCl2 , 200 mM NaCL , 50 µM ZnSO4 , 0 . 05% Brij 35 , 20% glycerol and 2 M GndHCl , for 16 h at 4°C and then against the same buffer containing 2 mM DTT without GndHCl , for 16 h at 4°C . After concentration with a 10 kDa cut-off Amicon Ultra-15 Centrifugal Filter ( Millipore ) , the enzyme preparations were stored with 40% glycerol at −20°C for activity assays . The enzymatic activity of purified recombinant Mmps was confirmed using well characterized substrates [38] , [42] . Four µg of synthetic fibronectin ( Sigma ) were incubated alone or together with 250 ng of purified Mmp1 , with or without pre-incubation with 1 mM Batimastat inhibitor ( Sigma ) for 30 min at room temperature . The reaction buffer was 0 . 1 M Hepes , 0 . 1 M NaCl ( pH 7 . 4 ) . After incubation for 18 h at 37°C , samples were analyzed by 7 . 5% SDS–PAGE in Tris–Tricine gels and stained with Coomassie Brillant Blue . The enzymatic activity of purified recombinant Mmp2 was analyzed by using the synthetic OmniMMP™ fluorogenic substrate Mca-Pro-Leu-Gly-Leu-Dpa-Ala-Arg-NH2 . AcOH ( Enzo Life Sciences ) . The substrate at 1 µM and Mmp2 at 200 mM were incubated in assay buffer 50 mM HEPES , 10 mM CaCl2 , 0 . 05% Brij 35 and 10 µM ZnCl2 pH 7 . 0 for 1 h at 37°C . For its inhibition , Mmp2 was previously incubated with 1 mM Batimastat for 30 min at room temperature . The emission at 393 nm for 1 h and the emission spectra between 350 and 450 nm were measured in a JASCO FP-6500 espectrofluorometer at 37°C ( Ex . : 328 nm ) . The PDF peptide was synthesized at NeOmps ( France ) ; the primary sequence is YNSELINSLLSLPKNMNDA; since it was originally synthesized for coupling to a carrier during antibody production an additional tyrosine ( Y ) at position 1 was included . Synthetic PDF was purified by reverse-phase high performance liquid chromatography ( HPLC ) , and evaluated by matrix assisted laser desorption ionization time-of-flight ( MALDI-TOF ) mass spectrometry ( MS ) ( Cequibiem , Universidad de Buenos Aires , Argentina ) . The lyophilized peptide was dissolved in 0 . 1 M Hepes , 0 . 1 M NaCl ( pH 7 . 4 ) and aliquoted and stored at −20°C for further use . One hundred and fifty µg of PDF were incubated with 1 µg of purified Mmp1 or Mmp2 , with or without pre-incubation ( 30 minutes at room temperature ) with 1 mM Batimastat inhibitor ( Sigma ) for 5 , 15 and 60 minutes at 37°C in 0 . 1 M Hepes , 0 . 1 M NaCl ( pH 7 . 4 ) in a final volume of 100 µl . Reactions were stopped by the addition of 50 µl of 1% ( v/v ) trifluoroacetic acid ( TFA ) and the final volume was made up to 500 µl with milli-Q water . The intact/parent peptide and peptide fragments generated by peptidase activity were resolved and quantified by reverse-phase HPLC using a C18 Beckman 5 µm ( 4 . 6 mm×25 cm ) column and detection at 214 nm [67] . Peptides were eluted with a linear gradient from 0% to 60% acetonitrile in 0 . 1% TFA at 1 ml/min flow during 1 hour . The differential peaks were analysed by mass spectrometry . Molecular masses of intact peptides and the products of Mmp1 degradation were determined ( CEQUIBIEM , University of Buenos Aires ) . Samples were desalted through reversed-phase ZipTip ( Millipore , MA ) following manufacturer's instructions and analyzed on an Ultraflex II MALDI TOF TOF ( Bruker Daltonics ) in Reflectron Positive mode and Lift mode using standard instrument settings , and HCCA matrix . Statistical analyses were performed with the InfoStat package version 2009 ( Grupo InfoStat , FCA , Universidad Nacional de Córdoba , Argentina ) . Normality was tested using Shapiro-Wilks test and the homogeneity of variance was assessed with Levene's test . In all the graphs , experimental groups with different letters indicate statistically significant differences . To illustrate with an example , groups with letters AB are not statistically different from groups coded either with an A or a B but they are statistically different from groups with a letter C . p<0 . 05 was considered statistically significant . For structural plasticity analysis and circadian PDF and ANF-GFP immunoreactivity a two way ANOVA with Circadian Time ( CT ) and Genotype as factors was performed . In the case of structural plasticity analysis , each independent set of crosses ( including one vial per condition ) was considered as a blocking factor to reduce the variability between experiments . Locomotor activity was analyzed by a two way ANOVA with Genotype and Treatment ( RU or Vehicle ) as factors . During two way ANOVA analysis , the significance of the interaction between the two factors ( Genotype×CT or Genotype×Treatment ) was first analyzed . If the interaction was statistically significantly different ( i . e . , p<0 . 05 ) a Duncan multiple comparison test over the Interaction was performed . In the cases where the interaction was not significantly different , we performed the statistical analysis for each factor separately ( analysis of principal effects ) . As a consequence , in those cases , it was not possible to perform all the combinatorial comparisons between experimental groups but only the ones indicated in the corresponding figures . For qRT-PCR analysis differences between genotypes were studied by a one way ANOVA . For the statistical analysis of axonal crosses per ring ( shown in Figure S1 ) a repeated measured design was applied where Genotype and CT were considered external factors and Ring an internal factor repeated in space . To simplify the analysis only the axonal crosses with rings , 1 , 3 and 6 were taken into account . To analyze ANOVA assumptions , Box's test for homogeneity of variance and covariance matrices , and Mauchly's sphericity test were performed . In all the cases replicates indicate the number of independent experiments . The number of flies used per experiment is indicated in the legend of each particular figure and was adjusted to minimize the internal variance between individuals and considering the specific requirements of the technique . | Circadian clocks have evolved as mechanisms that allow organisms to adapt to the day/night cyclical changes , a direct consequence of the rotation of the Earth . In the last two decades , and due to its amazing repertoire of genetic tools , Drosophila has been at the leading front in the discovery of genes that account for how the clock operates at a single cell level , which are conserved throughout the animal kingdom . Although the biochemical components underlying these molecular clocks have been characterized in certain detail , the mechanisms used by clock neurons to convey information to downstream pathways controlling behavior remain elusive . In the fruit fly , a subset of circadian neurons called the small ventral lateral neurons ( sLNvs ) are capable of synchronizing other clock cells relying on a neuropeptide named pigment dispersing factor ( PDF ) . In addition , a number of years ago we described another mechanism as a possible candidate for contributing to the transmission of information downstream of the sLNvs , involving adult-specific remodeling of the axonal terminals of these circadian neurons . In this manuscript we describe some of the molecular events that lead to this striking form of structural plasticity on a daily basis . | [
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| 2014 | Mmp1 Processing of the PDF Neuropeptide Regulates Circadian Structural Plasticity of Pacemaker Neurons |
Perception of extracellular signals by cell surface receptors is of central importance to eukaryotic development and immunity . Kinases that are associated with the receptors or are part of the receptors themselves modulate signaling through phosphorylation events . The rice ( Oryza sativa L . ) XA21 receptor kinase is a key recognition and signaling determinant in the innate immune response . A yeast two-hybrid screen using the intracellular portion of XA21 , including the juxtamembrane ( JM ) and kinase domain as bait , identified a protein phosphatase 2C ( PP2C ) , called XA21 binding protein 15 ( XB15 ) . The interaction of XA21 and XB15 was confirmed in vitro and in vivo by glutathione-S-transferase ( GST ) pull-down and co-immunoprecipitation assays , respectively . XB15 fusion proteins purified from Escherichia coli and from transgenic rice carry PP2C activity . Autophosphorylated XA21 can be dephosphorylated by XB15 in a temporal- and dosage-dependent manner . A serine residue in the XA21 JM domain is required for XB15 binding . Xb15 mutants display a severe cell death phenotype , induction of pathogenesis-related genes , and enhanced XA21-mediated resistance . Overexpression of Xb15 in an XA21 rice line compromises resistance to the bacterial pathogen Xanthomonas oryzae pv . oryzae . These results demonstrate that Xb15 encodes a PP2C that negatively regulates the XA21-mediated innate immune response .
Protein kinases regulate most cellular signal transduction pathways including cell growth and proliferation , cellular differentiation , morphogenesis , gene transcription , and immunity [1–3] . Adaptive immunity , restricted to vertebrates , is characterized by the creation of antigen-specific receptors through somatic recombination in maturing lymphocytes [4] . In contrast , innate immunity , common to both animals and plants , is mediated by a set of defined receptors referred to as pathogen recognition receptors ( PRRs ) . Recognition of pathogen-associated molecular patterns ( also called microbe-associated molecular patterns ) or pathogen-derived avirulence ( Avr ) molecules by PRRs triggers signal transduction pathways mediated by activation of mitogen-associated protein kinase ( MAPK ) cascades and transcription factors [4 , 5] . These pathways lead to a core set of defense responses including accumulation of defense related molecules , increases in reactive oxygen species , calcium fluxes , and programmed cell death ( PCD ) [4–6] . In animals , recognition of pathogen-associated molecular patterns in extracellular compartments or at the cell surface is largely carried out by the Toll-like receptor ( TLR ) family containing leucine rich repeats ( LRRs ) in the extracellular domain [6] . TLRs associate with the interleukin-1 receptor-associated kinase ( IRAK ) family [7] and with receptor interacting-protein ( RIP ) kinases [8] via adaptor proteins . In plants , cell surface recognition of pathogen-associated molecular patterns or pathogen-derived Avr molecules is largely carried out by the non-RD class of receptor kinases ( RKs ) [6 , 9] . These “non-RD” kinases typically carry a cysteine ( C ) or glycine ( G ) before the conserved aspartate ( D ) residue . In contrast , the larger group of “RD” kinases have an arginine ( R ) immediately preceding the conserved catalytic aspartate ( D ) [10 , 11] . The RD class of kinases includes nearly all receptor tyrosine kinases and most characterized plant receptor serine/threonine kinases [10] . The non-RD class includes members of human IRAKs and RIPs , Drosophila Pelle , and members of plant RKs belonging to the IRAK family [10 , 12 , 13] . Plant genome analyses have revealed the presence of a large family of these non-RD IRAK kinases , with more than 45 encoded in the Arabidopsis genome and more than 370 found in the rice genome [10 , 14] . Members include the Arabidopsis flagellin RK ( FLS2 ) , the Arabidopsis elongation factor Tu RK ( EFR ) [15 , 16] , the rice XA26 and Pi-d2 RKs [17–19] , and the rice XA21 RK that mediates recognition of the Gram negative bacteria Xanthomonas oryzae pv . oryzae ( Xoo ) [18 , 20] . Because the presence of the non-RD motif in IRAK kinases is correlated with a role of the protein in pathogen recognition , there is great interest in understanding how they are regulated [10 , 21] . Unlike the majority of RD kinases , where phosphorylation of the activation loop is critical for activation [22] , the mechanism of non-RD kinase regulation , in which many non-RD kinases do not autophosphorylate the activation loop , remains to be elucidated [10] . The juxtamembrane ( JM ) domain , a region of the RK that is N terminal to the kinase domain , has been suggested to be important for regulation of non-RD RKs and to serve as a high affinity binding site for downstream signaling proteins [10 , 23] . Although non-RD IRAK kinases are clearly essential for innate immunity in both plants and animals , sustained or highly induced immune response can be harmful [24] . It is therefore necessary that PRR signaling through non-RD kinases be under tight negative regulation . For example , misregulation of the non-RD IRAK1 in mice induces activation of nuclear factor κB ( NF-κB ) and increases inflammatory responses to bacterial infection [25 , 26] . Dephosphorylation of kinases by protein phosphatases ( PPs ) is a common mechanism for downregulating kinase-mediated signaling [27] . PPs are classified into two major classes: tyrosine phosphatases and serine/threonine phosphatases , depending on their substrates [27 , 28] . In humans , a group of MAPK phosphatases ( MKPs ) including MKP1 , MKP5 , and dual specificity PPs , negatively regulate the innate immune response [24 , 27 , 29] . In contrast to animal systems , negative regulation of kinases involved in plant innate immunity is not well understood . One important class of negative regulators are PP 2Cs ( PP2Cs ) , a group of serine/threonine phosphatases that function as monomers and require Mn2+ and/or Mg2+ for activity [28 , 30] . One of the best characterized plant PP2Cs is the Arabidopsis kinase-associated PP ( KAPP ) , which interacts with many RKs including FLS2 , CLAVATA1 ( CLV1 ) , somatic embryogenesis RK 1 , brassinosteroid-insensitive 1 ( BRI1 ) , and BRI1-associated RK 1 [31–36] . Overexpression of KAPP in Arabidopsis results in loss of sensitivity to flagellin treatment , suggesting that KAPP negatively regulates the FLS2-mediated defense response [33] . Arabidopsis CLV1 controls stem cell identity in shoot and flower meristems . In vitro , a CLV1 fusion protein can phosphorylate KAPP . Conversely , KAPP dephosphorylates the kinase domain of CLV1 in vitro [3 , 31 , 37 , 38] . So far , KAPP is the only PP known to be involved in regulation of RK-mediated signaling and to be associated with RKs in plants [31 , 33] . The rice XA21 RK is one of a few plant PRRs that has been studied in depth [18] . Despite the clear biological role for XA21 in the rice innate immune response , very little is known about the mechanism by which XA21-mediated resistance is regulated . Although the rice KAPP protein emerged as a good candidate for being a negative regulator of the XA21-mediated innate immune response , it does not interact with XA21 [39] . This suggests the presence of another protein that negatively regulates XA21-mediated signaling pathway . In this study , we report the identification and characterization of rice XA21 binding protein 15 ( XB15 ) , which encodes a novel PP2C . Transgenic rice lines overexpressing Xb15 display compromised Xa21-mediated resistance to Xoo strains carrying AvrXa21 activity . Conversely , Xb15 Tos17 insertion mutants display cell death , constitutive induction of PR genes , and enhanced XA21-mediated resistance upon Xoo infection . Subsequent biochemical experiments show that XB15 can dephosphorylate autophosphorylated XA21 in a temporal- and dosage-dependent manner and that a serine residue in the XA21 JM domain is required for XB15 binding . Our findings are consistent with a model in which XB15 associates with the XA21 JM domain to negatively regulate the XA21-mediated innate immune response and cell death .
The rice XA21 protein is representative of the large class of non-RD RKs predicted to be involved in plant innate immunity [10 , 18 , 20] . To elucidate the mechanism of XA21-mediated resistance and to identify its interaction partners , we performed a GAL4-based yeast two-hybrid screen using a rice cDNA library constructed from Oryza sativa spp . Indica line , IRBB21 . IRBB21 is an isogenic line of the Indica variety IR24 carrying an introgression at the Xa21 locus [40] . It has been previously reported that both the JM region ( the portion of the cytoplasmic domain between the transmembrane sequence and the kinase domain ) and the C-terminal region of RKs , can serve as high affinity binding sites for downstream signaling proteins [23] . Therefore , the entire XA21 predicted intracellular region , including the JM , kinase , and C-terminal domains , called XA21K668 ( 668–1 , 025 amino acids ) , was used as bait for this screen . Of the 7 × 107 transformants screened , a total of eight unique clones both grew on selective media lacking histidine and tested positive for β-galactosidase reporter gene activity . These clones were named XA21 binding proteins ( XBs ) . We have previously reported characterization of another Xb , called Xb10 , which encodes a WRKY transcription factor [41] , and Xb3 , which encodes an ubiquitin ligase [42] . No RKs were isolated in this screen . Here , we report the functional characterization of one of the isolated interacting proteins , XB15 , the only PP among the isolated XBs . To assess whether the XA21 JM domain was important for the physical interaction of XB15 with XA21 , we generated a new construct , XA21K ( TDG ) ( residues 705–1 , 025 ) , which lacks the JM domain . XA21K ( TDG ) was then used as bait in a yeast two-hybrid assay with XB15 ( Figure 1A ) . Activation of the lacZ reporter gene was dependent on the simultaneous presence of pAD-XB15 and pLexA-XA21K668 . XA21K ( TDG ) lacking the JM domain did not interact with XB15 . This result suggests that the JM domain contains specific amino acids needed for XB15 binding in yeast and that these residues may serve as a docking site for XB15 . The JM region ( XA21JM ) lacking the kinase domain was not sufficient for the interaction with XB15 ( Figure 1A ) . XB15 also failed to interact with the catalytically inactive mutant XA21K668K736E , in which lysine 736 is substituted with glutamic acid [43] , suggesting that XA21 kinase activity is also important for the interaction . On the basis of these results , we hypothesized that a residue ( s ) in the JM domain phosphorylated by XA21 may serve as a docking site for XB15 . It has previously been reported that XA21 autophosphorylates residues Ser686 , Thr688 , and Ser689 in vitro [44] . To test if these residues are important for XB15 binding , we assessed whether mutations in these sites affected binding activity . We found that all three XA21 single mutants , XA21S688A , XA21T688A , XA21S689A , as well as the triple mutant XA21S688A , T688A , S689A maintained interaction with XB15 . We next assessed the role of three previously uncharacterized Ser and Thr residues ( Ser697 , Ser699 , and Thr705 ) in the JM region of XA21 . When Ser697 is mutated to Alanine , interaction with XB15 is abolished ( Figure 1A ) . This result indicates that Ser697 in the XA21 JM domain is critical for interaction with XB15 . To confirm that the absence of interaction was not due to lack of expression of the fusion constructs in yeast cells , we performed protein gel blot analysis with anti-XB15 or anti-LexA antibodies . All yeast cells transformed with AD-Xb15 carried an 80-kDa band cross-reacting with the anti-XB15 antibody ( unpublished data ) . In Western blot analysis using an Anti-LexA antibody , yeast cells expressing LexA-XA21K668 or -XA21 variants , displayed bands with molecular weights corresponding to the correct size of each fusion protein ( Figure 1B ) . Yeast cells expressing the vector control produced a protein of 24 kDa that reacted with the anti-LexA antibody . Xb15 carries a 3 , 219-bp open reading frame that consists of three introns with lengths of 94 bp , 1 , 070 bp , and 138 bp and four exons . The ORF is predicted to encode a 639 amino acid protein ( Figure 2A ) with a molecular mass of 69 . 2 kDa and an isoelectric point of 5 . 4 . XB15 is similar in overall structure to known plant PP2Cs , with a conserved C terminus ( 240–630 amino acids ) , which is predicted to have phosphatase catalytic activity ( Figure 2A , underlines ) . It has a unique N terminus ( 1–239 amino acids ) with no similarity to proteins with known function . The catalytic domain is interrupted by approximately 100 amino acids with no similarity to any sequences in the database ( Figure 2A , with italics ) . XB15 contains six amino acids residues ( four aspartic acids , one glutamic acid , and one glycine ) known to interact with two Mg2+ or Mn2+ ions to form a binuclear metal center in Arabidopsis POL and human PP2Cα [45–47] . We next compared the PP2C domain of XB15 with other evolutionary related proteins from the reference plant species rice and Arabidopsis , such as Arabidopsis POL and POL-like proteins ( PLLs ) ( Figure 2B ) . XB15 shows significant similarity to PP2Cs from other organisms including Arabidopsis POL ( 47% identity ) and shares several features with the Arabidopsis POL and PLLs such as unique intron/exon boundaries and an insertion in the same region of the PP2C catalytic domain . Figure 2B shows the phylogenetic relationship among POL and PLL genes from rice and Arabidopsis based on the amino acid sequence of the last three exons . Arabidopsis POL and PLL1 cluster in one branch of the tree , consistent with the similar primary phenotype of pol and pll1 mutants—phenotypic suppression of clv mutants [48 , 49] . These two proteins have also been shown to regulate the balance between stem-cell maintenance and differentiation and are closely related to Wuschel , which encodes a homeodomain transcription factor expressed in shoot meristems [50] . XB15 groups with Arabidopsis PLL2 , PLL3 , PLL4 , and PLL5 , showing the greatest similarity to PLL4 and PLL5 with 57 . 5% and 56 . 3% identity , respectively . pll4 and pll5 mutants develop abnormal leaves that are altered in shape [48] . To date , no function has been reported for Arabidopsis PLL2 or PLL3 , or any of the rice PLLs including XB15 , leaving the functional role of these proteins unknown . To explore the role of Xb15 in resistance , we examined whether constitutive overexpression of Xb15 would affect resistance to Xoo . We first generated an Xb15 tagged N-terminal tandem affinity purification ( NTAP ) construct under the control of the ubiquitin promoter ( Ubi ) [51 , 52] and then introduced this construct into the rice cultivar Kitaake using our established transformation procedures [53 , 54] . The resulting transgenic plants ( NTAP-XB15 , pollen donor ) were crossed with another Kitaake line possessing an N-terminal Myc-epitope-tagged XA21 , under control of its native promoter ( Myc-XA21 , pollen recipient ) . The presence of the Myc-Xa21 and/or Ntap-Xb15 gene in the resulting F1 was confirmed by the PCR analysis ( unpublished data ) . Next , we tested the expression of NTAP-XB15 and Myc-XA21 in the cross ( XA21/XB15 17A-21 and 19A-72 ) using an anti-Myc antibody and peroxidase-anti-peroxidase ( PAP , for detecting the TAP tag ) ( Figure 3A ) . Bands corresponding to the predicted molecular mass of Myc-XA21 and NTAP-XB15 were detected at approximately 95 and 140 kDa , respectively confirming that the crossing was successful and that the progeny express both NTAP-XB15 and Myc-XA21 . Although equivalent amounts of protein were analyzed , no signals were observed in nontransgenic Kitaake plants . Six-week-old progeny from self-pollinated XA21/XB15 17A-18 , 17A-21 , 19A-64 , and 19A-72 were then inoculated with Xoo PR6 strain PXO99 expressing AvrXA21 and examined for cosegregation of genotype with phenotype by PCR analysis and measurement of the length of Xoo-induced lesions . All Myc-Xa21 plants overexpressing Ntap-Xb15 ( Myc-Xa21/Ntap-Xb15 , +/+ ) by PCR analysis showed accumulation of NTAP-XB15 by protein gel blot analysis and displayed enhanced susceptibility to Xoo PR6 compared to transgenic plants carrying Myc-Xa21 alone ( +/− ) ( Figure 3B ) . Western blot analysis revealed a higher accumulation of NTAP-XB15 protein in XA21/XB15 19A line compared to XA21/XB15 17A , which correlated with longer leaf lesions developed by Xoo PR6 , indicating that XB15 negatively regulates the XA21-mediated defense pathway in a dosage-dependent manner . We could barely distinguish a difference in XA21 accumulation with homozygous or heterozygous Myc-XA21 transgenic plants ( Figure S1 ) . Therefore , to rule out the possibility that this phenotypic difference is caused by a Xa21 dosage effect , we selected segregants ( F2 ) carrying Myc-Xa21 and analyzed the next generation ( F3 ) to determine which segregants were heterozygous or homozygous for Myc-Xa21 . After PCR genotyping 20 progeny from each line , we identified 17A-18–1 , 17A-18–2 , and 19A-72–5 as homozygous for Myc-Xa21 ( unpublished data ) . The remaining segregants are heterozygous for Myc-Xa21 . Inoculation analysis revealed that there is no correlation between the number of copies of Myc-Xa21 and the enhanced susceptibility phenotype in the NTAP-XB15 overexpression lines ( Figure 3B ) . These results indicate that the enhanced susceptibility observed in the crossing population ( F2 ) is caused by NTAP-XB15 overexpression and is not due to the presence of fewer copies of Myc-Xa21 . Figure 3C shows a picture of two typical leaves from each of the following inoculated rice plants: F2 segregants of XA21/XB15 17A and 19A , and transgenic plants overexpressing NTAP only ( NTAP ) 16 d after Xoo PR6 inoculation . While the Myc-XA21 plants lacking Ntap-Xb15 ( +/− , XA21/XB15 17A-18–2 and 17A-21–10 ) were highly resistant , showing short lesions ( approximately 2 cm ) , inoculated leaves of crossed lines ( +/+ ) , XA21/XB15 17A-21–7 and 19A-72–5 , developed typical water-soaked , long lesions ( approximately 8–10 cm ) . In inoculated NTAP-XB15 lines lacking Xa21 ( −/+ , XA21/XB15 19A-72–3 and 17A-21–8 ) , there was no significant difference from segregants not carrying Myc-Xa21 and Ntap-Xb15 ( −/− ) in lesion lengths . These results demonstrate that overexpression of XB15 reduces XA21-mediated resistance to Xoo . We quantified the effect of XB15 overexpression on bacterial growth by monitoring bacterial populations on XA21/XB15 17A and 19A plants ( F2 ) inoculated with Xoo PR6 ( Figure 4A ) . For both growth curve and lesion length analysis up until 4 d after inoculation ( DAI ) , there was no significant difference in bacterial growth in any of the lines . However , at 12 DAI , Xoo PR6 populations in the Kitaake and NTAP lines reached approximately 1 . 5 × 109 colony-forming units per leaf ( cfu/leaf ) : whereas , population in Myc-XA21 plants leveled off at fewer than 8 . 7 × 107 cfu/leaf . In XA21/XB15 19A carrying Myc-Xa21 and Ntap-Xb15 ( +/+ , 19A-72–5 , −6 , −8 , and −11 ) , Xoo PR6 populations grew to 8 . 5 × 108 cfu/leaf , a 10-fold increase compared to segregants carrying Myc-Xa21 alone ( +/− , 19A-72–1 , −10 and 17A-21–6 , −10 ) . A significant difference in bacterial growth between the lines was observed continuously up to 16 DAI . We also measured the length of Xoo-induced lesions on all rice lines ( Figure 4B ) . The progeny of XA21/XB15 19A and 17A ( +/+ , 19A-72–14 and 17A-21–15 ) displayed enhanced susceptibility to Xoo PR6 with lesions ranging in length from 4–10 cm compared to the segregants carrying Myc-Xa21 alone ( +/− , 17A-21–10 ) , which showed 2–3 cm lesion lengths . Segregants overexpressing Ntap-Xb15 but lacking Myc-Xa21 ( −/+ ) did not show significant difference in bacterial populations and lesion lengths , as compared to Kit and NTAP control . To further investigate the in vivo function of Xb15 , a knockout mutant line was identified from a collection of rice mutants generated by random insertion of the endogenous retrotransposon Tos17 ( http://tos . nias . affrc . go . jp/∼miyao/pub/tos17/ ) [55] . One mutant line ( NF9014 ) carried a Tos17 insertion in the third exon of the Xb15 gene ( Figure 5A ) . This insertion resulted in a lack of expression of the full-length Xb15 mRNA compared to the wild type Nipponbare plants when tested with appropriate primers by reverse transcription-PCR ( RT-PCR ) ( Figure 5B ) . 18S ribosomal RNA ( 18S rRNA ) was used as an internal control . The homozygous insertion mutant line ( −/− ) showed a severe cell death phenotype marked by the appearance of small necrotic lesions that were apparent at the vegetative stage ( Figure 5C ) . Of the 24 segregants of 6-16-30 , six out of eight of the heterozygotes ( +/− ) displayed the cell death phenotype . Two of the heterozygotes displayed a severe cell death phenotype similar to that observed in the homozygous individuals ( −/− ) . In contrast , all nine homozygous ( −/− ) plants displayed a cell death phenotype and seven of these were very severe ( Figure S2 ) . We next tested if the cell death phenotype correlated with alterations in PR gene expression . Total RNA was extracted from the mutant line and the Nipponbare control . RT-PCR was performed with primers targeting PR genes as molecular markers . Figure 5D shows that the expression of full-length Xb15 was not detected in the Tos17 mutant line . In contrast , all the defense-related genes tested , PR1a , PR1b , PR10 , Betula verrucosa 1 ( Betv1 , major birch allergen ) , and probenazole-inducible protein 1 ( PBZ1 ) were highly expressed in the insertion mutant lines ( −/− ) but barely expressed in Nipponbare wild type ( WT ) or the no-insertion null plants ( +/+ ) . Both the internal controls ( 18S rRNA and elongation factor 1 α [EF1α] ) showed constitutive expressions in all tested plants . To confirm that the cell death phenotype of the Tos17 insertion mutant is due to the loss of function of XB15 , we constructed a plasmid for the gene-specific knockdown of Xb15 by RNA interference ( RNAi ) with an inverted repeat of a specific fragment of Xb15 cDNA under control of the Ubi promoter . The protein level of XB15 in Xb15 RNAi transgenic rice plants was severely reduced compared with the Kitaake control ( Figure S3A ) . We observed a similar cell death to that detected in the Tos17 insertion mutant line in several independent RNAi lines ( Figure S3B ) . Taken together , these results show that loss-of-function of XB15 induces expression of defense-related genes and elicits a cell death phenotype , suggesting a negative regulatory role for Xb15 in the defense signaling pathway . We have shown that the Xb15 Tos17 insertion mutant line ( NF9014 ) accumulates PR gene transcripts ( Figure 5 ) . On the basis of this result , we hypothesized that these lines would be more resistant to Xoo . However , because Nipponbare plants are already moderately resistant to Xoo PR6 and because we are pushing the limits of sensitivity of our bacterial assay , we were unable to detect enhanced resistance in these lines ( unpublished data ) . To further explore the role of Xb15 in resistance , we carried out additional experiments in the Kitaake genetic background , which is highly susceptible to Xoo PR6 ( Figures 3 and 4 ) . In this experiment , we crossed the Xb15 Tos17 insertion mutant line ( NF9014–1 ) ( pollen recipient ) with a Kitaake line containing Myc-XA21 under control of its native promoter ( pollen donor ) . We then tested if the loss of XB15 function alters the XA21-mediated defense response . We were able to simultaneously test for both reduced and enhanced resistance by taking advantage of the fact that the resistance conferred by XA21 is developmentally regulated . At the juvenile two-leaf stage ( ∼2 wk old ) , the plants are fully susceptible . Full resistance develops only at the adult stage [56] . We therefore inoculated progeny ( F4 ) from the cross ( NF9014–1/Myc-XA21 ) at 3 wk old when XA21 resistance is not yet fully active . At this stage of development , XA21 rice plants are only partially resistant ( approximately 40% of resistance to that of 6-wk-old plants ) [56] . Because lesion development continues to 16 d ( Figure 4 ) , we measured the lesion lengths at 18 d to capture as large a difference as possible ( Figure 6A ) . We found that progeny carrying Xa21 and the Xb15 Tos17 mutation ( xb15/xb15 , Xa21/− ) , displayed enhanced resistance to Xoo PR6 with lesions ranging length from 1–2 cm compared to the segregants carrying Xa21 alone ( Xb15/Xb15 , Xa21/− ) , which showed approximately 4-cm lesion lengths . The difference was statistically significant . In contrast , although enhanced resistance was observed in the Xb15 Tos17 mutant lines lacking Xa21 ( xb15/xb15 , −/− ) as compared to the wild-type Xb15 line ( Xb15/Xb15 , −/− ) , this difference was not statistically significant ( Figure 6A ) . This result indicates that , using our currently available methods , the enhanced resistance caused by the Xb15 mutation can be detected only in the presence of XA21 . To further validate the enhanced resistance phenotype conferred by the Xb15 Tos17 mutation in the XA21 genetic background , we measured bacterial populations in these lines . In Figure 6B , we show that Xoo populations multiplied to 9 . 2 × 107 cfu/leaf in the XA21 plants carrying the Xb15 Tos17 mutation ( xb15/xb15 , Xa21/− ) . In contrast , in the absence of the Xb15 Tos17 mutation ( Xb15/Xb15 , Xa21/− ) , bacterial populations reached to 3 . 5 × 108 cfu/leaf a 3 . 8-fold increase compared to the Xb15 Tos17 mutant lines . These results clearly show that loss of XB15 function enhances XA21-mediated resistance . To elucidate the mechanism of XB15 negative regulation of XA21-mediated resistance , we initiated in vitro biochemical studies . We first examined whether Xb15 encodes a functional PP2C and if alterations in XA21-mediated resistance are caused by the putative PP2C activity of XB15 . Full length XB15 ( GST-XB15FL ) tagged with an N-terminal tagged glutathione-S-transferase ( GST ) recombinant fusion protein and GST protein alone were expressed in E . coli . A smaller clone expressing the PP2C catalytic region alone , without the N-terminal extension ( GST-XB15ΔN ) was also expressed to check its possible regulatory function . We purified and assayed the recombinant proteins for phosphatase activity by measuring the release of phosphate from a phosphorylated synthetic peptide [57] . Figure 7A shows that in the presence of the substrate , both GST-XB15FL and GST-XB15ΔN , but not GST alone , catalyze the release of 240–350 pmoles of phosphate . This reaction was inhibited by the serine/threonine phosphatase inhibitor , sodium fluoride ( NaF ) , but not by vanadate , a tyrosine phosphatase inhibitor . These results indicate that Xb15 encodes a protein serine/threonine phosphatase not a tyrosine phosphatase , as predicted on the basis of a sequence analysis that places XB15 in the PP2C clade . To experimentally test which class of serine/threonine phosphatases XB15 falls into , we incubated the recombinant GST-XB15FL , GST-XB15ΔN , and GST proteins with the substrate in various reaction buffers optimized for activity for different protein PP classes , PP2A , PP2B , and PP2C ( Figure 7B ) [57] . As predicted , XB15 enzyme activity was detected in PP2C buffer but not in PP2A and PP2B buffer . The presence of Mg2+ in the PP2C buffer was required for full activity . Significant inhibition was observed in PP2B buffer with saturating amounts of calmodulin and additional Ca2+ . While GST-XB15FL showed a slightly lower PP activity than GST-XB15ΔN , unlike POL , inhibition of phosphatase activity by the N-terminal domain appears to be minimal ( Figure 7A and 7B ) . To test whether plant-expressed XB15 has PP2C activity , we used the NTAP-XB15 transgenic plants described above . The NTAP tag includes two IgG binding domains from Staphylococcus aurous protein A and a calmodulin binding peptide linked by a TEV protease cleavage site [51 , 52] . This tag has been successfully used for purification of native protein complexes from yeast , plants , and animals [52 , 58 , 59] . The NTAP-tagged XB15 ( NTAP-XB15 ) and NTAP alone were purified with IgG-agarose and assayed for their phosphatase activity in PP2C buffer . Figure 7C shows that NTAP-XB15 purified from transgenic rice plants carries PP2C activity , but not purified NTAP alone or boiled NTAP-XB15 . These experiments demonstrate that Xb15 encodes a serine/threonine protein PP2C . XB15 was originally isolated as one of XA21 binding proteins using the yeast two-hybrid screen . To rule out the possibility of false positive caused by nonspecific binding to XA21 , the specificity of the interaction between XA21 and XB15 was confirmed using a GST pull-down assay with immobilized GST-XA21K668 . GST-XA21K668 and HIS-XB15ΔN were expressed in E . coli and purified using glutathione and Nichel nitrilotriacetic ( Ni-NTA ) agarose beads , respectively . The purified recombinant HIS-XB15ΔN protein was incubated with glutathione beads bound to either GST-XA21K668 or GST alone in a binding buffer containing 2 mM MgCl2 . Figure 8A shows that the recombinant protein HIS-XB15ΔN specifically interacts with GST-XA21K668 ( lane 2 ) but not GST ( lane 3 ) or glutathione beads ( lane 1 ) . To further investigate the association between XA21 and XB15 in vivo , we used the Myc-XA21 transgenic plants described above . Anti-Myc antibody detected a 140-kDa polypeptide in transgenic plants carrying Myc-Xa21 but not in the control line Kitaake ( Kit ) ( Figure 8B ) . When the Myc-XA21 protein is enriched after immunoprecipitation ( IP ) with agarose conjugated anti-Myc antibody , an additional 100-kDa product was detected . We have previously reported that the 140-kDa polypeptide is Myc-XA21 and the 100-kDa polypeptide is a proteolytic cleavage product of Myc-XA21 ( Myc-XA21cp ) [42] . We then developed a polyclonal antibody against a synthetic peptide of XB15 ( anti-XB15 ) to detect the XB15 protein in planta and confirmed its specificity against XB15 from rice extracts . Anti-XB15 detected a major band of 70 kDa , which is close to the predicted size of XB15 ( 69 . 2 kDa ) ( Figure 8C ) . In the NTAP-XB15 overexpression line 17A and 19A , there was a significant increase in NTAP-XB15 ( 95 kDa ) compared to endogenous XB15 ( 70 kDa ) . When rice protein extracts were incubated with agarose conjugated anti-Myc antibody , an immune complex containing a 140-kDa polypeptide was precipitated and detected after western blot analysis with anti-Myc antibody in Myc-XA21 transgenic plants inoculated with Xoo PR6 but not in the Kitaake control ( Figure 8D ) . Although the same amount of total protein extract was used for each immunoprecipitation , the amount of Myc-XA21 protein precipitated by anti-Myc-antibody accumulated to greater amounts at 12 and 24 h after Xoo PR6 inoculation as compared to the 0-h time point . Next , we examined whether XB15 was co-immunoprecipitated with XA21 in the immune complex . A 70-kDa polypeptide was detected with the anti-XB15 antibody in Xoo PR6-inoculated protein extracts . This band was not detected in Kitaake lacking Myc-Xa21 and was barely detectable in mock-treated Myc-XA21 plants . The association between XB15 and XA21 was detectable by 12 h after inoculation with Xoo PR6 and had significantly increased after 24 h . As a control , similar experiments were performed with agarose-conjugated anti-Myc antibody presaturated with Myc peptide to check for a nonspecific interaction of XB15 with Myc peptide; we detected no interaction between XB15 and the Myc peptide ( unpublished data ) . These results demonstrate a direct interaction between XB15 and XA21 in leaves , consistent with our in vitro observations . We have previously shown that XA21 encodes a protein kinase and that autophosphorylation of the XA21 kinase domain occurs via an intramolecular mechanism [43] . Because XB15 is associated with XA21 both in vitro and in vivo , because the overexpression of XB15 compromises the XA21-mediated resistance , and because XB15 possesses PP2C activity , we hypothesized that XB15 could directly dephosphorylate XA21 . We tested this hypothesis by checking if XB15 could dephosphorylate the in vitro autophosphorylated XA21 . After expression in E . coli and purification , GST-XA21K668 bound to glutathione beads was autophosphorylated with [γ-32P] in vitro . Autophosphorylation was monitored by radioactive incorporation of γ-32P-ATP into the bead-binding fraction ( unpublished data ) and by SDS-PAGE . The exposure to X-ray film shows that the fusion protein , GST-XA21K668 is capable of autophosphorylation ( Figure 9A , 0 min ) . The 32P-autophosphorylated GST-XA21K668 was incubated with or without 1 μg of purified recombinant HIS-XB15ΔN in the presence of PP2C buffer for the indicated time ( Figure 9A ) . While XA21K668 remained labeled for over 60 min in the absence of XB15 ( unpublished data ) , dephosphorylation of XA21K668 was detectable after 1 min of incubation with XB15 . Additionally , dephosphorylation of XA21K668 by HIS-XB15ΔN was not detected in the presence of serine/threonine PP inhibitor , NaF ( 20 mM ) or when HIS-XB15ΔN ( 1 μg ) was boiled ( unpublished data ) suggesting that the dephosphorylation of XA21 is caused by the PP2C activity of XB15 . In further support of this conclusion , increasing the amount of HIS-XB15ΔN ( 1–10 μg ) resulted in a dosage-dependent dephosphorylation of GST-XA21K668 ( Figure 9B ) . Although XA21-dependent phosphorylation of XB15 was examined with recombinant proteins purified from E . coli at various time points and different conditions , we did not detect transphosphorylation of XB15 by XA21 ( unpublished data ) . From these results , we conclude that XA21 is a substrate of XB15 and that the XB15 phosphatase can effectively dephosphorylate XA21 . To further elucidate XB15 function , we assessed its subcellular localization . According to MultiLoc ( http://www-bs . informatik . uni-tuebingen . de/Services/MultiLoc ) [60] and PSORT ( http://www . psort . org/ ) [61] , XB15 has a putative nuclear-localization sequence ( Figure 2 ) and is predicted to be a nuclear protein . To investigate the in vivo cellular distribution of XB15 , a targeting experiment was performed using smGFP2 , as a fluorescent marker [62] . We fused the entire Xb15 coding region without the termination codon to the smGFP2 gene ( Figure S4A ) , and the resulting construct was introduced into rice Kitaake protoplast cells by PEG-mediated transformation [63 , 64] . Localization of the fusion protein was determined by visualization with an Olympus FV1000 confocal microscope . We introduced the smGFP2 gene alone into protoplast cells as a control . As shown in Figure S4B , the fusion protein was mainly localized to the plasma membrane of the rice protoplast cell , whereas the control smGFP2 was uniformly distributed throughout the cell except the large central vacuole . As a positive control for plasma membrane targeting , we cotransfected protoplasts with vector expressing a H+-ATPase-red fluorescent protein ( dsRed ) fusion protein , which also localized to the plasma membrane [65] . A close overlap was observed between the green and red fluorescent signals of XB15-smGFP2 and H+-ATPase-dsRed , respectively . This experiment was repeated with protoplasts extracted from transgenic rice carrying Xa21 gene and same localization of XB15 to the plasma membrane was observed ( unpublished data ) . We next tested if the large green fluorescent protein ( GFP ) tag would inhibit XB15 phosphatase activity . XB15-smGFP2 and smGFP2 alone were purified from transiently transformed protoplasts with agarose conjugated anti-GFP antibody and assayed for their phosphatase activity in PP2C buffer ( Figure S5 ) . We found that this purified XB15-smGFP2 carries PP2C activity similar to that observed for NTAP-XB15 ( Figure 7C ) , but not purified smGFP2 alone or boiled XB15-smGFP2 . Taken together , these results suggest that the tagged protein is functional and that , in vivo , XB15 is primarily targeted to the plasma membrane . Because its membrane targeting appears to be constitutive external signals are likely not required for its membrane translocation . This observation is also consistent with the predicted plasma membrane-localization of the intact XA21 protein [44] .
XB15 possesses an active PP2C domain in its C-terminal region . On the basis of sequence comparison and phylogenetic analysis among PP2Cs from the rice and Arabidopsis databases , XB15 shows relatively high similarity with Arabidopsis POL , PLL4 , and PLL5 ( Figure 2B ) . All cluster into a small subgroup of the rice/Arabidopsis phylogenetic tree . Abnormal leaf development was observed in Arabidopsis knockout mutants for pll4 and pll5 and transgenic plants overexpressing Pll5 [48] . Despite the sequence similarity with POL and PLLs , we observed no disorders of leaf development in the Xb15 knockout mutant and RNAi plants . Additionally , transgenic plants overexpressing Xb15 displayed normal leaf development . These results indicate that XB15 has distinct functions to that of the Arabidopsis POL and PLLs . The apparent differences in the function of XB15 and Arabidopsis POLs may be caused by their different localizations . Arabidopsis POL contains a putative nuclear-localization motif [47] and is predicted to be a nuclear protein according to the prediction methods for subcellular location , MultiLoc , and PSORT . In contrast , the fusion protein , XB15-smGFP2 is mainly localized to the plasma membrane of rice protoplast cells , suggesting that the in vivo function of XB15 is primarily related to the plasma membrane itself or to other membrane-localized proteins ( Figure S4 ) . Based on sequence analysis , XA21 is predicted to localize to the plasma membrane like other RKs , but its subcellular localization has not yet been demonstrated in vivo . Our in vivo data of XB15 combined with the putative localization of XA21 suggest that the interaction between XB15 and XA21 occurs at the plasma membrane , and may be similar to KAPP , which interacts with its target RKs at the plasma membrane to attenuate their signaling [34 , 36 , 67] . In animals , some receptor tyrosine kinases are controlled through autophosphorylation of their JM domains . For example , the mouse ephrin and KIT receptors , and human Fms ( Feline McDonough Sarcoma ) -like tyrosine kinase 3 , interact with protein tyrosine phosphatases via phosphorylated tyrosine residues in their JM domains . These interactions negatively regulate receptor tyrosine kinase-mediated signaling [68–71] . In plants , only a few RKs have been shown to autophosphorylate residues in their JM regions . These include barley HvLysMR1 , legume symbiosis RK , Arabidopsis BRI1 , and rice XA21 [43 , 72–76] . In spite of the presumed importance of the JM domain in plant RK-mediated signaling , no downstream target proteins that bind the JM regions and regulate RK signaling in a phosphorylation-dependent manner have yet been identified . XB15 failed to interact with the XA21K ( TDG ) mutant lacking the XA21 JM region and the catalytically inactive mutant XA21K668K736E ( Figure 1 ) , suggesting that XA21 autophosphorylation in the JM region is critical for the XB15/XA21 interaction [43] . Although autophosphorylation of XA21 has not been demonstrated in yeast , there have been many reports showing that plant proteins maintain their function when they are expressed in yeast as heterologous proteins [77–79] . For example , an Arabidopsis kinase , GSK3/shaggy-like protein rescues the phenotype of its yeast homolog mck1 , suggesting that plant kinases in yeast cells undergo the necessary post-translational modification for their in vivo functions [78] . Autophosphorylation of the JM residues Ser686 , Thr688 , and Ser689 was previously shown to be important stabilizers of XA21 protein levels [44] . XA21 proteins with mutations in these residues maintained interaction with Xb15 , suggesting that these phosphorylation sites mainly function in the stability of XA21 protein but are not directly involved with mediating the downstream signal transduction cascade . In contrast , when Ser697 is mutated to Alanine , interaction with XB15 is abolished , indicating that Ser697 is essential for XA21 binding with XB15 in yeast . The Ser697 mutation does not affect kinase activity of XA21S697A , suggesting that Ser697 serves as a high affinity binding site rather than as a regulator of kinase activity ( unpublished data , X . Chen , P . E . Canlas , M . Chern , D . Ruan , R . Bart , et al . ) . Taken together , these results suggest that XA21 requires its own kinase activity and residues in the JM region for interaction with XB15 . In support of this hypothesis , our preliminary results suggest that transgenic rice carrying the XA21 variant , XA21S697A , display enhanced resistance to Xoo PR6 at the 3-wk-old stage ( unpublished data , X . Chen , P . E . Canlas , M . Chern , D . Ruan , R . Bart , et al . ) . These results indicate that Ser697 is essential for XA21 binding with XB15 and that in the absence of XB15 binding to XA21 , negative regulation is compromised leading to enhanced resistance . In both animals and plants , PCD is a highly regulated process involved in immunity and other functions [66] . Many mutants exhibiting spontaneous PCD , initially isolated in maize [80] , have now been identified in other plants , including Arabidopsis , barley , and rice [80–86] . Analysis of these mutants has led to the identification of genes that regulate cell death [87] . The Xb15 Tos17 insertional mutant line , NF9014 , and Xb15 RNAi transgenic rice lines , RNAiXB15 , display spontaneous cell death on the leaves during development under green house condition ( 26–28 °C ) in the absence of obvious stress or disease . All of the tested defense-related PR genes commonly associated with the PCD and defense response [87–89] are constitutively expressed in the NF9014 mutant . In addition , progeny ( F4 ) carrying both Xa21 and the Xb15 Tos17 mutation derived from the cross ( NF9014–1/Myc-XA21 ) shows enhanced resistance to Xoo PR6 , indicating that XA21-mediated resistance is enhanced in the absence of XB15 ( Figure 6 ) . In the absence of XA21 , we do see enhanced resistance , although this difference is not statistically significant . We believe this unexpected result is due to the limitations in sensitivity of our assay . The PCD-like phenotype observed in plants lacking Xa21 suggests the presence of an additional signaling cascade that is negatively regulated by a functional XB15 ( Figure 5 ) . There are many examples of PP2Cs that function as negative regulators of multiple kinase cascades . In addition to Arabidopsis KAPP , which associates with at least five RKs [31–36] , mouse PP2Cε dephosphorylates and negatively regulates MKKKs , apoptosis signal-regulating kinase 1 and transforming-growth-factor-β-activated kinase 1 ( TAK1 ) [90] . In this report , we provide evidence that XB15 regulates at least one , but likely more , RK-mediated pathways , controlling PCD-like lesions . Figure 10 compares our working model for XA21-mediated innate immunity with the human TLR4 and Arabidopsis FLS2 signaling cascades . TLR4 contains an extracellular LRR that is critical for transmitting the lipopolysaccharide signal ( LPS ) across the cell membrane [91] . An adaptor molecule , myeloid differentiation factor 88 ( MyD88 ) , associated with the Toll/interleukin-1 receptor ( TIR ) intracellular domain of TLR4 , recruits IRAK4 [5] . The activated IRAK4 rapidly phosphorylates the non-RD kinase IRAK1 , and leaves the receptor complex to interact with the tumor necrosis factor receptor associated factor 6 ( TRAF6 ) [92] . TRAF6 is autoubiquitinated and forms a complex with TAK1 , which functions as a MAPK kinase kinase [93 , 94] . The activated kinase TAK1 mediates downstream events , such as the activation of inhibitor of κ kinase , p38 , and Jun-N-terminal kinases , which lead to the activation of transcription factors including NF-κB and activating protein-1 [5 , 95] . Similar to TLR4 , Arabidopsis FLS2 also carries an extracellular LRR domain that recognizes a pathogen-associated molecule; in this case a small peptide called flg22 [15] . FLS2 contains an intracellular non-RD kinase , which , when activated , transduces the signal to a MAPK cascades [96–98] . WRKY transcription factors in the nucleus are activated by the MAPK cascades and turn on downstream target genes [97] . KAPP has been shown to negatively regulate FLS2-mediated signaling through overexpression studies , although the mechanism has not yet been elucidated [33] . In rice , the XA21 LRR domain is responsible for race-specific recognition of Xoo strains carrying AvrXA21 [20 , 99] . XA21/AvrXA21 binding is hypothesized to activate the non-RD kinase domain leading to XA21 autophosphorylation and/or transphosphorylation of downstream target proteins [20 , 100] . In support of this hypothesis we have observed a strong interaction between XB15 and XA21 in Xoo PR6-inoculated rice plants but not in untreated rice ( Figure 8D ) , suggesting that Xoo inoculation induces XA21/XB15 complex formation . Our data indicate that Ser697 in the XA21 JM region is required for XB15 binding . XA21 transphosphorylates the RING finger ubiquitin ligase XB3 , which is required for effective XA21-mediated resistance [41 , 92] . We have also shown a direct , regulatory role of XB10 ( OsWRKY62 ) in the XA21-mediated response [41] , suggesting another layer of conservation between the Arabidopsis FLS2 and rice XA21 signaling pathways . In the nucleus , XB10 and other WRKY transcription factors ( unpublished data , Y . Peng , L . E . Bartley , and P . C . Ronald ) [41] either activate or repress defense-related genes such as PR1 and PR10 . The XA21-mediated defense response is also regulated by rice negative regulator of resistance ( NRR ) that interacts with NH1 [53 , 54] , the rice ortholog of NPR1 , a key regulator of the SA-mediated defense pathway ( unpublished data ) . This result demonstrates cross-talk between the XA21- and NH1-mediated pathways . In contrast to animal PPs that inactivate phosphorylated MAPKs to negatively regulate the innate immune response [24] , XB15 directly interact with the non-RD kinase domain of XA21 . This study suggests that XA21 is a substrate of XB15 and that the phosphatase activity of XB15 attenuates the XA21-mediated innate immune response . Future studies will be directed at identifying the XA21 phosphorylated residue ( s ) targeted by XB15 for dephosphorylation .
Rice ( Oryza sativa L . ) plants were maintained in the green house . The growth chamber was set on a 16-h light and 8-h dark photoperiod , a 28/26 °C temperature cycle , and 90% humidity . Healthy and well-expanded leaves from 6-wk-old rice plants were used for Xoo PR6 inoculation and nucleic acid or protein extraction . XA21K668 and XA21K ( TDG ) were amplified using the primer pairs 5′-CACCATGTC ATCACTCTACTTGCTTA-3′/5′-TCAGAATTCAAGGCTCCCA-3′ and 5′-CACCATGACAGATGGTTTCGCGCCGACC-3′/-TCAGAATTCAAGGCTCCCA-3′ , respectively , and cloned into pENTR/D-TOPO/D vector ( Invitrogen ) . XA21K668K736E , XA21K668S686A , XA21K668T688A , XA21K668S689A , XA21K668S686A , T688A , S689A , and XA21K668S697A were constructed by site-directed mutagenesis using the Quick Change kit ( Stratagene ) , according to the manufacturer's protocols . The specific primers for the mutagenesis were 5′- GTTGCAGTGGAAGTACTAAAGCTTGAAAATCC-3′/5′- GGATTTTCAAGCTTTAGTACTTCCACTGCAAC-3′ ( for XA21K668K736E ) , 5′- AAGGGAGCCCCTGCAAGAACTTCCATG-3′/5′- CATGGAAGTTCTTGCAGGGGCTCCCTT-3′ ( for XA21K668S686A ) , 5′- GCCCCTTCAAGAGCTTCCATGAAAGGC-3′/5′- GCCTTTCATGGAAGCTCTTGAAGGGGC-3′ ( for XA21K668T688A ) , 5′- CCTTCAAGAACTGCCATGAAAGGCCAC-3′/5′- GTGGCCTTTCATGGCAGTTCTTGAAGG-3′ ( for XA21K668S689A ) , 5′- AAGGGAGCCCCTGCAAGAGCTGCCATGAAAGGCCAC-3′/5′- GTGGCCTTTCATGGCAGCTCTTGCAGGGGCTCCCTT-3′ ( for XA21K668S686A , T688A , S689A ) , and 5′- CACCCATTGGTCGCTTATTCGCAGTTG-3′/5′-CAACTGCGAATAAGCGACCAATGGGTG-3′ ( for XA21K668S697A ) . The positive clones were verified by DNA sequencing and then using Gateway LR Clonase ( Invitrogen ) , moved into the yeast two-hybrid vector pNlexA carrying the BD domain ( Clontech ) . Positive clones were confirmed again by DNA sequencing . AD-Xb15 was from the clone pAD-GAL4–2 . 1 identified from a cDNA library . The purified plasmid DNAs from AD vectors and BD vectors were cotransformed into the yeast cell pEGY48/p8op-LacZ ( Clontech ) using the Yeast transformation kit , Frozen-EZ yeast transformation II ( Zymo Research ) . We followed the detailed procedure from the manual of Matchmaker LexA Two-Hybrid System ( Clontech ) . Full-length cDNA corresponding to Xb15 was amplified by PCR with primers 5′-CACCATGGGCAACTCCCTCGCCTG-3′/5′-TTACACGCAGGATCTCCAAATC-3′ . PCR fragments were purified and subcloned into the pDEST15 or 17 ( Invitrogen ) , which expresses the recombinant protein with an N-terminal GST or HIS tag , respectively . The resulting expression vectors were transformed into the bacterial host strain BL21 ( DE3 ) pLysS ( Invitrogen ) , and expression of protein was induced at midlog phase ( 1 mM isopropyl β-D-thiogalactosiadse , 3 h , 28 °C ) . Recombinant proteins were purified by affinity chromatography using Glutathione Sepharose 4B ( Amersham ) or Ni-NTA bead ( Qiagen ) . For the purification of the recombinant protein from rice , leaves of 5- to 6-wk-old NTAP-XB15 transgenic plants were harvested essentially as previously described [51 , 52] . Five grams fresh weight of rice shoots were ground to a fine powder in liquid nitrogen . Crude protein extracts were prepared in four volumes of Extraction Buffer I ( 20 mM Tris-HCl , [pH 8 . 0] , 150 mM ethylene-diamine-tetra-acetic acid ( EDTA ) , 2 mM benzamidine , 0 . 1% IGEPAL , 10 mM β-mercaptoethanol , 20 mM NaF , 1 mM phenylmethanesulfonylfluoride [PMSF] , 1% Protease cocktail ( Sigma ) , 2 μg/ml leupeptin , 2 μg/ml antipain , and 2 μg/ml aprotinin ) . The extract was passed through a fine sieve , filtered through two layers of miracloth , and centrifuged twice at 13 , 000 g for 30 min at 4 °C . The cleared supernatant was mixed with 50 μl of IgG Sepharose beads ( Amersham ) and incubated at 4 °C for 2 h on Labquake Rotisserie ( Barnstead Thernolyne ) . After centrifugation at 3 , 000 g for 30 sec , IgG supernatant was discarded and the collected IgG beads were washed four times in 5 ml Extraction Buffer I lacking protease inhibitors and twice in 1 ml of 5 mM ammonium acetate ( pH 5 . 0 ) . The protein was eluted with 1 ml of 0 . 5 M acetic acid ( HOAc ) ( pH 3 . 4 ) , neutralized with 100 μl of 1 M Tris-HCl ( pH 8 . 0 ) , and concentrated by acetone precipitation . The purified NTAP-XB15 protein was used for PP activity assay and protein gel blot analysis . Purified recombinant proteins , GST-XA21K668 , HIS-XB15 , and GST were used for pull-down assays as described [101] , except that buffers were supplemented with 2 mM MgCl2 . Bound proteins were eluted by boiling in SDS sample buffer , separated by SDS-PAGE , and detected by immunoblotting with a anti-histidine antibody ( Quiagen ) . Recombinant GST or GST-XA21K668 were incubated with glutathione-sepharose beads ( Amersham ) overnight at 4 °C and washed three times for 10 min at 4 °C with phosphate buffer saline ( PBS ) . The beads were equilibrated with Binding Buffer ( 20 mM Tris [pH 7 . 5] , 150 mM NaCl , 2 mM MgCl2 , 0 . 5 mM dithiothreitol ) to which a protease inhibitor mixture ( Roche ) was added . Recombinant HIS-XB15 was incubated with GST or GST-XA21K668 ( 20 μg ) bound to glutathione beads . After 2 h of incubation at 4 °C , the beads were washed four times for 20 min in binding buffer . Proteins bound to the bead were eluted with SDS sample buffer , separated by SDS-PAGE , and processed for immuno-blotting . To co-immunoprecipitate Myc-XA21 and XB15 , total proteins were extracted from 5 g of leaf tissue in 25 ml of ice-cold Extraction Buffer II ( 0 . 15 M NaCl , 0 . 01 M Na-phosphate [pH 7 . 2] , 2 mM EDTA , 1% Triton X-100 , 10 mM β-mercaptoethanol , 20 mM NaF , 1 mM PMSF , 1% Protease cocktail [Sigma] , 2 μg/ml leupeptin , 2 μg/ml antipain , and 2 μg/ml aprotinin ) . After filtering through Miracloth ( Calbiochem ) followed by centrifugation twice at 13 , 000 g for 20 min at 4 °C , the supernatant was mixed with 50 μl of agarose conjugated anti-Myc antibody ( Santa Cruz ) and incubated at 4 °C for 2 h . The beads were then washed four times in 1 ml of Extraction Buffer II without proteinase inhibitors . The proteins were eluted with 4× Laemmli loading buffer . Protein blot analyses were performed . To co-immunoprecipitate XB15-smGFP2 , total proteins were extracted from protoplasts transformed with Xb15-smGFP2 in 5 ml of ice-cold Extraction Buffer II and 20 μl of agarose conjugated anti-GFP antibody ( Santa Cruz ) . For the anti-XB15 antibody , synthetic peptides and monospecific antibodies were made as a service by Pacific Immunology . Detailed information about their methods can be obtained at Pacific Immunology ( http://www . pacificimmunology . com/ ) . Epitope selection was directed to hydrophobic , flexible regions of the proteins . The epitope , TRALLARTEKFQDSADL was used . Peptides were synthesized and conjugated to keyhole limpet hemocyanin . Rabbits were immunized with peptide in complete Freund's adjuvant , followed by three boosts in incomplete Freund's adjuvant . Monospecific antibodies were affinity purified to the synthetic peptides bound to C3-SEP-PAK cartridges . Antibodies were conjugated to horseradish peroxidase for use in Western blots . For Xoo inoculation , rice plants were grown in the greenhouse normally until they were 6 wk old , unless stated otherwise , and transferred to the growth chamber . The Xoo strain Philippine race 6 ( PR6 ) was used to inoculate rice by the scissors-dip method [18 , 53] . Only the top two to three expanded leaves of each tiller were inoculated . For Xoo colony counts from inoculated leaves , 20 cm of leaf tissue from the top , including lesions and tissue showing no lesions , was ground up and resuspended in 10 ml water to harvest bacteria . The extract was diluted accordingly and plated out on peptone sucrose agar ( PSA ) plates containing 15 mg/l cephalexin . The transgenic lines Myc-XA21 T330-16-1 was used as the pollen recipient in a cross with pollen donor NTAP-XB15 17A and 19A . Over 50 seeds were recovered from the each T330-16-1/17A and T330-16-1/19A cross . The nature of the F1 hybrid was confirmed by PCR amplification of 820-bp fragment spanning part of the Myc tag and Xa21 in the Myc-Xa21 construction using primers 5′-GAGCAAAAGCTGATTTCTGAGGAGGAT-3′/5′-ACCACCTAGCTTGTTTTCTCTGAC-3′ and 650-bp fragment spanning part of the NTAP tag and Xa21 in the Ntap-Xb15 construction using primers 5′-ATGCCCAAGCCCCAAAGGACTACG-3′/5′-GAAGCTTGGACGGCGCCACCCATACGAC-3′ . Rice transformation was constructed as described previously [53] . Agrobacterium EHA105 was used to infect rice callus for transformation . Transformants of rice cultivars Kitaake carrying Ntap-Xb15 or Myc-Xa21 were selected using hygromycin . For NTAP-XB15 detection , rabbit PAP soluble complex ( Sigma ) was used at a final dilution of 1:5 , 000 for 2 h . Bands were visualized using the SuperSignal West Pico Chemiluminescent Substrate ( Pierce ) according to standard protocol . PCR was performed using the gene-specific oligonucleotide primers , 5′-GGGATCCAATGGGCAACTCCCTCGCCTG-3′/5′-GGGATCCACGCAGGATCTCCAAATC-3′ . Subsequently , the termination codon of the Xb15 cDNA was removed . The PCR-amplified product was fused in-frame to the coding region of soluble-modified green fluorescent protein ( smGFP2 ) ( kindly provided by K . H . Paek ) [63 , 64] . Transient expression of green fluorescent protein ( GFP ) fusion constructs and H+-ATPase-dsRed ( kindly provided by I . Hwang ) were performed by introducing the plasmid into the rice protoplasts using the PEG-mediated transformation method [63 , 64] . Images were collected with an Olympus FV1000 confocal microscope . GFP was imaged under the following conditions: excitation: 488 nm; DM 405/488/543; emission: 500–530 nm . DsRed was imaged under the following conditions: excitation: 543 nm; DM 405/488/543; emission: 560–620 nm . Images were collected through a 40× ( NA: 1 . 00 ) oil immersion lens . All images are the result of 2 kalman line averages , and , where appropriate , sequential scans were used to prevent cross talk . Images were analyzed using the Olympus Fluorview software ( Ver 1 . 4a ) and coded green ( for GFP ) or red ( for dsRed ) . For RT-PCR analysis , total RNAs were extracted from leaves after each treatment and then the RT reaction was performed following the manual for QuantumRNA 18S Internal Standards ( Ambion ) . PCR analyses were performed with primers pairs , 5′-TTATCCTGCTGCTTGCTGGT-3′/5′-GGTCGTACCACTGCTTCTCC-3′ ( for PR1a ) , 5′-AGGTATCCAAGCTGGCCATT-3′/5′-GGCGTAGTCGTAGTCGCTCT-3′ ( for PR1b ) , 5′-CGCAGCTCACATTATCAAGTCAGA-3′/5′-GAAGCAGCAATACGGAGATGGATG-3′ ( for PR10 ) , 5′-GCAGGGAGCGTATACAAGACCAA-3′/5′-CACGCCACAGTAACATGACCACAA-3′ ( for Betv1 ) , 5′-CAACAGTCGAAGGGCAATAATAAGTC-3′/5′-ACTGCCACACCTCCCACATTG-3′ , 5′-ATGGCTCCGGCCTGCGTCTCCGA-3′/5′-GGCATATTCGGCAGGGTGAGCGA-3′ ( for PBZ1 ) , 5′-TCATTCGATGGATCAGTCGGG-3′/5′-ATGCTCTGGTCACCTTCAGCG-3′ ( for Xb15 ) , and 5′-CAACAGTCGAAGGGCAATAATAAGTC-3′/5′-ACTGCCACACCTCCCACATTG-3′ ( for EF1α ) . The amplified products were then resolved by gel electrophoresis . Phosphatase activity was measured according to the instructions provided by the manufacturer by using a nonradioactive serine/threonine phosphatase assay system ( Promega ) . The color was allowed to be developed for 15 min , and the absorbance was measured at 600 nm with plate reader ( Bio-Rad ) . The composition of the buffers used in the assay was: PP2A 5× buffer ( 250 mM imidazole [pH 7 . 2] , 1 mM EGTA , 0 . 1% 2-mercaptoethanol , 0 . 5 mg/ml BSA ) , PP2B 5× buffer ( 250 mM imidazole [pH 7 . 2] , 1 mM EGTA , e50 mM MgCl2 , 2 mM CaCl2 , 250 μg/ml calmodulin , 0 . 1% 2-mercaptoethanol ) , PP2C 5× buffer ( 250 mM imidazole [pH 7 . 2] , 1 mM EGTA , 25 mM MgCl2 , 0 . 1% 2-mercaptoethanol , 0 . 5 mg/ml BSA ) . Purified agarose-bound fusion proteins , GST-XA21K668 , were washed with kinase buffer ( 50 mM HEPES [pH 7 . 4] , 10 mM MgCl2 , 10 mM MnCl2 , 1 mM dithiothreitol ) . Autophosphorylation experiments were carried out in 30-μl volumes containing 20 μl of agarose-bound protein ( 5 μg ) and 20 μCi of [γ-32P]ATP ( 6 , 000 Ci/mmol ) ( PerkinElmer Life Science ) . The reaction was stopped after 30 min by adding 10 μl of Laemmli loading buffer and boiling for 5 min . The proteins were separated by SDS-PAGE ( 7 . 5% or 10% ) . After staining with Coomassie Brilliant Blue G-250 , the gel was dried and exposed to x-ray film . To dephosphorylate the phosphorylated fusion proteins by HIS-XB15ΔN , the 32P-labeled XA21K668 proteins were washed with PP2C buffer and incubated with HIS-XB15ΔN . The resulting proteins were resolved by SDS-PAGE as described above . A 1 , 920-nt cDNA fragment encoding full-length XB15 protein was amplified from a rice cDNA using primers , 5′-CACCATGGGCAACTCCCTCGCCTG-3′/5′-TTACACGCAGGATCTCCAAATC-3′ . The PCR product was cloned into pENTR/D-TOPO ( Invitrogen ) according to the instructions provided by the manufacturer and the insert confirmed by sequencing . For Overexpression in rice , the Xb15 cDNA in pENTR/D-TOPO was recombined into the final Ubi-NTAP-1300 vector using Gateway LR Clonase ( Invitrogen ) . Ubi-NTAP-1300 is a pCAMBIA-1300 ( AF234296 ) derivative with an additional expression cassette containing the maize ubiquitin promoter and a nopaline synthase 3′-polysdenylation region , to which the NTAP/Gateway cassette was added [52] .
The rice cDNA locus identification numbers ( TIGR; http://rice . tigr . org/ ) are as follows: Xb15 , Os03g60650; PR1a , Os07g03710; PR1b , Os01g28450; PR10 , Os12g36830; Betv1 , Os12g36850; PBZ1 , Os12g36880; EF1s , Os03g08010 . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for Xb15 cDNA sequence is NP_001051726 . | Resistance to pathogens is critical to plant and animal survival . Plants , unlike animals , lack an adaptive immune system and instead rely on the innate immune response to protect against infection . To elucidate the molecular mechanism of plant innate immunity , we are studying the signaling cascade mediated by the rice pathogen recognition receptor kinase XA21 , which confers resistance to the bacterial pathogen Xanthomonas oryzae pv . oryzae . We demonstrate that XA21 binding protein 15 ( a protein phosphatase 2C ) negatively regulates XA21-mediated signaling resistance . This finding provides significant insight into regulation of receptor kinase-mediated immunity . | [
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| 2008 | Rice XB15, a Protein Phosphatase 2C, Negatively Regulates Cell Death and XA21-Mediated Innate Immunity |
Systematic mappings of the effects of protein mutations are becoming increasingly popular . Unexpectedly , these experiments often find that proteins are tolerant to most amino acid substitutions , including substitutions in positions that are highly conserved in nature . To obtain a more realistic distribution of the effects of protein mutations , we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M . HaeIII , a DNA methyltransferase . During this drift , multiple mutations gradually accumulated . Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations . Despite being averaged across many different genetic backgrounds , about 67% of all nonsynonymous , missense mutations were evidently deleterious , and an additional 16% were likely to be deleterious . In the early generations , the frequency of most deleterious mutations remained high . However , by the 17th generation , their frequency was consistently reduced , and those remaining were accepted alongside compensatory mutations . The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M . HaeIII’s natural orthologs . The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap . Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces , and thereby a more realistic view of the mutational space of proteins .
The ability to reliably measure and predict the effects of amino acid mutations in proteins is of fundamental importance to protein engineering and design and for understanding protein evolution and human genetic variation . Data regarding the effects of individual mutations originate from two major sources: sequence analysis of natural proteins , and laboratory experiments . Phylogenetic analyses enable insights regarding how protein sequences diverge [1 , 2] and what dictates the purging of mutations [3 , 4] . Protein phylogenies also allow us to predict whether a given mutation might be deleterious or neutral , assuming that the fitness effects of mutations correlate with their occurrence in orthologous sequences ( reviewed in [5–7] ) . The algorithms are relatively accurate in predicting disease-causing mutations [8 , 9] . However , many nonsynonymous SNPs predicted to have a deleterious effect are not clearly associated with a disease phenotype [10] , either because they are rare [11] or because a deleterious effect in a single gene often results in no phenotype at the organismal level [12] . Indeed , the effects of mutations at the isolated protein and organismal levels do not necessarily overlap . Predictors may also fail in assigning deleterious effects to mutations in highly conserved sites that when mutated experimentally appear to be neutral [13] . Exhaustive datasets listing the effects of all mutations within a given gene/protein , independently of organismal effects , would therefore greatly improve prediction [14] . Systematic experimental mappings of the effects of mutations within one given gene/protein are therefore crucial for understanding protein evolution , as well as an attractive resource for improving predictions [15 , 16] and for refining protein design algorithms [17] . Experiments that systematically map the effects of mutations in a given protein are generally conducted through either saturation mutagenesis , using NNS codons to diversify individual sites [18–21] , or random mutagenesis along the entire gene using error-prone replication [22] . In both cases , the diversified gene repertoires are subjected to selection that purges deleterious mutations , and then sequenced to identify which mutations are tolerated . Recently , advanced gene synthesis technologies and deep sequencing have yielded exhaustive mappings ( for examples see [23–38]; reviewed in [16] ) . However , although deep mutational scanning provides a powerful means of studying protein structure-function , there remain challenges that are yet to be tackled [16] . Foremost , the relevance of the results of laboratory experimental mappings our understanding of natural protein evolution may be limited . Specifically , there is a disagreement between the trends indicated by experimental mappings versus natural protein diversity . In silico analyses of natural protein diversities suggest that the vast majority of mutations are deleterious [39–43] . Given enough drift , mutations at other sites enable the acceptance of certain deleterious substitutions . But at a background of a given sequence , most substitutions would result in the loss of configuration stability and/or function [41 , 43–45] . Experimental mappings , however , mostly portray a different picture–the majority of mutations are tolerated ( for examples , see [25–27 , 33 , 36 , 46 , 47] ) . Accordingly , a poor correlation between the acceptance of mutations in the laboratory and the occurrence of the same exchanges in natural orthologs of the studied protein has been noted [25–27 , 33 , 36] . For example , positions that are 75–90% conserved in Hsp90 tolerated a range of amino acids some of which are not seen in any ortholog [25] . However , at lower expression levels , these mutations did reveal deleterious fitness effects , thus indicating that the sensitivity of the experimental system is a key parameter [48] . The comparison of results from different experimental mappings is also problematic . The experiments not only address different proteins , but also apply different mutagenesis strategies and methods of determining the effects of mutations . In some cases the measured effects of mutations relate to growth of the host organism ( e . g . , antibiotics resistance ) and in others to the biochemical function of the targeted protein in isolation ( e . g . levels of fluorescence , or of DNA methylation , as applied here ) . Nonetheless , the disagreement between tolerance in the laboratory and occurrence amongst natural sequence raises several questions . Does the absence of a given exchange within natural orthologs indicate its deleterious fitness effect , or does the sparse and sporadic sampling of natural sequences prevents reliable prediction ? Do laboratory experimental setups adequately reproduce the constraints that shape protein sequences in nature , or do tolerance or acceptance of a mutation in the laboratory have limited relevance to the evolutionary history/future of a protein . Finally , obtaining a realistic distribution of the fitness effects ( DFE ) of protein mutations remains a worthy goal [36 , 46 , 49 , 50] . To address the above questions , and obtain a more realistic distribution of fitness effects of protein mutations , we have set up a laboratory system that better mimics the manner by which protein sequences diverge in nature . To this end , we performed 17 iterative rounds of random mutagenesis and purifying selection . This laboratory experiment does not address crucial elements of natural drifts ( mutation rates , population sizes , and organismal fitness demands ) . It does , nonetheless , mimic the process of prolonged accumulation of mutations under purifying selection to maintain the protein’s structural and functional integrity ( hence the term ‘neutral drift’ ) . As a model , we used a bacterial DNA methyltransferase , M . HaeIII , which can be readily placed under purifying selection in the laboratory . At different rounds along this prolonged drift , the ensembles of gene variants that survived the purifying selection were subjected to deep sequencing . The naïve , unselected mutational repertoire was similarly sequenced . This enabled us to determine the frequency of occurrence , and hence the relative fitness effects of all single nucleotide mutations in M . HaeIII . As described in the following pages , our results differed from those of other experimental mappings in several key respects .
M . HaeIII is a DNA methyltransferase isolated from Haemophilus aegyptius . Being part of the bacterial restriction-modification system , this enzyme selectively methylates GGCC DNA sequences , and thereby protects DNA from digestion by the cognate endonuclease , HaeIII . Sequence specific methylation-restriction offers a facile way of performing laboratory evolution . As described in earlier works [51 , 52] ( S1 Fig ) , M . HaeIII's open reading frame was randomly mutated using PCR with an error-prone polymerase , cloned into an expression plasmid and transformed to E . coli . In each bacterium , the encoding plasmid is methylated , or not , depending on whether the M . HaeIII gene variant it encodes is properly folded and functional . Following ‘expression’ of the plasmid encoded M . HaeIII variants in individual bacteria , the plasmids were pooled and digested with HaeIII , thus selecting for M . HaeIII’s native specificity [51 , 52] ) . The starting point for these experiments was a variant of M . HaeIII optimized for soluble and functional expression in E . coli . This variant carried four consensus substitutions replacing the amino acid in M . HaeIII with the one that dominates in all M . HaeIII’s orthologs [50] . For the sake of simplicity we refer to this variant as wild-type M . HaeIII . These consensus substitutions are likely to have a stabilizing , compensatory effect , and spontaneously accumulate in accelerated , laboratory drifts . They may thus allow a larger variety of deleterious mutations to be accepted , especially during the first rounds of mutagenesis [53] . In Haemophilus aegyptius , M . HaeIII is under a strong and constitutive selection pressure imposed by the presence of the cognate restriction enzyme HaeIII–a DNase that would cause chromosomal breaks unless the genome is methylated at all HaeIII sites . The HaeIII restriction-modification system is naturally encoded by single copy chromosomal genes [54 , 55] . In our experimental system , M . HaeIII was encoded by a multi-copy plasmid ( ~400 copies per cell ) . To avoid unrealistic enzyme doses , expression was driven from a tightly controlled promoter with no induction . Although M . HaeIII’s levels in Haemophilus aegyptius are unknown , its expression level in the E . coli cells of our experimental setup is extremely low ( a similar plasmid showed no detectible GFP signal when inducer levels were ≤20 μg/ml [56] , and we used no inducer ) . This basal expression level was nonetheless sufficient to enable wild-type M . HaeIII to methylate all GGCC sites , not only in the encoding plasmid , but also within the E . coli host's chromosome , as is the case with natural methyltransferases [51] . M . HaeIII underwent 17 rounds of random mutagenesis , at an average mutational rate of 2 . 2±1 . 6 nucleotide mutations per gene per generation followed by purifying selection ( i . e . , digestion of the encoding plasmids with HaeIII nuclease ) . To avoid false positives due to mutations in GGCC sites , the applied M . HaeIII’s coding sequence ( ORF ) contained no GGCC sites whist the encoding plasmid contained 14 such sites including three sites within the antibiotic selection marker . Each round , selection was repeated three times ( i . e . , repeated isolation of plasmid DNA pool from the grown bacteria , digestion with HaeIII , and transformation into E . coli ) . Subsequently , the drifted M . HaeIII’s ORF was mutagenized and recloned into a fresh plasmid for the next round of selection . We ensured the same level of selection pressure and the absence of bottlenecks throughout: ≥105 independent transformants were passed to the next round ( effective population size , Ne > 105 ) . The drifting M . HaeIII thus met the conditions that essentially eliminate the possibility of mutations fixing by chance ( 1/Ne <10−5 ) . Mutations that were enriched are therefore likely to have provided a selective advantage , most typically , as shown below , a compensatory effect . By the 17th round , the drifted genes carried on average of 18±1 . 6 mutations per gene in total , and 9 . 6±0 . 7 nonsynonymous mutations per gene ( determined in parallel by deep sequencing and conventional Sanger sequencing of the full length ORFs of randomly chosen genes; see ‘Dynamics of the laboratory drift‘ below ) . The mutational spectrum of the unselected , naïve gene pool ( dubbed G0 ) , and of the pools after three ( G3 ) , seven ( G7 ) and seventeen ( G17 ) rounds of selection , were analyzed by Illumina high-throughput sequencing . Mutations were identified using a script that aligned all codon triplets to the reference gene , wild-type M . HaeIII ( S1 File ) . The background frequency that stems from the Illumina sequencing PCRs and sequencing errors was determined using the sequencing data for the region upstream of the randomly mutated ORF of M . HaeIII ( the N-terminal fused His tag that was part of the encoding vector and was hence not subjected to mutagenesis ) . In this manner , the frequencies of all single , double and triple nucleotide mutations were determined by the fraction of sequence contigs that carry a mutation out of all contigs that covered the respective position . The amino acid mutational frequencies were subsequently determined by summing up the frequencies of all codon triplets that yield a given amino acid ( See S1 , S2 and S3 Files ) . In theory , M . HaeIII’s sequence space includes 6 , 580 possible amino acid mutations; i . e . , 329 positions , each mutated to all other 19 different amino acids or to a stop codon ( Table 1 ) . However , the immediate mutational space originates from single nucleotide mutations . Subsequent nucleotide mutations within the same codon were found ( dubbed double and triple mutations; Table 1 ) , but at very low frequencies ( in G17—an average of 0 . 052% and 0 . 003% of nonsynonymous double and triple mutations ) . However , these double and triple mutations only appeared at later stages , and after many other positions had changed due to single nucleotide mutations . Single nucleotide missense mutations also dominate polymorphism , and thus , our analysis focused on their effects . We thus examined all 1 , 957 possible missense single point mutations , namely all amino acid exchanges accessible by single nucleotide mutations; Table 1 ) . The effects of stop codons were also examined as described in ‘Tolerance of nonsense mutations’ . All possible single nucleotide mutations were detected in the unselected G0 library at the raw data level– 329X9 = 2 , 961 possible single nucleotide mutated codons , that in turn yield 1 , 957 possible single nucleotide amino acid mutations ( raw data provided as S2 File ) . However , 77 mutations at G0 were observed at lower than background frequencies . That a mutation is observed under what we defined as the background frequency is not necessarily an indication that it did not occur . The background frequency was derived from averaging the frequencies for relatively few positions compared to the measured ones ( 20 positions versus 329 ) and thus it is conceivable that a small fraction of the latter ( <1% ) will deviate from this average . Indeed , out of the 77 mutations , 35 were detected in the later , selected rounds , and some were even enriched . The remaining 42 mutations were also observed with under background frequencies in later rounds . We therefore assume that they were strongly purged and assigned them as eliminated mutations . Overall , our analysis related to the complete set of 1 , 957 single nucleotide missense mutations with >98% ( 1 , 915/1 , 957 ) of these being covered with complete confidence . The spectrum of mutations covered by our experiment was dictated by the genetic code , M . HaeIII’s DNA sequence , and by the nucleotide substitution matrix that underlined our mutagenesis protocol . Although we used an engineered , error-prone DNA polymerase , the obtained spectrum of mutations was similar to that naturally observed in E . coli . Specifically , a transition/transversion ratio of ~1 . 3 was observed in our naïve repertoire ( G0 ) similar to what has been observed in the comparison of closely related E . coli genomes ( 0 . 91 or 1 . 3 , [57 , 58] , S1 Table ) . The variability in mutation frequencies along M . HaeIII positions in the unselected G0 library was relatively high ( 1 . 07 ± 0 . 24% mutations/position ) . Thus , mutation frequencies varied not only by the type of base substitution ( e . g . transitions , transversion; S2A Fig ) , but also according to the position of the mutated base along M . HaeIII’s gene . To verify that this variability is not the outcome of limited sampling in G0 ( the naïve repertoire that underwent only one round of mutagenesis ) we compared the frequencies of synonymous mutations in the unselected library , G0 , and in the selected one , G3 . As expected , synonymous mutations were under relatively weak selection ( detailed below ) and thus their frequencies , certainly within the early rounds , largely reflect the rate of mutagenesis . Indeed , the frequencies of synonymous mutations in G0 and in G3 were highly correlated ( R = 0 . 9 , S2B and S2E Fig ) . By G17 , the correlation was still significant although weaker indicating some degree of selection on synonymous mutations ( R = 0 . 6; S2C Fig ) . The observed frequencies in the unselected library , f ( G0 ) , therefore appear to provide a reliable measure for the positional rates of occurrence of mutations in all 17 mutagenesis steps of the drift . However , for the 77 mutations ( out of 1 , 957 ) with lower than background frequencies in G0 ( Table 1 ) , the rate of the occurrence , f ( G0 ) , was based on the base substitution table derived from G0 ( S2A Fig ) . Mutations were retained , purged or enriched in each round of our experiment . The change in frequency along the drift therefore reflects the effects of selection per each mutation or , as defined here , their relative fitness effects ( Wrel ) . The frequency of a given mutation in a given round ( f ( Gn ) ) is dictated by its relative fitness ( Wrel ) , and relates to the frequency of this mutation in the previous round , f ( Gn-1 ) plus the frequency of re-occurrence at round n . For example , the frequencies of neutral mutations ( Wrel = 1 ) are essentially equal to their cumulative rate of occurrence ( f ( Gn ) ~ n f ( G0 ) ) . Conversely , the frequencies of deleterious mutations ( Wrel < 1 ) decrease from round to another , in an exponential manner , and their observed frequency is lower than expected from their rate of occurrence ( f ( Gn ) < n f ( G0 ) ) . The opposite applies for beneficial mutations ( Wrel > 1 ) . However , since the genes in our drifting ensembles contained multiple mutations , and the applied sequencing approach does not reveal the specific mutational composition of individual genes , the Wrel values measured here relate to the effect of a given mutation at the background of many different genetic compositions . For better and for worse , the measured Wrel values therefore represent an average that ignores epistatic interactions between mutations . This averaging has obvious drawbacks , and may cause biases due to hitchhiking and clonal interference ( e . g . a highly deleterious mutation would result in every other mutation on the same gene having a low Wrel ) However , under our experimental setup , new mutations are reintroduced in each round of mutagenesis , allowing multiple resampling of the effects of each given mutation at the background of many different mutations , and thus reducing the probability of hitchhiking . Indeed , as indicated below , there are clear indications that hitchhiking and clonal interference did not bias the observed Wrel values . We also note that , in general , allele frequencies , and thereby fitness effects of mutations , are measured in populations comprising individuals with different genetic backgrounds with certain caveats [59–61] . Foremost , the number of sequenced alleles needs to be in the order of thousands [59]–a demand that is amply met in our experiment . Thus , if a mutation is on average purged ( Wrel <<1 ) , we can conclude that it has deleterious effects on M . HaeIII’s structure and/or function independently of the specific genetic background . We used the following model to derive the relative fitness effect , Wrel , from the mutational frequencies observed in the selected versus unselected libraries ( see Methods for details ) . Following the first round: f ( G1 ) =f ( G0 ) ⋅Wrel , For the subsequent rounds , the observed mutational frequency , f ( Gn ) , is derived from the mutations inherited from the previous round f ( Gn-1 ) plus the mutations newly incorporated in this round . The latter corresponds to the frequency of this mutation in the naïve , unselected ensemble , f ( G0 ) , as discussed above: f ( Gn ) =[f ( Gn−1 ) +f ( G0 ) ]⋅Wrel ( 1 ) Eq ( 1 ) corresponds to a geometrical series that has no closed solution . Thus , to derive the Wrel values of each mutation , we calculated the expected frequency ratio ( f ( Gn ) f ( G0 ) ) for a series of discrete Wrel values from absolutely deleterious ( Wrel = 0 ) to highly beneficial ( Wrel = 3 . 5; see Methods and S3 Fig ) . In this manner , each of the 1 , 957 amino acid mutations measured by the deep sequencing ( each derived from the respective single nucleotide mutation; Table 1 ) , were assigned a Wrel value . We used the variability in the relative fitness effects of synonymous mutations and nonsense mutations to categorize the effects of nonsynonymous mutations [36] . The distribution of synonymous mutations was consistent with their low impact on fitness relative to nonsynonymous mutations ( Fig 1A , ‘Syn’ ) . The average Wrel value , and standard deviation , for synonymous mutations were found to be 0 . 82±0 . 12 for G3 , 0 . 84±0 . 15 for G7 , and 0 . 91±0 . 1 for G17 ( Table 2 ) . Given our hypothesis that other works overestimated the tolerance of mutations , we preferred to under- rather than over-estimate the fraction of deleterious mutations . Accordingly , for the assignment of a deleterious fitness effect , we chose a conservative threshold of two standard deviations under the mean of the relative fitness effect of synonymous mutations ( X¯−2SD ) . The X¯−2SD values obtained were 0 . 58 , 0 . 55 and 0 . 72 for G3 , G7 and G17 , respectively ( Table 2 ) yielding an average of 0 . 62 for all 3 ensembles . We therefore used Wrel ≤ 0 . 6 as the threshold for indicating purging and consequently a deleterious fitness effect of a mutation . However , since , as detailed below , mutations with Wrel values of 0 . 6–0 . 8 were found to be systematically purged as the drift progressed , suggesting that in effect they are not neutral . This Wrel range was therefore classified as ‘nearly-neutral’ ( Fig 1A ) . The X¯+2SD threshold was similarly applied for categorizing beneficial mutations , thus stetting the threshold for beneficial mutations as Wrel > 1 . 1 . Within this threshold , only 4 out of 321 synonymous mutations were defined as beneficial relative to 33 nonsynonymous mutations . Indeed , within the 0 . 6–1 . 1 Wrel range defined here as neutral , >93% of the synonymous mutations observed in the three selected ensembles ( G3 , G7 , G17 ) were assigned as neutral ( Table 2 ) . The potential deleterious or beneficial effects of the remaining 7% were not analyzed here . The selection acting on synonymous mutations may , amongst other factors , relate to different codon usage in E . coli . Overall , given the applied thresholds , the likelihood of misassignment of neutral mutations as deleterious or beneficial was < 4% ( Table 2 ) . The Wrel threshold for defining ‘highly deleterious’ mutations was derived from the distributions of nonsense mutations that are , beyond doubt , deleterious ( see also ‘Tolerance of nonsense mutations‘ below ) . The average Wrel value , and standard deviation , for nonsense mutations were found to be 0 . 042±0 . 15 for G3 , 0 . 028±0 . 13 for G7 , and 0 . 020±0 . 11 for G17 ( Table 2 ) . Thus , a threshold of X¯+2SD , i . e . , Wrel ≤ 0 . 3 , was chosen for categorizing highly deleterious mutations . In summary , nonsynonymous mutations were categorized as ‘Deleterious’ if their Wrel values were ≤ 0 . 6 , and ‘Highly deleterious’ if Wrel ≤ 0 . 3 ( including eliminated mutations , Wrel = 0 , i . e . , when the net frequency of a mutation was zero ) . Mutations were assigned as ‘Nearly-neutral’ if their frequencies in the selected populations were in the range of Wrel = 0 . 6–0 . 8 ( X¯−2SD of the distribution of synonymous mutations ) and ‘Neutral’ in the range of Wrel = 0 . 8–1 . 1 . Finally , enrichment in the selected repertoires ( Wrel >1 . 1 , X¯+2SD of the distribution of synonymous mutations ) indicated a ‘Beneficial’ fitness effect . As can be seen in Fig 1A , the distribution of relative fitness effects of synonymous mutations centered near neutrality ( Wrel ~1; see Table 2 for mean and standard deviation ) . In contrast , the distribution of the nonsynonymous mutations encompasses primarily deleterious mutations . Overall , ~67% out of all the possible nonsynonymous single nucleotide mutations ( ~1 , 310/1 , 957 ) were found to be deleterious , even within the conservative threshold of Wrel of ≤ 0 . 6 ( Table 2 , ‘nonSyn’ ) . Removal of the 42 mutations that were observed below background frequency in all repertoires , and assigned as eliminated , has almost no impact on the fraction ( ~1 , 268/1 , 915 = ~66% ) . Note that Fig 1A shows the derived fitness effects of all possible single nucleotide mutations regardless of their frequencies in the drifting populations . Indeed , a similar fraction was assigned as deleterious in all the three selected libraries ( Fig 1A , ‘nonSyn’ ) . The effect that selection had on purging deleterious mutations is clearly seen in the distribution of their frequencies ( Fig 1B ) . This distribution shifted during the drift: from ~15% of all mutations denoted as deleterious in G3 , to only ~3% in G17 ( Fig 1C ) . Further , by our conservatively chosen threshold , mutations with assigned Wrel from 0 . 6 to 0 . 8 were considered as ‘Nearly-neutral’ . However , mutations within this Wrel range were systematically purged throughout the drift , from ~22% in G3 to ~7% in G17 ( Fig 1C ) , indicating small yet consistent deleterious effects . Further , as discussed below , these mutations were accepted at the background of beneficial mutations , most likely owing to their compensatory effect ( as discussed in the section below ) . If all mutations with Wrel ≤ 0 . 8 are considered , then ~83% of all possible mutations in M . HaeIII have a deleterious effect . In agreement with the reduction in the frequency of deleterious mutations , the total frequency of beneficial mutations ( Wrel > 1 . 1 ) increased consistently , from ~2% in G3 to ~10% in G17 . Consistent with the distribution of Wrel values being the same along the drift ( Fig 1A ) , we also observed that the Wrel values per given mutation remain largely the same along the drift , i . e . , when derived from the sequenced frequencies in G3 , G7 or G17 ( S3B and S3C Fig ) . This was despite the fact that the average number of mutations per gene increased form 2 . 4 in G3 to 9 . 6 in G17 . It therefore seems that hitchhiking and/or clonal interference did not significantly bias our data , and that the derived Wrel values that average the effect of a given mutation over different genetic background are relevant . The discrepancy between our results and the results of other experimental mappings is likely due to differences between measuring the effect of a single , or at most a few mutations , to measuring the effect of accumulated mutations over a long mutational drift . To support this hypothesis , we examined the rate of accumulation of mutations along the various rounds of the drift . To this end , in addition to deep-sequencing of the gene ensembles of 3 rounds ( G3 , G17 and G17 ) , we also performed conventional Sanger sequencing of the full length ORFs of randomly picked variants from each round . As expected , the total number of mutations per gene ( Nt ) increased linearly throughout the drift , certainly up to the 15th round , at the average of 1 . 16 mutations/gene/round ( Fig 2A ) . However , as the drift progressed , the intensity of purged nonsynonymous mutations increased as indicated by the ratio of nonsynonymous to synonymous mutations ( Na/Ns; whereby Na denotes the average frequency of nonsynonymous mutations and Ns the average frequency of synonymous mutations; Fig 2B ) . Specifically , the first round ( G1 ) exhibited a Na/Ns ratio of 2 . 6 , only mildly lower than 3 . 2 –the ratio in G0 , the unselected repertoire . However , by the 5th round , the Na/Ns ratio dropped to a value of ~1 . By the 14th round , the accumulation of nonsynonymous mutations ( Na ) had slowed down , in addition to a slowdown in the total accumulation of mutations ( Nt; Fig 2A ) . That the tolerance to mutations decreased as the drift progressed is also reflected in the continuous decline in frequency of deleterious mutations along the drift . About a third of all possible mutations were eliminated by the 3rd round ( Wrel = 0 , ‘Eliminated’ ) whereas deleterious mutations ( Wrel = 0 . 01–0 . 6 ) were observed in the drifting ensembles throughout the 17 rounds . However , their frequency was small and remained constant throughout ( ~0 . 6 deleterious mutations/gene; Fig 2C ) . Thus , as mutations gradually accumulated , the relative frequency of deleterious mutations became increasingly lower–in G17 their fraction became 0 . 06 ( 0 . 6 out of the ~9 . 6 nonsynonymous mutations/gene ) relative to 0 . 26 in G3 ( 0 . 6 out of the ~2 . 4 nonsynonymous mutations/gene ) . In effect , the majority of mutations that did accumulate beyond G3 were neutral ( Figs 1B and 2C ) . This finding also indicates that hitchhiking and/or clonal interference does not significantly bias our data . In addition to the accumulation of the neutral mutations ( Wrel = 0 . 8–1 . 1 ) beyond G3 , the later rounds were accompanied by the enrichment of beneficial mutations ( Fig 1C ) . The beneficial mutations ( Wrel > 1 . 1 ) are likely to be compensatory mutations , increasing the global stability of M . HaeIII , or locally interacting with a specific deleterious mutation . The applied sequencing method does not reveal the specific mutational composition of individual genes , and thus , there is no way of detecting enriched , beneficial mutations that have a specific , local compensatory effect . However , as previously shown [53] , mutations that were enriched in a prolonged neutral drift were experimentally confirmed to have global , stabilizing effects that compensate for a wide range of deleterious destabilizing mutations . The global compensatory effect can also be deduced from the identification of most enriched mutations as consensus mutations ( S2 Table; see also Ref . [53] ) . Further , under selection for the acquisition of five different new DNA target specificities [51] , the same mutations were rapidly fixed in all the evolved lines irrespective of which new specificity was selected ( S2 Table ) . Compensatory mutations are essential for the acquisition of new functions because mutations that confer new functions tend to severely undermine protein stability [62–64] . By G17 , each gene carried , on average , 1 . 99 beneficial mutations relative to 0 . 08 in G3 ( Fig 2C ) . Conversely , the fraction of enriched , beneficial mutations in the drifting genes ( out of all nonsynonymous mutations ) became 0 . 21 in G17 relative to 0 . 03 in G3 ( Fig 1B and 1C ) . Thus , not only was the mutational tolerance limited beyond G3 , but also , the acceptance of nearly-neutral mutations ( Wrel = 0 . 61–0 . 8 , Fig 1 ) was dependent on the co-accumulation of compensatory mutations . The ability to tolerate highly deleterious mutations at the onset of the drift , but not once mutations further accumulate , is also vividly exemplified by the tolerance of nonsense mutations–mutations leading to stop codons ( S4 Fig ) or frameshifing insertions/deletions ( InDels ) . The occurrence and tolerance of InDels in the selected G17 M . HaeIII library has been described [52] , indicating that certain nonsense mutations were tolerated to some degree due to translational slippage that results in a correctly translated protein despite a frame-shifted gene . However , the levels of full length , functional proteins translated from frame-shifted genes is obviously much lower than for wild-type , in some cases as little as 1% [52] . The second form of nonsense mutations are stop codons . At the onset of the drift , at least one stop codon mutation , in position 176 , was moderately tolerated ( Wrel in G3 = 0 . 74 ) . Stop codons in other positions were also found , although with lower Wrel values ( S4A Fig ) . However , once other mutations that reduce protein dose and/or function accumulated , nonsense mutations were almost entirely purged ( e . g . Wrel for position 176 in G7 = 0 . 47 , and in G17 = 0 . 24 ) . By the later rounds , stop codons were found almost only after position 324– a region that is not under functional selection ( S4 Fig ) . The stronger purging effect of nonsense mutations in later rounds was also manifested in an increasing fraction of nonsense mutations being assigned a Wrel value of 0 ( Figs 1 , ‘nonSense’ , and S4B ) . Several laboratory mutational tolerance experiments indicated the acceptance of mutations in positions that are highly conserved in natural orthologs [21 , 25–27] . We therefore examined to what degree M . HaeIII's orthologs predict acceptance in our experiment; namely , do the measured relative fitness effects of mutations ( Wrel ) correlate with the degree of divergence of the corresponding position in M . HaeIII’s natural orthologs ? To address this question we first compared the experimental Wrel values to the natural evolutionary rates of the respective positions ( Fig 3A ) . We used Rate4Site whereby the calculated rates relate to the degree of physico-chemical change exerted by sequence exchanges , and to the phylogenetic distances [65] . We found that positions that exhibit slow evolutionary rates ( i . e . , are highly conserved in nature; log2μ ≤ −2 , or μ ≤ 0 . 25 [44] ) show no , or low acceptance to mutations in the laboratory drift . Conversely , positions with high evolutionary rates tend to show high experimental tolerance to mutations . This trend is seen along the primary sequence ( Fig 3A ) as well as in the 3-dimensional structure ( Fig 3B and 3C ) . Given that we have mapped the effects of all possible single nucleotide mutations in M . HaeIII , we further examined how well their effects could be predicted from an alignment of orthologous sequences . There are many ways of predicting the effects of mutations from multiple sequence alignments . Certain biases are inevitable; foremost , prediction is highly dependent on sequence sampling–the number and the phylogenetic distribution of available sequences that are evolutionary related to the protein in question . Other biases relate to phylogenetic relatedness of the orthologs to the reference sequence and the manner by which the degree of divergence is calculated . A meaningful measure uses profile scores ( position-specific scoring matrices ) that take into account not only the frequency of sequences in which a given position varies , but also the physico-chemical nature of exchanges ( reviewed in [5 , 66] ) . Given the epistatic nature of sequence evolution , tolerance of mutations is largely not a matter of 'if' but of 'when'–namely , given enough drift , exchanges in even the most conserved sites may be tolerated [2 , 45 , 67] . PROVEAN ( Protein Variation Effect Analyzer , http://provean . jcvi . org ) is a predictor that takes into account phylogenetic distances [68] . Thus , the PROVEAN score function considers the physiochemical impact of amino acid exchanges alongside the evolutionary distance between the reference protein and the homolog ( s ) in which a given exchange is observed . We submitted to PROVEAN the reference sequence ( the wild-type M . HaeIII gene ) and a multiple sequence alignment of 105 orthologs ( S5 Fig ) . The program computed a score predicting how deleterious each possible amino acid exchange in M . HaeIII might be . The algorithm’s default thresholds are: scores ≤ -2 . 5 are predicted as deleterious , and scores > -2 . 5 as neutral [68] . We then compared the predicted PROVEAN score to the measured Wrel values for the 1 , 957 single nucleotide nonsynonymous mutations . Overall , a clear-cut trend is seen ( Fig 3D ) –mutations found to be deleterious in the laboratory drift ( Wrel ≤ 0 . 6 ) tend to show low PROVEAN scores ( ≤ -2 . 5 ) , whereas the accepted ones show high scores ( > -2 . 5 ) . From PROVEAN’s point of view as a predictor of deleterious mutations , true positives occurred at a rate of 83 . 3% ( S3 Table ) . Namely , out of the 1 , 234 mutations that were evidently deleterious in the laboratory drift ( Wrel ≤ 0 . 6 ) , 1 , 028 were correctly categorized by PROVEAN as deleterious ( score ≤ -2 . 5 ) . True negatives–mutations predicted by PROVEAN as neutral and found to be so in the drift , occurred at a rate of 63 . 5% ( 459 out of the 723 accepted mutations in the laboratory drift , Wrel > 0 . 6 , were scored with PROVEAN values of > -2 . 5 ) . When excluding mutations with borderline effects ( mutations categorized as nearly-neutral , with Wrel values 0 . 61–0 . 8 ) , the effects of the remaining set of mutations ( 1 , 649 out of 1 , 957; Wrel ≤0 . 6 , or > 0 . 8 ) were , as expected , better predicted by PROVEAN ( Fig 3E ) . Specifically , the ability to predict the effect of neutral mutations ( Wrel > 0 . 8 ) increased to 72% , ( accuracy of 80 . 5% , S3 Table ) . Notably , SIFT , a predictor similar to PROVEAN but that with no phylogenetic correction , showed lower prediction accuracy than PROVEAN ( 75 . 3% accuracy , with 73% true positives for deleterious mutations , and 82 . 2% true negatives for neutral mutations , Wrel > 0 . 8; S6 Fig and S3 Table ) . Furthermore , the effects of mutations are best described on a continuum scale rather than a binary classification of deleterious versus neutral . The inclusion of phylogenetic distances , as in PROVEAN , also generates a continuous score that in turn seems to correlate well with the experimental Wrel values ( S6C and S6D Fig ) . Further support to the conclusion that phylogenetic distance is a crucial factor in prediction is provided by the fact that neutral/beneficial drift mutations ( Wrel > 0 . 8 ) are decreasingly observed in orthologs as their sequences further diverge from M . HaeIII’s . In total , 39% of the single nucleotide exchanges observed in orthologs were found as neutral/beneficial in the background of M . HaeIII ( Wrel > 0 . 8 , S7A Fig ) . Of these , 54% ( i . e . , 21% of all single nucleotide ortholog-observed exchanges ) appear in close orthologs ( ≤35% divergence relative to M . HaeIII , S7B Fig ) . Thus , sequence exchanges observed in orthologs with higher sequence identity are more indicative of a neutral fitness effect in the reference sequence ( M . HaeIII in our case ) than when the very same exchanges appear in diverged orthologs ( >50% divergence ) . Finally , we examined how the experimentally measured fitness effects correlate with predicted structural and functional constrains , and to what degree these constraints apply to the sequence diversity seen in orthologs of M . HaeIII . To this end , a multiple sequence alignment was derived for M . HaeIII ( S5 Fig ) . The alignment gave a set of 2 , 000 exchanges that are observed in at least one ortholog–dubbed ‘ortholog-observed’ ( 845 , single nucleotide exchanges , and the remaining 1 , 155 being double and triple exchanges ) . The complementary set of ‘ortholog-unobserved’ was accordingly derived , and included 4 , 251 exchanges that are not observed in any known ortholog ( of which 1 , 112 are single nucleotide exchanges ) . We then compared the experimental set of ‘neutral mutations’ ( Wrel ≥ 0 . 8 , i . e . , excluding ‘nearly-neutral’ ) to ‘ortholog-observed’ , and the set of ‘deleterious mutation’ Wrel ≤ 0 . 6 ) to ‘ortholog-unobserved’ . This comparison is a priori problematic . The background at which these two sets of mutations occurred differs fundamentally: the maximal divergence in the laboratory drifted G17 sequences was ~3% ( an average of 9 . 6 mutations per a length of 329 amino acids gene ) . Accordingly , whereas our experimental set comprises only single nucleotide mutations , most natural amino acid exchanges relate to two or three nucleotide exchanges within the same codon ( 1 , 155 out of 2 , 000 orthologs-observed , and 3 , 139 out of 4 , 251 orthologs-unobserved ) . Despite the above caveat , we found that drift mutations with ‘neutral/beneficial’ effect ( Wrel > 0 . 8 ) , and accordingly , orthologs-observed exchanges , share the same biophysical constraints with respect to M . HaeIII’s configurational stability and enzymatic function ( Fig 4 ) . Specifically , mutations predicted using the FoldX force field to be highly destabilizing ( ΔΔG ≥ 2 kcal/mol; [69 , 70] ) were purged in the laboratory drift ( ‘deleterious’ Wrel ≤0 . 6 ) and also in the natural diversity ( ‘orthologs-unobserved’ , Fig 4A and 4B ) . Mutations in positions close to M . HaeIII’s active-site followed the same trend ( Fig 4C and 4D ) . The overlap between the biophysical constrains acting in nature and in the laboratory constraints was also indicated by ‘local closeness’–a structural measure of the degree of structural connectivity of a residue to other residues [71 , 72] ( Fig 4E and 4F ) . Furthermore , exchanges found in close orthologs appear to obey the above biophysical constraints to a larger extent than those in more diverged ones [41] ( S7B and S7C Fig ) .
Our results illustrate the limitations inherent to the experimental methodologies used for measuring the fitness effects of mutations in the laboratory , and in deducing from these experiments how proteins evolve in nature . In general , the current state-of-the-art experimental mappings artificially widen the threshold for acceptance of mutations , such that the early accumulating mutations have no apparent effect on the protein’s fitness [73] . This wider experimental threshold is driven by various factors , including: ( i ) higher protein stability ( e . g . , the stabilizing mutations we included in , or fusion tags known to increase solubility ( e . g . [26] ) ; ( ii ) Gene and protein copy numbers that are typically orders-of-magnitude higher than the natural ones; ( iii ) Growth environments that are less demanding than the natural ones; ( iv ) Proteins being under selection for one task out of several tasks they perform in nature under variable conditions . Such wider thresholds result in a higher , if not unrealistic tolerance of mutations relative to nature [74 , 75] . Once this threshold is exhausted , the loading of additional mutations results in a rapid collapse [47] . Indeed , the dynamics of our neutral drift experiment indicate that the very same deleterious mutations , including nonsense mutations , which are tolerated in the early rounds , are completely purged as the drift progressed ( Figs 1C and S4; see also [76] ) . The continuous loading of mutations , as applied in our study , appears to portray a more realistic picture with respect to the fraction of deleterious mutations . The distribution of fitness effects of mutations ( DFE ) derived from this experiment is different from the distributions derived from previous experiments , namely that ~30% of mutations are deleterious , and the remaining largely neutral [25–27 , 33 , 46] . In contrast , this experiment indicates the anticipated continuum , rather than the generally assumed bimodal distribution [49 , 74 , 77] ( Fig 1A ) . Further , even under the most conservative threshold , 67% of the mutations have evident deleterious effects ( Wrel ≤ 0 . 6 ) . However , purging is also consistently seen for mutations we categorized as ‘nearly-neutral’ ( Wrel = 0 . 61–0 . 8 , Fig 1B ) . Individually these mutations may be close to neutrality , but collectively they impact fitness , analogously to a population’s drift load [50 , 78] . This is apparent by the acceptance of ‘nearly-neutral’ mutations being accompanied by the enrichment of compensatory mutations ( Fig 1 ) [79] . If what we categorized as nearly-neutral mutations are included , ~81% of all possible amino acid mutations that derived from single nucleotide mutations are potentially deleterious . The much higher fraction of nonsynonymous mutations with deleterious effects observed in our experiment as compared to other experiments may relate to variations in the mutational tolerance of one protein vs . another . However , M . HaeIII does not seem to be a particularly slow evolving protein–the distribution of the positional evolutionary rates , and specifically the relative histogram areas of the fast versus slow evolving positions , are in agreement with a fast evolving protein [44] . Regardless , it is clear that , at present , comparing the DFEs obtained for different proteins is problematic because the experimental methodologies used to obtain these DFEs vary so much . The sensitivity of detection of fitness effects is also limited in laboratory setups by high noise levels as well as by the limited number of generations along which fitness is examined [74] . We also note that in reality , protein sequences drift in a gradual manner and via single nucleotide exchanges . Thus , the fitness effects measured for all 19 possible amino acids per position often reflect leaps in sequence space that are not taken by natural evolution . The results of our laboratory drift also support the hypothesis that natural protein drift is punctuated by deleterious and compensatory mutations . The order of their accumulation may differ , also in relation to the mutational rates . At high mutational rates , as applied here , compensatory substitutions may follow the deleterious ones [80–83] . At low mutation rates , however , mutations that initially accumulated as neutral may enable the fixation of deleterious ones [44 , 83] . In any case , the DFE obtained here suggests that , whereas upon drifting in nature , exchanges may be fixed by chance ( the neutral theory ) , their fitness effects are rarely neutral–they are nearly always deleterious or compensatory [41 , 84] ( Fig 1A ) . Compared to previous reports ( for example see [25–27 , 33 , 36] ) , tolerance vs . purging of mutations in our prolonged drift shows much better correlation to the positional evolutionary rates , and to specific exchanges observed in the natural diversity , i . e . , in M . HaeIII’s orthologs ( Fig 4 ) . Such a correlation is a priori problematic . The representation of natural sequences is sporadic , especially with horizontally transferred genes that encode specialized functions such as M . HaeIII . Thus , that a certain exchange is not observed , or rarely observed in the currently known sequence does not necessarily mean it is deleterious . Nonetheless , our data seems to coincide with what had been deduced from other analyses of orthologous sequences , namely that at a given background , the vast majority of mutations are deleterious [39–41 , 43] ( Fig 1 ) . Our data also support the notion that the exchanges found in close orthologs are more likely to be neutral than those in more diverged ones [41] ( S7 Fig ) . Exchanges in highly diverged orthologs are tolerated by virtue of being compensated by exchanges at other positions [41 , 45 , 67] and therefore tend to be context-specific . However , despite the above caveats , it seems that the effects of mutations can be predicted from the natural diversity of orthologs with relatively high accuracy , particularly when ‘nearly-neutral’ mutations with borderline effects are excluded ( Fig 3E ) . A systematic exploration of the performances of various predictors and prediction parameters is beyond the scope of this work . Nonetheless , it appears that the prediction seems improved when the phylogenetic distance of orthologs is taken into account [85 , 86] ( S6 and S7 Figs ) . Likewise , comparing the results of different experimental mappings of mutational effects is inherently problematic . These mapping experiments used different proteins , different mutagenesis and screening , or selection , strategies , and different ways of assigning the ‘fitness’ values to mutations . As experimental approaches of systematic mapping develop further , standard experimental and data analysis procedures may develop that will enable more meaningful comparisons . Further , the biophysical constraints acting to limit drift both in the laboratory and in nature overlap , indicating universal constraints that dictate purging of sequence exchanges [87] ( Fig 4 ) . Thus , including structural considerations , possibly ‘local closeness’ as an integrated parameter [71] , may greatly improve prediction of the effect of mutations [88 , 89] , as already shown for certain predictors [8 , 89–93] . Overall , the application of the experimental setup described here provides a better understanding of how protein sequences diverge in nature , as well as a new dataset that can be used for improving the prediction accuracy of the effects of protein mutations , and specifically of single nucleotide polymorphisms .
A modified M . HaeIII wild-type gene , carrying four stabilizing mutations [51] , and no GGCC sites in its open reading frame , was cloned with an N-terminal His-tag into pASK-IBA3+ vector ( IBA , using NcoI and NotI; the vector also carried 14 GGCC sites , 3 of which were located within the ampicillin resistance gene ( See supplementary Fig 3 in [51] ) . Plasmids were transformed into E . coli ER2267 ( EcoK r- m- McrA- McrBC-Mrr- ) in which GGCC DNA methylation is not toxic [94] . Transformants were selected by growth on ampicillin . Random mutagenesis was performed as described previously [52] . Briefly , M . HaeIII's ORF ( open reading frame ) was amplified by PCR with an error-prone polymerase ( GeneMorphII Mutazyme , Stratagene ) . The mutagenic PCR was optimized to an average of 2 . 2 mutations per gene . Each round of evolution , or generation ( noted as 'G' ) , included the following steps ( S1 Fig ) : ( i ) The pool of M . HaeIII genes from the previous round was randomly mutated , recloned using the NcoI and NotI sites , transformed into E . coli and plated on agar plates containing ampicillin . About 106 individual transformants were obtained in each round . ( ii ) Colonies grown at 37°C overnight were combined , plasmid DNA was extracted and digested with HaeIII ( 10–20 units , in 50 μl of NEB buffer 2 , for 2 hours at 37°C ) , and re-purified ( PCR purification kit , QIAGEN ) . ( iii ) The recovered plasmid DNA was re-transformed for another round of enrichment . Each round of drift included one cycle of mutagenesis and three cycles of enrichment ( transformation , growth , plasmid extraction and digestion ) . The naive library , G0 , relates to the transformed plasmid DNA derived from cloning of a repertoire of ~105 individual M . HaeIII genes after the first round of mutagenesis and prior to selection by HaeIII digestion . The samples of the naive ( G0 ) and the selected libraries from Rounds 3 , 7 and 17 ( assigned as G3 , G7 and G17 ) were prepared as described previously [52] . Briefly , the pools of M . HaeIII's open reading frame were PCR-amplified , purified , and concatenated by self-ligation ( using XhoI restriction sites at both ends of the PCR [51] ) . Sequencing libraries were prepared and sequenced according to manufacturer's protocol at the Weizmann Institute's high throughput-sequencing core facility . The obtained sequencing reads ( ~40 nts ) were mapped to the reference sequence of wild-type M . HaeIII with two methods: ( i ) Using NCBI blastn v2 . 2 . 20 [95] with parameters: e-value cutoff 0 . 0001 , word size 7 , and allowing up to 6 mismatches and requiring a minimal alignment length of 24 consecutive nts , as previously described [96 , 97]; and ( ii ) Using Novoalign v2 . 07 . 00 with parameters: c 4 Hash step-size 6 [96] . Point mutations , insertions and deletions were assigned based on the mapping of the sequencing reads to the reference sequence as previously described [97 , 98] . Every mismatch or gap in the reads alignment relative to the wild-type reference was recorded per each nucleotide position , and further analyzed using custom Perl scripts ( available at: https://github . com/tawfiklab/HTS_codon_analyzer ) . Only codons that were intact within the 40 nt reads were included . Processing of the observed mutation counts per codon was done primarily with Excel ( see S1 File ) . All possible single nucleotide mutations were detected in the raw data of the unselected G0 library ( 329X9 = 2 , 961 possible single nucleotide mutated codons that in turn comprise the 1 , 957 possible single nucleotide amino acid mutations; S2 File and S8 Fig ) . However , Illumina sequencing exhibits a considerable background level of mutagenesis due to PCR amplifications as well as sequencing errors . Potential sequencing artifacts , specifically mutations that were observed at the edges of reads ( where sequencing errors are more frequent ) , were filtered out ( S1 File ) . The background rate was determined using the region upstream of the randomly mutated open reading frame of M . HaeIII ( the N-terminal fused His tag that was not subjected to mutagenesis , S2 File , S8 Fig and S4 Table ) . Thus , the average background frequency was subtracted from the mutational frequencies to give the net positional frequencies that were used to calculate the Wrel value of each amino acid mutation ( the final analyzed data can be found in S3 File , including for double and triple mutations that were not analyzed here ) . Mutational frequencies were determined for every possible codon mutation ( 63 including single , double and triple nucleotide mutations ) as the number of reads with a given mutation ( s ) divided by the total number of reads that mapped the corresponding position . The frequencies of all mutational events that led to the same amino acid were combined . Orthologous sequences to M . HaeIII were collected using BLASTP search within the REBASE database [99] . Within the range of 25–75% identity , 105 non-redundant family members were aligned using MUSCLE [100] ( S5 Fig ) . The maximum likelihood phylogenetic tree was calculated using PhyML with the LG matrix [101] . The position-specific evolutionary rates ( μ ) were calculated by Rate4Site [65] . The positional rate is calculated that indicates how fast this site evolves relative to the average rate across all sites in the input alignment . Mutation-specific scores were calculated using PROVEAN and SIFT programs using the default parameters and M . HaeIII sequence as a reference . Both software are available on the homepage of the J . Craig Venter Institute: the SIFT tool is at http://sift . jcvi . org [102] , and the PROVEAN tool is at http://provean . jcvi . org [68] . To ensure the consistency of this analysis , we provided the set of orthologs sequences in FASTA format ( S5 Fig ) rather than using the BLAST search in the PROVEAN webserver . The PROVEAN calculated scores were subsequently provided by Dr . Yongwook Choi . FoldX was used to predict the stability effects of mutations relative to wild-type M . HaeIII . The crystal structure of M . HaeIII ( PDB id 1dct ) [103] was first optimized using the FoldX RepairPDB function . Subsequently , all possible single mutants ( 19 different amino acids , at each position ) were calculated by the BuildModel mutation engine , and relative stability of mutants was obtained ( ΔΔG = ΔGWT-ΔGMUT ) . Distances of residues from the reaction center were defined as the shortest distance between the closest residue atom and either the sulfur of the catalytic cysteine or the methyl group of the SAM cofactor . These were calculated based on M . HaeIII in complex with the DNA ( PDB id 1dct ) [103] . The coenzyme distances were derived from a homology model based on M . HhaI in complex with SAM ( PDB id 2hr1 ) . Local closeness was calculated SPACER web server ( available at http://allostery . bii . a-star . edu . sg/ ) [71] using default parameters and M . HaeIII structure as a reference ( PDB id 1dct ) [103] . | Understanding and predicting the effects of single nucleotide polymorphisms ( SNPs ) is of fundamental importance in many fields . Systematic experimental mappings of the effects of such mutations within a given gene/protein comprise an essential experimental tool for determining protein function and for refining models of protein evolution , as well as an important resource for improving prediction algorithms . Here , we present the results of a laboratory system that mimics the manner by which protein sequences diverge in nature: a prolonged process of gradually accumulating random mutations that retain the protein’s structure and function . The change in frequencies of mutations over generations , as obtained by deep sequencing , enabled us to assess the relative effects of all possible SNPs at the background of an accumulating number of mutations . Compared to previous reports , we found that > 80% of all possible amino acid exchanges have potential deleterious effects , with 67% being clearly deleterious . Tolerance vs . purging of mutations in our prolonged drift also showed better correlation with natural diversity . Overall , our experimental setup provides a better understanding of how protein sequences diverge in nature , plus a new basis for improving the prediction accuracy of the effects of protein mutations , and specifically of SNPs . | [
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| 2015 | Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations |
Current Chagas disease vector control strategies , based on chemical insecticide spraying , are growingly threatened by the emergence of pyrethroid-resistant Triatoma infestans populations in the Gran Chaco region of South America . We have already shown that the entomopathogenic fungus Beauveria bassiana has the ability to breach the insect cuticle and is effective both against pyrethroid-susceptible and pyrethroid-resistant T . infestans , in laboratory as well as field assays . It is also known that T . infestans cuticle lipids play a major role as contact aggregation pheromones . We estimated the effectiveness of pheromone-based infection boxes containing B . bassiana spores to kill indoor bugs , and its effect on the vector population dynamics . Laboratory assays were performed to estimate the effect of fungal infection on female reproductive parameters . The effect of insect exuviae as an aggregation signal in the performance of the infection boxes was estimated both in the laboratory and in the field . We developed a stage-specific matrix model of T . infestans to describe the fungal infection effects on insect population dynamics , and to analyze the performance of the biopesticide device in vector biological control . The pheromone-containing infective box is a promising new tool against indoor populations of this Chagas disease vector , with the number of boxes per house being the main driver of the reduction of the total domestic bug population . This ecologically safe approach is the first proven alternative to chemical insecticides in the control of T . infestans . The advantageous reduction in vector population by delayed-action fungal biopesticides in a contained environment is here shown supported by mathematical modeling .
Chagas disease , caused by infection with the parasite Trypanosoma cruzi , is the most important vector-borne disease in Latin America . It currently affects about 7–8 M people [1] . The blood-sucking insect Triatoma infestans ( Hemiptera , Reduviidae ) is the most widespread and relevant vector of the disease in the southern region of South America . The major target of vector control programs are well-established domestic populations , although peridomestic and sylvatic habitats may also harbor populations of significant size . Residual spray with chemical insecticides has been the major vector control strategy , showing a significant success in reducing the transmission in many areas of the so called Southern Cone Initiative ( Argentina , Bolivia , Brazil , Chile , Paraguay and Uruguay ) launched in 1991 [2] . However , sustained financial and human resources are essential to decrease and maintain domestic populations to an acceptable low level in order to interrupt or reduce vectorial transmission of T . cruzi . In addition , after about 30 years of pyrethroid application , pyrethroid resistance foci are increasingly being documented in Argentina and Bolivia [3 , 4] . Despite their recognized efficacy , the major drawback of using the traditional pyrethroid spraying is its limited effectiveness in certain regions , such as the Gran Chaco area [5] . In particular , insecticide efficacy depends not only on the mode and frequency of application and on the characteristics of the dwelling structures , but also on the potential development of insecticide resistance . Systematic and periodic insecticide application covering most sites and refuges within infested houses ( difficult to achieve in inaccessible remote areas ) , together with house improvement ( to reduce potential sites for house infestation ) are required in order to achieve suppression of T . infestans [5 , 6] . A reduction of the indoor population of triatomines is the best way to decrease the risk of human infection with T . cruzi [7] . Among alternative control methods , a box containing a formulation of the fungus Beauveria bassiana , has been tested in the laboratory and in the field [8] . The killing ability of this fungal formulation was similar against pyrethroid-susceptible ( Py-S ) and pyrethroid-resistant ( Py-R ) T . infestans strains [9]; furthermore , successive applications of B . bassiana were estimated to significantly reduce the risk of acquiring T . cruzi through an infective bite [9] . The horizontal transmission ( auto-dissemination ) of fungal conidia was estimated at different bug densities , showing to contribute significantly to the overall infection of the insect population [10] . Pheromone-based traps are of widespread and increasing use to detect , lure-and-kill , or disrupt mating , for many agronomy insect pests [11 , 12]; most if not all of the attractants are volatile [13] . However , much less research related to such tools is available for the control of animal and human insect-borne diseases . Carbon dioxide is a well-known attractant of most blood-sucking arthropods including T . infestans [9 , 14–16] , though a major limitation is the cost , operation and efficacy of its use under field conditions [17] . Triatomine population growth and regulation depends on several environmental factors [18] , the most relevant being temperature and humidity [19] , habitat and refuge [20] , and diet [21] as well as their own density , i . e . , intra-specific competition [22 , 23] . Several mathematical models of triatomine population dynamics have been developed , including some models that incorporate triatomine population control; however , most of them have focused on the effect of insecticide spraying [24 , 25] , with the exception of a mathematical model for biological control of Rhodnius prolixus using the parasitoid Ooencyrtus trinidadensis [26] . Stevens et al . [7] modeled the effect of intervention strategies , such as insecticide spray rate and spraying efficiency . In the absence of sylvatic populations , the frequency of insecticide application was predicted to have the largest influence on house infestation , and the vector’s population reproductive rate R0 to be most sensitive to the spraying rate . The possible impacts of environmental stochasticity on the evolution of vector-borne diseases have also been investigated mathematically [27] . Several evaluations of the potential of B . bassiana as an agent of biological control against triatomines have been carried out [9 , 10 , 28–30] , as well as various biological aspects of the relationship between triatomines and B . bassiana , such as modes of action of the pathogen [31–33] , the effects of molting and starvation on the susceptibility of Rhodnius prolixus to the pathogen [34] , and the contribution of the horizontal transmission of conidia to the overall population infection events [9 , 10] . It has also been shown that B . bassiana is compatible with deltamethrin and can be used combined with this chemical without being exposed to metabolic detrimental effects [35] . However , no model for predicting the dynamics of triatomine vector populations after exposure to this entomopathogenic species has been developed . The purpose of this work was i ) to evaluate reproductive and survival parameters of T . infestans after exposure to the entomopathogenic fungus B . bassiana , combined with signals attractive to the bugs in infective boxes , measured both in the laboratory and in field assays , and ii ) to develop a mathematical model of the effect of B . bassiana on T . infestans populations based on parameter estimates of those laboratory and field results . This information will allow an evaluation of the potential effectiveness of B . bassiana infection on the population dynamics of T . infestans , and the possibility of reducing T . infestans domestic populations .
The T . infestans colony of pyrethroid-susceptible ( Py-S ) insects used for laboratory assays was reared at 30°C , 50–60% relative humidity , under a 12:12h photo cycle , and fed on chickens every seven days . The colony is renewed yearly by incorporating first generation insects , usually from Formosa province , provided by the Servicio Nacional de Chagas , Cordoba , Argentina . For the laboratory bioassays we used insects two weeks after molting and after 1 week of a blood meal , except otherwise specified in the text . A commercial powder formulation ( WP ) of B . bassiana strain GHA provided by Laverlam International ( Butte , MT , USA ) containing 1 . 27×1011 conidia/g ( 98% viable ) was formulated with diatomaceous earth ( DE ) ( Perma-Guard Inc . , Albuquerque , NM , USA ) in a conidia:DE ratio ( 2:1 w:w ) . DE produced no mortality at this dose . The commercial fungus powder was suspended in distilled water containing 0 . 01% Tween 80 for insect susceptibility bioassays . Virgin females and males ( 15–20d old ) were used 2–3d after blood-feeding on chickens and paired at random; each pair was placed on separate containers and checked for copulation completion following established protocols in our laboratory [36]; non copulated females were discarded . Twenty-eight females were randomly selected and allowed to be in contact with the fungal powder ( 2 . 6 × 108 conidia/cm2 ) for 5 minutes [10] . Another set of 18 randomly selected females were used as controls . All females ( infected and non-infected ) were maintained in separate containers at 28°C and 60%RH . Reproductive parameters ( oviposition , fecundity and fertility ) were recorded daily until death in infected females and for eight weeks in control females . Oviposition refers to the number of females laying eggs per total females; fecundity refers to the number of eggs laid per female per day , and the fertility refers to the percentage of the laid eggs that hatched . Fifth instar T . infestans exuviae were washed with distilled water , left to dry on filter paper and ground with mortar and pestle until obtaining a fine powder that was then mixed with distilled water ( 0 . 038 g/ml ) . For bioactivity assays , this suspension was applied with a brush on half of the bottom’s internal surface of a 15×12 cm cardboard box ( called the test box ) and let to dry . Four replicates were set and , to completely randomize the assay , the location of the painted zone was rotated in each replicate 90° clockwise . The test box had two rectangular holes of 2 . 5×0 . 6 cm in two opposing sides at ground level to allow for the entry of insects; the test box was placed in the center of a larger container ( 60×60×50 cm ) with the upper side covered with muslin cloth . Twenty-five 5th instar nymphs were released at random in the bottom of the larger container . Four and 24 h after the start of the experiment , the two boxes were opened and a picture was taken; the insects inside the exuviae-painted and the exuviae-free boxes were counted . To evaluate the performance of an attraction-infection device , we used similar boxes treated as described above , and the conidia:DE formulation ( 2:1 w:w ) powder was added over the same surface ( 100 mg/box ) . Twenty-five 5th instar non-infected nymphs were released in the container . Twenty four hours later , the boxes were opened and the insects collected , and then maintained individually at rearing conditions . Bug mortality was checked daily; dead insects were kept in individual humid chambers to confirm fungal infection [9] . Another set of infection assays were performed employing similar boxes without the exuviae suspension ( controls ) . Three replicates of the treatment and of control assays were carried out . Median lethal time ( MLT ) was estimated as Σ ( daysn × dead insectsn ) /total dead insects . The device consisted in a 3-mm medium density fiberboard ( MDF ) box of 16×11×4 cm , with five lateral holes placed at the same level than that of five internal racks ( 3 cm width ) containing the conidia:DE mixture ( 2:1 ) ( A scheme is shown in Fig 1 ) . Before incorporating the fungal mix , the upper side of the racks and the internal sides of the boxes were covered with the exuviae suspension . The field box was tested in rural houses of the Salvador Mazza Department , Salta Province , Argentina ( 22° 03’ S , 63° 41’ W; 804 masl ) , previously reported heavily infested with pyrethroid-resistant ( Py-R ) T . infestans [9] . A total of 13 houses were selected based on reports of vector presence . The number of insects recorded by visual observation ( see below ) varied from 10 to 63 per house . The selected houses were mostly made of adobe; indoor walls were partially plastered , with zinc roofs and rustic cement floors . Households ranged from two to eleven people per house , in 1–2 bedrooms; dogs were reported to sleep outside . Peri-domiciliary structures were chicken coops . The experiment was started in November 2009 . Prior to the assays , a two-person team searched by sight and counted the triatomines in room areas , household goods , and beds ( one man-hour/house ) ; no flushing out treatment was applied in order to avoid physical damage or affecting the behavior of the bugs . Six boxes per room containing each 1g of fungal formulation ( conidia:DE mix , 2:1 w:w ) were placed in each house wall , close to the insect shelters . One month later ( December 2009 ) , a 3-person team searched thoroughly for dead insects inside the boxes and their surroundings . Dead bugs were collected and stored separately in labeled containers and transported to the laboratory to verify fungal infection; the infection boxes were replaced by new ones . One month later ( January 2010 ) , the boxes were removed; and all insects detected ( dead and alive ) were collected using the flushing-out method with 0 . 2% tetramethrin ( Icona , Buenos Aires , Argentina ) as an irritant agent , and transported to the laboratory in labeled flasks . Mortality was checked until 40 days after the boxes were removed , and cadavers were analyzed to verify fungal infection . The differences between the mean values obtained in the experiments described in 1 . 3 , 1 . 4 , and 1 . 5 were determined by the Student’s t-test ( P < 0 . 05 ) . Instat 3 . 05 ( GraphPad Software Inc . , San Diego , CA ) was used for all statistical analyses . We developed a stage-specific matrix model based on the seven stages of T . infestans: the egg , the five nymphal instars , and the adult stage; the model is a female-only model; the nymphal stages cannot be differentiated by sex , but no sex-ratio correction was needed because the sex-ratio of eggs after being laid is 1:1 , and there is no sex-bias in the susceptibility to the fungal pathogen . The time unit of the model was one day . As our interest was to model the infection process of T . infestans by B . bassiana we added to this basic matrix model the infected individuals ( with only six stage-specific classes , because eggs do not get infected by B . bassiana ) . Fig 2 shows a graphical representation of the matrix model in terms of a life cycle diagram , where the asterisks identify infected individuals; the upper row shows the non-infected insects with their probability of surviving within the same stage given by Gi ( the “self-loop” arrows ) , while the probability of surviving within the same stage and molting to the next stage is given by Pi ( the straight-line arrows ) ; here i identifies the stage; f represents the force of attraction of all boxes to the vector population per time unit , so that for non-infected individuals Pi and Gi are multiplied by ( 1-f ) . The lower row shows the infected insects with Pi and Gi as before , where θi ( 0≤ θi ≤ 1 ) is a measure of the mortality induced by the pathogen B . bassiana; that is , if the probability that a non-infected individual does not die before the next time unit is Pi +Gi , then for an infected individual that probability is reduced by the factor θi , that is it becomes ( Pi +Gi ) θi . The so-called “transitions” Pif ( the “curly” arrows ) refer to the probability that an individual will survive in the same stage and get infected at the next time unit ( it will “pass” from Ni to Ni* ) . In this model , we ignore the probability that an individual will acquire the infection and molt to the next stage in the same time unit ( that is , in the same day ) . The egg stage has three entering arrows: eggs that were laid by non-infected and infected bugs , and eggs that having been laid in a given time unit , are surviving to the next time unit; however , the exit from the egg stage is only one arrow: from the egg to the first non-infected instar ( an egg can only become a non-infected N1 , with a “transition” given by G1 ( 1-f ) ) . The fecundity components are F and F* , for non-infected and infected bugs , respectively , both in units of female eggs/female/day ( the long curved arrows pointing to the eggs at the top and at the bottom of the diagram ) . The corresponding algebraic expression of this life cycle diagram is given in S1 File , which also provides an example of a numerical matrix . Some of the main differences between the model and the field conditions are summarized in Table A in S3 File . In order to adapt field data and the model we converted the sampled house number of bugs of our field data in total number of bugs based on published relationships between the one man-hour/house collection and total house bug population ( Table B in S3 File ) . Also , the pooled nymph field population estimates were separated into nymph stages based on published bug stage distribution ( Table C in S3 File ) . To compare the model´s results with the field data we had to estimate some of the model parameters ( Efficacy , α and N ) , and also the values of two new parameters called “Development time factor” and “Survival factor” , to correct those two variables from laboratory to field conditions ( Table D in S3 File ) . For that purpose the model was then processed assigning to each of the five parameters a possible value selected from a uniform random distribution ( within established lower and upper limits ) trying to minimize the cumulative sums of squares function ( SSQ ) between the simulated and observed values of the four variables of interest: infected and non-infected nymphs and adults , respectively ( Table E in S3 File ) . The model was used to simulate the house conditions for 60 days ( the time frame of the field assays ) , with the field initial conditions and the best parameter estimates; 100 simulations were ran to obtain results for 100 combinations for each parameter , and the simulation results and the field data were compared with boxplots , for each of the four variables of interest ( number of nymphs and adults infected and non-infected ) . This study was performed within the guidelines established by the Bioethic Regulations of the Public Health Ministry of the Province of Salta , Argentina . Before the study , the objectives and the experimental protocol were explained during meetings with the community leaders , health professional and technical staff . Each head of family of these illiterate populations provided oral consent to perform the assay , after receiving the same information in the presence of local Vector Control Program officers , who participated as mediators . Oral consent was documented by a spreadsheet . All animal care and laboratory experimental protocols were approved by theDirective Board of the INIBIOLP and carried out following the AVMA Animal Welfare Policies ( https://www . avma . org/kb/policies/pages/default . aspx ) and AVMA Guidelines on Euthanasia ( https://www . avma . org/KB/Policies/Documents/euthanasia . pdf ) ( Instituto de Investigaciones Bioquímicas de La Plata’s Animal Welfare Assurance No . A5647–01 ) .
Triatomine aggregation behavior is relevant to the survival and reproduction of T . infestans ( and therefore to its population fitness ) because it facilitates finding daylight shelters to evade predators , thus remaining motionless ( and safe ) for most of the day . The epicuticle lipids of T . infestans are involved in this behavior; among them , the free fatty acid fraction was identified as partially responsible for the aggregation response [8 , 42] . Other cuticle components also elicit mating behavior in adult males upon contact [36] . However , none of these components have been tested in field assays . To take advantage of these aggregation properties of the epicuticle , and to enhance the attraction effect of the B . bassiana infection boxes , we used an exuviae suspension , with a lipid composition similar to that of the juvenile cuticle [43] which resulted in a significant aggregation response of T . infestans to conspecific cuticle-like signals [8]; the aggregation response persisted at least up to one month . The incorporation of this aggregation cue increased the efficacy of the bioinsecticide-containing box in a relatively short time frame; our present results show that bug mortality was significantly higher ( 59 . 0 ± 9 . 6% ) when the fungus was combined with the aggregation cue , compared with 33 . 3 ± 5 . 3% mortality when the infection box contained only the fungus . We are not aware of similar attempts combining an entomopathogenic fungi and a non-volatile attractant cue in other vector control strategies . In the typical attract-kill paradigm extensively used for some agronomy pests [11 , 12] , the attractant is usually a low molecular weight volatile semiochemical that exerts its action at a certain distance [13]; we have not found any evidence on the use of relatively high molecular weight , short-range attractant compounds . Among potentially useful volatile signals , the limitations of using carbon dioxide under field conditions have been mentioned already mentioned [9 , 17] . The chemistry of volatile defensive signals emitted by triatomines has been known for many years [44 , 45]; they have been used for bug collection and infestation monitoring in long term field experiments ( up to 6 months ) , showing a superior performance than the traditional man-hour survey collections [46] . However , no evidences are available on their efficacy in short term evaluation assays . T . infestans populations have marked fluctuations under certain conditions , with about two population cycles per year , at least in the central region of Argentina [47] , with fecundity as one of the main parameters affecting population size and also impinging on its population stability . In our results not only the percent of infected ovipositing females was significantly lower ( 57% ) as compared to controls , but also total lifetime offspring production after fungal infection was reduced in about 70% of the corresponding controls , mainly due to the shortened longevity of adult infected females . When 18 complete cohort studies of T . infestans taken from different bibliographic sources were used , we found similar results: the average fecundity of un-infected females was 0 . 86 ♀eggs/♀/day , about 70% higher than for the infected females ( 0 . 51 ♀eggs/♀/day ) ( see Table B in S2 File ) . Both T . infestans adults and nymphs are similarly susceptible to the entomopathogen B . bassiana [9 , 10]; however our summer field assays showed that the adult population mortality ( 52 . 1 ± 7 . 5% ) was higher than that of the juvenile stages ( 20 . 1 ± 5 . 1% ) . Sixty days after starting the assay , the mean percentage mortality was estimated as the total number of dead insects/house compared to total number of insects detected ( dead and alive ) /house ( Table 3 ) . These results are probably related to seasonality; nymph’s populations show a marked annual peak during the hot season [47]; on the contrary , the same authors reported a scarce 2% fluctuation for the adult population during summer . Similar previous trials in winter , when bug population is relatively stable and inactive [47] , were performed at a neighboring rural locality distant about 30 km from the current experimental site , using the same fungal formulation ( 1 g per box ) and the same number of boxes ( 6 boxes per room ) , showing 52 . 4% overall bug mortality one month after setting the boxes [9] . That is , after 30 days , bug mortality was similar to our present results , despite the 2008 assay was carried out in the winter season , and CO2 was used to attract the triatomines . A constrain in the experiments here described was the timing . It has already being addressed that the seasonal timing of vector control interventions can greatly affect its efficacy [48 , 49] . Gorla and Schofield [47] proposed the cold season ( June-August ) as the most appropriate , because recovery of any surviving populations would be inhibited by low temperatures . However , operational difficulties forced us to perform our field assays in the warm season ( late Spring to Summer ) . In vector indoor control the most important advantages of the use of biopesticides over chemical insecticides are the smaller risk of developing host resistance , and minimal risk to the environment and other organisms [50] . In contrast to the well-known irritant effect of chemical insecticides , the powdered fungal formulation was not avoided , but rather showed some attractiveness to T . infestans . During the biopesticide experiments , insects exhibited a characteristic camouflage behavior: using their front legs to partially cover themselves with soil dust; and they did the same with the dusty powder formulation . The paradigm of “instant-kill requirement” of chemical insecticides can be contrasted to the “slow-kill” bioinsecticide effect , with the latter showing many advantages . Mycoinsecticides elicit no irritation response on the host , and by reducing the survival of the host without instant killing ( i . e . , slow-kill ) , allow for the fungus a long term control with lower possibilities of developing resistance [51] . This was shown by Read et al . [52] , using a mathematical model of the use of mycoinsecticides with mosquitoes transmitting malaria . Also in relation to mosquitoes , Koella et al . [53] extended the idea of a late mode of action by biopesticides , and claimed that entomopathogenic fungi might act as an “resistance-evolution-proof” insecticide for mosquito control because of its relatively late action in the mosquitoes’ life-cycle , allowing the reproduction of part of the adult population before they are killed , thus preventing the evolution of resistance . We have observed that fungal infection can affect triatomine feeding behavior; i . e . , shortly after insects are infected with fungi ( 2–3 days ) , most of them are not apt to extend their proboscis to suck blood; this effect is similar to the one reported in infected mosquitoes [54] . As T . cruzi transmission is highly related to early diuresis after feeding , the poor feeding aptitude induced by B . bassiana not only reduces the fecundity ( strongly dependent upon blood ingestion ) but also the potential of disease transmission ( lack of quantitative data prevented us to include this information in the model ) . The mathematical model we developed is a relatively simple one , and we consider it as a first step in developing a more realistic model . E . g . , this is a deterministic model , despite both demographic and environmental stochasticity ( unpredictable spatio-temporal variability in environmental conditions and feeding sources , as well as variability in life history traits ) are among the main factors of the fluctuations in the density of triatomine populations . Notwithstanding the strictly deterministic characteristics of the model here developed , and the various simplifications we resorted to , our sensitivity analysis results proved extremely useful; they tell us that the number of boxes ( n ) and the boxes’ efficacy factor ( E ) , are the main drivers of the reduction of the total bug population , with the former being about four times as strong as the latter in their effects . This result implies that , being E an arbitrary factor to compensate for potentially overestimating the effect of the fungi from laboratory results , the performance of B . bassiana as a biological control agent should be quite reliable , and that whatever environmental condition that decreases the efficacy may be compensated by increasing the number of boxes . This is clearly seen in Fig 5 , which shows how E and n are related to maintain the net population growth rate RPo≈ 1 . The extremely low “importance” of the infectiveness period ( μ ) , RPo and REo parameters in the behavior of the model , also suggest the structure and the demographic and epidemiological parameters are reasonable because μ and REo subsume the essential parameters of the dynamics of infection: μ is related to the average time that an infected individual remains infective ( 1/μ ) , and REo represents the average number of infective contacts caused by one infected individual; we can then conclude that even if our laboratory and field estimates of those parameters would have been flawed , their impact on the model’s predictions would be minimal . Additionally , it indicates that as the behavior of the model is more sensitive to external factors such as the efficacy ( E ) and the number of boxes ( n ) than to the biological parameters , we are confident on the prospects of our model´s predictions , because they depend essentially on factors that can be manipulated during the biological control campaign operations . Furthermore , the population reduction obtained in the field assays after 2 month intervention , using 6 boxes per room , is similar to that predicted by the model ( for 6 boxes/room ) when E ~ 0 . 12 , a conservative value for the box efficacy ( Fig 6 ) . This figure shows that for this number of boxes , the condition R0< 1 can be attained with an extremely low efficacy ( E = 0 . 06 ) . Modifying the number of boxes , the model predicts that the population can still be decreased using 3 boxes , but with higher efficacy ( α = 0 . 16 ) ; on the contrary , at extremely low efficacy ( E = 0 . 02 ) , 20 boxes would be needed for R0< 1 . The good fit obtained in the comparison between model prediction and house field data ( Fig 8 ) suggests that , despite the original model’s structure was not intended to simulate a real house , the model’s performance can be considered satisfactory . Nevertheless , to be more realistic future versions of the model should include bioclimatology , house construction materials , number of rooms in the house , number of people , number of animals , and peri-domiciliary structures , a task that was beyond our original purpose . The future of chemical control of T . infestans based on indoor insecticide spraying as the sole tool for bug suppression is threatened by growing incidents of pyrethroid-resistant population detection , together with the known operational and financial difficulties to spray a large number of sparsely populated small villages located in remote areas . Thus , it is evident the urgent need to modify the conventional vector control methodologies , at least in the Gran Chaco area . Under this avenue , it is relevant to gain additional understanding of the effect of biological tools in vector population control-per se or jointly applied with chemicals- , and we think that our combined results of field , laboratory and mathematical modeling suggest that a formal pilot program of T . infestans population biological control with B . bassiana is in order . | Triatomine bugs are the causative agent of Chagas disease ( American trypanosomiasis ) , they transmit the parasite Trypanosoma cruzi by infectious blood-sucking . In the southern Cone of South America , Triatoma infestans is the major disease vector . The efficacy of chemical control strategies is mostly based on pyrethroid application; however , the growing reports of resistant bug populations in Argentina and Bolivia urges to find innovative vector control measures . Among alternative control tools , entomopathogenic fungi were already shown to be effective against T . infestans; furthermore , pyrethroid-resistant insects are unable to counter entomopathogenic fungal infection . Pheromone-based technologies are widely used to help improve the insecticide killing efficacy; here we will show the usefulness of the fungal biopesticide combined to T . infestans contact aggregation pheromone to reduce bug population . That this low cost , low-tech tool can help control T . infestans , regardless their susceptibility to current chemical insecticides , will be shown by field , laboratory and mathematical modeling assessment . | [
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| 2015 | Biological Control of the Chagas Disease Vector Triatoma infestans with the Entomopathogenic Fungus Beauveria bassiana Combined with an Aggregation Cue: Field, Laboratory and Mathematical Modeling Assessment |
Despite the importance of intracellular signaling networks , there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ . One prominent view involves stable signaling machines with well-defined quaternary structures . The combinatorial complexity of signaling networks has led to an opposing perspective , namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes . Since many hypotheses regarding network function rely on how we conceptualize signaling complexes , resolving this issue is a central problem in systems biology . Unfortunately , direct experimental characterization of these complexes has proven technologically difficult , while combinatorial complexity has prevented traditional modeling methods from approaching this question . Here we employ rule-based modeling , a technique that overcomes these limitations , to construct a model of the yeast pheromone signaling network . We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations . To contrast the ensemble behavior , we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines . We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed . This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo . In some cases , e . g . core enzymatic activities like protein synthesis and degradation , machines assembled via hierarchical energy landscapes may provide functional stability for the cell . In other cases , such as signaling , ensembles may represent a form of weak linkage , facilitating variation and plasticity in network evolution . The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function , evolution and engineering of signaling networks .
Much of our reasoning about the function of biological systems relies on the formation of multi-subunit protein complexes [1] . In some cases , such as the ribosome and the proteasome , these complexes take the form of intricate molecular machines with well-defined quaternary structures [2]–[4] . The overall structure of complexes formed during signal transduction , however , is considerably less clear . There are a few well-characterized signaling machines , like the apoptosome , and some have argued that the majority of structures produced by signaling networks would have a machine-like character [5] , [6] . Most of the complexes formed during signal transmission and processing have not had their global three-dimensional structures experimentally determined , however , and as such we currently do not know the extent to which signaling occurs via machines [7] . Despite this uncertainty , the machine-like perspective on signaling complexes is pervasive in the literature , if often implicit; for instance , one commonly represents signaling networks graphically by drawing large complexes in which all of the relevant proteins interact simultaneously [8]–[14] ( Fig . 1A ) . Although such diagrams are often presented as compact summaries of a set of interactions , they are certainly evocative of a machine-like structure , and lead naturally to analogies between signaling complexes and highly ordered objects such as circuit boards [7] , [9] . One issue that complicates this machine-based picture is the fact that the protein interaction networks that underlie cellular signaling exhibit considerable combinatorial complexity; that is , they can ( theoretically ) generate anywhere from millions to 1020 or more unique molecular species [7] , [15]–[17] . For example , even a single PDGF receptor dimer has ∼105 possible phosphorylation states , many of which could be ( stably ) occupied by any given molecule [7] , [18] . A similar problem arises in protein folding: a polypeptide chain could theoretically adopt so many conformations that it is a priori difficult to understand how a protein folds quickly and stably into a single native structure [16] , [19] , [20] . Proteins have evolved energy landscapes with specific features in order to overcome this problem ( which is known as the “Levinthal paradox” ) . In order to assemble well-defined signaling machines , signaling networks would similarly need to evolve specific “chemical potential landscapes” in order to drive the system to a specific set of quaternary structures [16] , [19] . Mayer et al . have speculated , however , that signaling networks might not need to assemble machine-like structures at all in order to function [7] . This “pleiomorphic ensemble” hypothesis posits that heterogeneous mixtures of complexes drive cellular responses to external signals . Early work , based on systems of Ordinary Differential Equations ( ODEs ) that considered a few hundred molecular species , indicated that more diffuse “network” models of signaling could generate reasonable signaling behavior [21] , [22] . The dearth of computational methods that can handle combinatorially complex networks has made it difficult to fully test the ensemble hypothesis in realistic networks , however [16] . As such , it is currently unclear if ensembles could even produce reliable responses to signals , or if there is any functional or evolutionary difference between networks that employ ensembles vs . machines . Over the past 10 years , a set of rule-based methods have been developed that allow one to model the behavior of biological systems without an a priori reduction in the set of possible species that can be formed [11] , [16] , [21] , [23] , [24] . Given a model consisting of a specific set of protein interaction rules , we can exactly sample sets of protein complexes ( or “conformations” ) from the astronomically large set of all possible complexes the model can generate . In this work we employed these methods to investigate the possibility of signaling via ensembles in silico . We focused on the pheromone response network ( Fig . 1A ) , one of multiple mitogen-activated protein kinase ( MAPK ) cascades in Saccharomyces cerevisiae . This thoroughly characterized signaling cascade involves the scaffold protein Ste5 , which is thought to be a nucleation point for the formation of signaling complexes ( Fig . 1B ) and prevent crosstalk [8]–[10] . Since similar MAPK cascades are found in eukaryotic cells from yeast to humans [25] , this network represents an excellent model system for exploring the influence of combinatorial complexity on signaling dynamics . In our initial model , we included only those interactions ( and their requisite molecular contexts ) that have been explicitly characterized experimentally . We found that this model is able to fit available data on the response of the network to pheromone , despite exhibiting significant ensemble character . We also constructed an alternative set of rules that could assemble a scaffold-based signaling machine , similar to those typically drawn to graphically summarize the cascade [8]–[14] ( Fig . 1A ) . Although this model does fit some of the available data , we found that it could not replicate the “combinatorial inhibition” of the pathway observed at high levels of Ste5 overexpression [12] , [26]; instead , it displayed considerable robustness to such changes . We also demonstrated that TAP/MS , a common technique for experimentally determining the components of “molecular machines” via binary interactions [27] , [28] , could not distinguish between the complexes formed in these two models , despite their radically different character . Direct experimental tests of the ensemble hypothesis thus require the application of assays that can measure three-way or higher-order interactions , such as fragment complementation , fluorescence triple correlation spectroscopy or single-molecule approaches [29]–[35] . Our findings indicate that ensembles can indeed reliably transmit and process extracellular information , and their inherent plasticity in response to perturbations like scaffold overexpression implies that they may play a role in facilitating the evolutionary variation of signaling systems within cells [36] .
A summary of the molecular interactions underlying the yeast pheromone response network may be found in Fig . 1A . Briefly , the signaling cascade is initiated by the interaction between extracellular pheromone molecules and a G-protein coupled receptor ( GPCR ) , which induces dissociation between the α subunit ( Gpa1 ) and βγ subunits ( the Ste4-Ste18 complex , hereafter referred to as Ste4 ) of the G-protein [37] . Ste4 then recruits the scaffold protein , Ste5 , which dimerizes , binds numerous kinases ( Ste20 , Ste11 , Ste7 ) and promotes a phosphorylation cascade resulting in dual-phosphorylation and activation of the MAPK , Fus3 [38] , [39] . As mentioned above , the vast majority of graphical depictions of this cascade involve simultaneous binding of all requisite proteins to Ste5 ( Fig . 1A ) [8]–[14] , however to our knowledge there is no explicit experimental evidence that such a large scaffold-based complex is actually formed during signaling . Active Fus3 then translocates to the nucleus , regulating the expression of numerous mating-related genes via the transcription factor Ste12 [10] . To create a dynamical model of this cascade , we constructed a set of rules for these interactions and other events ( e . g . post-translational modification , protein synthesis and degradation , nucleotide transfer ) . The rules themselves , which follow mass-action kinetics , were primarily derived from two sources: an online model ( http://yeastpheromonemodel . org ) [40] and an ODE model [11] , both of which are based on comprehensive literature searches ( Section 1 in Supporting Information Text S1 ) . In our initial model , if a reaction ( e . g . efficient phosphorylation of Fus3 by Ste7 ) requires conditions that have been experimentally characterized ( e . g . Ste7 also bound to Ste5 ) , they are explicitly represented in the rule . We added no additional constraints to this model , in order to: ( a ) see if existing knowledge of these interactions is sufficient to produce realistic network dynamics ( Fig . 2 ) and ( b ) characterize whether they result in machine- or ensemble-like character . The rule set , written in the Kappa rule-based modeling language [41] , contains 232 rules , 18 protein and 8 gene agent types and is available as a separate supporting file ( “ensemble . ka” in Protocol S1 ) . This model displays considerable combinatorial complexity: even if we only focus on complexes containing the Ste5 scaffold , the system can generate over 3 billion unique molecular structures ( Section 3 . 5 in Text S1 ) . We thus employed KaSim , an open source simulator for Kappa models , to consider the dynamics of the system without a reduction in its combinatorial complexity . Our general simulation strategy is described in detail in the Materials and Methods section and Section 2 in Text S1; a graphical schematic can be seen in Fig . 3A The model described above has two types of parameters: initial copy numbers ( i . e . concentrations ) for each of the 18 protein agents and stochastic rate constants for each of the 232 rules . We obtained the initial conditions directly from experimental measurements of copy number in yeast cells [40] , [42] . The stochastic rate constants were obtained from a combination of experimental data and parameter fitting . Briefly , 7% of the rate constant parameters in the model have been directly measured for yeast proteins , 68% were estimated from measurements on related proteins in other networks and 25% were completely unknown and thus given approximate values . In order to reproduce experimental observations with our model , we identified 111 rules that were likely to influence experimentally characterized trends and varied their rate constants . We found that only 25 of these parameters had a strong impact on the dynamics of important observables in the model , and so we only modified those values during our fitting procedure . Of these 25 , 22 had original estimates obtained from related proteins . In those cases , we restricted variation of the parameters to an increase or decrease of about one order of magnitude , to maintain similarity between the fitted value and the original estimate . Two of the remaining parameters had no available estimate , and so we restricted variations in those parameters to a biologically realistic range ( a table with ranges for each type of parameter is available in Section 1 . 2 in Text S1 ) . Finally , one parameter , the Gpa1 degradation rate , had been measured experimentally; we restricted variation in this parameter to a less than five fold change , a reasonable range given the inherent error in the experimental measurement [43] . Further details on how we identified and varied these parameters may be found in Section 1 . 2 in Text S1 . Since each simulation of this model requires over three hours of CPU time , we could not perform fits using standard techniques , nor could we employ statistical methods to understand the probabilistic structure of the parameter space [44] , [45] . Therefore , we manually altered these 25 parameters ( subject to the above constraints ) and simulated the model with the updated rate parameters . We iteratively applied this procedure until the model successfully replicated the dose-response behavior of Fus3 with respect to pheromone ( Fig . 2A ) [13] , [14] , the temporal dynamics of G-protein activation ( Fig . 2B ) [37] , and other experimental observations ( Figs . S1 , S2 , S4 , and S5 in Text S1 ) . To test the robustness of our results to the particular simulation method , we translated our rules into the related BioNetGen Language ( BNGL ) and used the same parameters to simulate the model using the BNGL simulator NFsim [23] . The two software packages produced exactly the same dynamics for these rules ( Figs . S4 , S5 and Section 2 . 2 in Text S1 ) . The BNGL version of the model is also available as a supporting file ( “ensemble . bngl” in Protocol S1 ) . Given the large number of parameters in the model compared to the amount of data available for fitting , one should not construe the above results as implying this model represents a uniquely valid description of the system . Indeed , as we demonstrate below , even fairly different rule sets can provide ( roughly ) equivalent fits to this data; we thus cannot make any claims regarding the identifiability of the parameters or even the rule set itself [45] , [46] . The point in this case is that it is possible to find some set of parameters that replicate the data , indicating that this model is at least consistent with available observations . To determine if the model described above signals through ensembles , we implemented a pairwise comparison between the sets of complexes produced in two independent simulations i and j , using the Jaccard distance , which we refer to as “compositional drift” [16]:where Ci represents the set of unique complexes in simulated cell i , Δ and are the symmetric difference and union set operators , respectively , and |X| is the cardinality of set X . Given the complexes present in two simulated cells , drift is the number of complexes unique to either one cell or the other , divided by the total number of complexes in the union of the two cells . Drift can thus be interpreted as the probability that a complex found in one cell is not found in the other at a particular point in time . For example , d = 0 indicates identical sets of complexes , whereas d = 1 means the sets are pairwise disjoint . We only performed this comparison between multiple simulation replicates that started from exactly the same steady-state initial condition; thus d = 0 at t = 0 for all of our simulations ( Fig . 3A; Sections 2 . 3 and 3 . 3 in Text S1 ) . Note that this calculation takes into account any difference between complexes , whether the difference is in binding partners , phosphorylation states , or otherwise . Analysis of other potential criteria for differentiating complexes yielded similar results to those discussed below ( Fig . S9 and Section 3 . 3 in Text S1 ) . We observed a marked increase of drift between simulations with pheromone ( and thus signaling activity ) as opposed to those without pheromone ( Fig . 3B ) . At peak Fus3 signaling activity ( t = 360 seconds ) , around 80% of all unique complexes were exclusive to one simulation or the other ( Fig . 3B ) . Such small overlap indicates that individual cells utilize different sets of signaling complexes , consistent with the ensemble hypothesis [7] , [16] . To confirm that this high level of drift is not an artifact of our chosen parameters , we generated over 1000 rule sets with randomized rate parameters ( Section 2 . 4 in Text S1 ) . In Fig . 3C we see the distributions of drift values among scaffold-based signaling species for both the validated model and models with randomized parameters at peak Fus3 signaling . Although the average random parameter set has somewhat lower drift than observed in our original parameter set , approximately 97% of the drift values from the models with randomized parameters were nonetheless greater than 0 . 8 . The high level of drift among signaling species thus likely arises from the rules and interactions themselves rather than specific rate constants . While the results in Fig . 3C indicate relatively high levels of heterogeneity at a particular time point , it could be that two different simulated cells utilize the same set of complexes , just at different times during signal transduction . We thus considered the differences between cells based on the union of all the unique complexes they sampled across the time points in our simulations ( i . e . the points in Fig . 3B ) . We found that using the union of complexes across times only reduced absolute drift levels by about 10% , indicating a high degree of diversity between simulated cells across the entirety of the signaling dynamics ( Fig . S10 in Text S1 ) . Our analysis of drift across time points raised the question of whether an individual simulation i maintains a specific set of complexes , or if the set changes over time . To answer this question , we used an alternative drift calculation , termed autodrift: di ( t , t+Δt ) instead of d ( i , j ) . We found that simulated cells employ rapidly changing sets of complexes during peak signaling times in this model ( Fig . 3D ) . Autodrift increased as a double exponential , with a longest time scale of approximately 0 . 5 s ( Fig . 3D , inset , and Section 3 . 3 in Text S1 ) . Indeed , within 5 seconds the difference between a cell and its past self achieves levels of drift similar to that observed between two completely independent cells in the population . This is consistent with observations from both modeling and experimental studies of epidermal growth factor signaling in mammals , where a diverse set of phosphorylated species forms rapidly during signaling [47] , [48] . The rapid increase in drift also highlights the transient nature of the ensembles of complexes that are generated . It is possible that the putative ensembles in this case merely represent a set of highly similar ( though technically distinct ) signaling species that form around a large “core” signaling complex . We thus examined in detail the structures of the scaffold-based species at various time points in our simulations . If a core complex were present , we would expect to see substantial conservation of protein binding patterns ( ignoring phosphorylation state ) in the set of unique complexes . Though Ste5 dimers are present in ∼70% of species during peak signal throughput , conservation significantly declines as the binding pattern is expanded to include more proteins ( Fig . 4A ) . In fact , not once did we find a Ste5 dimer bound to all its potential interaction partners , indicating that the complex used in the standard graphical depiction of this phosphorylation cascade is one that would very rarely , if ever , occur in simulations of this model ( Fig . 1A ) [8]–[14] . It is possible that complexes in the ensemble model still assemble around a consistent core structure , just not the traditional representation of a scaffold-based core signaling complex that we intuitively expect ( Fig . 1A ) . Since there are over 3 billion possible scaffold signaling structures in this model , however , we could not search for this core by enumerating all possibilities and looking at conservation patterns as in Fig . 4A . We thus used a straightforward clustering analysis to search for an alternative core structure . The signaling species generated in our model were clustered on the basis of the structural similarity between complexes , represented in this case by the graph edit distance metric , which is simply the number of changes ( or edits ) that would be required to form one complex starting from another . This distance accounts for differences in the members of a complex ( i . e . the removal of a protein from a complex increases the distance ) as well as differences in phosphorylation state , etc . ( Fig . S12 and Section 3 . 4 in Text S1 ) . We implemented a hierarchical clustering algorithm based on this distance . Briefly , the algorithm chooses a representative complex from each cluster , called the “clustroid , ” which is the complex with the lowest average graph edit distance to all other complexes in its cluster ( Section 3 . 4 in Text S1 ) . At each level of the hierarchy , the algorithm combines the two clusters whose clustroids are most similar , that is those with the minimum graph edit distance ( i . e . the minimum between-cluster distance , or MBCD ) . This algorithm is initialized with each complex in its own cluster ( meaning the complex is its own clustroid ) and continues until the original set of complexes is partitioned into a given number of clusters . This number , which we call the “cutoff , ” is a free parameter and is relatively arbitrary in our case ( Fig . S15 and Section 3 . 4 in Text S1 ) , so we repeated the clustering algorithm with numerous different cutoff values . We calculated the size of the largest conserved structural pattern as a function of the cutoff value for each cluster that contained ten or more complexes . We found that , on average , this conserved pattern contained less than 2 proteins ( Fig . 4B ) , indicating substantial dissimilarity among clustered proteins; cutoff values producing clusters with 4 or more proteins in the conserved subgraph were very rare ( Fig . S15 in Text S1 ) . These results , combined with the dissimilarity between clusters generated from independent simulations ( Fig . S13 in Text S1 ) and the high levels of drift we observe ( Fig . 3B–D ) , underscore the strong ensemble character of this model . The findings described above indicate that heterogeneous ensembles of complexes can indeed transmit and process extracellular information with levels of noise comparable to those observed experimentally ( Figs . 2–4 ) . To understand if machine-like complexes could also produce reliable signaling behavior , we constructed an alternative model with the goal of assembling signaling machines , which we defined to be stable , multi-subunit kinases based around the scaffold Ste5 [1] , [5] , [9] . Specifically , the machine we focused on consists of a Ste5 dimer , with each scaffold protein bound to a Ste4–Ste20 dimer and two kinases , Ste11 and Ste7 ( Fig . 1A ) . Upon assembly and activation , this decameric structure binds and phosphorylates Fus3 according to standard mass-action kinetics [9] . In contrast to the previous model , we were forced to introduce a priori assumptions ( neither experimentally supported nor specifically refuted ) in order to generate stable signaling machines . The simplest possible approach would be to create rules and rates that render the desired machine complex incredibly stable . The decamer , however , is essentially never generated in our original model's simulations ( Fig . 4A ) , so a machine model based purely on increasing the stability of the desired complex is unlikely to actually produce such machines in high quantities reliably . As mentioned above , this fact resembles the Levinthal paradox in protein folding: no matter how stable the native state of a polypeptide chain may be , proteins would essentially never fold if they randomly searched for this state on an otherwise “flat” energy landscape [19] , [20] . Alternatively , evidence suggests that molecular machines assemble hierarchically in vivo [49] , and so we added specific rules that determine the order in which binding and phosphorylation could occur between the scaffold and its associated proteins ( Fig . 1B , red arrows ) . This represents a hierarchical energy landscape ( extending the analogy to protein folding ) , where each consecutive step builds toward the formation of a “native” signaling machine [19] . For example , in the machine model , binding of Ste11 to the scaffold can only take place if Ste5 has dimerized and each scaffold is bound to a Ste4–Ste20 dimer . Beyond these scaffold assembly rules , no other alterations were made to the model . The resulting rule set is sufficiently complex that it is impossible to directly estimate the number of unique species that the machine model could form . We thus translated this model from Kappa into BNGL and used available BioNetGen tools to calculate the total number of species for this rule set [50]; as with our ensemble model , the Kappa and BNGL versions of the machine model are available as supporting files ( “machine . ka” and “machine . bngl” , respectively , in Protocol S1 ) . This analysis indicated that the machine model could only generate a total of 1106 possible scaffold-based structures , a decrease of over 6 orders of magnitude compared to the ensemble model ( Section 3 . 5 in Text S1 ) . The hierarchical assembly rules in this case thus drastically constrain the set of possible species that the model can sample . As with our original model , we subjected this alternative machine model to parameter variation and confirmed that it can reproduce experimental data ( Figs . S6 , S7 , S8 and Sections 1 . 8 and 3 . 2 in Text S1 ) . Although the dose-response and time-course trends of the machine and ensemble models are similar , they exhibit significantly different sets of signaling complexes . As expected , nearly half of all unique scaffold species in the machine model contained the decamer defined above ( Fig . 4C ) , indicating wide conservation of the desired core signaling complex , in contrast to the complete lack of conservation observed in the ensemble model ( Figs . 4A and 4B ) . The set of species sampled in the machine model also differed dramatically from those produced by the ensemble model . As a gross estimate of this difference , we considered the cumulative number of unique scaffold-based species obtained by a set of simulations; that is , the total number of unique complexes that are found in a group of N simulated cells . In the machine model , this number rapidly approaches a maximum value as N increases , saturating at around 800 after considering only 100 simulations ( Fig . 5A ) . The machine model thus samples about 70% of the 1106 possible scaffold complexes in a population of ∼100 cells . The behavior of the ensemble model is strikingly different , sampling a set of unique structures that is nearly two orders of magnitude greater than the machine model ( approximately 70 , 000 , Fig . 5A ) , and failing to saturate even after considering a population of 600 simulated cells . Although the total number of sampled species across these 600 cells is large , it is only 0 . 0022% of the 3 billion species the ensemble model could theoretically generate . As one might expect given the results of Fig . 5A , we observed large differences in drift during peak signal output between the two models . On average , only 55% of unique scaffold complexes were exclusive to one of two simulations in the machine model , as opposed to 90% in the ensemble model ( Fig . 5B ) . As with the ensemble model , we generated 1000 alternative machine models with randomized parameter sets to determine if the level of drift in this case was an artifact of the parameterization of the model . Though the distribution of drift values was fairly wide across these randomized models , in every case we observed considerably less drift than for the validated or randomized ensemble model ( Fig . 5B ) . The rules underlying the machine model thus robustly produce dynamics that one might expect for well-established molecular machines like the ribosome or proteasome: a stable , heavily populated core structure with residual diversity arising from assembly intermediates and the association of substrates and/or regulatory factors . Since these two models can both reproduce general pheromone-dependent trends , one might ask if it is possible to differentiate machine- and ensemble-like signaling processes directly using available experimental techniques . The most natural approach would be tandem affinity purification in conjunction with mass spectrometry ( TAP/MS ) , which is widely employed as a high-throughput assay for the discovery and analysis of protein complexes [27] . For example , Gavin et al . employed a “socio-affinity” ( SA ) index designed to extrapolate binary TAP/MS interaction data in order to discover novel “eukaryotic cellular machines” via clustering analysis [27] . To determine whether this technique could discern the nature of in vivo signaling complexes , we characterized the signaling species generated in both the ensemble and the machine models using the SA index [27] . There is a high correlation between the SA scores produced from our two models' sets of species ( Fig . 6A ) ; clustering these scores using the commonly employed MCL algorithm [27] , [28] , [51] , [52] results in essentially the same set of complexes ( Fig . 6A , inset ) . This leads to the question of whether one could ever detect any functional differences between ensembles and machines in a signaling context . Previous work has established the presence of “combinatorial inhibition” [26] ( akin to the “prozone” effect [53] ) in this particular cascade; increased expression of the Ste5 scaffold leads to a maximal response , past which further overexpression leads to a decline in signal output [12] , [40] . We found that the ensemble model reproduces this behavior , while the machine model does not ( Fig . 6B ) . In the ensemble model , the eventual decrease in signal response arises because the high quantity of scaffold proteins lowers the probability of cascade components ( say , Ste7 and Ste11 ) binding the same scaffold dimer [26] , [53] , and so the rate of signal propagation is drastically reduced . The hierarchical assembly rules in the machine model , however , reduce drift by ensuring scaffold dimers can only bind Ste7 after Ste11 is already bound . Beyond a certain minimal point , increasing Ste5 concentration has no effect , since the only potential scaffold binding partners for Ste7 are already bound to Ste11 , and thus can propagate signal . To test if the difference in Fig . 6B was robust to variations in the rate parameters , we simulated 100 randomized ensemble models and 100 randomized machine models with three values of Ste5 concentration: Wild Type ( WT ) , 12 times WT ( 12× ) and 60 times WT ( 60× ) . We used these simulations to calculate the relative change in peak Fus3 activation ( ΔFus3pp ) between two pairs of scaffold concentrations: WT to 12× , and 12× to 60× . The validated ensemble and machine models both exhibit a positive ΔFus3pp ( 12× – WT ) , corresponding to an increase in Fus3 activation ( the peak in Fig . 6B ) ; all the randomized ensemble models , and most of the randomized machine models , displayed this same behavior ( Figs . S16 , S17 and Section 3 . 7 in Text S1 ) . In the ensemble model , increasing Ste5 to 60× WT concentration decreases response , yielding a negative ΔFus3pp ( 60×–12× ) , while the machine model exhibits an approximately constant response across these concentrations ( Figs . 6B and C ) . The randomized ensemble models also universally showed a decrease in Fus3 activation from 12× to 60× Ste5 concentration , indicating that combinatorial inhibition is a robust feature of the ensemble model . The randomized machine models , however , had mostly increases in Fus3 activation between these two concentrations , and in no case did we observe a decrease as large as that observed for the ensemble models ( Fig . 6C ) . The relative lack of combinatorial inhibition in the machine model is thus likely a feature of the rules themselves , rather than the specific parameters chosen . It should be noted that the machine considered here is an acyclic complex; that is , there are no ring-like motifs in the protein interaction map for Ste5 ( Fig . 1A ) [53]–[57] . Previous modeling studies indicate that ring-like structures can assemble efficiently into well-defined quaternary structures , at least in certain parameter regimes [57] . Nonetheless , overexpression of a single subunit in a heteromeric ring causes a marked decrease in the concentration of the assembled machine , indicating that ring-like structures can simultaneously exhibit a machine-like character and combinatorial inhibition [53] , [56] , [57] . We leave full consideration of the interplay between robustness and topology in the evolution hierarchical assembly pathways to future work [56] , [57] .
The nature of the signaling complexes formed during signal transduction is foundational to how we conceptualize and understand information processing in cells . This is particularly true of scaffolds , whose primary function is to serve as a platform for the formation of multicomponent complexes that transmit signals [9] . The question of whether these complexes align more with the machine or ensemble paradigm is thus crucial for developing a principled picture of the roles scaffolds play . For instance , it has been posited that Ste5 acts to insulate pheromone signals from activating other , related MAP kinase cascades by sequestering active Ste11 in a pheromone-specific complex . This view is inconsistent with the ensembles we observe , however , since those involve appreciable concentrations of free , active Ste11; in contrast , the machine model produces essentially no active Ste11 molecules that are not bound to the scaffold . The capacity of Ste5 to fulfill the role of insulator in this pathway , or the need to posit other mechanisms such as cross-inhibition [8] , [9] , is thus directly related to the degree of ensemble character the network displays , a fact that highlights the central role that reasoning about quaternary structure plays in developing and evaluating hypothetical signaling mechanisms . Our findings indicate that certain experimental methods , such as TAP/MS , are ill-equipped to directly resolve the structural details of signaling complexes in living cells . The difficulty in this case lies with the inherently binary nature of co-purification assays: they can tell us that two proteins interact in some way , but they tell us very little about the global structural context of the complexes in which those proteins are found . For example , in our computational TAP/MS experiment , we see that the overall pattern obtained by “tagging” each protein and recording its interaction partners is essentially the same for both the ensemble and machine models ( Fig . 6A ) . This is due to the fact that , while the types of quaternary structures formed varies considerably between the two models ( Fig . 4 ) , the probability of observing any given pairwise association between two proteins is essentially the same . Our results thus indicate that it is problematic to construe clusters obtained from TAP/MS data as representing “cellular machines” in the classic sense [1] , [27] . In contrast , experimental methods that can capture ternary or higher interactions ( i . e . the simultaneous association of three or more distinct proteins ) could be used to provide direct evidence for ( or against ) the hierarchical assembly of a signaling machine . For instance , in the machine model , Ste7 only binds Ste5 after Ste11 is already bound . Observation of Ste7-Ste5 association in the absence of Ste11 binding to Ste5 would thus provide evidence against the type of signaling machine considered here ( Fig . 1B ) . Methods such as fragment complementation assays and fluorescence triple correlation spectroscopy could likely be used to probe these types of ternary association dynamics [29]–[31] . Alternatively , recent advances in single-molecule ( super-resolution ) microscopy ( e . g . methods like PALM and STORM ) could potentially track the assembly of machine- or ensemble-like signaling complexes [32]–[35] . While direct experimental tests of the ensemble hypothesis are currently lacking , inherent functional differences between machine and ensemble models can be used to provide indirect evidence for or against a particular paradigm . For instance , the hierarchical assembly rules that are required to reliably construct a functional scaffold-based signaling machine prevent our machine model from replicating the experimental observation of combinatorial inhibition ( Fig . 6B ) [9] , [12] , [26] . Our analysis of machine models with randomized parameters indicate that this is likely a general observation: in order to exhibit combinatorial inhibition , signaling networks must have the capacity to sample large sets of complexes , ultimately leading to ensemble behavior ( Fig . 6C ) . Although more work is clearly needed to unambiguously resolve the question of machines vs . ensembles , our findings on combinatorial inhibition indicate that at least some degree of ensemble character is likely present in yeast pheromone signaling . It is also clear that the assembly pathways employed to form machines can have measurable , phenotypic consequences . As a result , even if one could determine experimentally the small set of machine-like complexes employed by some network , making a model that employs these machines , but ignores the mechanisms necessary to generate them [11] , [21] , may not accurately capture the response of the system to perturbations . The presence of ensemble character in signaling also highlights a potential evolutionary trade-off between machines and ensembles in terms of their phenotypic plasticity . Considering again the analogy to protein folding , adopting a well defined , thermodynamically stable tertiary structure clearly enables the function of a vast array of protein domains ( i . e . the general protein structure-function paradigm ) [58] . In some cases , however , it has been posited that “intrinsically unstructured” ( or unfolded ) protein domains may have a distinct functional or evolutionary advantage: for instance , they may display greater interaction plasticity , binding specifically yet transiently with a large number of protein targets [58] , [59] . Similarly , a protein with a robust , stable quaternary structure ( i . e . a machine ) [1] , [7] , [16] may be beneficial for the conservation of universal cellular tasks , like protein synthesis and degradation . In the case of signal transduction , however , ensembles may offer greater functional and evolutionary plasticity . For example , modifying Ste5 expression levels produces altered , but nonetheless functional , responses without the need to introduce complex , coordinated mutations to the reaction network's rule set ( Fig . 6B ) [36] . In this sense , both intrinsically disordered proteins and pleiomorphic ensembles may perform unique intracellular tasks precisely because they involve less well-ordered ( tertiary or quaternary ) structures . The ensemble character we observe could thus represent a form of weak regulatory linkage among genes , ultimately being responsible for the remarkable capacity of MAPK networks to exhibit different but meaningful phenotypes when they are re-wired , either through synthetic modifications or naturally over the course of evolution [9] , [25] , [36] , [60] . Since machines do indeed form in some signaling networks ( e . g . the apoptosome ) , there is likely a spectrum of structural specificity in the formation of complexes during signal transduction [1] , [6] , [7] . Indeed , one could modify the machine model presented here to include a finite probability of “off-pathway” binding events ( e . g . some chance that Ste7 will bind Ste5 even if Ste11 is not already bound ) . Such models could exhibit intermediate levels of both drift and combinatorial inhibition ( Figs . 5B and 6B ) ; future work on this and related systems will be necessary to understand the particular functional and evolutionary consequences of a particular degree of ensemble-like character in any given system . Nonetheless , our work clearly demonstrates that large , heterogeneous ensembles can indeed reliably transmit and interpret extracellular information [7] , [21] , [22] . This hints at the existence of a new paradigm for molecular computation , one in which the evolution or engineering of “local” interaction rules allows for robust information processing in the absence of “global” order ( i . e . a stable , multi-subunit signaling machine ) [1] , [5] . Understanding the consequences of this paradigm for robustness [61] , plasticity [9] , [36] and crosstalk [8] in signaling networks represents a crucial task for the emerging field of systems biology .
The models in this work were simulated using KaSim , a stochastic simulator for rule-based models based on the Kappa language that is capable of stochastically sampling all possible species a given model can generate ( Fig . 5B; Section 2 . 1 in Text S1 ) [23] , [24] . The model is initialized with a set of ( mostly ) monomeric protein agents and simulated for 1000 seconds without pheromone to generate a steady-state population of N untreated “cells . ” We treated the cells with pheromone , and generated a set of N′ independent hour-long simulations from each steady-state starting cell . All of the complexes present in the simulation were recorded at logarithmically spaced time intervals . Compositional drift calculations were performed using these “snapshots;” we only performed this calculation between simulations that started from exactly the same initial conditions ( Fig . 3A ) . We performed similar simulations to determine both dose-response and the time course trends . Further simulation details may be found in Section 2 . 3 in Text S1 . Simulation data was fit to a set of exponential models using nonlinear least-squares regression . We found that a double exponential function was the best fit for the data upon analysis of the residuals and the statistical significance of the estimated model coefficients . The functional form of the model and the full statistical analysis can be found in Section 3 . 3 in Text S1 . We focused primarily on the scaffold-based species for the analysis of structural conservation and subsequent clustering . These were defined as any complex that included a Ste5 agent or that could bind a free Ste5 agent . We created a vector notation to uniquely identify any scaffold-based complex to simplify the calculation of the graph edit distance between any two complexes ( Figs . S11 , S12 , and Section 3 . 4 in Text S1 ) . We then implemented the clustroid-based hierarchical clustering approach described in the main text . Other clustering criteria , such as standard single- and complete-linkage , gave similar results ( Section 3 . 4 in Text S1 ) . We extracted all the binary interactions from the set of complexes generated by our simulations , artificially creating “bait” and “prey” association data . This computational version of the TAP/MS experimental procedure was used to generate the SA scores [27] . The MCL clustering algorithm [52] was then employed to generate the “functional modules” generally associated with such data sets [51] . More information on the SA score calculation and clustering algorithm can be found in Section 3 . 6 in Text S1 . | Intracellular signaling networks are central to a cell's ability to adapt to its environment . Developing the capacity to effectively manipulate such networks would have a wide range of applications , from cancer therapy to synthetic biology . This requires a thorough understanding of the mechanisms of signal transduction , particularly the kinds of protein complexes that are formed during transmission of extracellular information to the nucleus . Traditionally , signaling complexes have been largely perceived ( albeit often implicitly ) as machine-like structures . However , the number of molecular complexes that could theoretically be formed by complex signaling networks is astronomically large . This has led to the pleiomorphic ensemble hypothesis , which posits that diverse and rapidly changing sets of transient protein complexes can transmit and process information . Our goal was to use computational approaches , specifically rule-based modeling , to test these hypotheses . We constructed a model of the prototypical yeast mating pathway and found significant ensemble-like behavior . Our results thus demonstrated that ensembles can in fact transmit extracellular signals with minimal noise . Additionally , a comparison of this model with one tailored to generate machine-like complexes displayed notable phenotypic differences , revealing potential advantages for ensemble-like signaling . Our demonstration that ensembles can function effectively will have a significant impact on how we conceptualize signaling and other processes inside cells . | [
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| 2013 | Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes |
Nutrition is known to interact with genotype in human metabolic syndromes , obesity , and diabetes , and also in Drosophila metabolism . Plasticity in metabolic responses , such as changes in body fat or blood sugar in response to changes in dietary alterations , may also be affected by genotype . Here we show that variants of the foraging ( for ) gene in Drosophila melanogaster affect the response to food deprivation in a large suite of adult phenotypes by measuring gene by environment interactions ( GEI ) in a suite of food-related traits . for affects body fat , carbohydrates , food-leaving behavior , metabolite , and gene expression levels in response to food deprivation . This results in broad patterns of metabolic , genomic , and behavioral gene by environment interactions ( GEI ) , in part by interaction with the insulin signaling pathway . Our results show that a single gene that varies in nature can have far reaching effects on behavior and metabolism by acting through multiple other genes and pathways .
The question of how phenotypic plasticity evolves has been the subject of vigorous debate ( reviewed in [1] , [2] , [3] ) , as has the related question of whether allelic variation in single genes can have large impacts on plasticity [3] . Phenotypic plasticity is defined as the degree to which the environment can change or modify the phenotype . Genotype-environment interaction ( GEI ) is genetic variation in phenotypic plasticity . The genetic variation in GEI is needed for the evolution of an adaptive level of phenotypic plasticity [4] . Here we abbreviate “the phenotypic plasticity of one genotype in different environments” to “plasticity” . Recent studies show that quantitative trait loci can have large effects on GEI [5] and that traits with GEI responses to nutrition can be correlated with GEI in the expression of a relatively small number of genes [6] . In this paper , we examine GEIs resulting from variation in a single gene , in response to food deprivation . We begin the process of determining the mechanism by which alleles of this gene affect plasticity . By quantifying the proportion of a large number of gene expression and metabolite traits in which the gene is involved in GEI , we also provide experimental data on the extent of the gene's pleiotropy and its allelic contributions to plasticity . The foraging ( for ) gene of D . melanogaster encodes a cGMP dependent protein kinase [7] , [8] . Naturally occurring for alleles give rise to the rover and sitter behavioural morphs . As larvae , rovers move more and feed less in the presence of food than sitters , but don't differ in locomotion in the absence of food [9] , [10] , [11] . Food deprivation causes larval rovers to behave more like sitters [12] . Like their larval counterparts , adult rovers and sitters also differ in food-related behaviours . The sucrose response of rovers in a proboscis extension assay is higher than in sitters and the patterns of walking after feeding on a sucrose drop in sitters exhibits higher turning rates than in rovers [13] , [14] , [15] . In the present study , we investigate the response of adult rovers and sitters to well-fed ( Fed ) and food deprived ( FD ) conditions . Through global profiling of gene expression and metabolites , we find that rovers have greater changes in gene expression profiles and metabolite levels in response to food deprivation than do sitters , and that the insulin pathway is required for this rover-sitter difference . Allelic variation in for also influences the allocation of energy stores to lipids as compared to carbohydrates in fed flies . We conclude that allelic variation in for has a major effect on multiple aspects of food-related plasticity and GEI .
The food-leaving assay measures the proportion of flies that traverse a maze after leaving a vial containing sucrose in agar ( see Methods and Figure S1 ) . This “food-leaving” behaviour shows significant GEI interaction ( Figure 1 ) . To determine how general this pattern of GEI is we repeated these experiments rearing flies on a variety of different food media and in all cases we found significant and similar patterns in GEI ( Table S1 for descriptions of food media and statistical tables ) . Specifically , rover scores increase more from FD to Fed environments than sitter scores . The direction of the GEI is positive ( I>0 ) in all cases . We examined Fed and FD forR and fors2 heads to determine compounds most strongly associated with the for response to feeding state , using Fourier Transform Ion Cyclotron Resonance Mass Spectroscopy ( FTICR MS , Methods ) to detect 750 putative metabolites . We found a significant influence of for ( forR or fors2 ) and feeding state on compounds with the molecular weight ( MW ) and chemical properties of triacylglycerols ( TAG ) and polysaccharides ( PS ) ( Figure 2A and 2B; Table S2A , S2B ) . There was a significant main effect of for in carbohydrates , and significant GEI in both carbohydrates and lipids , but in opposite directions ( I>0 for lipids , I<0 for carbohydrates ) . The largest differences were found in smaller MW compounds . Incorporating MW into ANOVA of TAG compounds gave p ( for×food ) = 1 . 3·10−13 and p ( for×MW ) = 1 . 9·10−5; PS compounds had p ( for×food×MW ) = 0 . 03 ( Table S2B ) . ( Note that the term food in the ANOVA describes the feeding state of Fed vs . FD and the term for refers to the strains forR or fors2 ) . Thus , GEI interactions are found for metabolites but their direction I depends on metabolite type . Rovers have a larger drop in lipids than sitters in the change from Fed to FD , while sitters have a greater drop in carbohydrates than rovers . Whole-fly spectrophotometric measures of total carbohydrates , lipids , and proteins ( Methods ) showed that adult rovers had almost twice as much energy stored in whole-body lipid and about half the energy stored in carbohydrates compared to adult sitters , whereas protein levels normalized to dry weight were not significantly different between genotypes ( Figure 3 gives full statistics ) . Thus , for genotype strongly affects energy storage strategies . A main effect of genotype in fed flies is consistent with an allocation shift between storage of energy as lipids and as carbohydrates . If for affects both behavioural and metabolic GEI and plasticity in a food-dependent manner , how is this reflected at the level of gene expression ? To examine for's effect on transcript levels we performed whole-genome microarray analysis on heads of rovers and sitters and sitter mutants under Fed and FD conditions ( Methods ) . Array results were verified using qRTPCR on two genes with strong rover-sitter differences and involved in carbohydrate metabolism [Treh , trehalase , and CG10924 , human homolog is PCK1 phosphoenolpyruvate carboxykinase 1 ( soluble ) ] ( Figure S3 ) . Overall , the expression of genes involved in the breakdown of food to provide energy ( catabolism ) was significantly altered ( had strong GEI ) , with rovers decreasing and sitters increasing their expression when food is present ( I<0 ) . For instance , glycogen phosphorylase ( GlyP ) , which regulates glycogen breakdown , is highly significant ( Figure 4A , q ( for×food ) = 0 . 000057 ) . The differing genetic background between fors and fors2 has a main effect on GlyP ( q ( BG ) = 1 . 64·10−7 ) , but the response to food of each sitter genotype is similar ( q ( BG×food ) = 0 . 33 ) , so it is meaningful to speak of a GEI common to both types of sitters when compared to rovers . Conversely , expression of many genes in pathways for synthesis of proteins ( anabolism ) significantly increased in rovers in the Fed condition , exemplified by eukaryotic translation initiation factor 4A ( eIF-4A; Figure 4B , q ( for×food ) = 0 . 0011 ) . I is positive for eIF-4A and negative for GlyP . We investigated whether functionally related genes show common modes of regulation as defined by the sign of I ( see Text S1 , Supplementary Methods , Group Level ANOVA ) , and found effects consistent with the catabolism-anabolism pattern described above . Catabolic groups such as carbohydrate ( Figure 4C ) , glycogen , chitin , and amino acid breakdown genes , and mitochondrial oxidative phosphorylation complexes I–V had significant negative I values ( Table S3 ) . Anabolic groups associated with protein biosynthesis ( Figure 4D ) , such as gene splicing , translation initiation , ribosomal proteins , and post-translational protein modification complex had significant positive I values ( Figure 4D , Table S3 ) . In Table S3 , we list Gene Ontology groups with significant GEI , ordered from most positive to most negative I [groups with greater GO term specificity as per DAVID [16] GO levels 3–5 were used] . The most inclusive GO groups relating to catabolism/anabolism are group 9056 “catabolic process” which has significant negative for×food interaction I ( p = 0 . 009 , n = 140 genes ) while the “biosynthetic process” group 9058 has positive I ( p = 0 . 008 , n = 300 genes; as these two groups were specifically singled out for testing we report p rather than FDR-adjusted q ) . Some gene groups with significant for GEI go beyond a simple anabolism - catabolism dichotomy . In particular , functional groups involved in neural and muscle function – neurotransmitter secretion groups , synaptic transmission , postsynaptic membrane , ion channels , GABA and calcium-binding EGF domains – all have negative I values ( Table S3 ) . How does the genetic background ( BG ) difference between fors and the other two strains ( forR and fors2 ) affect GEI of functional groups ? Most groups in Table S3 have highly significant FDR-corrected q values for the main effect of background . However , only a few groups have significant BG×food interactions , and these are all groups with positive for×food I values , associated with transcription , splicing , translation , or post-translational modification of proteins . Thus , at the functional group level , gene groups with significant positive for GEI I often have significant interactions with the genetic background but those with negative I values do not . We used two methods to quantify the contributions of for , food , and BG to gene expression over all genes above cut off , not just those with interactions . First , we performed a principal components analysis ( PCA ) on log2 gene expression levels . Five PCA components were identified . The first component ( explaining the largest amount of variance , see below ) correlates strongly with main effect of BG , followed by components correlating with main effects of food and then for , with components 4 and 5 ( explaining similar amounts of variance ) correlating with the interactions of BG×food and for×food . Thus , genetic effects rank in the order BG>for>BG×food>for×food when measured by variance explained . Our second method uses the Storey-Tibshirani false discovery rate ( FDR ) analysis to estimate the proportion of genes with significant effects [17] . This method uses a mixture-model approach to estimate the proportion π0 of genes matching the null hypothesis of no effect . Then πalt = 1−π0 estimates the proportion of genes matching the alternative hypothesis of an effect . One interpretation of πalt is that is the proportion of genes that would show significant effects , after FDR , if we had large numbers of replicates . π0 and hence πalt depend not only on the true rate of differential expression of genes ( DEG ) but also on the signal to noise ratio of the array technology . Thus , for low expression genes with poorer signal to noise ratio π0 will be higher even if the true proportion of DEG is unchanged . We calculated π0 both for the top-1000 highest expressing genes and for increasingly large groups of genes based on minimum expression levels . If signal to noise is the only factor affecting π0 then the intercept of the curve of π0 values based on expression is an estimator of 1-DEG . In practice we found good agreement between the latter method and π0 for the top 1000 genes , so we report the top-1000 figure . This analysis was done for each ANOVA p-value ( for all main and interaction effects ) . The πalt = 1−π0 for a given set of p-values ( e . g . for main effect p values ) estimates the true DEG rate for that effect . Our top-1000 πalt values were BG = 0 . 84 , food = 0 . 73 , BG×food = 0 . 63 , for = 0 . 59 , and for×food = 0 . 57 . That is , among the top-1000 genes by mean expression level , 84% had a main effect of BG , 73% of food , 63% showed BG×food , 59% had a main effect of for and 57% had for×food GEI . Thus , although for×food GEI affects the smallest proportion of top-expressing genes , it still has an effect on 57% of these genes . The interaction between for and feeding state could be due either to the genotypes responding in equal amounts but opposite directions to feeding ( same plasticity of each genotype ) , or to one genotype responding more than the other ( differences in magnitude of plasticity ) . To quantify differences in plasticity between rovers and sitters , we calculated an index of plasticity called Relative Nutrient Sensitivity ( RNS ) for any given trait as the difference between the size of the trait's rover response to food and the size of the sitter response: RNS = ( |rover change|−|sitter change| ) /C ( where C = 1 for log2 transformed data , otherwise C = mean level of trait ) . In other words , for each trait compared ( e . g . , behaviour , metabolite , gene expression ) , RNS compares the absolute magnitude of for-dependent changes in response to food rather than their direction ( see Methods ) . When RNS>0 , rovers show a larger response; when RNS<0 , sitters respond more . When we tabulated RNS for behaviour , metabolites , gene expression , and functional gene categories , we found RNS>0 ( rovers change more ) in 8 of 9 behaviour cases ( 89% , Figure 5A , p = 0 . 03 ) . For metabolites , gene expression , and functional gene categories , a significant majority of traits had RNS>0 ( Figure 5B–5D , p<10−15 and Table S4 ) . Thus , for a large majority of behavioural , metabolic , and gene expression traits , rovers exhibit greater food-related plasticity than sitters . This is true whether fors2 or fors are used in comparison to forR . The rover-natural sitter RNS comparison is more rover-biased ( has more cases with rovers changing more than sitters ) than the rover-mutant sitter comparison , but the correlation between the two is good ( for genes , r = 0 . 83 , p<10−15 ) , so we show the conservative , common genetic background , rover-sitter mutant distributions in Figure 5 . The direction of for-dependent response to feeding state ( GEI I ) and plasticity ( RNS ) are different measures , as shown in Table S3 . Most groups with significantly negative GEI I have RNS>0 . Of the traits with RNS<0 , most are mitochondrial groups involved in fatty acid beta oxidation or oxidative phosphorylation complexes I–V . Among metabolites ( Table S2A , Table S4 ) only the group containing PS polysaccharides has significant RNS<0 . So , sitters are more plastic than rovers ( have a higher magnitude of change in response to food ) for a small subset of traits having to do with sugars ( among metabolites ) or mitochondrial catabolic pathways ( functional groups ) , but rovers are more plastic for the majority of gene and metabolite groups . We hypothesized that the for-dependent metabolic plasticity might be mediated by the insulin signaling pathway . This is because rovers exhibit a higher plasticity in response to feeding state and the insulin signaling pathway is a key regulator of the response to food [18] . In the cell , binding of DILPS ( Drosophila insulin-like peptides ) to the insulin receptor ( InR ) triggers a signaling cascade with major effects on gene expression [19] . Protein translation is increased via phosphorylation of key members of the TOR pathway by the kinases Akt1 ( dPKB ) and Pk61C ( dPDK ) [20] , [21] . Negative homeostatic control of insulin signaling occurs on the transcriptional level – when signaling is high , foxo is phosphorylated by Akt1 and sequestered in the cytoplasm , but when signaling drops foxo translocates to the nucleus where it stimulates transcription of genes such as InR and the negative regulator of translation Thor ( d4EBP ) [22] , [23] , [24] . At the transcript level , then , many insulin pathway genes have an inverse relationship to the level of insulin signaling . Our results show that transcription of positive regulators decreased more in fed rovers than sitters , resulting in a negative GEI interaction coefficient I for the group of positive regulators as a whole ( Figure S2 ) . This normal inverse relationship between transcription and insulin signaling is more evident in rovers than sitters . As with RNS , this is true whether mutant or natural sitters are compared , but again as for RNS , the difference between rovers and natural sitters is larger than the difference between rovers and mutant sitters , suggesting that the genetic background of natural sitters may intensify this difference . The finding that rovers show larger responses to food , and the known role of insulin signaling in the response to food , suggests that rovers might also show a larger impact of changes to insulin signaling . We therefore tested whether for interacts with the insulin signaling pathway by means of quantitative complementation crosses for epistasis between mutant insulin pathway genes and alleles of for [25] , [26] , [27] . We crossed each of the three for genotypes to loss of function mutants of the fly insulin receptor InR , phosphatidylinositol-3-kinase Pi3K92E ( or Dp110 ) , and foxo ( Methods ) . InR and Dp110 are positive regulators of insulin signaling; foxo is a negative regulator . Based on our gene expression data , we hypothesized that rovers had higher insulin signaling than sitters , so we expected crosses of rovers with loss of function insulin mutants to be more sitter-like than their controls . We tested food-deprived adults of the resulting 18 trans heterozygote genotypes and compared food-leaving scores of for;mutant to the for;Balancer which controlled for genetic background effects ( see Methods ) . Recall that food-deprived homozygous rovers show low levels of food leaving behaviour ( Figure 1 ) while sitters have higher levels . As expected , the control for;Balancer flies show the previously found lower level of behavioural response for rovers compared to sitters ( Figure 6A and 6B , solid lines ) , indicating no direct effects of , or interactions with , the balancer chromosome background . There is , however , a significant epistatic interaction in the for;InR and for;dp110 flies ( Figure 6A and 6B dashed lines; Table S5 ) . Rovers crossed to these insulin pathway mutants become more sitter-like . In contrast , the interaction with negative regulator foxo is not significant ( Table S5 ) . Mutants of positive regulators of insulin signaling make rover food-leaving behaviour more like sitters ( reduces RNS ) , while a mutant of negative regulator foxo trended towards making rovers less like sitters ( increases RNS ) . This suggests that there is a significant ( epistatic ) interaction between for and the two positive regulators of the insulin signaling pathway tested here . This is consistent with rovers experiencing greater shifts in insulin signaling effects between Fed and FD states than sitters . There are also differences between natural and mutant sitters in the interaction with InR ( Table S5 ) suggesting that the difference in genetic backgrounds between these strains may also affect this interaction . For this behavioural measure , natural sitters are intermediate between rovers and mutant sitters , a difference from the trend found in gene expression overall ( via RNS , Figure 5 ) or in regulators of insulin signaling ( Figure S2 ) . Thus the effect of the background difference between natural sitters and the other strains varies between gene expression and behavioural measures . We performed a bioinformatic meta-analysis comparing our array results to those from three published microarray studies which manipulated insulin/Tor signaling [28] , [29] , [30] . This provides additional evidence for transcriptional parallels between for and insulin . We use these studies to identify sets of genes which were up- or down-regulated by the manipulation of insulin/Tor signaling , and which had high enough expression levels in our data for reliable comparison . To ensure independence of the three analyses we used sets of genes which did not overlap between studies ( see Table S6 for gene selection criteria ) . For each up- or down-regulated set of genes identified from a study we calculated the mean log2 fold change between rovers and mutant sitters when Fed or FD . This gave four comparisons per study ( up/down regulated in study 1 , 2 or 3×Fed/FD in our data ) . In other words , we used the 3 independent studies to tell us which genes may be transcriptionally regulated by insulin signaling . We then used our data to ask , for the same genes , what the rover-sitter difference in expression is under the two food conditions . Our hypothesis is that rovers have higher insulin signaling when Fed than sitters , but not necessarily when FD . Hence we predict that genes requiring insulin signaling for their expression should have higher expression levels in Fed rovers than in Fed sitters , but that this difference may not exist in FD rovers and sitters . Similarly , if genes are shown in the independent study to have lower expression when insulin signaling is high ( or equivalently , higher expression when insulin expression is reduced ) , then we predict those genes should have lower expression in Fed rovers than in Fed sitters . In the first study , Buch et al . [28] ablated dilp3 secreting cells in adults and used microarrays to compare ablation lines which had reduced insulin signaling to that of controls . Figure 7A shows a summary of the four rover-sitter comparisons for this study . Bars on the right labelled “expression down” are for genes whose expression was reduced by dilp3 ablation ( i . e . insulin signaling increases expression of these ) and bars on the left ( “expression up” ) are for genes whose expression was increased by dilp3 ablation ( genes repressed by insulin signaling ) . Genes with expression reduced by dilp3 ablation ( Figure 7A , left ) show a negative GEI interaction sign I ( rovers higher when food deprived , mutant sitters higher when fed ) . Conversely genes increased by dilp3 ablation ( Figure 7A , right ) show a positive GEI interaction sign I ( no difference when food deprived , rovers higher when fed ) , in accordance with our predictions . Figure 7B gives the four comparisons for genes whose expression was changed by foxo overexpression [29]; Figure 7C is for genes changed by rapamycin treatment [30] . In each case the pattern is similar to dilp3 ablation: genes with expression increased by a manipulation equivalent to lowering insulin/Tor signaling ( genes reduced by insulin ) show the negative I GEI interaction , while genes whose expression is reduced by the manipulation ( genes increased by insulin ) show positive I . Full statistics are given in Table S6 . This table also shows that the pattern in I is more significant when natural sitters are used in the analysis than when only mutant sitters are used , so the trends shown in Figure 7 apply to both mutant and natural sitters . In summary , the patterns of GEI interaction strength I in rover-sitter gene expression of genes affected by three different manipulations of insulin/Tor signaling in three independent studies [28] , [29] , [30] are consistent with our hypothesis in each study . Genes requiring insulin signaling for expression show positive rover-sitter I and genes inhibited by insulin signaling show negative rover-sitter I .
Expression of insulin pathway genes such as InR and Pi3k92E ( Dp110 ) is inversely related to the strength of insulin signaling via the foxo transcription factor: foxo is retained in the cytoplasm when signaling is high , but translocates to the nucleus and stimulates transcription of pathway genes when signaling is low [23] , [24] . Since insulin gene expression is opposite to insulin signaling strength , our finding of greater negative transcriptional effects on insulin pathway genes in rovers ( Figure S2 ) suggests the presence of greater positive insulin signaling in rovers . Since insulin signaling upregulates anabolism and reduces catabolism [19] , [42] , this is consistent with the patterns we find in genes involved in anabolic ( fed rover-biased ) and catabolic ( fed sitter-biased ) processes . And genes which require insulin for expression are higher in fed rovers , while genes repressed by insulin tend to be higher in fed sitters ( Figure 7 ) . The finding of genetic interactions between for and genes in the insulin signaling pathway raises questions for future investigation . Insulin signaling in flies can reduce flow through tricarboxylic acid cycle and oxidative phosphorylation and increase flow through the pentose-phosphate shunt , freeing pyruvate and acetyl CoA for lipogenesis and increasing NADPH and precursors for biosynthesis [43] . This is consistent with patterns in the Fed adult rover catabolic groups ( Figure 4C , Table S3 ) . Instead of accumulating energy as fat , Fed adult sitters accumulate carbohydrates . Because of the lower density of fat and its higher caloric content , rovers store more energy per unit mass than sitters , a difference which should have implications for life history characteristics such as starvation resistance ( see below ) . Several studies note changes in fat stores in flies with mutations in insulin signaling genes . These include , loss of fat in melted mutants [44] , gain of fat in InR , chico and Pi3K ( also called Dp110 ) mutants [45] , [46] and Pi3K-overexpression in larvae increases accumulation of nutrients in fat [47] . Nuclear foxo reduces fat , phenocopying starvation [44] , [48] , and it reduces head fat body insulin signaling [49] . Thus , there may be multiple different effects of insulin-related genes on fat . Could foxo mediate lower sitter fat levels ? Rapamycin treatment ( which acts downstream of foxo specifically on the Tor signaling pathway ) also produces similar patterns of effects ( Figure 7C ) . Hence , indirect effects of insulin signaling on the Tor pathway could also be involved . In support of this , PDK/Pk61C is the gene in the insulin pathway showing strongest transcriptional regulation in rovers versus both natural and mutant sitters ( Figure S2B ) . PDK phosphorylates ribosomal S6 kinase ( S6k ) , part of Tor regulation of translation [20] , [21] , [50] . Indeed , overexpression of Tor has been shown to increase triglycerides in adult male flies [44] . Genes repressed by foxo , rapamycin , or dilp3 ablation are rover-biased in Fed flies , while genes increased by insulin/Tor knockdown are rover-biased in FD flies ( Figure 7A–7C ) ; that is , if a gene's expression is increased by insulin/Tor signaling , it tends to be higher in Fed rovers , while if it's expression is increased by inhibition of insulin/Tor signaling , it tends to be higher in FD rovers . This is an example of the more general trend illustrated in Figure 5 , where the change between Fed and FD flies is larger in rovers than in sitters for behaviours ( 89% ) , metabolites ( 84% ) , genes ( 77% ) , and gene groups ( 77% ) . In mammals , a reduced physiological response to food is a sign of insulin resistance; whether this is true of sitters waits further testing . Our data provides direct evidence through genetic crosses and considerable correlational information through patterns of insulin gene expression and meta-analysis that the GEI effects of foraging in Fed and FD flies are mediated at least in part through interaction with the insulin/Tor signaling pathways . Whole-body energy stores in fed rovers and sitters differ: how might fatty rovers and starchy sitters differ in life-history ? A number of life-history and ecological parameters have been shown to be related to lipid or carbohydrate reserves in flies , including flight capacity , starvation and desiccation resistance . Diptera in general and fruit flies in particular are dependent on glycogen reserves and hemolymph sugars to fuel flight muscles [51] , [52] , [53] . Glycogen phosphorylase , which has strong GEI in rovers and sitters , is a rate limiting enzyme for glycogen mobilization to support flight [54] ( Figure 4A ) . Flies selected for postponed ageing show increased flight duration , glycogen reserves and resistance to desiccation , [55] , while desiccation-selected flies show higher glycogen levels [56] . Glycogen , desiccation resistance , longevity and stress resistance may form a cluster of correlated traits in flies [57] . Lipid content of adult flies correlates with starvation resistance [58]; among lifespan-selected and other lines , starvation resistance was correlated with lipid content and not glycogen [55] . This correlation extends to sibling species D . simulans [59] . In a cricket species where lipids can be used to support flight , a trade-off between lipid reserves for flight and for egg production has been reported [60] , [61] . The rover-sitter system , with its dichotomous Y-allocation [62] of energy stores to lipids and carbohydrates , may therefore be useful for studying single-gene influences on traits with costs and benefits associated with energy use and storage including flight capacity , desiccation and starvation resistance . The patterns of GEI and I between foraging and food ( Fed vs . FD ) are very consistent for genes whose primary function is in anabolic or catabolic pathways , with I positive in anabolic groups and negative in catabolic groups . However , there are many more genes with significant for GEI . An important set of such genes is involved in nerve and/or muscle function ( Table S3 ) . PKG affects synaptic plasticity in mammals [63] , [64] , [65] , [66] and learning and memory in flies [33] , [34] . The possibility that PKG may cause GEI through its role in regulating ion homeostasis in nerves and muscles deserves further examination . PRKG1 , the mammalian homolog of for , regulates calcium and potassium fluxes in smooth muscle relaxation where it is associated with the myosin phosphatase complex , Ca++ATPases , and potassium ion channels [67] . We find a cluster of gene groups with I<0 associated with muscle and actin cytoskeleton , including genes such as wupA ( troponin-I ) and Prm ( paramyosin ) . These are some of the genes whose expression is most correlated with for in a coexpression analysis across humans , flies , worms , and yeast [68] . Calcium/potassium levels are important in synaptic function and plasticity , and mutations in potassium channel genes affect habituation in the giant-fiber axon escape reflex in flies [69] , [70] . Habituation of the giant-fiber escape reflex differs in adult rovers and sitters [31] . Rover-sitter differences in PKG are also associated with different voltage-dependent K+ currents in larval neuromuscular junctions , along with differences in neuronal excitability , neurotransmitter release , and synaptic transmission [71] . Rover-sitter differences in neural thermotolerance arise from differences in the regulation of K+ channel activity via a circuit involving PKG , PP2A , and ion channels [72] . Thus our demonstration of rover-sitter differences in gene expression of genes involved in neurotransmitter release , postsynaptic membranes , and calcium- and potassium-channels supports previous studies . It will be important to determine whether foraging interacts epistatically with other genes influencing K+ currents in neurons and muscles . It is also of interest to investigate whether the metabolic effects of allelic variation in for are independent of , or are tied to PKG's effects on ion homeostasis and neural function . Our study used only a few strains of flies and thus does not speak to the importance of for-mediated effects on the genome in natural populations . However , we are able to consider allelic effects at the for locus relative to the genetic background effects in a principal components analysis which identifies genetic background ( BG ) and food ( Fed vs . FD ) as the most important factors , followed by the interaction of BG and food , for genotype main effects , and for interaction effects . Using the Storey-Tibshirani method to estimate the true proportion of differentially expressed genes πalt shows that for GEI affects 57% of the highest expression genes . We also found that the effect of the natural sitter background was to intensify gene expression contrasts with rovers but to reduce behavioural contrasts . Thus , an important future step is to quantify the relative importance and roles of for and other genes in a wider variety of natural genetic backgrounds . Our results also speak to evolutionary questions about pleiotropy , epistasis , and plasticity . Pleiotropic genes may affect few traits when redundancy , degeneracy [73] , or compensations in gene networks buffer the effects of mutations [74] , while mutations in other genes produce large changes [75] . The number of traits influenced by a gene follows a power law , with a few genes having widespread affects [74] , [76] . It has been proposed that the use of naturally occurring alleles or mild mutations is more relevant to studies of epistasis and network stability than the more common use of knockouts or severe loss of function mutations [73] , [77] , [78] . The question of whether some genes can increase phenotypic plasticity and thus whether selection can act to increase or decrease plasticity has been the subject of much debate [2] . In for we have an example of a gene with naturally occurring alleles maintained in a stable polymorphism in the wild [79] . We demonstrate that in adult flies for interacts pervasively with food , producing pleiotropic GEI in behaviours , lipids and carbohydrates , and gene expression . Our quantitative plasticity measure reveals that for×food GEI is often due to rovers having significantly higher food-related plasticity than sitters . We show that for interacts with genes of the insulin signaling pathway to produce some of these effects . The foraging gene may thus provide a suitable context for resolving some of these questions relating to phenotypic plasticity and selection .
The following allelic variants of the chromosome-2 foraging ( for ) gene were used in this study: the forR ( natural rover ) , fors ( natural sitter ) variants and fors2 ( a sitter mutant strain made on a forR genetic background ) [9] , [13] . All strains share common isogenic third chromosomes from forR and common X chromosomes . Genetic variation on the small fourth and Y chromosomes was not controlled . For tests of epistasis we used three null mutants of genes in the insulin signalling pathway ( see below ) . Flies were reared on medium b for behavioural assays , gene array and metabolite experiments ( Table S1A ) [80] . For additional behavioural assays reported in Table S1B , S1C flies were raised in media described in Table S1A , S1C . Flies were raised in 40 mL plastic vials containing 10 mL food medium in 12/12 h light/dark cycle ( lights on 08:00 ) , 25±1°C , 70±5% relative humidity ( standard conditions ) . Flies were collected 0–2 days post eclosion , separated under light CO2 anaesthesia , then reared in groups of 25–30 for 4 days . 12–13 males and 12–13 females were used in each rearing group . Adult rearing was done under standard conditions as described above . Flies were transferred to test media the night before behaviour tests . Test media consisted of 10 mL of food medium ( Fed ) , or 10 mL 1% agar for food deprivation ( FD ) tests in vials . Flies were tested in the morning ( 9–12 a . m . ) after 16–18 hours under Fed or FD conditions . A plexiglass maze was used for the food-leaving assay; the maze is as described [80] and is shown in Figure S1 . Each morning mazes were conditioned by passing through one sample of 25 natural sitter flies before testing commenced . Mazes were placed horizontally on a light table with a uniform light intensity of 1000 lumens . Flies were placed in a10×75 mm borosilicate glass tube ( the “sugar entry tube” ) containing 0 . 5 mL 0 . 25 M sucrose in 1% agar for 15 min prior to test . At start of testing , the sugar tube with flies is placed in the entry port of the maze . Empty glass collection tubes are placed in the 9 exit ports of the maze . After 3 min , flies in collection tubes at exit ports are counted , as are flies remaining in the sugar tube . Food-leaving score is ( flies in collection tubes ) / ( total flies ) . All treatment conditions were tested on at least 3 different test days . To test whether the rover and sitter for alleles interact epistatically with null alleles of the three genes involved in the insulin signaling pathway we used a form of quantitative complementation , a method of complementation developed for testing quantitative effects , in this case , gene interactions [27] . We asked if one copy of a mutant allele of a gene involved in insulin signaling , in the presence of each of the for alleles , changes food-leaving behaviour . Crosses were made between rover ( forR ) , natural sitter ( fors ) , and mutant sitter ( fors2 ) strains and balanced loss of function mutants in three insulin signaling pathway genes: ( a ) InR , the insulin receptor ( mutant allele: InR93dj-4 [81] ) ; ( b ) Pi3K92E/Dp110 , the phosphatidylinositol 3-kinase catalytic subunit ( Dp110B [25] ) ; and ( c ) foxo , ( foxo21 [22] ) . Strains carrying mutations in the insulin signaling pathway were: ( a ) In ( 3R ) GC25 , InR93Dj-4/TM3 , Sb1 ( Bloomington stock 9554 ) [82]; ( b ) yw;P[ry+ , gH] , Dp110B/TM3 , Ser , y+ ( C ) [83]; ( c ) foxo21/TM6C [22] . All mutants of the insulin signaling pathway were maintained heterozygous with balancer chromosomes which did not carry mutations in these insulin signaling genes and were on a sitter background . Epistasis is identified as a two-way statistical interaction between the variant ( rover or sitter ) and test genotype ( mutant or control ) [26] ( see below ) . The balancer heterozygotes control for effects of natural variation in the genetic background that can result in increases or decreases in food leaving behaviour . Fourier Transform Ion Cyclotron Resonance Mass Spectrometry ( FTICR MS ) was used to analyze homogenized fly heads ( equal numbers of males and females ) from 5–7 day post eclosion forR and fors2 strains harvested in the morning . Food Deprived ( FD ) flies had been restricted to water in agar 12 hours before collection . Samples were taken in triplicate . Values shown are Signal to Noise ( S/N ) ratios . Each sample was analysed as described [84] using the DiscovaMetrics [85] package producing parent ion molecular weights accurate to within 0 . 0005 daltons; compound identifications were cross-checked against Kegg Ligand [86] and Metlin [87] databases . FTICR MS has maximum sensitivity to metabolites in the 100–1000 Dalton range , accuracy of 0 . 0001 Dalton , and uses six different buffer/ionization modes ( Table S2 ) each detecting different classes of metabolites . For instance , compounds such as sugars and phosphates are detected in mode 1102 using a polar buffer and negative ion electrospray , while mode 1203 uses a non-polar buffer and positive atmospheric pressure chemical ionization mode to detect compounds such as triacylglycerols . Whole body lipid and carbohydrate analysis was performed separately on males and females of 5–7 day post-eclosion forR , fors , and fors2 strains . For both lipid and carbohydrate analyses , results were standardized against dry weight . For lipids , the ether extraction method was used as described [88] . In brief , flies were frozen in liquid nitrogen , then weighed to the nearest 0 . 01 mg in groups of 5–10 flies on a Mettler Toledo XS205 balance . Flies were then dried at 60°C for 24 hours and reweighed . Lipids were then extracted in 1 mL of ether for 24 hours , after which ether was decanted and flies were dried at 60°C for 24 hours and weighed . Total carbohydrate levels were determined using amyloglucosidase digestion followed by spectrophotometric determination of total glucose using NAD to NADH reduction [89] . Sigma kit GAHK20 . Briefly , hexokinase catalyzes phosphorylation of glucose in the presence of ATP to Glucose-6-phosphate ( G6P ) , which is then oxidized to 6-phospho-gluconate in the presence of oxidized nicotinamide adenine dinucleotide ( NAD ) in a reaction catalyzed by glucose-6-phosphate dehydrogenase ( G6PDH ) . During this oxidation , an equimolar amount of NAD is reduced to NADH . The consequent increase in absorbance at 340 nm is directly proportional to glucose concentration . Protein was measured using the bicinchonic acid ( BCA ) method . Total energy content was calculated based on ratios of 9∶4∶4 Kcal/gm for fats∶carbs∶protein . Affymetrix Drosophila Genome 1 . 0 cDNA microarrays were used to evaluate effect of foraging genotype and feeding state on transcript levels in adult heads . Flies homozygous for each of the 3 alleles , forR , fors , and fors2 , were raised to 5–7 days post-eclosion , and given Food or FD treatments as described above . Samples of flies ( equal numbers of males and females ) were frozen in liquid nitrogen , and heads were separated by sieving . RNA was extracted as described [90] . Triplicate RNA samples for each treatment were hybridized to Drosophila Genome 1 . 0 microarrays , for a total of 18 arrays . N = 3 within each treatment . Expression levels produced by MAS 5 . 0 were normalized by quantile normalization [91] of log2 transformed data . Full MIAME information and expression set data is filed as GEO accession GSE14371 . Pathway analysis and gene ANOVA were performed as described in Statistical methods . Analysis of variance was used to detect significant GEI for individual genes after False Discovery Rate correction for multiple testing [17] . Levels of expression of 2 genes ( Treh , CG10924 ) were confirmed by quantitative reverse transcriptase polymerase chain reaction analysis ( Figure S3 , Text S1 ) . Heads of male and female forR and fors2 flies were raised as in the microarray analysis and then were frozen in liquid nitrogen . RNA was extracted using the Trizol method ( 15596-018 , Life Technologies ) and further purified using the Qiagen RNeasy kit ( 74106 , Qiagen ) . The amount of RNA in each sample was determined using a Nanodrop spectrophotometer ( ND-1000 ) and sample quality verified using 260/280 micron absorbance ratios . Scores of each trait are analysed with two-way Analysis of Variance ( ANOVA ) to determine whether significant GEI exists ( see detailed procedures below ) . The Storey-Tibshirani False Discovery Rate ( FDR ) [17] is used for multiple testing correction and estimation of π0 using the qvalue package as implemented by Storey [17] , with default parameters . Thus ANOVA p values have been replaced by FDR q values , and q<0 . 05 is deemed significant . RNS measures which strain has higher plasticity and is defined as RNS = ( |rover change|−|sitter change| ) /C ( for log2 transformed data C = 1 , else C = mean of all treatments ) . That is , RNS compares the absolute magnitude of changes in response to food and is positive when rovers change more than sitters . For ANOVA of gene expression data with two sitter strains and one rover , a modified general linear model design matrix was used ( Text S1 , Supplementary Methods ) to ensure unbiased estimation . Briefly , factors RS ( rover or sitter ) , food ( Fed or FD ) , and BG ( genetic background , 2 levels , 1 for rovers and mutant sitters , another for natural sitters ) were analysed including main effects and the interactions RS×food and BG×food . A reduced model omitting the interaction BG×food was also fitted . For each gene , the first and second models were compared using Schwartz's Bayesian Information Criterion ( BIC ) [92] to determine whether to report full or reduced model results . Thus if interaction of BG and food was significant ( as determined by BIC ) we reported statistics from the full model , else from the reduced model . FDR correction was then applied to p-values from the selected model . For group-wise ANOVA analysis of groups of metabolites or genes , a linear model as above , with the addition of a factor G with one level for each gene or compound was used; this is similar to adjusting each gene or compound to have a mean of zero , but accounts more conservatively for lost degrees of freedom due to the adjustment . Only transformed variables with approximately equal variances are used in group-wise ANOVAs . Microarray data is log2 transformed , then subjected to quantile normalization and a variance-equalizing monotonic transform . After these steps variances for the top-expressing 60% of genes were approximately equal and data was normally distributed . For group-wise ANOVA of metabolites , log2 data was used . Data was tested with a covariate of molecular weight ( MW ) . If MW or its interactions with food and genotype were significant , the ANCOVA with MW is reported; otherwise group-wise ANOVA results are reported . For ANOVA of behavioural experiments where behaviours may vary from day to day ( Day effect ) , Day was added as a random factor to the ANOVA described above for single genes , and significance of this mixed-model was determined by F-tests of fixed factor terms to their interactions with Day . See Table S1 and Table S5 for examples . In the quantitative complementation crosses in Table S5 , the interaction of for with a factor representing the presence or absence of the mutant insulin gene is tested . That is , we test for epistasis rather than GEI . The ANOVA analysis is identical in format to that just described , with the factor representing presence/absence of insulin mutant replacing the food factor . | Normal individual differences in the foraging ( for ) gene of the fruit fly Drosophila melanogaster result in two behavioral types called rover and sitter . Larval rovers show a greater behavioral response to changes in their food environment than sitters . The for gene makes an enzyme called PKG , which is found in the head of the fly , as well as in most other organisms , including humans . Here , we demonstrate that adult rovers and sitters differ in their metabolic response to changes in their food environment . We measure metabolites in rovers and sitters and show that rovers store energy predominantly as lipids , whereas sitters store it as carbohydrates . We also examine expression levels of genes in rover and sitter heads when the flies are well-fed or food-deprived . We find that for affects levels of gene products involved in carbohydrate and fat metabolism and insulin signaling . We confirm an interaction between for and insulin signaling genes by using genetic mutants to measure their combined effects on fly food-leaving behavior . Our results show that natural variation in this single gene can affect plasticity of large numbers of traits . | [
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| 2009 | The Drosophila foraging Gene Mediates Adult Plasticity and Gene–Environment Interactions in Behaviour, Metabolites, and Gene Expression in Response to Food Deprivation |
Protein Direct Coupling Analysis ( DCA ) , which predicts residue-residue contacts based on covarying positions within a multiple sequence alignment , has been remarkably effective . This suggests that there is more to learn from sequence correlations than is generally assumed , and calls for deeper investigations into DCA and perhaps into other types of correlations . Here we describe an approach that enables such investigations by measuring , as an estimated p-value , the statistical significance of the association between residue-residue covariance and structural interactions , either internal or homodimeric . Its application to thirty protein superfamilies confirms that direct coupling ( DC ) scores correlate with 3D pairwise contacts with very high significance . This method also permits quantitative assessment of the relative performance of alternative DCA methods , and of the degree to which they detect direct versus indirect couplings . We illustrate its use to assess , for a given protein , the biological relevance of alternative conformational states , to investigate the possible mechanistic implications of differences between these states , and to characterize subtle aspects of direct couplings . Our analysis indicates that direct pairwise correlations may be largely distinct from correlated patterns associated with functional specialization , and that the joint analysis of both types of correlations can yield greater power . Data , programs , and source code are freely available at http://evaldca . igs . umaryland . edu .
Contacts among residues largely determine a protein’s three-dimensional structure . For proteins sharing a common structure , such contacts generally produce correlated substitution patterns between residue pairs . Over evolutionary time substitutions at one residue position often result in compensating substitutions at other positions in order to maintain critical interactions . This allows the prediction of protein structural contacts based upon multiple sequence alignment ( MSA ) covariance analysis . Early approaches were only partially successful , with a major shortcoming the confounding effect of indirect correlations: When residues at positions i and j correlate , as do those at positions j and k , then residues at positions i and k may also correlate even though they fail to interact directly . Direct Coupling Analysis ( DCA ) and related methods [1–8] have overcome this problem by disentangling direct correlations from indirect coupling effects . As used here , the term DCA refers to all such approaches . DCA constitutes a major breakthrough in protein structure prediction and is currently being applied successfully on a large scale [9] . DCA programs employ a variety of algorithmic strategies , including sparse inverse covariance estimation ( PSICOV ) [4] , pseudo-likelihood maximum entropy optimization ( EVcouplings-PLM ) [5 , 6] ( CCMpred ) [10] and multivariate Gaussian modeling ( GaussDCA ) [11] . DCA methods are evaluated by comparing those residue pairs with the highest direct coupling ( DC ) scores to residue-to-residue contacts within protein structures . Currently this involves using , for example , ROC curves [11] , the Matthews correlation coefficient [12] , F1 scores , or often the positive prediction value ( PPV ) . Such measures are applied by labeling data points according to a binary classification scheme; for DCA , those residue pairs that are a specified distance apart within a benchmark structure ( e . g . , ≤ 5 Å ) are labeled as positives and other pairs as negatives . However , there are reasons to criticize such measures in particular circumstances [13] . In particular , it is not clear how to assess the significance of such measures when comparing different proteins or distinct structures . To standardize such comparisons , it is desirable to obtain a measure of statistical significance , which also provides insight into how surprised we should be with a given result . As illustrated here , one can use such a measure to determine whether it is better to base DC scores on an MSA of more closely related proteins rather than on an entire superfamily MSA . Given a set of structures for a protein superfamily , a significance measure can help identify those of greatest interest: Direct couplings between pairs of residues presumably are due to selective constraints maintaining functionally important structural interactions . Hence , those protein structures that exhibit the most biologically relevant interactions should achieve the highest level of significance . One could therefore use a significance measure to select among alternative structural models generated by homology or by ab initio structure prediction methods . One may also adapt such a measure to evaluate the degree to which high DC scores are associated with properties other than 3D structural contacts . As illustrated here , for example , one may determine whether those residues most distinctive of a particular protein family are overrepresented among the highest DC-scoring residue pairs . Here we describe a method to estimate , in various contexts , the statistical significance of the correspondence between DC scores and either protein structural contacts or other protein properties . Unlike the current practice of selecting for analysis an arbitrary number of the highest scoring pairs ( e . g . , 1 . 5 times the MSA length [14] ) , our approach determines the optimal number of such pairs automatically based on a statistical criterion , while adjusting automatically for the number of multiple hypotheses tested . Unlike binary classification schemes , our approach takes into account both the order of each residue pair based on DC scores and their ranks based on 3D pairwise distances; hence , it treats the structurally closest residue pairs having high DC scores as of higher biological relevance than such pairs having low DC scores . By providing a quantitative measure of significance , our approach can detect subtle yet important features of the data that qualitative measures would fail to distinguish from background noise . We illustrate this approach by investigating: the relative performance of alternative methods; the biological relevance of alternative structures; subtle structural changes associated with the transition state of Ran GTPase; the contribution of homo-oligomer interfaces to aggregate DC scores; DCA’s dependence on the sequences included in the input MSA; and the correspondence between DCA pairwise correlations and correlated patterns associated with protein functional specialization .
Abstractly , given an array of elements ordered by a primary criterion ( e . g . , as used here , DC scores ) , we ask how well it agrees with a secondary criterion ( e . g . , 3D pairwise distances ) that distinguishes and ranks a subset of the elements . More specifically , we seek to identify an optimal initial cluster of elements of the array ( defined by a cut ) , as measured by a relevant p-value . Our approach is based upon Initial Cluster Analysis ( ICA ) [15]; see Methods . For reference , Table 1 provides a summary of the variables used below . ICA answers the question: Given a random array of length L , containing D '1's ( representing distinguished elements ) , and L—D '0's , what initial cluster , consisting of elements up to and including a cut point X , contains the most surprising number d of '1's , and what is its probability of occurring ? ( Below , we call the d '1's in an initial cluster “left-distinguished elements . ” ) For L = 18 and D = 7 , for example , one such array is “101101100000010001” , with optimal cut point X = 7 ( underlined ) , yielding d = 5 . Here we note that , in practice , to distinguish elements within our array , we frequently rank all the elements , and distinguish those with rank ≤ D . We then might denote our example array as “401603200000070005” with digits > 0 denoting the ranks of distinguished elements . ICA ignores these ranks when choosing the optimal X , whereas we would prefer the d distinguished elements to the left of X to have superior ranks ( i . e . , lower numbers ) than those to the right . To generalize ICA to exploit ranking information we incorporate a ball-in-urn model to calculate a ranking specific p-value Pb . For a specific cut point X that yields d left-distinguished elements , we imagine first coloring red , among all D distinguished elements , those d elements with superior ranks ( e . g . , with the smallest pairwise distances ) ; and then recording the number R that are red among the left-distinguished elements . Ideally , all the left-distinguished elements will outrank the remaining distinguished elements , yielding R = d , but more generally higher values of R are better; in the example of the previous paragraph , D = 7 , d = 5 and R = 4 . Given the null hypothesis that rankings are random , we may then use the cumulative hypergeometric distribution to calculate the probability Pb that ≥ R of the left-distinguished elements are red: Pb=[∑i=Rd ( di ) ( D−dd−i ) ]÷ ( Dd ) . This corresponds to drawing d balls from an urn containing D balls , of which d are red; note that the number of balls drawn here equals the number colored red . A low value of Pb is reported for a cut with a surprising number , among its d left-distinguished elements , having the d smallest pairwise distances . Before it corrects for optimizing over all possible cuts , ICA can be understood as calculating a p-value Pa for finding d distinguished elements to the left of a cut point X . Because the calculation of Pa ignores ranking information , it will be independent of Pb , and these two p-values may therefore be combined to yield a joint p-value PJ [16–19] using the formula PJ=PaPb ( 1−lnPaPb ) . Low values of PJ may arise from low values of Pa , or Pb , or of both . PJ can provide a statistically stronger measure than Pa alone of the congruence of two orderings , here derived from DC scores and 3D distances . The p-values P we report in this paper correspond to PJ , after it has been corrected for optimization over the multiple cut points X considered , as described in [15] . One may wish to optimize as well over various values of D , but in the current application larger values of D are then almost always preferred , due to the indirect couplings considered below . We therefore choose a fixed D , based upon a maximum allowed 3D distance within a reference structure . To summarize , in order to apply the theory above to the question of how well DCA actually uncovers direct contacts within proteins , we proceed as follows . Given an MSA , a method to calculate DC scores for all column pairs , and a reference structure corresponding to one of the sequences in the MSA , we consider only those pairs of MSA columns separated by ≥ m intervening positions within the reference sequence , with m = 5 by default . Ordering these column pairs by descending DC score yields our array of elements , of length L . We then distinguish those D elements whose 3D distance per the reference structure is ≤ r Å , with r = 5 by default , and rank them by increasing distance . ( This distance is defined as the minimum between sidechain atoms , including hydrogens , of the paired residues . For glycine , Cα and its attached hydrogen serve as the sidechain atoms . ) ICA's original Pa depends only upon the specification of these pairs as distinguished , whereas PJ takes account as well of their rankings , through the ball-in-urn derived Pb . As we will show below , for this application Pa is , in general , far smaller than Pb . However , we have found PJ to provide , in general , greater statistical power than Pa for analyzing protein sequence-structural relationships , and our focus in this paper is to illustrate its use . Currently DCA performance is often evaluated using the positive prediction value ( PPV ) , defined as the percentage of observed reference-structure 3D contacts corresponding to a fixed number x ( e . g . , x = 100 [11 , 20] ) of the highest DC-scoring column pairs . In contrast , the cut point X is not fixed but chosen to optimized significance . Because the number of column pairs grows with increasing MSA length ℓ , x is often chosen , using a parameter F , as x = F × ℓ . Typical values of F range from 0 . 5 to 1 . 5 [10 , 14] . Since we propose the S-score as a replacement for PPV , we compare these two metrics below in several ways . To aid these comparisons , we define Px as the probability , based on the cumulative hypergeometric distribution , of d being at least the value observed for a constant value of x = F × ℓ . We define PF as the estimated joint p-value that combines Pb and Px: PF=PxPb ( 1−lnPxPb ) =PF×lPb ( 1−lnPF×lPb ) . Implementation and availability . We implemented these algorithms and statistical models in C++ as the STARC ( Statistical Tool for Analysis of Residue Couplings ) program , which , along with the source code , is freely available at http://evaldca . igs . umaryland . edu . Here and below , for an estimated or theoretical p-value P we define a corresponding s-score as S = −log10 P . Our theory should yield accurate p-values and s-scores for randomly generated , or shuffled arrays . However , in the present application many column pairs within an MSA are interrelated ( e . g . , {i , j} , {j , k} and {i , k} ) , possibly affecting their DC scores as well as the corresponding distances derived from a structure . To test whether computed p-values remain valid given such interrelationships we generated , based on randomization of each of six MSAs with six corresponding structures , sets of random p-values , as described in Methods . We define Ŝ , as a function of S , to be -log10 of the proportion of observed ( simulation-based ) s-scores that are greater than or equal to S . If our p-value calculations are accurate , Ŝ should equal S to within stochastic error . In Fig 1 we plot , for S from 2 to 5 , the Ŝ obtained from 100 , 000 p-values for MSAs in which the residues in each column and the order of the columns for each MSA were randomly permuted ( termed column-permuted MSAs ) . This operation retains the distribution of column relative entropies observed in the original MSA . For comparison , we plot as well the Ŝ obtained from an equivalent number of shuffled DC arrays ( see Methods ) . These arrays abolish interrelationships among DC-scores , and so conform better to theory . The straight , solid line represents the agreement of Ŝ with theory , and dashed curves represent error ranges of two standard deviations . As can be seen , within stochastic error , Ŝ agrees well with theory for the shuffled arrays . ( Because we can generate p-values rapidly for shuffled arrays , we have confirmed the accuracy of Ŝ in this case for S ≤ 8 . ) For the MSA with the largest number of sequences ( 146 , 217 ) among the six used for column-permuted simulations , and corresponding to structure 1wznA , the values of Ŝ deviate consistently below the error bounds corresponding to slightly inflated s-scores . When we randomly removed all but 5 , 117 ( 3 . 5% ) of the sequences in this MSA , however , this effect was essentially eliminated ( see 1wznA plot in Fig 1 ) . Values of Ŝ for the other column-permuted MSA simulations exhibit less of a tendency to deviate outside of the error bounds . Based on these examples , it appears that for large alignments , s-scores may be slightly inflated , but it is not clear why this is so . We cannot be sure that values of Ŝ for randomized MSAs will conform to theory beyond the range tested . However , we may apply s-scores in a manner similar to that of Z-values . A Z-value is the distance between a raw score and the population mean in units of standard deviation . One may convert a Z-value into a p-value under the assumption ( based on the Central Limit Theorem ) that the variables are drawn from a normal distribution . Although this assumption is typically invalid for raw scores far away from the mean , Z-values still provide a useful metric for assessing significance . Extreme s-scores likewise provide a useful measure of statistical significance even though the true distribution may depart from the theoretical distribution used here . We ran the STARC program on the output from four DCA programs , EVcouplings ( EVC ) [5 , 6] , GaussDCA with Frobenius norm ranking ( GSF ) [11] , PSICOV ( PCV ) [4] , and CCMpred ( CCM ) [10] , each applied to thirty protein domain MSAs with reference 3D contacts ≤ 5 Å ( Table 2 ) . For a given MSA , better performing programs should typically generate more significant results , and thus generally higher s-scores . We have studied , through the score S , the correspondence between a multiple alignment's DC scores and the pairwise distances implied by the structure for a particular sequence in the alignment . However , to calculate S , there are typically many structures to choose among , and these may differ in important particulars . Recent studies [27–32] have demonstrated that high DC scoring pairs that are distant in certain benchmark 3D structures may come into contact within alternative conformations or across homo-oligomer interfaces , and have thereby provided insight into protein biophysical and dynamic properties . Other studies [33 , 34] have combined DCA with correlation analyses involving larger groups of structurally and/or functionally correlated residues , thereby generating further insight . Here we illustrate the application of our method to these sorts of studies . To the degree to which DC scores capture the pairwise correlations imposed by the functional requirements common to a protein family , we expect the S yielded by a particular structure to reflect the degree to which that structure exhibits critical interactions characteristic of the family . In other words , S may measure the degree to which a specific structural conformation is biologically relevant . To investigate this , we consider three cases—human Ran GTPase , Gna1 N-acetyltransferase from C . elegans , and the bacterial ( E . coli ) clamp loader complex . Using available structures for each of these , we add hydrogen atoms using the Reduce program [21] to better discriminate among residue-to-residue contact distances . A previous DCA analysis [31] found that the heavy atom distance distribution for directly coupled residue pairs exhibited local maxima at 2 . 8 Å and 3 . 7 Å , which were interpreted as corresponding to the donor-acceptor distance of hydrogen bonds and to hydrophobic interactions , respectively . Here we choose to focus on hydrogen bond interactions . Since our analyses explicitly model hydrogen atoms , we calculate S using a maximum structural distance of 2 . 6 Å , which , based on the sum of the van der Waals radii for hydrogen plus either nitrogen or oxygen [35] , corresponds to an upper bound on the hydrogen-acceptor distance of hydrogen bonds .
STARC-computed s-scores quantify a DCA method’s ability to detect 3D residue-to-residue contacts . When used in combination with the Wilcoxon signed rank test , they yield a significance measure of the performance of one method versus another and can quantify , as well , a method’s tendency to detect indirect couplings . Larger domains tend to yield higher s-scores due to enhanced statistical power . However , this is not a confounding factor when s-scores are used to compare different DCA methods applied to the same MSA and 3D structure or to compare alternative structures based on the same DCA method and MSA . The following advantages of S-scores over SF-scores and PPV may be noted: S-scores are not biased toward any particular method , but rather correspond to the optimal value of F for each method and reference structure under consideration; this may reveal important features that would otherwise be overlooked . S-scores avoid ranking inconsistencies due to one’s choice of fixed values of F . S-scores can tap into additional information regarding each structure’s possible biological relevance , as illustrated here . S-scores take into consideration not only the number and arrangement of ( false negative ) contacting residue pairs to the right of a potential cut point , but also the ordering of those pairs based on their 3D distances within a reference structure . And , unlike PPV , S-scores provide a measure of statistical significance . Of course , researchers also have the option of computing SF , thereby obtaining both a measure of significance and an assessment of program performance as functions of F . We could further develop the STARC statistical model by considering the arrangement of the d distinguished pairs before X . A pair with a higher DC score should be more likely than one with a lower DC score to correspond to a 3D interaction . Ideally , the d pairs should thus be arranged in order of decreasing DC score . To measure how closely a DCA method’s output comes to achieving this configuration , we may first define a permutation π by ranking the d distinguished pairs based on 3D distance , with smaller distances receiving superior ranks ( i . e . , lower numbers ) , and then define τ ( π ) =12∑i=1d ( π[i]−i ) 2 . One may show that τ is an integer function that for random permutations is symmetrically distributed about its mean μ = ( d3−d ) /12 , with standard deviation σ=μ/d−1 . For d ≤ 16 one may compile exact p-values for τ by exhaustive enumeration , and for d > 16 estimate them using either a Gaussian approximation or Monte Carlo simulation . However , it is unclear whether these p-values are independent of Pa and Pb , and whether there is biological benefit to including this order in our statistical model . We plan to investigate these questions . An important potential application of our approach , which is beyond the scope of this study , is the evaluation of MSA accuracy without the need for benchmark alignments , which typically contain a relatively small number of sequences and whose accuracy may be uncertain [46] . Our proposed approach would proceed on the assumption that , given available structures , more accurate MSAs will yield higher values of S . We are developing this approach , which should benefit from the large amount of sequence and structural data becoming available . Our analysis of Ran , Gna1 and the DNA clamp loader complex suggests that S may be useful for evaluating the biological relevance of alternative structural conformations of the same protein and for characterizing the nature of conformation-specific interactions . Viewing direct couplings as functionally imposed constraints and proteins as molecular machines , S may measure the degree to which a particular crystal structure captures a protein in a mechanistically important state . If so , then analyzing in what ways various residue pairs contribution to S may provide mechanistic clues . Likewise , comparative analyses among MSAs corresponding to a protein’s subfamily , family and superfamily may provide mechanistic clues regarding functional specialization . Our analysis here also suggests that one may use STARC to search for the most biologically relevant among the many structures often available for a major protein superfamily .
For the thirty STARC analyses in Table 2 , we obtained high quality crystal structures from the Protein Data Bank ( PDB ) ( www . rcsb . org/pdb ) . The pdb and chain identifiers are given in column 1 of Table 2 . Likewise , the coordinates for the Ran , Gna1 and DNA clamp loader analyses were obtained from the PDB; their pdb identifiers are given in Tables 4 , 5 and 6 , respectively . For all analyses , hydrogen atoms were added using the Reduce program [31] , except for the pdb coordinate file for 3f1lA in which hydrogens were already present . Hence , residue-to-residue distances are based on any two atoms , including hydrogens , albeit ignoring main chain to main chain interactions . This allows better discrimination among hydrogen bond interactions based on subtle differences in contact distances . EVcouplings ( EVC ) was run over the EVfold website ( http://evfold . org ) using the pseudo-likelihood maximization ( PLM ) option with default settings . For each analysis , taking as input the sequence corresponding to the reference structure as the query , EVcouplings uses jackhmmer [45] to create an MSA , from which it then computes the DC scores . The score file and the corresponding PDB coordinates serve as the input to STARC . We also used the jackhmmer alignment as input to the other programs . The GaussDCA program was run with Frobenius norm ranking ( with default parameters ) ; this was done interactively under Julia ( www . julialang . org ) . PSICOV version 2 . 4 was run using the author recommended –p and –d 0 . 03 options and the jackhmmer alignment reformatted by the fasta2aln program , which is included with the PSICOV package ( http://bioinf . cs . ucl . ac . uk/downloads/PSICOV ) . CCMpred version 0 . 3 . 2 ( https://travis-ci . org/soedinglab/CCMpred ) was run with default settings again using the reformatted alignment . Note that the output from GaussDCA , CCMpred and PSICOV does not include the query sequence , which , along with the DC scores , were provided as input to STARC . We performed two types of simulations for each of the six MSAs in Fig 1 , which are labeled by their corresponding pdb identifiers , 3fhkF , 1ijxA , 4cmlA , 1k30A , 1olzA , 1wznA , and which correspond to analyses in Table 2 . These MSAs vary substantially in their numbers of aligned columns ( 127 to 481 ) and aligned sequences ( 1 , 042 to 146 , 217 ) , and in the degree of shared sequence similarity . For the first type of simulation , we randomly shuffled the DC score array for each of 100 , 000 runs . This simulation corresponds to the theory behind the ICA algorithm , which is described in the next section . The second type of simulation corresponds more closely to a STARC analysis by computing a DC score array from a simulated MSA . For each MSA , we first randomly permuted the residues in each aligned column and then randomly permuted the order of the columns in the MSA ( termed a column-permuted MSA ) . Next , using these simulated MSAs as input to the CCMpred program [10] , DC scores were computed for each of 100 , 000 runs . Finally , for each run , STARC was applied using as input the DC scores and the corresponding protein structure . We describe Initial Cluster Analysis ( ICA ) in detail elsewhere [15] , but summarize the approach briefly here . ICA seeks to determine whether a set of distinguished elements within a linear array is clustered significantly near the start of the array and , if so , what is the most significant initial cluster of these elements . Abstractly , given a linear array of length L containing D '1's ( the distinguished elements ) and L-D '0's , it considers a generative model in which the '1's occur with particular and differing probabilities before and after a cut point X in the array . For any particular X it is relatively easy to calculate a likelihood L ( X ) of the array of data , and one may optimize L ( X ) by simply evaluating it for all possible X . However , the values of L ( X ) for close values of X are highly correlated , dependent upon a calculable "density of independent trials" ρ ( X ) . Because ρ ( X ) is not constant but rather grows approximately as the reciprocal of X's distance from 0 or L , simply optimizing L ( X ) inherently favors , a priori , small or large values of X . Therefore , if one's application suggests no such bias , choosing to optimize L ( X ) /ρ ( X ) rather than L ( X ) for a given array of '0's and '1's may be a better strategy . This is referred to in [15] as using "flattened priors" , and is the approach we take here . ICA estimates the effective total number of independent trials implicit in either optimization , which it uses in calculating a p-value for the optimal X from its corresponding Pa . This provides a mathematically principled way to define an optimal initial cluster of distinguished elements , balancing the claims of very short and dense clusters with those of longer but sparser clusters . We have extended ICA here by taking account not only of distinguished elements within an array , but of a ranking assigned to these elements as well . Thus we seek here initial clusters not only with a high density of distinguished elements , but clusters in which these elements have relatively better rankings . Our s-score may be understood as providing a measure of the congruence between two orderings , as well as , simultaneously , an assessment of statistical significance . We evaluated the performance of alternative DCA methods using the Wilcoxon signed-rank test [22] , first dividing each S by the total number of residue pairs L . For CCMpred , EV-couplings and GaussDCA , these normalized s-scores then approximately follow a Gaussian distribution , as indicated by the Shapiro-Wilk test statistic [47] ( p = 0 . 52 , 0 . 60 , and 0 . 09 , respectively ) . For PSICOV the Shapiro-Wilk test score corresponded to p = 0 . 04 , which is slightly below the acceptance threshold of p > 0 . 05 . The STARC program uses a modified version of the Initial Cluster Analysis ( ICA ) algorithm [15] to find the optimal score S , as described above . Alternatively , as an option , it will calculate SF given a specified F . STARC converts PSICOV and GaussDCA formatted DC score files into EVcouplings format automatically; this requires as input the query sequence in fasta2aln format . We modified the CCMpred source code and recompiled the program to generate PSICOV-formatted DC score files . The source code for STARC is freely available at: http://evaldca . igs . umaryland . edu/ . BPPS [39 , 48] partitions the sequences in a superfamily MSA into families and subfamilies . It uses Markov chain Monte Carlo ( MCMC ) sampling to stochastically move sequences between subgroups , while modifying each subgroup’s characteristic pattern . BPPS also identifies and removes unrelated or aberrant sequences . We applied BPPS here to generate both family and subfamily MSAs for sequences of interest . Here we also use STARC to assess the correspondence between pairs of BPPS-defined pattern residues and high DC-scoring pairs . | The success of Direct Coupling Analysis ( DCA ) for protein structure prediction suggests that multiple sequence alignments implicitly contain more structural information than had previously been realized , and prompts deeper investigations of the sequence correlations uncovered by either DCA or other approaches . To aid such investigations and thereby broaden the utility of and improve DCA , we describe an approach that measures the statistical significance of the association between DCA and either 3D structure or correlated patterns associated with functional specialization . This approach can be used to evaluate the relative performance of DCA methods , their ability to distinguish direct from indirect couplings , and the potential biological relevance and mechanistic implications of alternative conformations and homodimeric interactions . | [
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| 2018 | Statistical investigations of protein residue direct couplings |
Nucleophosmin ( NPM ) is a multifunctional nuclear phosphoprotein and a histone chaperone implicated in chromatin organization and transcription control . Oncogenic Kaposi's sarcoma herpesvirus ( KSHV ) is the etiological agent of Kaposi's sarcoma , primary effusion lymphoma ( PEL ) and multicentric Castleman disease ( MCD ) . In the infected host cell KSHV displays two modes of infection , the latency and productive viral replication phases , involving extensive viral DNA replication and gene expression . A sustained balance between latency and reactivation to the productive infection state is essential for viral persistence and KSHV pathogenesis . Our study demonstrates that the KSHV v-cyclin and cellular CDK6 kinase phosphorylate NPM on threonine 199 ( Thr199 ) in de novo and naturally KSHV-infected cells and that NPM is phosphorylated to the same site in primary KS tumors . Furthermore , v-cyclin-mediated phosphorylation of NPM engages the interaction between NPM and the latency-associated nuclear antigen LANA , a KSHV-encoded repressor of viral lytic replication . Strikingly , depletion of NPM in PEL cells leads to viral reactivation , and production of new infectious virus particles . Moreover , the phosphorylation of NPM negatively correlates with the level of spontaneous viral reactivation in PEL cells . This work demonstrates that NPM is a critical regulator of KSHV latency via functional interactions with v-cyclin and LANA .
Nucleophosmin ( NPM ) is a multifunctional nucleolar phosphoprotein that constantly shuttles between nucleus and cytoplasm [1] . It functions as a molecular chaperone and has been linked to a number of cellular processes from ribosome maturation and transcriptional control to apoptosis ( reviewed in [2] ) . NPM is reported to associate with unduplicated centrosomes and to dissociate from centrosomes upon phosphorylation on Thr199 by cyclin E ( NM001238 ) -CDK2 ( NM001798 ) , which coincides with the initiation of DNA replication and centrosome duplication [3] , [4] . It is often highly expressed in tumors , and the NPM1 ( M26697 ) gene is frequently targeted by genetic alterations in various lymphomas and leukemias ( reviewed in [2] ) . NPM can contribute to oncogenesis through various mechanisms , and has been linked to both tumor promoting and suppressing processes . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is an oncogenic human DNA virus in the family of γ-herpesviruses . KSHV infection is associated with Kaposi's sarcoma ( KS ) and certain B-cell malignancies such as an AIDS-related form of non-Hodgkin lymphoma , called primary effusion lymphoma ( PEL ) , and Multicentric Castleman's disease ( reviewed in [5] ) . Similar to other herpesviruses , KSHV life cycle displays latent and lytic phases . Majority of the tumor cells in KS and PELs are latently infected [6]–[8] . Lytic replication phase can be induced ( viral reactivation ) by a variety of intra- and extracellular factors , including hypoxia , cytokines and chemical agents , such as histone deacetylase ( HDAC ) inhibitors or protein kinase C agonists ( reviewed in [9] ) . In addition , interactions of the viral machinery with components of the cellular signaling pathways and cellular transcription factors play an important role in the viral reactivation . Latency-associated nuclear antigen ( LANA ) , the product of ORF73 gene ( AF305694 ) tethers the circular viral DNA to the host chromosomes [10] , [11] , and is required for the maintenance of the KSHV episomal genome [12] , [13] . During latency LANA actively represses transcription of the KSHV lytic reactivator ORF50 gene ( YP001129401 ) as well as several other lytic genes [14]–[18] . Viral cyclin ( v-cyclin; U79416 ) is a latent KSHV gene that is transcribed from the same promoter element as LANA [19]–[21] . v-cyclin is structurally similar to cellular D-type cyclins and forms an active kinase complex with cellular CDK6 kinase ( NM001259 ) . The v-cyclin-CDK6 kinase phosphorylates not only the pRb protein ( M15400 ) , a common substrate of the cellular cyclin D-CDK6 complex , but also a large repertoire of unique cellular substrates such as p27KIP1 ( AF480891 ) , p21CIP1 ( U03106 ) , ORC-1 ( U40152 ) , CDC6 ( U77949 ) , caldesmon ( M64110 ) , and Bcl-2 ( M14745 ) ( reviewed in [22]–[24] ) . We have previously demonstrated that ectopic expression of v-cyclin causes NPM redistribution from the nucleolus to the nucleoplasm [25] . Here we have addressed the functional relationship of v-cyclin and NPM in endothelial cells and naturally KSHV-infected PEL cells .
To explore a functional relationship of the v-cyclin-NPM association , previously noted by us [25] , in patient derived PEL cell lines we size fractionated BC-3 cell lysates and assayed for elution of NPM and v-cyclin . NPM and v-cyclin were found to co-elute at 180–110 kDa ( Figure S1A; fractions marked 1–4 ) . Subsequently , v-cyclin containing complexes were purified from these fractions and analyzed for co-precipitated CDK6 and NPM ( Figure S1B ) , and assayed for in vitro kinase activity ( Figure S1C ) . As shown by us before [26] , v-cyclin-associated kinase activity was detected toward p27KIP1 and p21CIP1 . Interestingly , v-cyclin associated kinase activity was also observed towards a 37 kDa protein most notably in fraction 2 and to a lesser extent in 3 , which co-migrated with NPM ( Figure S1C ) . These results suggested that v-cyclin associated kinase phosphorylates co-purified NPM in BC-3 PEL cells . The notion that v-cyclin phosphorylates NPM in patient derived PEL cells was supported by recent identification of NPM Thr199 CycE-Cdk2 phosphorylation site as a target of v-cyclin-CDK6 in vitro [27] . To confirm that NPM is phosphorylated by v-cyclin on Thr199 in our experiments , U2OS cells were transfected with expression vectors for Myc-tagged v-cyclin or a vector control together with eGFP-tagged wild-type NPM ( eGFP-NPM ) , or its phosphorylation site mutant T4A , with threonine to alanine mutations of NPM codons of 199 , 214 , 234 , 237 , and mutant T3A in which all but threonine 199 is replaced by alanine [28] . Phosphorylation of NPM was analyzed by immunoblotting using phospho-NPM antibody ( pNPM Thr199 ) , which recognizes NPM phosphorylated on its CDK2 ( NM001798 ) , phosphorylation site at threonine 199 [29] . Phosphorylation of NPM was only detected in cells co-transfected with v-cyclin and the eGFP-NPM retaining an intact Thr199 site ( i . e . eGFP-NPM or eGFP-NPM T3A; Figure S1D ) . To gain more insight into the biological relevance of v-cyclin induced phosphorylation on NPM , we next analyzed NPM phosphorylation in non-infected and KSHV-infected cells . To this end , SLK and EA . hy926 endothelial cells were infected with a recombinant KSHV , rKSHV . 219 [30] , and analyzed for phosphorylation of NPM by immunoblotting with the pNPM Thr199 antibody . Infection with rKSHV . 219 induced prominent phosphorylation of NPM on threonine 199 in both cell lines , which interestingly , was accompanied by an increase in CDK6 protein levels ( Figure 1A and B ) . NPM phosphorylation was detectable only at very low levels in uninfected cells ( Figure 1; control lanes , see Figure S2 for longer exposure ) . To demonstrate that NPM phosphorylation in KSHV-infected cells was dependent on v-cyclin , we silenced v-cyclin expression in the rKSHV . 219-SLK cells using retrovirus-mediated RNA interference . We chose to use rKSHV . 219-SLK cells instead of PELs since it has been reported that silencing of v-cyclin in PELs leads to cell death [31] . To this end , rKSHV . 219-SLK cells were transduced with retrovirus expressing sh-RNA against v-cyclin ( sh-v-cyclin ) or a scrambled control sh-RNA ( sh-Scr ) . Silencing of v-cyclin expression was confirmed by immunoblotting after 48 hours , and resulted in a 78% decrease in v-cyclin ( Figure 1A ) . Phospho-NPM signal was markedly attenuated ( 4 . 8-fold ) in cells expressing the sh-v-cyc as compared to cells expressing the sh-Scr , confirming that v-cyclin is required for NPM Thr199 phosphorylation in KSHV-infected endothelial cells ( Figure 1A , top panel ) . V-cyclin is transcribed from a common transcription start site for three latency associated genes ( v-cyclin , v-FLIP ( U90534 ) , and LANA ) [32] . The resulting transcript is spliced to yield two messages , one tricistronic encoding LANA , v-cyclin , and v-FLIP and one bicistronic message encoding v-cyclin and v-FLIP . The bicistronic transcript is more abundant of the two [20] . Therefore , targeting of v-cyclin by RNAi results in concurrent depletion of v-FLIP [31] . To rule out the possibility that the inhibition of NPM phosphorylation in sh-v-cyclin expressing cells is due to the loss of v-FLIP , rKSHV . 219-SLK cells expressing sh-v-cyclin or sh-Scramble were reconstituted for v-FLIP by transducing them with a retrovirus expressing v-FLIP ( v-FLIP-pBMN ) or empty vector ( pBMN ) . Consistent with the bicistronic nature of the v-cyclin transcript , expression of sh-v-cyclin reduced the level of v-cyclin and v-FLIP transcripts to 27% and 48% , respectively , of the levels in control cells ( sh-v-cyclin -; Figure S3A ) . This change was accompanied by significant reduction in NPM Thr199 phosphorylation in the sh-v-cyclin cells expressing either pBMN ( v-FLIP - ) or v-FLIP-PBMN ( v-FLIP + ) , suggesting that reconstitution of v-FLIP had no effect on NPM phosphorylation ( Figure S3B ) . This data further underscores the importance of v-cyclin in phosphorylation of NPM in KSHV-infected endothelial cells . As inhibition of v-cyclin or v-FLIP has been shown to induce apoptosis in PEL cells [31] , [33] , [34] we analyzed whether RNAi for v-cyclin resulted in changes in the cell cycle or increased cell death . To this end expression of cyclin A ( U66838 ) as a relevant S-phase marker and apoptosis by TUNEL assay were determined . No significant differences in the cyclin A levels ( Figure S3B ) or in TUNEL positivity ( data not shown ) were observed in the sh-v-cyclin cells as compared to the sh-Scr cells . Previous studies have shown that CDK6 is the major kinase partner of v-cyclin in PEL cells [35] , [36] . We therefore sought to confirm that also CDK6 is required for NPM phosphorylation in KSHV-infected cells . To this end , we depleted CDK6 in the rKSHV . 219-infected endothelial EA . hy926 cells , and , in addition , CDK4 ( M14505 ) and CDK6 ( NM 001259 ) in BC-3 cells using lentivirus-mediated RNA interference . Cells stably expressing sh-RNAs specific for the aforementioned kinases or control sh-RNA ( sh-Scr ) were first assayed for the levels of respective kinases . Immunoblotting analysis indicated 80% decrease of CDK6 in EA . hy926 , and a 98% and a 60% decrease of CDK6 and CDK4 in BC-3 , respectively ( Figure 1B and 1C ) . The specificity of silencing was also controlled by reciprocal immunoblotting of the kinases in BC-3 cells ( i . e . anti-CDK6 for sh-CDK4-expressing cells and vice versa; Figure 1B ) . Silencing of CDK4 resulted in a 3 . 6-fold compensatory increase in CDK6 . This is in accordance with previous studies that have illustrated that CDK4 activity is dispensable for most cell types , mostly due to the compensation by the highly related CDK6 [37]–[39] . Suppression of v-cyclin associated kinase activity consequent to CDK6 silencing was confirmed by an in vitro kinase assay in BC-3 cells ( Figure S4 ) . Depletion of CDK4/6 neither induced a cell cycle arrest ( Figure S5 ) nor increased apoptosis ( data not shown ) in the sh-CDK expressing BC-3 cells . Importantly , we observed that NPM phosphorylation was significantly reduced in rKSHV . 219-EA . hy926 ( 4 . 8-fold ) and BC-3 cells ( 3 . 7-fold ) silenced for CDK6 as compared to cells stably expressing the scrambled control ( Figures 1B–D ) or sh-CDK4 ( Figures 1C and 1D ) . In contrast , concomitant with an increase in CDK6 level in the CDK4-silenced cells , the phosphorylation of NPM was increased by 1 . 8-fold . Taken together , the results indicate that v-cyclin phosphorylates NPM on Thr199 by activating CDK6 in the KSHV-infected endothelial and BC-3 cells , which are both biologically relevant cell types in KSHV-infections and malignancies . A study by Si et al . [40] reported that NPM associates with LANA and the terminal repeats of the KSHV genome in PEL cells . That LANA and NPM interact is supported by gel filtration chromatography and co-immunoprecipitation analysis of LANA and NPM in BC-3 cells ( Figure S6 and [37] ) . Given that NPM is detected in complexes with two KSHV latent proteins , which are transcribed from a common promoter upstream of the LANA gene [19] , [32] , we considered it reasonable to test whether v-cyclin affects LANA-NPM association . We transfected U2OS cells stably expressing LANA or GFP ( Sarek et al . , unpublished data ) with a Myc-tagged v-cyclin expression vector ( Myc-v-cyclin ) or with an empty vector as a control . After 48 hours , whole cell extracts were collected and subjected to immunoprecipitation with anti-LANA or -NPM antibodies followed by reciprocal immunoblotting . This revealed that co-expression of v-cyclin facilitated association of NPM with LANA ( Figure 2A; middle and rightmost panels ) . To further address the NPM-LANA interaction in naturally KSHV-infected PEL cells , and the dependency of this interaction on CDK6 , the sh-Scr and sh-CDK6 BC-3 cells shown in Figure 1B were subjected to gel filtration chromatography to assess for LANA and NPM co-elution . NPM and LANA were found to co-elute and co-precipitate in fractions marked 1–3 , corresponding to ca . 700-440 kDa ( Figure S7 ) . Interestingly , silencing of CDK6 reduced co-precipitation of NPM and LANA in these fractions ( Figure 2B ) , further indicating that the v-cyclin-CDK6 kinase activity supports the NPM-LANA interaction . We then examined the requirement of NPM phosphorylation on Thr199 for NPM-LANA interaction , by performing anti-LANA immunoprecipitation from cell extracts of U2OS cells expressing LANA and transfected with expression vectors for Myc-v-cyclin , empty vector control , and the eGFP-tagged NPM wt or its phosphosite mutant constructs used in Figure S1D . As shown in Figure 2C ( right panel ) , LANA associated with the eGFP-tagged NPM wt and T3A mutant in the presence of v-cyclin , while interaction with the T4A mutant was severely compromised . Together these results indicate that the phosphorylation of Thr199 on NPM by v-cyclin–CDK6 is needed for LANA-NPM interaction . To explore the role of NPM in KSHV pathobiology we first silenced NPM in BC-3 and BCBL-1 PEL cell lines using lentivirus-mediated RNA interference . NPM protein level was decreased by 77% and 80% in BC-3 and 83% and 80% in BCBL-1 cells stably expressing two different sh-RNAs for NPM , respectively ( sh1-NPM and sh2-NPM ) as compared to cells expressing a control sh-Scr ( Figure 3A ) . Fibrillarin ( AC005393 ) , another nucleolar protein , and tubulin were used as loading controls and were unaffected . There were no major effects on mitotic activity and proliferation ( data not shown ) , or on nucleolar morphology based on localization of fibrillarin ( Figure S8 ) , or phase contrast in the selected population of cells silenced for NPM ( data not shown ) . Furthermore , no obvious change was observed for LANA levels in cells expressing sh-RNAs against NPM ( as shown for BCBL-1 cells in Figure 3B ) . KSHV is predominantly latent in PEL cells , but can be induced to lytic replication phase with chemical HDAC inhibition . NPM was recently reported to recruit HDAC and to exert its repressive effect on transcription by inducing a change in local chromatin structure [41] . To address the function of NPM in PEL cells in this context , we first analyzed the association of NPM and LANA with HDAC1 ( D50405 ) by immunoprecipitation experiments . In agreement with the recently reported role of NPM as an HDAC recruiter , NPM was consistently present in reciprocal co-immunoprecipitations with HDAC1 in latent ( uninduced ) BCBL-1 cells ( Figure 3C ) and BC-3 cells ( data not shown ) . Consequently , we tested whether this interaction is affected by inhibition of HDAC activity with sodium butyrate ( NaB ) that leads to induction of lytic phase ( reactivation ) . The association between NPM and HDAC1 was greatly diminished upon NaB treatment while their protein levels remained unchanged ( Figure 3C ) . Interestingly , we found that LANA complexed with HDAC1 in the latently infected cells and even after HDAC inhibition by NaB ( Figure 3D ) . This interaction , however , was completely abolished upon NPM silencing , suggesting that NPM facilitates interaction of LANA with chromatin modifiers ( Figure 3D ) . NaB treatment has been shown to increase LANA acetylation and to diminish LANA interactions with chromatin , Sp1 transcription factor , and the early lytic ORF50/RTA promoter [42] . Given that NPM silencing reduced the association of LANA with HDAC1 , we decided to assess for acetylation of LANA in both NPM silenced and NaB-treated BCBL-1 cells . To this end we immunoprecipitated cellular extracts with antibody against acetylated lysine ( Ac-K ) or control IgG antiserum , followed by immunoblotting with anti-LANA antibody . As shown in Figure 3E treatment with NaB as well as silencing of NPM expression resulted in appearance of new bands in the subsequent LANA immunoblot suggesting of an increase in the acetylation of LANA [42] . However , we cannot exclude the possibility that we were detecting other acetylated proteins associated with LANA . Acetylation of NPM by the histone acetyltransferase p300 ( NP001420 ) has been reported to disrupt the nucleosomal structure [43] . We therefore analyzed the level of acetylation on NPM in the NaB-treated BC-3 cells , and found that it was increased upon HDAC inhibition ( Figure 3F ) . In line with this , p300 was co-immunoprecipitated with NPM from extracts of BCBL-1 cells treated with NaB ( Figure 3G ) . Protein acetylation is one possible mechanism modulating the interaction between LANA and NPM . Thus , we wanted to test whether this interaction is altered by NaB . Whole cell extracts of BCBL-1 cells treated with NaB ( 1 mM ) or vehicle for 24 hours were subjected to immunoprecipitation with anti-LANA followed by immunoblotting for NPM . Intriguingly , we found that NPM association with LANA was abolished in cell extracts pretreated with NaB ( Figure 3H ) . Given that NaB treatment leads to induction of viral reactivation , we considered it reasonable to rule out the possibility that expression of lytic genes would affect the LANA-NPM association . We have recently demonstrated that Pim-1 kinase ( NM002648 ) is a critical regulator of KSHV reactivation , and that depletion of Pim-1 expression by RNAi leads to inhibition of early steps of viral reactivation [44] . To this end we transfected BCBL-1 cells with siRNA oligonucleotides specific for Pim-1 ( Pim-1 siRNA ) or with a non-targeting control siRNA ( Scr siRNA ) . Cells were subjected to 1 mM NaB or vehicle treatment 48 hours post-transfection and lysates were collected after another 24 hours to confirm silencing of Pim-1 ( Figure S9A , left panel ) . The inhibition of viral reactivation by Pim-1 depletion was confirmed by quantitative real-time PCR ( Figure S9B ) and immunobloting with vIL-6 ( AAB61701 ) ( Figure S9C ) . Importantly , NPM association with LANA was still abolished by inhibition of HDAC activity in the Pim-1 depleted cell extracts ( Figure S9A , right panel ) , supporting that the NPM-LANA interaction was disrupted due to inhibition of de-acetylation , and not due to induction of lytic gene expression . As silencing of NPM seemed to induce an increase in LANA acetylation in PEL cells , an event previously linked to viral reactivation , we next addressed the expression of lytic replication markers in the NPM silenced BC-3 and BCBL-1 cells . Cells stably expressing a non-target control sh-RNA ( sh-Scr ) , sh1-NPM , and sh2-NPM were analyzed by immunofluorescence using antibodies against an early lytic marker ORF59 ( YP001129416 ) . In both cell lines , depletion of NPM expression led to a significant increase in ORF59 positive cells as compared to the sh-Scr expressing control cells ( shown for BCBL-1 in Figures 4A and 4B ) . Viral reactivation in sh1-NPM and sh2-NPM expressing BCBL-1 cells was further confirmed by quantitative real-time PCR ( qRT-PCR ) for the early lytic genes ORF50 and ORF57 ( YP001129410 ) as well as for a late lytic gene K8 . 1 ( Figure 4C ) . Reactivation of the complete lytic cascade of KSHV results in production of infectious progeny virions . We therefore wanted to investigate whether NPM silencing would lead to production of infectious virions in PEL cells . To this end , BCBL-1 cells stably expressing sh1-NPM , sh2-NPM , or sh-Scr were analyzed by immunoblotting for expression of the late lytic capsid glycoprotein K8 . 1 ( AF068829 ) , nine days post-transduction . Expression of K8 . 1 was induced in cells depleted for expression of NPM with sh1- or sh2-NPM , but not in cells expressing the control sh-Scr ( Figure 4D ) . To measure production of infectious KSHV particles upon NPM silencing , supernatants from sh1-NPM or sh-Scr expressing BCBL-1 cells were collected six , nine and 12 days post lentiviral transduction , pooled , and used directly or after concentration by ultracentrifugation to infect naive SLK cells . Infection of target cells was monitored by immunofluorescence with anti-LANA antibodies . At 48 hours after infection , 50–90% of the target SLK cells displayed the typical speckled signal from LANA with numerous dots per nucleus thus confirming that silencing of NPM expression led to induction of the full lytic replication cascade and production of infectious virions ( Figure 4E and F ) . Considering the essential role of LANA in KSHV latency and suppression of lytic viral transcription , as well as the observation that v-cyclin promotes LANA-NPM interaction , we sought to address whether there is a correlation between NPM Thr199 phosphorylation , v-cyclin expression , and the extent of spontaneous lytic replication in four different patient-derived KSHV-infected PEL lines ( BC-3 , BCBL-1 , BC-1 , JSC-1 ) , IHH ( a KSHV-positive lymphoblastoid cell line ) , and IHE ( a KSHV-negative cell line ) . To this end , cell extracts were analyzed by immunoblotting for expression of pNPM Thr199 , total NPM ( NPM ) , v-cyclin , and another latent KSHV protein vIRF3 as a control . Interestingly , we found an apparent correlation with the extent of NPM phosphorylation and v-cyclin expression in the KSHV-infected lymphoid cells ( Figure 5A ) . In accordance with our finding that phosphorylation of NPM is needed for the interaction between LANA and NPM ( Figure 2 ) , the LANA-NPM interaction was diminished in JSC-1 PEL cells , which had a very low level of NPM phosphorylation , as compared to BC-3 cells with highly phosphorylated NPM and prominent LANA-NPM co-precipitation ( Figure 5B ) . To examine whether NPM phosphorylation correlates with the level of spontaneous , un-induced viral reactivation , we performed qRT-PCR for expression of the early lytic transcripts ORF50 and ORF57 in the KSHV-infected PEL cells ( Figure 5C ) . Reactivation was also analyzed by immunofluorescence using antibodies against the early lytic marker ORF59 ( Figure 5D ) . Intriguingly , we found that the cells with lower phospho-NPM levels had higher level of spontaneous expression of the lytic markers compared to the cells with elevated phospho-NPM , suggesting that NPM is a critical regulator of latency in KSHV-infected lymphoid cells . To obtain further evidence about the correlation between , v-cyclin expression levels , extent of NPM phosphorylation on Thr199 , and spontaneous viral reactivation we over-expressed v-cyclin in BCBL-1 and JSC-1 cells using retroviruses expressing v-cyclin and GFP ( KpBMN ) or GFP ( pBMN ) as a control . Exogenous expression of v-cyclin ( determined by the number of GFP positive cells ) was achieved in 17% and 20% of BCBL-1 and JSC-1 cells , respectively ( data not shown ) , and confirmed by Western blotting at 48 hours post-transduction ( Figure 5E ) . As shown in Figure 5E , NPM phosphorylation on Thr199 increased about 1 . 8-fold in cells over-expressing v-cyclin as compared to cells infected with the control ( v-cyclin + and - , respectively ) as evaluated from the luminescence signal , and analyzed by Image J software . In accordance with the increase of pNPM Thr199 levels , qRT-PCR analysis demonstrated a 1 . 5 to 1 . 7-fold reduction ( in average ) in the spontaneous expression of the lytic transcripts ORF50 , ORF57 and K8 . 1 in cells over-expressing v-cyclin ( Figure 5F; v-cyclin + ) . Taken together , these results further support the role of v-cyclin mediated phoshorylation of NPM in controlling spontaneous viral reactivation in PEL cells . Our results suggest that KSHV infection and more specifically the activity of v-cyclin-CDK6 is responsible for the phosphorylation of NPM on Thr199 in latently infected cells . To determine whether NPM is phosphorylated also in KS tumors we stained primary cutaneous lesions of KS ( n = 6 ) with antibodies against pNPM Thr199 and total NPM . Immunohistochemistry for LANA confirmed KSHV infection and defined the tumor area ( Figure 6 , LANA ) . Sections of paraffin-embedded BC-3 cells stably expressing sh-CDK6 or control ( sh-Scr ) were used to verify the specificity of anti-pNPM Thr199 staining ( data not shown ) . NPM phosphorylation was observed in all of the KS lesions analyzed ( Figure 6 ) , suggesting a role for NPM phosphorylation in KSHV pathogenesis .
Latency is the predominant mode of viral persistence in KS and PEL tumors , and is considered to have a fundamental impact on KSHV tumorigenesis . In this study , we show that NPM is phosphorylated in KSHV-infected cells by v-cyclin-CDK6 . In addition , we show that NPM is phosphorylated in primary human KS tumors . Our study demonstrates the functional interaction between v-cyclin and NPM in a biologically relevant system , and establishes NPM as a substrate for the v-cyclin activated CDK6 in KSHV-infected endothelial cells and PEL cells . Our findings indicate that phosphorylation of NPM is essential for its association with LANA , and thus establishes the first functional link between the latent proteins v-cyclin and LANA . Furthermore , we demonstrate that NPM is a critical key regulator in KSHV latency . Our data indicates that in latently KSHV-infected cells NPM facilitates interaction of LANA with HDAC1 ( Figure 3D ) and the core histones ( data not shown ) . This is in accordance with the recent finding identifying NPM as a HDAC recruiter involved in transcriptional repression during differentiation [41] , and with earlier data showing that HDACs interact directly or indirectly with viral sequences to inhibit lytic gene expression [45] . A study by Lu et al . ( 2006 ) has shown that LANA-mediated repression of viral lytic transcription is mediated by LANA binding to the lytic reactivator ORF50/RTA promoter , which is de-repressed by acetylation of LANA [42] , probably due to acetylation disturbing the association of LANA with the chromatin modifiers at the ORF50 promoter . Here our results demonstrate that in cells depleted for NPM LANA is not anymore associated with HDAC1 ( Figure 3D ) . Moreover , depletion of NPM led to an increase in the acetylation of LANA ( Figure 3E ) , and induction of the complete lytic replication cascade ( Figure 4 ) . In line with these results , inhibition of HDAC activity by NaB is a widely used mechanism to induce KSHV reactivation . Further supporting the role of acetylation in the LANA-NPM interaction , NaB treatment led to an increase also in lysine acetylation of NPM ( Figure 3F ) , and to dissociation of the NPM-LANA complex ( Figure 3H ) . Interestingly , NaB treatment did not disrupt the interaction of LANA with HDAC1 ( Figure 3D ) , probably because the increase in LANA acetylation was achieved by chemical inhibition of the HDAC enzymatic activity rather than by interfering with its recruitment by NPM . Furthermore , it is possible that acetylation primarily modulates NPM-HDAC1 and NPM-LANA interactions in the ternary complex of NPM-HDAC1-LANA , but does not interfere with the HDAC1-LANA interaction after the recruitment of HDAC1 to the complex has occurred by phosphorylated NPM . However , if de-acetylation by HDAC-1 is chemically suppressed ( by NaB ) this results in an increase of NPM acetylation ( Figure 3F ) , dissociation of HDAC1 from NPM ( Figure 3C ) , and may allow a more stable interaction between p300 and NPM as suggested by the data in Figure 3G . Although also acetylation on LANA is elevated , this only affects its association to NPM , but not to HDAC-1 that would remain associated with LANA ( Figure 3D ) . NPM has also been implicated in the replication cycle of other viruses such as adeno-[46] , [47] , and adeno-associated viruses [48] , but the detailed mechanism for the function of NPM in these viral systems is not known . By analyzing several different PEL cell lines and one in vitro KSHV-infected lymphoblastoid cell line we detected significant differences in the level of phosphorylation of NPM , but not in total NPM , which correlated with v-cyclin expression ( Figure 5A ) . Furthermore , we found that the extent of NPM phosphorylation correlated with the low level of spontaneous reactivation in these cells . Taken together , our study identifies a key role for NPM and its phosphorylation in regulating the KSHV latency in PEL lymphocytes and de novo infected endothelial cells . The data demonstrating a significant reduction in the phosphorylation of NPM upon silencing of CDK6 expression in KSHV-infected endothelial and lymphoid cells ( Figure 1 ) may open up novel opportunities for developing targeted therapies for intervention and treatment of KSHV associated malignancies . Putative approaches could include development of CDK6-specific kinase inhibitors or addressing the effect of small molecule NPM inhibitors or RNA aptamers [49] , [50] on disruption of KSHV latency .
BC-1 , and JSC-1 PEL cell lines [51] , [52] were obtained from ATCC ( Manassas , VA ) . BC-3 and BCBL-1 cell lines [8] , [53] were kindly provided by E . Cesarman ( Cornell Medical College , NY ) , and IHH and IHE [54] by J . Haas ( University of Edinburgh ) . SLK cells were a kind gift of T . F . Schulz ( Hannover Medical School , Germany ) . PEL cell lines were cultured as described previously [26] . To induce KSHV lytic replication , cells were treated with 1 mM sodium butyrate ( NaB; Sigma , St Louis , MO ) for 24 hours . U2OS human osteosarcoma cells ( ATCC ) were grown as detailed in [26] . rKSHV . 219-infected endothelial cells were established and maintained as described in [54] . Infected cells were grown in the presence of 1 µg/ml of puromycin for about two weeks to obtain a 100% infected cell population . Production of retro- and lentiviral supernatants and CDK silencing in EA . hy926 and SLK cells were performed as described earlier [55] . To silence CDKs and NPM , the PEL cells were seeded at density 5×105/ml and transduced in a 50 ml culture flask using 2 ml lentiviral supernatants in the presence of 8 µg/ml polybrene ( Sigma ) . 24 hours after transduction , the culture was replenished with fresh media , and cells were kept for 48 hours , after which they were subjected to selection with 3 . 5 µg/ml puromycin ( sh-NPM , sh-Scr ) or 300 µg/ml hygromycin ( sh-CDK4 , sh-CDK6 , sh-Scr ) . For an acute depletion of v-cyclin expression in the rKSHV . 219-SLK cells , the cells were spin-transduced ( 2500 rpm; Heraeus Multifuge 3 S-R; Thermo Scientific ) for 30 min at room temperature with fresh amphotropic retroviruses expressing control sh-RNA ( sh-Scr ) or sh-RNA against v-cyclin ( sh-v-cyclin ) in the presence of 8 µg/ml polybrene . Cells were then returned to 37°C , 5% CO2 , and after 24 h of incubation viral supernatant was removed and replaced with fresh complete media . Cells were harvested for analysis 48 hours post-transduction . U2OS osteosarcoma cells were spin-transduced as described above with GFP- or LANA-expressing lentiviruses in the presence of 8 µg/ml polybrene , and the cells were incubated for 48 hours . Thereafter LANA and GFP-expressing cells were cultured in the presence of 4 . 5 µg/ml of blasticidin ( Sigma ) for at least two weeks . Stable expression of the transduced proteins was assessed by immunofluorescence and Western blotting using anti-GFP or anti-LANA antibodies . To study production of infectious virions upon the silencing of NPM expression , BCBL-1 cells ( 0 . 5×106/ml ) were transduced with sh-Scr and sh1-NPM lentiviral supernatants as described above . The medium was collected at day six , nine , and 12 post-transduction . The supernatants were cleared by centrifugation at 3 , 000×g for 15 min to remove cell debris and pooled together . The supernatants were concentrated at 21 , 000 rpm ( 60 , 000×g ) for 2 hours in a Beckman SW28 . 1 rotor , resuspended overnight in 1 ml ( 1/100 of the original volume ) of TNE buffer ( 10 mM Tris-HCl , 0 . 15 M NaCl , 1 mM EDTA [pH 7 . 8] ) , and used to infect naive SLK target cells . Immunoblotting , immunoprecipitations , kinase assay and size exclusion chromatography were carried out as detailed [26] . For detection of the p-NPM signal by western blotting see Text S1 . Total RNA was prepared by using the RNAeasy kit according to instructions from the manufacturer ( Qiagen , Valencia , CA ) . RT-PCR was performed with TaqMan Reverse Transcription Reagents kit ( Roche Diagnostics , Indianapolis , IN ) according to manufacturer's protocol . Real-time PCR conditions and set of primers for LANA , v-cyclin , v-FLIP , ORF50 , ORF57 , K8 . 1 and human beta-actin were essentially as described previously [56] , [57] . Sections of paraffin-embedded KS tumors were treated for antigen retrieval by autoclaving in 10 mM sodium citrate ( pH 6 . 0 ) for 2 min , and then pretreated for peroxidase blocking by incubation in 2 . 5% H2O2 for 30 min . Sections were processed by using Vectastain Elite ABC rabbit IgG or mouse IgG kits ( Vector Laboratories , Burlingame , CA ) . Sections were blocked for 30 min at room temperature in normal goat serum ( 1∶50 in PBS ) +0 . 3% BSA . The slides were incubated at +4°C overnight with primary antibodies to pNPM ( Thr199 ) or total NPM diluted 1∶200 or 1∶100 , respectively , in PBS +0 . 3% BSA followed by biotinylated anti-rabbit or anti mouse secondary antibody 1∶200 ( Vectastain ) , respectively in 0 . 3% BSA in PBS for 30 min at room temperature . Signal was amplified by ABC ( Vectastain ) and diaminobenzidine ( DAB; DakoCytomation ) reaction . The slides were then counterstained with hematoxyline and mounted by using GVA mount media ( Invitrogen , Carlsbad CA ) . Immunohistochemistry of LANA in KS tumors was performed essentially as described previously [55] . Signal amplification was performed by using a peroxidase ABC kit ( Vector Laboratories ) and followed by diaminobenzidine ( DAB; DakoCytomation ) reaction . The images were captured with a Zeiss Axioplan 2 microscope equipped with Zeiss Plan-Neofluar x63/0 . 75NA objective ( Carl Zeiss , Oberkochen , Germany ) . Images were acquired with a Zeiss Axiocam HRc CCD camera , using Zeiss AxioVision 4 . 6 SP1 software and processed with Adobe Photoshop 8 . 0 software ( Adobe , San Jose , CA ) . For complete list of Materials and Methods see Text S1 . | Latency is the predominant mode of viral persistence in KS and PEL tumors , and has a fundamental impact on KSHV tumorigenesis . Establishment and maintenance of latency involves a number of viral and cellular factors . This study provides a novel functional link between LANA and v-cyclin by showing that phosphorylation of nucleophosmin ( NPM ) by the v-cyclin-CDK6 kinase complex supports its interaction with LANA , and thus enables the transcriptional silencing of KSHV lytic genes needed for latency . These findings indicate that KSHV has evolved mechanisms to utilize host proteins for maintaining the latency , and underscores the role of NPM as a regulator of not only mammalian transcription but also of viral transcription . Taken together , our data suggests that a cellular protein , NPM , is a critical factor for the latency of this oncogenic human virus , and may thus represent an attractive novel target for intervention . | [
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| 2010 | Nucleophosmin Phosphorylation by v-Cyclin-CDK6 Controls KSHV Latency |
Cells of the immune system are derived from hematopoietic stem cells ( HSCs ) residing in the bone marrow . HSCs become activated in response to stress , such as acute infections , which adapt the bone marrow output to the needs of the immune response . However , the impact of infection-adapted HSC activation and differentiation on the persistence of chronic infections is poorly understood . We have examined here the bone marrow outcome of chronic visceral leishmaniasis and show that the parasite Leishmania donovani induces HSC expansion and skews their differentiation towards non-classical myeloid progenitors with a regulatory phenotype . Our results further suggest that emergency hematopoiesis contributes to the pathogenesis of visceral leishmaniasis , as decreased HSC expansion results in a lower parasite burden . Conversely , monocytes derived in the presence of soluble factors from the infected bone marrow environment are more permissive to infection by Leishmania . Our results demonstrate that L . donovani is able to subvert host bone marrow emergency responses to facilitate parasite persistence , and put forward hematopoiesis as a novel therapeutic target in chronic infections .
Emergency hematopoiesis in response to severe bacterial infection is associated with an expansion of hematopoietic stem/progenitor cells ( HSPCs ) and their progeny , mediated by a combination of environmental cues , including inflammatory cytokines and microbial products [1–4] . Activated , proliferating HSPCs and myeloid progenitor cells ( MPCs ) leave the bone marrow and migrate to the spleen , liver , and other inflamed target organs where they can directly contribute to hematopoiesis [1 , 5 , 6] . In the case of acute bacterial infection , the resulting increase in myeloid differentiation promotes the immune response that is required for clearance of the pathogen [3 , 4] . Similarly , increased myelopoiesis in response to an acute viral infection can be beneficial for helping to mount the appropriate T cell responses to eliminate infected cells [7] . Bone marrow homeostasis is usually restored after the infection is cleared . However , the potential impact of infection-adapted hematopoiesis on the ability of pathogens to persist and establish chronic infections has not been widely studied . Persistent inflammatory conditions , including chronic parasitic infections such as visceral leishmaniasis may lead to an acquired bone marrow failure , where the HSPCs and bone marrow stroma are no longer able to support blood homeostasis [8–10] . The underlying mechanisms are poorly understood , but the pathology clearly suggests that the parasite infection profoundly changes normal hematopoiesis: visceral leishmaniasis is generally associated with hepatosplenomegaly , extramedullary hematopoiesis , pancytopenia , and immunosuppression . Previous studies using the Balb/c mouse model of infection with Leishmania donovani suggest that the parasite does not directly infect HSPCs or MPCs . Instead , L . donovani establishes a persistent infection in bone marrow macrophages , which correlates with an enhanced MPC output by bone marrow and spleen , and the production of myeloid growth factors [10–12] . However , the contribution of enhanced myelopoiesis to the course of infection is not well understood . HSPC activation corresponds to changes in Wnt signaling activity , with the various intracellular signaling pathways promoting either activation or quiescence [13–15] . We have previously shown that the absence of the Wnt signaling receptor Frizzled6 ( Fzd6 ) results in defective stem cell self-renewal , completely abrogating their ability to reconstitute an irradiated host after transplant , and dampens HSPC expansion during LPS-induced emergency hematopoiesis [15] . Wnt signaling generally contributes to the establishment of T cell memory and regulates effector T cell responses [16 , 17] and leukocyte trafficking [18] , but its role in inflammation-induced myelopoiesis and the regulation of chronic infections is not known . We show here that experimental L . donovani infection induces the expansion of hematopoietic stem cell ( HSC ) -like cells and Sca1+ emergency MPCs in the bone marrow . The myeloid progeny of these emergency MPCs consists predominantly of Ly6Chi monocytes with a regulatory , suppressor cell-like phenotype . We further demonstrate that the expansion is functionally important , as monocytes generated in the presence of soluble factors extracted from the infected bone marrow are more permissive to infection , and a stunted emergency response such as seen in Fzd6-/- mice results in decreased parasite burden . Collectively our results support the hypothesis that Leishmania subverts the host bone marrow emergency response to promote its own proliferation and to allow for continued persistence of the infection .
L . donovani infection results in enhanced myelopoiesis in the bone marrow and spleen of Balb/c mice [11 , 12]; however , it is unclear which HSPCs the parasite targets and what functional consequences stem from the increased myeloid output . We initially followed the progression of L . donovani infection in the bone marrow of mice on C57Bl/6 background and observed a sharp increase in parasite burden beginning in the third week after infection ( Fig 1A ) . In parallel with parasite burden , the proportion and number of bone marrow Lin-Sca-1+c-Kit+ ( LSK ) cells and CD150+CD16/CD32-LSKs , which correspond to an HSC-like phenotype in uninfected mice , also increased , reaching a plateau between day 21 and day 28 , depending on the strength of the infection ( Fig 1B and 1C , S1 Fig ) . A similar expansion was also observed in the spleen: both LSKs and HSC-like cells were virtually undetectable in the naïve spleen , but their numbers continued to augment in infected mice through day 35 ( Fig 1D , S1 Fig ) . These results indicate that the most immature HSPCs are indeed affected by infection with L . donovani , and that this effect persists in time . Adult HSCs are usually dormant in the bone marrow , with more than two thirds of the cells residing in a quiescent state in the G0 phase of the cell cycle . However , they become readily activated under stress or in response to inflammatory cytokines [4 , 19 , 20] . Chronic infection with L . donovani induced HSC-like cells to enter cell cycle , resulting in a gradual loss of quiescent cells ( Fig 1E ) . This was accompanied by differentiation , as the proportion of CD150+ HSC-like cells that had acquired CD48 and thus represented multipotent progenitors with myeloid bias [19 , 21] also increased from 25% to 75% ( Fig 1F and 1G ) ; however , there was a significant expansion of the CD48- population as well . HSC activation and cell cycle entry have been shown to correlate with the induction of β-catenin-dependent Wnt signaling in non-infectious settings [13] . We observed a significant increase in intracellular levels of active β-catenin that was specific for CD150+ HSC-like cells ( Fig 1H ) , suggesting that Wnt signaling could contribute to regulating L . donovani-induced HSC expansion . Leishmaniasis is accompanied by an increase in circulating monocytes , and L . donovani induces the expansion and export to spleen of MPCs in Balb/c mice [11 , 22 , 23] . To better define the kinetics and the types of progenitors that were responding to the infection , we analyzed the bone marrow MPC compartment and observed that the number and proportion of granulocyte-monocyte progenitors ( GMPs; CD16/CD32+cKit+CD41-CD150-Lin- ) remained stable over time ( Fig 2A and 2B , S2 Fig ) . There was no specific change in the proportion of actively cycling GMPs ( Fig 2C , S2 Fig ) , and in contrast to HSC-like cells , the increase in active β-catenin levels was very modest ( Fig 2F ) . However , there was a striking upregulation of Sca1 expression on GMPs ( Fig 2D and S2 Fig ) , which translated into a decline in the numbers of Sca1-GMPs that are normally associated with steady-state hematopoiesis . The steady increase in the numbers of Sca1+ “emergency” GMPs ( Fig 2E; [24] ) together with the stable levels of total GMPs ( Fig 2A and 2B ) suggested that the major change brought about by L . donovani did not impact as much the numbers of MPCs as the type of progeny they generated . B lymphopoiesis was nearly completely abrogated at later stages ( S3 Fig ) , indicative of profound alterations to the bone marrow environment . GMPs as a population give rise to both granulocytes ( mostly neutrophils ) and monocytes . During resting hematopoiesis , approximately half of the bone marrow myeloid cells are granulocytes ( SSChiGR1hiLy6G+ ) , with the remaining half divided into Ly6Chi monocytes ready to enter the circulation and a Ly6Clo/- population that comprises alternative monocytes and macrophages in addition to immature stages of both monocytes and granulocytes . Ly6Chi monocytes represented the only myeloid cell type whose proportion steadily increased over time in the infected marrow ( Fig 3A ) . On day28 , we observed infected cells with monocyte as well as myeloblast morphologies in bone marrow smears ( Fig 3B ) . All monocytes contained a single parasite , while the myeloblast-like cells , similar to mature macrophages , could be found to house several . These latter , productively infected cells could represent either immature myeloid cells or potentially an intermediate stage in monocyte maturation towards a more macrophage-like morphology . To better evaluate monocyte differentiation and maturation , we sought to determine if the newly generated monocytes had acquired an altered , regulatory phenotype , similar to what has been reported in response to trypanosomal infection [25] . Ly6Chi monocytes rapidly acquired Sca1 expression , similar to the emergency GMPs ( Fig 3C and S3 Fig ) . In comparison , only a small percentage of Ly6Clo/- cells upregulated Sca1 . Ly6Chi monocytes also upregulated MHCII ( Fig 3C and S3 Fig ) and down-regulated Ly6C expression ( Fig 3D ) , which suggested that they had been exposed to IFN-γ [25] . To further support the hypothesis of monocyte developmental skewing , the Ly6Chi monocytes generated in L . donovani–infected mice also upregulated Galectin-3 ( Fig 3D and S3 Fig ) , which is associated with alternative macrophage activation [26] , IL-10 production [27] , and pro-fibrotic responses . These data collectively show that L . donovani subverts bone marrow hematopoiesis , enhancing the generation of emergency GMPs and the differentiation of monocytes that , due to their regulatory or immature phenotype , could represent safe targets and promote parasite expansion [22] . We have previously shown that the absence of the Wnt signaling receptor Frizzled6 ( Fzd6 ) dampens HSC expansion during LPS-induced emergency hematopoiesis [15] . Given that Wnt signaling was induced in L . donovani–activated HSCs ( Fig 1H ) , we sought to evaluate if Fzd6 was also important for the bone marrow response to the parasite , and if we could use Fzd6-deficient mice as a tool to investigate the importance of the bone marrow response in promoting parasite expansion . There was no significant difference in bone marrow LSK or HSC numbers between Fzd6-/- and Fzd6+/+ mice on day 14 , before the expansion ( Fig 4A ) or on day 21 ( S4 Fig ) . However , expansion was blunted in Fzd6-/- mice on day 28 post-infection ( Fig 4A ) , which corresponds to the peak of expansion ( Fig 1G ) . The difference in HSPC accumulation was not due to decreased proliferation , as Fzd6-/- HSCs entered the cell cycle at least as efficiently as their Fzd6+/+ counterparts ( Fig 4B ) . If anything , there was a decrease in the proportion of quiescent HSCs in Fzd6-/- bone marrow on day 28 ( Fig 4B ) , correlating with their previously reported self-renewal defect [15] , and indicating that the Fzd6-/- bone marrow responded to the infection . However , the response did not result in as great an accumulation of HSPCs . There was also no significant difference in β-catenin activation ( Fig 4C ) , similar to what we had previously reported at steady state [15] . These results suggested that Fzd6-/- mice could provide a model to investigate the impact of L . donovani–adapted hematopoiesis on parasite expansion . Fzd6-/- mice display no specific defects in the bone marrow MPC compartment at steady state ( S5 Fig ) or on day 14 post-infection , prior to the parasite-induced changes in hematopoiesis ( Fig 4D and 4E ) . However , on day 28 we observed a two-fold decrease in GMPs ( Fig 4D ) and more specifically in Sca1+ emergency GMPs ( Fig 4E ) . Similar to HSCs , there was no difference in cell cycle ( Fig 4F ) or β-catenin activity ( Fig 4G ) between Fzd6-/- and Fzd6+/+ GMPs; nor was GM-CSF receptor expression altered ( S6 Fig ) . We also evaluated the presence of common monocyte precursors ( cMoPs; Lin-CD115+cKithiFlt3-Ly6C+CD11b- , S5 Fig ) , and found that the number of cMoPs was slightly higher in L . donovani–infected bone marrow when compared to uninfected controls ( Fig 4H ) . cMoPs acquired Sca1 expression in the infected marrow , similar to GMPs and Ly6Chi monocytes ( Figs 2D , 2E and 3C ) . However , Fzd6-/- cMoPs were not expanded in response to L . donovani and a lower proportion had acquired Sca1 ( Fig 4H ) . Myeloid colony formation in culture was also decreased , with HSPCs isolated from infected Fzd6-/- bone marrow generating 3-times fewer CD11b+ myeloid cells than Fzd6+/+ controls ( Fig 4I ) . Together these data show that the presence of Fzd6 promotes parasite-induced HSPC expansion and skewing towards an emergency phenotype . To further investigate the impact of HSPC expansion on downstream myeloid differentiation during infection , we evaluated the acquisition of regulatory markers by Fzd6-/- Ly6Chi monocytes . There was no difference in the proportion of granulocytes ( SSChi GR1hi/Ly6G+ ) , mature Ly6Chi monocytes , or Ly6Clo monocytes/macrophages at steady state ( S7 Fig ) or on day 14 ( Fig 5A ) or day 21 ( S4 Fig ) . However , there was a specific decrease in the output of both GR1hi and Ly6Chi myeloid cells ( Fig 5A ) and F4-80+ CD11bint macrophages ( Fig 5B ) in the Fzd6-/- bone marrow on day 28 . In contrast to Fzd6+/+ cells , Fzd6-/- Ly6Chi monocytes did not downregulate Ly6C or Ccr2 ( Fig 5C and S8 Fig ) and expressed higher levels of Cxcr4 ( Fig 5D and S8 Fig ) . Ccr2 and Ly6C downregulation are generally associated with monocyte differentiation into macrophages [28–30] , while Cxcr4 expression has been recently shown to correspond to a transitional stage between cMoPs and mature monocytes [31] . Together , these data suggest that Fzd6-/- Ly6Chi monocytes were more immature and perhaps less likely to differentiate locally into macrophages . Both Fzd6-/- and Fzd6+/+ Ly6Chi monocytes expressed elevated Sca1 and MHCII ( Fig 5D and S8 Fig ) , indicative of an inflammatory microenvironment . In further support of their potential regulatory function , Ly6Chi monocytes from both groups upregulated Arginase1 expression , and expressed low levels of IL-10 ( Fig 5E and S8 Fig ) . Furthermore , there was no upregulation of iNOS by Ly6Chi monocytes or F4-80+ bone marrow macrophages from either Fzd6-/- or Fzd6+/+ mice ( Fig 5F and S8 Fig ) . Thus , Fzd6-/- Ly6Chi monocytes are produced in lower numbers , presumably due to decreased expansion of upstream progenitors , but they do not significantly differ in the expression of activation markers , such as MHCII , Sca1 , or iNOS . However , Fzd6-/- mice present with lower parasite burden in the bone marrow ( Fig 5G ) , suggesting that the decreased production of “safe targets” in the form of Ly6Chi monocytes might be sufficient to dampen parasite expansion , independent of their functional capacity . In further support of this hypothesis , we established a statistical correlation between bone marrow parasite burden and the number of CD150+ HSCs ( Fig 5H ) as well as bone marrow Ly6Chi monocytes ( Fig 5I ) , independent of mouse genotype or the stage of infection ( D14-D28 ) . To determine whether the differences in bone marrow were also reflected in peripheral organs , we evaluated the recruitment of myeloid cells also to liver and spleen , two major target organs in visceral leishmaniasis . Similar to the bone marrow , spleen myeloid compartment was still unchanged on day 14 , but the numbers of granulocytes and monocytes were all substantially increased by day 28 ( Fig 6A and 6B ) . Once more , there was no difference between Fzd6-/- and Fzd6+/+ mice at steady state ( S7 Fig ) , or earlier during the infection , before the onset of altered hematopoiesis ( Fig 6B and S4 Fig ) . We observed a specific decrease in Fzd6-/- GR1hi and Ly6Chi myeloid cells ( Fig 6A and 6B ) with no difference in Ly6Clo monocytes or F4-80+ CD11bint red pulp macrophages ( Fig 6A–6C ) . There was also a corresponding decrease in Ly6Chi monocytes in the liver ( S9 Fig ) . The decrease in myeloid cells correlated with decreased parasite burden on day 28 in Fzd6-/- spleen ( Fig 6D ) and liver ( S9 Fig ) . Wnt signaling has been reported to affect leukocyte infiltration [18]; however , there was no difference in the proportion of Ly6Chi monocytes in peripheral blood ( S4 Fig ) or in the relative ratio of Ly6Chi monocytes present in spleen as compared to bone marrow ( Fig 6E ) , suggesting that the decrease in Fzd6-/- peripheral monocytes was mainly due to differences in hematopoiesis . There was no difference in the expression of Sca1 , MHCII , Ly6C , or Ccr2 between Fzd6-/- and Fzd6+/+ Ly6Chi monocytes in spleen ( Fig 6F and , 6G ) or liver ( S9 Fig ) . Fzd6+/+ Ly6Chi monocytes expressed IL-10 ( Fig 6H and S8 Fig ) but did not upregulate iNOS ( Fig 6H ) , thus supporting the theory that they may have regulatory functions . A smaller proportion of Fzd6-/- Ly6Chi monocytes were IL-10+ . Wnt5a has been reported to directly induce IL-10 production in dendritic cells through a non-canonical pathway [32] , while β-catenin activation promotes TLR-mediated IL-10 secretion by human monocytes [33] . Overall these results suggest that the decreased monocyte output by the Fzd6-/- bone marrow translates into decreased monocyte numbers in the periphery , decreased IL-10 production , and an apparently improved control of parasite expansion . Improved T lymphocyte function [34–37] or macrophage-mediated parasite killing [38] could also contribute to diminished parasite expansion , independent of changes in myeloid differentiation . We first investigated T lymphocyte recruitment to the bone marrow and their ability to produce cytokines . There is no major difference in T lymphocyte development in the absence of Fzd6 [15] . There was also no major difference in the numbers of T lymphocytes in the bone marrow or spleen of Fzd6-/- mice when compared to controls ( Fig 7A and 7B and S10 Fig ) , as we observed a similar increase in CD4+ T lymphocytes in both groups . There was a slight but statistically significant increase in CD8+ T lymphocytes in the Fzd6-/- spleen . However , CD8+ T cells become progressively exhausted in chronic visceral leishmaniasis [39] . There was also no increase in cytokine production by either bone marrow or splenic T lymphocytes in response to parasite presented by bone marrow-derived dendritic cells ( Fig 7C and 7D ) , indicating that T lymphocytes were unlikely to explain the difference in parasite burden . To investigate the contribution of macrophages , we first generated bone marrow-derived macrophages ( BMDMs ) in culture from Fzd6-/- and Fzd6+/+ progenitors . We had not observed any striking differences in the Ly6Clo CD11b+ population that includes bone marrow macrophages at steady state ( S7 Fig ) , although the F4-80+ CD11bint macrophage population is decreased in mice infected with L . donovani ( Fig 5A and 5B ) . Similarly , there was no difference in the expression of CD11b , F4/80 , MHCII or Sca1 between Fzd6-/- and Fzd6+/+ macrophages at baseline ( S10 Fig ) . There was also no difference in their upregulation of MHCII in response to IFNγ ( S10 Fig ) , and we detected no significant changes in parasite uptake using fluorescence-labeled amastigotes ( Fig 7E and S10 Fig ) . However , there was an improvement in parasite control 72h post-infection in the presence of IFNγ ( Fig 7F and 7G ) , suggesting that Fzd6-/- bone marrow macrophages could partially contribute to the decreased parasite burden . Interestingly , exposure to the parasite resulted in massive upregulation of Sca1 on BMDMs ( S10 Fig ) , while IFN-γ alone had no impact . Sca1 expression in the Ly6Clo bone marrow population could therefore represent a biomarker for active infection rather than the IFN-dependent acquisition of a regulatory phenotype . L . donovani-infected macrophages produce TNF-α and GM-CSF , which have been suggested to promote myelopoiesis [12] . However , stromal macrophages represent a relatively small proportion of all bone marrow cells , with the vast majority of the marrow being occupied by the developing myeloid , lymphoid and erythroid cells . To obtain a more comprehensive picture of the bone marrow cytokine environment during infection , we used the supernatant from freshly harvested total bone marrow cells as a surrogate for the extracellular milieu and compared uninfected mice to L . donovani-infected mice at different time points . The levels of MIP1α/Ccl3 , IL-1 , and IFNγ-inducible factors , such as ICAM-1 , IP10/Cxcl10 and Cxcl9 were all increased in the chronic phase of the infection ( Fig 8A ) . Both IFN-α and IFN-γ were also upregulated on day 21 ( Fig 8A and 8B ) , at the beginning of the chronic phase , and could thus contribute to HSPC expansion in the bone marrow [4 , 7 , 24 , 40] . However , IFN-γ levels appeared to decrease back to or even below baseline levels by day 28 . There was no significant difference in the levels of IFN-γ , IFN-α , or myeloid growth factors GM-CSF , M-CSF , IL-3 , or IL-6 between Fzd6-/- and Fzd6+/+ mice . However , Fzd6-/- bone marrow environment demonstrated an even more dramatic upregulation of MIP1α and IL-1 , together with a stronger IFNγ response . These observations together with data from Figs 4 and 5 suggest that the reduced parasite burden in Fzd6-/- mice is at least partially the result of a different inflammatory microenvironment that could contribute to its own maintenance via altered myeloid differentiation . To investigate this hypothesis in culture , we used the bone marrow supernatants as source of growth factors and evaluated the expansion and differentiation of both Fzd6-/- and Fzd6+/+ bone marrow progenitor cells over four days . Supernatants from both Fzd6-/- and Fzd6+/+ infected bone marrow stimulated the emergence of GR1+/Ly6Chi/CD11b+ cells ( Fig 9A and S11 Fig ) ; however , their numbers were significantly lower in cultures that had been exposed to the Fzd6-/- environment . Similar results were obtained also with GMPs and LSKs , indicating that the cytokine environment present in the Fzd6-/- bone marrow significantly reduces HSPC expansion . The differentiation patterns were very similar when Fzd6-/- progenitor cells were used ( S11 Fig ) , suggesting that the initial steps of the progenitor cell response were not affected , similar to what we observed in vivo on day 14 ( Fig 4A–4E ) , or over the first few days after transplant [15] . Cell-intrinsic self-renewal defects would only appear after a much longer period . Thus , although L . donovani induces the production of factors that promote myeloid differentiation in both Fzd6-/- and Fzd6+/+ bone marrow , the Fzd6-/- cytokine environment reduces HSPC expansion , possibly via myelosuppressive factors , which in turn results in the generation of fewer GR1+/Ly6Chi monocytes both in culture ( Fig 9 ) and in vivo ( Fig 5 ) . To directly test whether the monocytes generated in the infected bone marrow environment were functionally different from normal in vitro—differentiated monocytes , we exposed the cultures to fluorescence-labeled L . donovani amastigotes . Less than 5% Ly6Chi monocytes generated in the naïve setting became infected and there was no increase in the proportion of infected cells from 24h to 72h ( Fig 9C–9E ) , although the proportion of monocytes containing efficiently replicating parasites increased over that period . In contrast , there was a 10-fold increase in the proportion of infected monocytes derived in the presence of supernatants coming from the infected bone marrow ( Fig 9C–9E ) , and this proportion increased over time . In both cultures , the proportion of Ly6Chi cells increased between 24h and 72h ( Fig 9B ) , which could indicate either continued differentiation or the loss of Ly6Clo/- cells due to cell death or increased adhesion . In summary , these results show that the L . donovani–infected bone marrow environment not only promotes myelopoiesis but also renders the newly generated cells more permissive to infection .
We have examined here the role of HSPC expansion and altered myeloid differentiation in promoting parasite proliferation during experimental visceral leishmaniasis . We demonstrate that L . donovani induces HSPC proliferation in the bone marrow , and promotes the generation of Sca1+ emergency GMPs and their differentiation into Ly6Chi monocytes expressing regulatory markers , such as Sca1 , MHCII , Galectin-3 and IL-10 . In contrast , an inefficient emergency response and diminished accumulation of emergency GMPs and Ly6Chi monocytes in Fzd6-/- mice corresponds to lower parasite expansion in bone marrow and in the periphery . We further show that the bone marrow cytokine environment is sufficient to promote HSPC expansion and myeloid differentiation , and to render the newly produced monocytes more susceptible to infection . Collectively our data support the hypothesis that L . donovani modulates the host hematopoietic program to support its own expansion . Leishmania parasites have developed multiple mechanisms to survive inside their classical targets , macrophages [38] , and to promote the recruitment of more monocytes to the site of infection [23] . Leishmania infection has also been associated with increased extramedullary myelopoiesis in the spleen [8 , 11 , 22] , which has been proposed to generate “safe targets” , or permissive monocytes for the parasite to replicate in [22] . Thus the parasite could not only modify its target cells to its liking but also promote their generation . L . donovani establishes a chronic infection in the bone marrow [12] , the predominant site of adult hematopoiesis , and chronic leishmaniasis is associated with pancytopenia and may ultimately lead to bone marrow failure [8 , 9] . Despite all this , the role of bone marrow in the pathogenesis of visceral leishmaniasis remains obscure . We have now characterized in detail the changes to the bone marrow HSPC compartment during experimental visceral leishmaniasis , and demonstrate that HSC-like cells and emergency GMPs are substantially expanded during the early phases of chronic infection . Emergency GMPs are associated with superior proliferative potential when compared to their classical Sca1- counterparts , and would thus result in the generation of more progeny in the periphery [24] . In addition , our data provide functional evidence that L . donovani–induced adaptations to hematopoiesis are indeed important for parasite expansion: bone marrow cytokine environment from infected mice was not only sufficient to induce HSPC expansion and myeloid differentiation in culture but also promoted the infection of newly generated monocytes . Conversely , parasite burden directly correlated with bone marrow HSPC and Ly6Chi monocytes numbers . Thus put together , our data strongly support the hypothesis that infection-adapted emergency myelopoiesis directly promotes parasite expansion and the persistence of a chronic infection . Monocytes have been proposed as safe targets for L . major [22] , and alternative monocytes contribute to the pathogenesis of L . brasiliensis [23]; however , the role of monocytes in the pathogenesis of visceral leishmaniasis is not well established [10] . Development of liver granulomas and the clearance of L . donovani in the liver are dependent on incoming monocytes and can be promoted with GM-CSF [41 , 42] , but the contribution of monocytes to the progression of infection in bone marrow and spleen is not known . Our results suggest that in contrast to liver , the accumulation of Ly6Chi/int monocytes in spleen and bone marrow is detrimental to the host , resulting in higher parasite burden . We observed an increased frequency of IL-10+ and Arginase1+ Ly6Chi monocytes in L . donovani-infected mice with no upregulation of iNOS , suggesting that the myeloid cells adopt indeed an M2-like phenotype . This could be due to the different environmental factors that promote their conversion to a regulatory phenotype [25] , permissive to infection but unable to kill the parasite . Bone marrow cytokine environment was responsible for mediating HSPC expansion and myeloid differentiation in culture . Our finding correlates with previous reports , suggesting that infected macrophages produce myeloid growth factors , such as GM-CSF , in culture [12] . However , our approach takes into account not only the infected macrophages but also other components of the bone marrow microenvironment , including the immature myeloid cells that compose the bulk of the marrow . When comparing the cytokine and chemokine environment from infected mice to uninfected controls , we saw that GM-CSF levels were actually downregulated in chronic infection , which could correlate with decreased leishmanicidal activity [42] . We also saw a corresponding increase in GM-CSFR expression on the surface of both GMPs and more mature myeloid cells , suggesting decreased ligand availability . These data suggest that the impact of a chronic in vivo infection with L . donovani on cytokine production may be very different from what is seen in culture . They also emphasize the need for more in depth analysis of various microenvironmental niches , as the cytokines present in different organs or even between separate sites within the same organ may have opposing effects . Chronic leishmaniasis is associated with anemia and pancytopenia , signs of diminished HSC function and bone marrow failure . We observed a decrease in SDF-1/Cxcl12 and an increase in MIP-1α/Ccl3 in the infected bone marrow environment . Cxcl12 is required for HSC maintenance in the bone marrow [43] , and a decrease in Cxcl12 levels is associated with HSC activation and release to peripheral sites , for example , during acute bacterial infection [44 , 45] . Conversely , Ccl3 has been shown to modify bone marrow HSC niches , thus promoting the maintenance of leukemic stem cells , for example , at the expense of normal HSCs [46 , 47] . Ccl3 can also promote the settlement of macrophages to the bone marrow [48] . Together , the decrease in Cxcl12 and the concomitant increase in Ccl3 observed in the bone marrow of L . donovani–infected mice are coherent with HSC activation and a gradual loss of function , and represent potential targets for intervention to prevent bone marrow failure . A better understanding of the signaling pathways underlying bone marrow alterations during visceral leishmaniasis may also result in the development of immunotherapies . For example , it should be possible to develop complementary therapies to be used in combination with parasitostatic treatment that would selectively promote the generation of monocytes capable of controlling and ultimately eliminating the parasite . Alternatively , it might be possible to interfere with monopoiesis in a short-term , preventive treatment , targeted for travellers visiting an endemic area . Lastly , long-term trained immunity [49–51] almost certainly involves epigenetic alterations at the HSPC level . Identifying the factors that stimulate HSPC expansion and the skewing of myeloid differentiation towards a permissive phenotype could thus pave the way for the design of vaccines dependent on innate immune cell function . Taken together , we report here a substantial activation and accumulation of HSC-like cells and Sca1+ emergency GMPs during the early stages of chronic L . donovani infection , concomitant with the sudden increase in bone marrow parasite burden . We further demonstrate that reduced HSPC expansion is associated with lower parasitemia and thus provide mechanistic evidence that L . donovani indeed subverts bone marrow hematopoiesis in vivo to support its own expansion . Our results support the hypothesis that emergency hematopoiesis directly contributes to the pathogenesis of visceral leishmaniasis , and suggest that HSPCs could represent an interesting therapeutic target .
C57BL/6 mice were purchased from The Jackson laboratory ( Bar Harbor , ME ) . Mice deficient in Frizzled 6 ( Fzd6−/− ) were first backcrossed to C57BL/6 for ten generations and then maintained under specific pathogen-free conditions in sterile ventilated racks at the animal facility of INRS-Institut Armand-Frappier ( CNBE ) , as described [15] . Female Fzd6-/- mice were compared to sex-matched Fzd6+/+ littermates unless otherwise noted . Leishmania donovani ( strain LV9 ) were maintained by serial passage in B6 . 129S7-Rag1tm1Mom mice , and amastigotes were isolated from the spleens of infected animals . Experimental mice were infected by injecting 2×107 amastigotes via the lateral tail vein . Bone marrow and splenic parasite burdens were determined either by limiting dilutions or by examining methanol-fixed , Giemsa stained tissue impression smears [52] . Data are presented as number of parasites per bone marrow ( one femur and one tibia ) or as Leishmani Donovan Units ( LDU ) . All procedures were in accordance with the Canadian Council on Animal Care guidelines and approved by the Comité institutionnel de protection des animaux of the INRS ( CIPA #1411–02 , 1210–06 , 1510–02 ) . Bone marrow was harvested by flushing tibiae and femora in phosphate-buffered saline ( PBS ) , and the cells were then passed through a 25-gauge needle to obtain a single cell suspension . PBS was supplemented with 0 . 1% bovine serum albumin ( BSA ) and 0 . 5mM ethylene-diamine-tetra-acetic acid ( EDTA ) for flow cytometry staining . See S1 Table for a complete list of antibodies . For intracellular staining , surface stained cells were fixed and permeabilized using the Foxp3 staining kit ( eBioscience , San Diego , CA ) and then incubated with appropriate antibodies . For cell cycle analysis , cells were first incubated for 30 min at 37°C with Hoechst #33342 ( Sigma-Aldrich , Oakville , ON , Canada ) in DMEM supplemented with 10% Premium FBS ( Wisent Bioproducts , St-Bruno , QC , Canada ) and 1 mM HEPES ( Life Technologies , Burlington , ON , Canada ) , followed by staining with surface antibodies and intracellular anti-Ki67 as described above . Samples were acquired with a four-laser LSR Fortessa flow cytometer ( BD Biosciences , Mountain View , CA ) and analyzed using BD FACS Diva software ( BD Biosciences ) or FlowJo ( for histogram overlays; Tree Star ) . Freshly isolated bone marrow cells from infected mice were collected on slides using a cytospin centrifuge ( Hettich , Tuttlingen , Germany ) at 800rpm for 5 min . The smears were air dried and stained with modified Wright-Giemsa stain ( Protocol Hema3; Thermo Fisher Scientific ) as per manufacturer’s instructions . Slides were mounted using Fluoromount-G ( SouthernBiotech ) and coverslips sealed with nail polish . Analyses of infected cells were performed using a Nikon Eclipse E800 microscope ( Nikon ) with a 100x oil immersion lens and images were captured with a digital camera ( COOLPIX 990; Nikon ) . Freshly harvested cells were seeded in duplicate into 35mm non-adherent petri dishes at a density of 104 cells/dish in methylcellulose medium containing stem cell factor , IL-3 , IL-6 , and Erythropoietin ( Methocult GF M3434 , Stem Cell Technologies , Vancouver , BC , Canada ) . The cultures were incubated at 37°C in 5% CO2 for 10 days and hematopoietic colonies were identified based on morphology under an inverted microscope . To analyze endogenous CD4 T cell responses , bone marrow-derived dendritic cells ( BMDCs ) were pulsed with fixed parasites for 24 h at 37°C . Splenocytes or bone marrow cells from infected animals were then added to BMDCs and incubated for 2 h at 37°C . Brefeldin A ( BD Pharmingen ) was added for an additional 4 h , after which cells were stained with appropriate antibodies as described above . Macrophages were derived from total bone marrow cells in IMDM supplemented with 10% very low endotoxin FBS and 15% L929-cell conditioned medium [53] . Purity of macrophage cultures was determined by flow cytometry 7 days after culture . Macrophages were then plated 2-3x106 cells per 35mm non-adherent petri dish and either left untreated or stimulated with 10ng/ml IFN-γ for 2h . IFN-γ-primed macrophages were infected with PKH26-labeled L . donovani amastigotes at MOI of 5:1 for 24h or 72h in the continued presence of IFN-γ . Macrophages were harvested from cultures and analyzed by flow cytometry . For imaging flow cytometry , cells were first surface-stained with anti-CD11b and anti-F4/80 and then fixed and counterstained with DAPI ( Life Technologies ) . Samples were acquired with Amnis Imagestream Mark II imaging flow cytometer ( EMD Millipore ) and analyzed with IDEAS v6 . 1 software using spot count function . Bone marrow cell supernatants were collected from naïve or L . donovani infected mice by harvesting cells from both hind legs into 2 ml PBS , followed by centrifugation . Supernatants were pooled from at least four mice per sample , and analyzed using a membrane-based Proteome profiler mouse cytokine/chemokine array kit ( R&D Systems ) . Array images were further analyzed using the NIH ImageJ image analysis software . Samples were normalized by subtracting pixel intensities from negative controls , and the fold changes for infected mice were determined as a ratio over naive mice of the same genotype . Levels of mIFN-α were determined by using mouse IFN-alpha Platinum ELISA ( eBioscience ) . Bone marrow cells were first enriched for progenitor cells using a Mouse hematopoietic progenitor cell enrichment kit ( StemCell Technologies , Vancouver , BC ) , and their purity was determined by flow cytometry . 5x105 Lin- BM cells per condition were divided in ten wells of a non-adherent 96-well plate ( BD Falcon ) , and cultured in IMDM supplemented with 10% very low endotoxin FBS ( Wisent ) and 30% bone marrow supernatant in the presence of 10ng/ml IL-3 , IL-6 and SCF for up to 4 days . Cells were harvested as indicated and their number and differentiation stage were analyzed by flow cytometry . Culture-derived monocytes were infected with PKH26-labeled L . donovani amastigotes at MOI of 5:1 for 24h or 72h . Cells were surface-stained with anti-CD11b and anti-Ly6C and then fixed and counterstained with DAPI ( Life Technologies ) for analysis by imaging flow cytometry as detailed above . Statistical significance was determined using ANOVA ( for multiple comparisons ) or two-tailed student's t test ( for comparing Fzd6+/+ and Fzd6-/- ) . P < 0 . 05 was considered significant . | Hematopoietic stem cells ( HSCs ) are responsible for the generation of all blood cells and thus play an important but often underappreciated role in the host response to infections . HSCs are normally dormant , but they can become activated in response to stress , such as infections . This stress response is meant to generate more blood cells and help the body to eliminate the invading pathogen . We have studied here the activation of HSCs in a mouse model of chronic infection with the parasite Leishmania donovani . We found that the parasite efficiently activates HSCs and steers them to produce large numbers of specific blood cells that are among the preferred targets of the parasite and become even more susceptible to infection when produced within the diseased environment . Using a mouse strain in which HSC activation cannot be sustained , we found that diminished HSC activity correlated with decreased parasite numbers . We therefore propose that HSC activation by the parasite promotes the infection and could be used as a new target for treatment . | [
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| 2017 | Infection-adapted emergency hematopoiesis promotes visceral leishmaniasis |
Host cell attachment by Toxoplasma gondii is dependent on polarized secretion of apical adhesins released from the micronemes . Subsequent translocation of these adhesive complexes by an actin-myosin motor powers motility and host cell invasion . Invasion and motility are also accompanied by shedding of surface adhesins by intramembrane proteolysis . Several previous studies have implicated rhomboid proteases in this step; however , their precise roles in vivo have not been elucidated . Using a conditional knockout strategy , we demonstrate that TgROM4 participates in processing of surface adhesins including MIC2 , AMA1 , and MIC3 . Suppression of TgROM4 led to decreased release of the adhesin MIC2 into the supernatant and concomitantly increased the surface expression of this and a subset of other adhesins . Suppression of TgROM4 resulted in disruption of normal gliding , with the majority of parasites twirling on their posterior ends . Parasites lacking TgROM4 bound better to host cells , but lost the ability to apically orient and consequently most failed to generate a moving junction; hence , invasion was severely impaired . Our findings indicate that TgROM4 is involved in shedding of micronemal proteins from the cell surface . Down regulation of TgROM4 disrupts the normal apical-posterior gradient of adhesins that is important for efficient cell motility and invasion of host cells by T . gondii .
Motility by apicomplexan parasites occurs by a unique form of locomotion called gliding , which relies on the apical secretion of adhesins followed by translocation of adhesin-receptor complexes along the cell surface to the back of the parasite [1] . Studies in T . gondii have elucidated the essential role of parasite F-actin in this process [2] , [3] , as well as a small myosin anchored in the inner membrane complex [4] , [5] . Gliding is extremely efficient and it provides the motive force for tissue migration [6] and for rapid invasion of host cells by T . gondii [7] . Similar forms of motility are important in the invasion of host cells by sporozoites of Cryptosporidium spp . [8] , [9] and Plasmodium sporozoites , both in its insect and vertebrate hosts [10] . Host cell invasion also requires the coordinated secretion of microneme proteins and rhoptries , which aid in adhesion and the formation of the vacuole that will ultimately house the intracellular parasite [11] . During invasion , the parasite squeezes through a constriction known as the moving junction , which demarks the closely apposed parasite and host cell membranes [12] . Recent evidence implicates proteins derived from the rhoptry neck ( so called RON proteins ) in forming this junction [13] , [14] and several of the RON proteins are inserted directly into the host cell membrane [15] , [16] . Aided by this mechanism , T . gondii is able to invade virtually all types of nucleated cells from a variety of warm-blooded animals . Micronemes contain a family of adhesive proteins ( referred to as MICs ) that contain a variety of domains involved in protein-protein interactions , which likely contribute to the wide host range of apicomplexans [17] . Microneme secretion depends on mobilization of intracellular calcium in the parasite [18] , and chelation of this signal blocks microneme secretion and prevents attachment , and consequently invasion of host cells [19] . Reverse genetic studies have documented the essential role of the microneme proteins AMA-1 [20] and MIC8 [21] in facilitating apical attachment , and signaling rhoptry secretion . MIC2 , which contains an integrin A-like domain and a series of thrombospondin repeats , is also essential for efficient invasion [22] . Conditional suppression of MIC2 impairs both helical gliding motility and host cell attachment , thus reducing invasion [23] . Similarly , the malaria orthologue TRAP is essential for invasion into salivary glands and liver hepatocytes [24] , [25] , [26] , [27] . In addition to mediating substrate attachment via their extracellular domains , MIC2 and TRAP also provide a connection to the parasite cytoskeleton , as shown by in vitro studies demonstrating a tight molecular interaction between their C-terminal tails and the F-actin-binding protein aldolase [28] , [29] . Recent evidence confirms that the molecular interaction between the tail of MIC2 and aldolase in T . gondii is essential for efficient invasion of host cells [30] . Secretion of MIC2 onto the parasite cell surface is accompanied by processing at the N-terminus [31] , an event that may be important for binding to certain receptors including ICAM1 [32] . Shedding of MIC2 into the supernatant is associated with proteolytic processing at the C-terminus [31] , releasing the extracellular domains into the supernatant . Shedding of adhesins such as MIC2 may be important for breaking the connection between the parasite and host cell , hence allowing completion of cell invasion . Mass spectrometry experiments demonstrate that shedding of MIC6 and MIC2 occurs by cleavage within their transmembrane domains [33] , [34] . Shedding of surface adhesins in malaria such as EBA175 also occurs by cleavage within the transmembrane domain , and this event is essential for sialic acid- dependent invasion of red blood cells by merozoites [35] . The conservation of a cleavage site between small hydrophobic residues in the transmembrane domain of parasite surface adhesins suggested that a rhomboid protease performed this task . Rhomboids are conserved serine proteases that cleave their substrates within the transmembrane domain [36] , based on unordered helical domains containing Ala and Gly [37] . In vitro biochemical studies have shown that MIC transmembrane domains function as substrates for heterologous rhomboids such as Rom1 from fly [37] . Toxoplasma gondii contains six rhomboids , one in the mitochondria , and five others that are expressed at different life cycles stages and localized in different cellular compartments [38] , [39] . ROM1 , ROM4 and ROM5 are expressed in tachyzoites of T . gondii [38] , suggesting that one or more of these proteases are important in the processing of adhesins , as described above . Previous studies have shown that suppression of TgROM1 , which is localized in the Golgi and micronemes , has a very slight effect on intracellular growth , but no effect on microneme adhesin processing [40] . In vitro expression in a heterologous system has been used to characterize the biochemical activities of TgROMs [38] , [41] . TgROM5 was by far the most active , as well as expressing activity against the widest range of substrates [38] . In contrast , no activity was detected for TgROM4 in this system [38] . TgROM4 is uniformly distributed on the surface of the parasite , while TgROM5 is localized at the back of the parasites , suggesting it may be responsible for shedding adhesins as they are translocated rearward [38] . Plasmodium spp . contains a similar diversity of ROMs [39] , and although it lacks a direct orthologue of ROM5 , the activity of PfROM4 shows broad specificity [35] . Although previous studies have suggested that apicomplexan invasion depends on proteolytic shedding of adhesins , the protease ( s ) involved in this final step has not been identified . Moreover , their different localizations suggests that ROM4 and ROM5 play different , although perhaps overlapping , roles in this process . To address the role of ROM4 in shedding of microneme proteins , we generated a conditional knockout ( cKO ) and tested it using a variety of in vitro assays . Our studies demonstrate that ROM4 plays an important role in the cleavage of surface adhesins , and that in its absence , invasion is impaired .
In order to determine the function of TgROM4 , we initially attempted gene disruption using double homologous crossover to replace the endogenous gene with the cat selectable maker , as described previously [40] . However , in three independent experiments , in which more than 100 separate clones were analyzed , we were unable to obtain gene knockouts by this approach ( data not shown ) . Therefore , we employed a conditional knockout strategy based on a Tet-transactivator system , described previously [5] . To accomplish this goal , a HA9-epitope tagged copy of TgROM4 was transfected into a T . gondii line expressing the Tet-transactivator , yielding a merodiploid clone . Addition of anhydrotetracycline ( Atc ) to this line was shown to suppress expression of the epitope-tagged copy ( data not shown ) . The endogenous TgROM4 gene was then disrupted in the merodiploid by replacement of the endogenous gene with the chloramphenicol acetyltransferase ( cat ) selectable marker under the control of a SAG1 promoter and flanked by genomic regions of the TgROM4 gene . Successful replacement left only the regulatable HA9-tagged copy of TgROM4 ( Figure 1A ) . To verify proper integration at the correct locus , PCR analysis was performed using primers from the cat gene combined with primers to flanking genomic regions of the endogenous ROM4 locus that lie outside of sequences included in the knockout construct ( Figure 1A ) . Amplification of a 1 . 5 kb fragment with primer pairs F1-R1 ( see Table S1 for sequences ) confirmed that proper integration was achieved ( Figure 1B ) . Similarly , amplification with primers F2-R2 generated a 2 . 4 kb PCR fragment , demonstrating replacement of the endogenous gene with cat ( Figure 1B ) . Based on PCR screening , two conditional knockout ( cKO ) clones ( i . e . cKO1 and cKO2 ) , were selected for further analysis . The degree of HA9-ROM4 down-regulation in the presence of Atc was quantified using quantitative RT-PCR to detect transcripts of the tagged gene compared to actin mRNA levels as an internal control . Following growth in the presence of Atc for 96 hr , expression at the mRNA level was reduced to ∼28% for cKO1 and ∼13% for cKO2 relative to wild type ROM4 levels ( Table 1 ) . To visualize expression of the HA9-ROM4 protein , intracellular parasites were grown in the absence or presence of Atc for 72h , fixed and stained for immunofluorescence using a mouse anti-HA mAb followed by goat anti-mouse IgG conjugated to Alexa488 ( green ) ( Figure 1C ) . In the absence of Atc , TgROM4-HA9 was distributed on the surface of intracellular parasites as shown by co-localization with T . gondii surface antigen 1 ( SAG1 ) ( red ) . However , following treatment with Atc there was no detectable staining of the HA9-tagged TgROM4 protein , although staining of the surface SAG1 antigen was unchanged ( red ) ( Figure 1C ) . To quantify the suppression of HA9-ROM4 , parasite lysates were analyzed by western blot following growth in Atc for different intervals . Expression of HA9-ROM4 was substantially reduced following culture in Atc for 48h , and the protein was essentially undetectable by 96h ( Figure 1D ) . When the signals from western blots were quantified by densitometry , and compared to loading standards of untreated parasites , the level of shutdown was ≥99% at the 96h time point . In contrast , no change was observed in actin levels following growth in Atc . Collectively , these findings indicate TgROM4HA9 was greatly reduced following extended treatment with Atc . Consequently these conditions were used to examine the phenotype of the TgROM4 cKO . To analyze the phenotype of TgROM4 suppression , we tested the ability of parasites to form plaques on monolayers of HFF cells using standard methods reported previously [42] . Suppression of TgROM4 , did not lead to a defect in plaque formation ( data not shown ) , a result that is similar to the suppression of MIC2 , reported previously [23] . To provide a more sensitive and quantitative assessment of growth , we examined the ability of the parasite to lyse monolayers of HFF cells as determined by absorbance at 570 nm following staining with crystal violet . Following growth in Atc for a total of 96h , suppression of TgROM4 in the cKO clones resulted in significantly decreased monolayer lysis when compared to untreated clones at an inoculum of 104 parasites/well ( open vs . closed symbols in Figure 2A ) . This effect was overcome at higher inocula where a single round of replication was sufficient to cause substantial lysis of the monolayer . The slightly decreased lysis of the Atc-treated merodiploid vs . the untreated merodiploid parasites at the 104 dose may be a consequence of prolonged exposure to Atc ( open vs . closed red circles , Figure 2A ) . However , the Atc-treated cKO and merodiploid clones showed statistically significant differences ( P≤0 . 005 ) , indicating that the decrease in monolayer lysis was due to absence of TgROM4 and not due to nonspecific effects of Atc exposure . Since the lytic assay was unable to distinguish between effects on invasion , replication , egress , or reinvasion of host cells , it was necessary to employ other assays to determine the exact nature of the cKO phenotype . Parasites were grown in the presence or absence of Atc for 96h and then the number of parasites per vacuole was quantified at different time periods over a single round of intracellular replication . When expressed as the average number of parasites/vacuole it was evident that the suppression of HA9-ROM4 had no effect on the rate of intracellular replication ( Figure 2B ) . Collectively these results imply that normal expression of TgROM4 is not essential for cell replication but that it functions to facilitate another step in the lytic cycle . To determine the role of TgROM4 in host cell invasion , we utilized a red-green differential antibody staining assay to quantify parasite attachment and invasion into host cells following a brief infectious pulse , as described previously [40] . Following growth in Atc for 96h , host cell invasion was significantly reduced in both cKO clones , albeit more strongly in cKO2 consistent with greater suppression ( Figure 3A , Table 1 ) . In contrast , the number of extracellular parasites was increased by 2–4 fold for both Atc-treated cKO clones ( Figure 3A , red bars ) . Conversely , the addition of Atc to the parental merodiploid parasites had no effect on the proportion of parasites that attached or invaded into host cells ( Figure 3A ) . When the data from multiple experiments was combined and expressed as the % of total parasites that were intracellular , treatment with Atc for 96h resulted in 48% decrease in invasion for cKO1 ( P≤0 . 05 ) and 73% decrease in invasion for cKO2 ( P≤0 . 001 ) ( Figure 3B ) . Similar defects in cell invasion for cKO1 and cKO2 treated with Atc were observed when parasites were allowed to invade for up to 120 min ( data not shown ) , indicating that the phenotype for decreased cell invasion was not simply a consequence of the short invasion pulse . We also examined the effect of ROM4 suppression on egress from host cells , a process that relies on microneme secretion . Stimulation of intracellular parasites ( 36 h post-invasion , total for 78 h Atc treatment ) with calcium ionophore using a protocol described previously [43] , showed normal levels of egress by the cKO clones grown in the absence and presence of Atc ( data not shown ) . Together these results indicate that suppression of TgROM4 decreased the efficiency of host cell invasion while concomitantly increasing attachment by T . gondii . During host cell invasion , the parasite makes intimate contact between its apical end and the host cell plasma membrane . At this interface , the host cell and parasite plasma membranes are in close contact and there is a visible constriction as the parasite migrates through a narrow waist , referred to as the moving junction ( MJ ) [7] . Although appreciated for decades from light and electron microscopy studies , the true dynamics of this interface only recently became known with the identification of protein components that reside there , including the rhoptry neck protein 4 ( RON4 ) [13] , [15] . RON4 is discharged early in invasion and it marks the MJ by the presence of a tight ring , visible by immunofluorescence staining . Consequently , localization of RON4 provides a convenient means of staging the process of invasion . We used a modified immunofluorescence staining protocol to evaluate the ability of T . gondii to properly form a MJ and migrate into host cells . To determine the location of the MJ during invasion , parasites were scored based on the location of the RON4 ring ( in green ) ( Figure 4A ) . In combination , we evaluated migration of the parasite through the junction using differential staining of the surface antigen SAG1; first to detect the extracellular portion ( in red , prior to detergent ) and then to detect the intracellular portion of the parasite ( in blue , following detergent ) . Individual parasites were thus classified as being attached , but not forming a MJ complex ( no ring ) , or having initiated invasion , in which case they were classified based on the degree of progression past the junction ( Figure 4A ) . Following prolonged treatment with Atc to suppress TgROM4 , the cKO clones were largely unable to form a MJ , as seen by the large percentage of parasites being classified as having no ring ( Figure 4B ) . Consistent with this , the cKO clones grown in Atc also showed lower numbers of intracellular parasites , when compared to culture in the absence of Atc ( Figure 4B ) . The cKO2 clone showed a greater impairment in junction formation and also showed a lower frequency of apically positioned RON4 rings ( Figure 4B ) . Those cKO parasites that did correctly form a MJ , migrated through this interface with a similar efficiency , as shown by the fact that the proportion of parasites at the middle and posterior stages did not differ between the merodiploid and cKO clones , regardless of whether cultured in Atc or not ( Figure 4B ) . Taken together , these results indicate that in the absence of TgROM4 , parasites attach to the host cell but fail to form a moving junction . The ability of T . gondii tachyzoites to move across substrates or host cell surfaces has been previously characterized using video microscopy [44] . Productive gliding , which leads to invasion , is characterized by a clockwise , helical pattern that produces a net forward motion . In contrast , circular gliding and twirling , while commonly observed , do not lead to invasion of host cells [18] , [44] , [45] . To assess the effect of TgROM4 suppression on parasite gliding motility , we captured parasite gliding by time-lapse video microscopy and classified the types of motility based on previously reported patterns . When the time-lapse images were merged together into a composite frame , helical gliding ( H ) appeared as a series of crescent-shaped arcs , while circular gliding ( C ) was seen as a tight circular pattern ( Figure 5A ) . Similar to wild type control parasites , these patterns predominated in the cKO clones grown in the absence of Atc ( Figure 5A , see supplemental Videos S1 , S3 ) . Treatment of the cKO parasites with Atc resulted in a preponderance of the third pattern called twirling ( T ) , which appeared as a “pin-wheel” pattern in the merged images ( Figure 5A , see supplemental Videos S2 , S4 ) . Quantification of these patterns from a series of time-lapse videos revealed that the majority of cKO parasites treated with Atc displayed twirling movements ( Figure 5B ) . Conversely , the untreated cKOs more often underwent helical and circular gliding , similar to the merodiploid control ( Figure 5B ) . Of the minority of Atc-treated cKO parasites that did not undergo twirling motility , most of these showed helical rather than circular gliding ( Figure 5B ) . A predominance of helical trails was also seen when the cKO clones were treated with Atc and evaluated using a static gliding assay based on staining of trails for surface membrane proteins that are deposited on the substrate ( data not shown ) , as described previously [44] . However , the static assay failed to detect the large proportion of twirling parasites , which do not leave detectable trails on the substrate . Comparison of the rates of movement between the cKO and merodiploid parasites treated with Atc did not detect a difference in average speed of motion ( data not shown ) . Thus far , the phenotype of the TgROM4 cKO consisted of impaired motility and cell entry , while adhesion to host cells was increased . Together with the previous suggestions that TgROM4 may process surface adhesins [41] , lead us to examine the steady state levels of microneme proteins on the surface of extracellular parasites . Normally micronemal proteins are rapidly released from the surface following constitutive secretion , such that the steady state surface levels are quite low [31] , [46] . The exception to this pattern was AMA1 , which remains detectable on the surface for much longer than the others [17] , presumably due to slower turnover . To determine if the absence of TgROM4 activity leads to an increase in cell surface adhesins , we examined the cKO parasites for the levels of MIC1 through MIC6 , AMA1 , and SAG1 by staining with specific antibodies and flow cytometry . Following 96h of culture in Atc , the cKOs had increased levels of MIC2 , MIC3 and AMA1 detectable on their surface , in comparison to the untreated cKO parasites ( Figure 6A ) . The levels of MICs 1 , 4 , 5 and 6 detected on the cell surface were unchanged by the suppression of TgROM4 ( data not shown ) . SAG1 , which is not cleaved by surface proteases , remained unchanged and was used as an internal control ( Figure 6A ) . These results suggest that in the absence of TgROM4 , surface adhesins accumulate to higher levels than normal . This increase is seen for proteins that normally have low surface expression such as MIC2 , and also those that have a higher steady state level of surface staining , such as AMA1 . Collectively , these results imply that TgROM4 affects the rate of shedding of a variety of substrates , independent of their intrinsic turnover rates . Previous studies have revealed that MIC2 is initially secreted at the apical end , rapidly translocated to the posterior pole , and shed from the surface by proteolysis [19] , [47] . To visualize differences in the surface expression of MIC2 , extracellular parasites were stimulated with ionophore and then stained by immunofluorescence . Parasites were examined cells at 2 min and 15 min post-stimulation to compare the surface expression of MIC2 . At 2 min post-stimulation , surface MIC2 staining was upregulated , consistent with previous reports , and this result was similar in the merodiploid and cKO1 clone grown both in the absence and presence of Atc ( data not shown ) . In contrast , the pattern of surface staining at 15 min post-stimulation was radically different . Although the merodiploid grown under either condition or the cKO1 line grown in the absence of Atc had cleared the majority of MIC2 from the surface , substantial staining was still detected for the cKO1 clone grown in the presence of Atc ( Figure 6B ) . A similar result was observed for cKO2 under Atc treatment ( data not shown ) . The pattern of surface staining for MIC2 in the cKO clone grown in Atc was diffuse and extended across the majority of the surface , rather than being confined to either pole ( Figure 6B ) . Previous studies have emphasized that following secretion of MIC2 onto the apical end of the parasite , rapid proteolysis results in shedding of the extracellular domain into the supernatant [31] . This event is thought to occur due to the action of a rhomboid protease , although the precise protease ( s ) involved has not been defined [38] , [41] . The flow cytometry data provided evidence that TgROM4 may facilitate cleavage of surface adhesins that are constitutively released and that in its absence , surface adhesins accumulate . Previous studies have defined several potent triggers that activate calcium-dependent microneme secretion , and this can readily be detected by examining supernatants for the extracellular portion of MIC2 , which is shed into the supernatant [31] , [47] . We examined the ability of TgROM4 cKO clones to process and shed MIC2 into the supernatant following secretion . The level of shedding of MIC2 was markedly decreased in the Atc-treated cKO2 clone compared to the untreated control ( −Atc ) ( Figure 7A , top blot ) . Reduced shedding was a result of the suppression of TgROM4 , since merodiploid parasites were able to cleave MIC2 at similar rates in the presence or absence of Atc ( Figure 7A , bottom blot ) . Quantification of the efficiency of processing revealed that there was an almost 6-fold decrease in the level of MIC2 shed into the supernatant by the Atc-treated cKO2 clone ( Figure 7B ) . In three independent experiments , the average level of suppression of shedding of MIC2 into the supernatant was ∼80% ( data not shown ) . Together with data presented above , these findings indicate that TgROM4 facilitates cleavage of MIC2 and that in its absence this adhesin accumulates to higher levels than normal on the parasite surface .
Cell motility and invasion by apicomplexans requires the coordinated control of polarized secretion of adhesins at the apical end , translocation along the parasite surface , and shedding into the supernatant . Previous studies have suggested that proteolytic processing at the C-terminus of MICs releases key surface adhesins from the cell surface , although the role of specific proteases in this process has not been established . Using a regulated expression system , we demonstrate that ROM4 plays an important role in shedding of cell surface adhesins for T . gondii . Suppression of ROM4 led to increased levels of MIC2 , and a subset of other adhesins , on the parasite cell surface , as well as decreased shedding into the supernatant . Absence of ROM4 led to enhanced twirling motility and increased cell attachment; however , parasites were unable to efficiently form a tight apical junction , hence host cell invasion was severely impaired . Our findings indicate that ROM4 acts to increase the efficiency of cell surface micronemal protein processing , which in turn maintains the apical to posterior gradient of adhesive proteins that appears necessary for efficient cell invasion . Protein secretion , translocation , and processing are critical for motility and cell invasion by apicomplexan parasites . Studies of the micronemal protein MIC2 have played a significant role in our understanding of these events . MIC2 is delivered to the apical end of the parasite surface by exocytosis of micronemes; this process occurs constitutively and appears to be strongly upregulated on contact with host cells [46] . Similar to most micronemal proteins , MIC2 does not enter the vacuole but is swept backward during invasion , ultimately being shed from the surface prior to entry [46] . This process can be mimicked in the absence of host cells by artificially elevating the levels of MIC2 on the parasite surface by inducing secretion [19] . Shedding into the supernatant involves processing at the C-terminus [31] , which occurs in the transmembrane domain [34] , consistent with rhomboid proteases being responsible . The rearward translocation of MIC2 requires a functional actin cytoskeleton in the parasite and progression is blocked by cytochalasin D , although interestingly shedding is not inhibited in this circumstance [19] . MIC2 connects with the actin cytoskeleton via the bridging function of aldolase both in vitro [29] and in vivo [30] , facilitating the rearward translocation of MIC2 by the motor complex . Release of the adhesins from the parasite membrane is also important to break contacts with the substrate and hence allow forward migration , or completion of cell entry . Support for this model comes from a mutant of MIC2 containing Ala-Ala substitution of a Lys-Lys motif just outside the transmembrane region: this mutant form of the protein resists normal shedding resulting in a dominant negative phenotype [48] . In these MIC2 processing mutant cells , adhesion is enhanced but parasites lose polarity and are inefficient at establishing apical attachment and invasion of cells [48] . Apicomplexan parasites contain a conserved family of rhomboids that have been implicated in processing of cell surface adhesins [39] , although the functions of these proteases have not been extensively studied in parasites . Toxoplasma gondii and related coccidians contain two rhomboid paralogues known as ROM4 and ROM5 that are expressed in tachyzoites and bradyzoites of T . gondii [38] . Previous experiments indicate that ROM5 is highly active based on a heterologous assay , while ROM4 is not [38] . Combined with their different cellular localizations , it was suggested that ROM5 was the more likely enzyme to process MICs , since it concentrates at the posterior end of the parasite [38] . In contrast , ROM4 has a peripheral surface pattern that would place it in the proximity of substrates before they reach the posterior end , hence risking their premature release from the surface . Thus , it was unclear from previous data whether these two enzymes share the role of processing surface adhesins , or if ROM4 performs a completely different function . To address the function of ROM4 , we attempted gene knock out studies using a double crossover strategy . After repeated attempts , we were unable to generate knockouts by this strategy , suggesting the gene was essential , or at least that knockouts likely have a distinct disadvantage in vitro . Instead we turned to a regulated expression system , which has been used previously to study essential genes in T . gondii [49] . We were able to achieve very tight down-regulation of HA9-ROM4 in the conditional knockout background . Under the conditions used here , we observed >90% suppression of ROM4 at the protein level , resulting in a significant impairment of cell invasion , yet no discernable effect on parasite replication . Using a different strategy to disrupt function ( dominant over-expression of a catalytically inactive enzyme ) others have reported a defect in intracellular replication ( Dominique Soldati pers . comm . ) . This difference may reflect a separate role for ROM4 during intracellular replication that is not apparent under conditions we have tested here , where low levels of residual ROM4 activity remain . Defining the requirement for low levels of TgROM4 expression could be further explored by generating a clean knockout using the newly developed methods for enhanced homologous recombination in Ku80 deficient cells [50]; a methodology that was not available in T . gondii at the outset of this work . Nonetheless , we were able to appreciate highly significant phenotypes in cell attachment and invasion that were associated with substantial suppression of ROM4 . Somewhat surprisingly , ROM4 was observed to affect the efficiency in processing of cell surface adhesins including MIC2 , AMA1 and MIC3 . Shedding of MIC2 was reduced by approximately 80% , suggesting residual ROM4 or another protease , perhaps ROM5 , was still able to process this protein , albeit less efficiently . The simplest interpretation of our findings is that ROM4 acts as a sheddase by directly cleaving micronemal proteins that have a conserved rhomboid site in their transmembrane domains . Under this assumption , ROM4 is expected to directly cleave MIC2 at a conserved site for rhomboid proteases present in the transmembrane domain [37] . In contrast , MIC3 does not contain a transmembrane domain , but rather has been reported to associate with MIC8 [51] , another putative rhomboid substrate . Such an in vivo activity for ROM4 was not anticipated from prior studies using a heterologous assay where it failed to show any activity [38] . This difference may reflect a necessary co-factor for activation that is only present in the parasite . As expected , suppression of ROM4 did not affect the soluble micronemal protein MIC5 , which lacks a transmembrane domain . Somewhat surprisingly , suppression of ROM4 also did not affect the complex of MIC1 , MIC4 , and MIC6 , only the latter of which has a transmembrane domain [52] . The rhomboid recognition sequence in the transmembrane domain of MIC6 is highly similar to MIC2 [37] , so the absence of an effect on MIC6 is intriguing . These findings may indicate that ROM4 has distinct preferences for regions outside the direct cleavage site , which is otherwise highly conserved among these substrates [53] , or alternatively that processing of MIC6 is influenced by different sensitivity to the level of shutdown achieved here . An alternative possibility is that ROM4 does not act directly on MIC substrates , but rather enhances the activity of another sheddase , possibly ROM5 . Such an accessory role has not been previously seen for rhomboids , but cannot be strictly ruled out from the data presented here . The phenotypes of the ROM4 cKO allow us to place it in the cascade of events that occurs during cell invasion by T . gondii . Previous studies have shown that MIC2 facilitates binding to host cells and hence is important for efficient invasion [23] . MIC2 may also participate in invasion directly by providing a linkage between the motor proteins and attachment , thus driving the parasite through the junction , although this role has not been specifically demonstrated [54] . AMA1 is necessary for tight apical binding and for initiation of the junctional complex , and in its absence , parasites are able to secrete the contents of rhoptries but remain peripherally attached and do not invade efficiently [20] . MIC8 is also essential in this pathway as conditional mutants fail to secrete rhoptries and hence cannot form a junction or invade the host cell [21] . In contrast to these prior conditional mutants that either decrease attachment to the host cell ( AMA-1 ) or show normal binding ( MIC8 ) , TgROM4 cKO parasites actually bound better to host cells by a factor of 3–4 fold . This is likely attributable to decreased processing of cell surface adhesins such as AMA1 , MIC2 , and MIC3 , all proteins that have previously been implicated in attachment . The lack of correlation between enhanced binding and invasion can be explained by the finding that ROM4 cKO parasites have lost directional attachment and hence fail to form an apical complex . This phenotype is similar to that of a MIC2 processing mutant described previously [48] , and strongly suggests that the phenotype resulting from suppression of ROM4 is due to the effect on MIC2 shedding , and perhaps other adhesins . Hence , while TgROM4 is not absolutely essential for survival , its presence affects adhesin shedding , and as such it is necessary for efficient invasion . The exact role of ROM5 in processing adhesin complexes is still not precisely defined . Its position at the posterior pole of the parasite , and its extremely high activity , still make it the best candidate for shedding of adhesin-receptor complexes , prior to completion of cell invasion . Thus far it has not been possible to directly disrupt TgROM5 , and efforts are underway to generate conditional knockouts , thereby better defining its function ( s ) . The importance of ROM4 in processing surface adhesins seems at odds with the previously proposed model that adhesive complexes should translocate to the posterior end of the cell prior to being released . If ROM4 processes adhesins along the entire length of the cell , this might decrease the efficiency of translocation , and hence impede motility . To reconcile the model with these new observations , we hypothesize that TgROM4 acts as a sheddase to remove unnecessary adhesins that normally accumulate on the cell surface , perhaps selectively removing those that are not productively engaged in attachment . By acting as a constitutive sheddase , ROM4 may help maintain an apical to posterior adhesin gradient that would accomplish several important goals ( Figure 8 ) . First , it would help mask the adhesins from the immune system and potential neutralization by antibodies . Secondly , it would facilitate apical attachment , as well as assure directional motility . The phenotype of the ROM4 cKO is particularly informative in this regard as in the absence of this protease , the parasite expresses MIC2 in a peripheral rather than apical pattern and consequently the parasite binds non-discriminately to host cells . Additionally , gliding is impaired as the parasite remains stuck by the posterior end . Although it is able to twirl extensively , it apparently cannot break the attachment to the substratum in order to move forward . Collectively these defects impede the parasite's ability to form a tight apical junction and hence successfully invade the host cell . In summary , our studies suggest that ROM4 is important to maintain an apical to posterior gradient of microneme adhesins , thus assuring directional gliding , apical attachment , and efficient host cell invasion .
T . gondii tachyzoites were maintained by growth on monolayers of human foreskin fibroblasts ( HFF ) in Dulbecco's Modified Eagles Medium ( DMEM ) ( Invitrogen , Carlsbad , CA ) supplemented with 10% fetal bovine serum ( FBS ) , 2 mM glutamine , 20 mM HEPES ( pH 7 . 5 ) , and 20 µg/ml gentamicin . Chloramphenicol ( 20 µg/ml ) ( Sigma-Aldrich , St . Louis , MO ) , phleomycin ( 5 µg/ml ) ( Invitrogen ) , and anhydrotetracycline ( Atc ) ( 1 . 5 µg/ml ) ( Clontech , Palo Alto , CA ) were added to culture medium as indicated . Parasites were harvested after natural egress and passed through a 3 . 0 micron polycarbonate filter to remove host cell debris , as described previously [40] . A knockout construct referred to as plasmid pΔR4 was engineered using the selectable marker cat , which confers resistance to chloramphenicol , controlled by 5′ and 3′ SAG1 flanking sequences . This cat cassette was in turn flanked by 2 kb of sequences upstream of the start and downstream of the stop codons of TgROM4 ( sequences retrieved from http://ToxoDB . org ) . Two tandem YFP genes expressed under the control of the T . gondii alpha tubulin promoter ( provided by Boris Striepen ) were inserted downstream into the SacII site of pΔR4 generating the plasmid pΔR4YFP . Direct KO of TgROM4 in RH stain parasites was attempted by transfection of the pΔR4YFP plasmid into wild type parasites in three independent experiments . After several rounds of positive selection with chloramphenicol , the population was sorted for YFP negative cells by FACS ( DAKO , Glostrup , Denmark ) and 3 parasites/well were deposited into 96-well plates containing HFF monolayers . Following growth in complete media for 7 days , wells containing a single plaque were screened using PCR analysis ( Table S1 ) , amplifying a 400 bp fragment of the HA9 tagged copy and a 119 bp fragment of a 5′-end intron within the endogenous gene . Clones were considered as potential knockouts only if they contained the larger 400 bp fragment and lacked the smaller intron DNA fragment . More than 100 YFP-negative clones were screened by PCR; however , no direct knockouts were detected . To generate a tagged copy , the TgROM4 open reading frame ( NCBI accession number: AY596193 ) was amplified with primers that added a N-terminal HA9 tag and inserted in the previously described vectors p7TetOS1 and p7TetOS4 ( provided by Dominique Soldati ) , between an EcoR1 and Pac1 restriction site , downstream of the tetracycline inducible promoters [49] . The resulting plasmids were called pS1HA9-ROM4 and pS4HA9-ROM4 . Transactivator-expressing parasites , referred to as TATi [49] , were cotransfected by electroporation with pS4HA9-ROM4 and a plasmid containing the ble selectable marker driven by SAG1 flanking sequences , conferring the resistance to phleomycin , as described previously [40] . Following 2 rounds of selection , clones were obtained by limiting dilution on HFF monolayers grown in 96-well plates . Single cell merodiploid clones containing a copy of the endogenous ROM4 gene and an epitope-tagged ( S4HA9-ROM4 ) copy , were identified by SDS-PAGE and Western blotting or immunofluorescence microscopy using an anti-HA9 mouse monoclonal antibody ( mAb ) F-7 ( Santa Cruz , Santa Cruz , CA ) to detect the tagged copy of the gene . A single parasite clone expressing the tagged , regulatable copy of HA9-TgROM4 was used to generate conditional TgROM4 knockout lines following transfection by electroporation with 50 µg of pΔR4YFP linearized with PspOMI restriction endonuclease . Following positive and negative selection , single cell clones were screened to identify knock-outs of the endogenous TgROM4 locus by PCR , as described above . Parasites were grown in the presence of 1 . 5 µg/ml Atc for 48h , harvested as described above , and used to infect HFF cell monolayers grown on glass coverslips . Parasites were grown an additional 24h ( 72 h total ) in the presence of 1 . 5 µg/ml Atc , washed 3 times with PBS and fixed with 4% paraformaldehyde for 20 min . Samples were permeabilized in 0 . 1% TritonX-100 ( Sigma ) for 10 min and subsequently blocked with 5% FBS and 5% normal goat serum ( Gibco ) for 20 min . To detect the HA9 epitope , mAb F-7 was added to the coverslips for 1h , washed and followed by goat anti-mouse IgG Alexa 488 ( green ) secondary antibody ( Invitrogen ) for 1h . Coverslips were then blocked with normal mouse sera and incubated for 1h with mAb DG52 against surface antigen 1 ( SAG1 ) directly conjugated to Alexa 594 . Coverslips were washed and mounted with Prolong Gold antifade reagent containing 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen ) . Fluorescence images were obtained with a Zeiss Axioplan microscope equipped with phase-contrast and epifluorescence optics using a 63× oil immersion lens ( N . A . = 1 . 3 ) . Images were collected with a Zeiss AxioCam cooled CCD camera directed by Zeiss Axio Vision software ( Version 4 . 5 ) and processed using similar linear adjustments in Adobe Photoshop CS2 ( Adobe Systems Inc . , San Jose , CA ) . Parasites were cultured in presence or absence of 1 . 5 µg/ml Atc for two lytic cycles ( 96h total ) and total RNAs were extracted as described previously [40] . One microgram of total mRNA was used to reverse transcribe TgROM4 and TgACT1 using Super-Script III reverse transcriptase according to the manufacturer's instructions ( Invitrogen ) . Quantitative PCR ( qPCR ) was performed using a SmartCycler ( Cepheid , Sunnyvale , CA ) , 2 µl of reverse-transcribed cDNA and primer pairs ( see Table S1 ) to amplify TgROM4 and TgACT1 . Data analysis was conducted using SmartCycler software ( Cepheid ) . The relative TgROM4 expression levels were calculated as the fold change using the formula 2−ΔΔCT , where ΔCT threshold cycle ( CT ) of actin - CT of TgROM4 and ΔΔCT = CT of wild type parasites grown in absence of Atc - ΔCT of TgROM4 parasites grown in the presence of Atc , as described previously [40] . Three independent experiments were performed and values are representative of one experiment . Parasites were cultured in the presence of 1 . 5 µg/ml Atc 24h prior to inoculation of 96-well plates seeded with confluent HFF monolayers . Infected monolayers were cultured in the presence of 1 . 5 µg/ml Atc for an additional 72 h , washed in PBS , fixed with 100% ethanol and stained with 0 . 1% crystal violet ( Sigma ) . Parasite growth was determined by the loss of monolayer integrity as monitored by absorbance at 570 nm using an EL800 multiwell plate reader ( Bio-Tek Instruments , VT ) . Values were expressed as means of 4 replicates each from two separate experiments that were pooled . To monitor the rate of intracellular growth , parasites were grown for 96h in 1 . 5 µg/ml Atc , harvested following natural egress , and used to infect monolayers of HFF cells grown on coverslips . Infection was performed by incubation of parasites with the host cells for 1 hr , followed by extensive rinsing and return to culture in complete medium with or without Atc . At 12 , 24 and 36 hr post infection , coverslips were fixed and stained by immunofluorescence as described above . The average number of parasites per vacuole was determined by microscopic examination and counting 50 or more vacuoles from each of three coverslips at each time point per sample . Values represent mean ± SD from a representative experiment . Invasion assays were performed based on differential staining of intracellular vs . extracellular parasites as previously described [40] , with minor modifications . Parasites were grown for 96h in 1 . 5 µg/ml Atc , harvested following natural egress , and resuspended in HHE buffer ( Hanks Balanced Salts ( Sigma ) , 1 mM EGTA , and 10 mM HEPES , pH 7 . 4 ) . Freshly egressed parasites were added to glass coverslips containing sub-confluent monolayers of HFF cells . After 15 min incubation at 37°C/5% CO2 , coverslips were washed in PBS , and fixed in 4% paraformaldehyde in PBS . Extracellular parasites were detected by staining with DG52 directly conjugated to Alexa-594 ( red ) , followed by washing . Monolayers were permeabilized with 0 . 25% Triton X-100 and the total parasite population ( extra- and intracellular ) was stained with mAb DG52 directly conjugated to Alexa-488 ( green ) . Coverslips were washed and mounted with Prolong Gold antifade reagent containing DAPI ( Invitrogen ) . Slides were examined by epifluorescence microscopy and the numbers of intracellular ( green ) , extracellular parasites ( red ) and host cell nuclei ( blue ) were counted from 5 fields per coverslip . Values were expressed as the average number of parasites/host cell and the percentage of total parasites . Values represent means ± SEM of 3 independent experiments . Parasites were grown for two lytic cycles in 1 . 5 µg/ml Atc , harvested following natural egress , and resuspended in invasion media ( DMEM , 20 mM HEPES , pH7 . 4 and 3% FBS ) . Parasites were added to glass coverslips containing sub-confluent monolayers of HFF cells for a 15 min invasion pulse , followed by washing and fixation in 4% paraformaldehyde in PBS . Extracellular parasites were detected by staining with mAb DG52 directly conjugated to Alexa 594 ( red ) . The moving junction was detected using a rabbit polyclonal antibody to TgRON4 , provided by John Boothroyd , and visualized using a goat anti-rabbit IgG Alexa 488 ( green ) secondary antibody . Monolayers were permeabilized with 0 . 05% saponin ( Sigma ) and the total parasite population ( extra and intracellular ) was stained with mAb DG52 directly conjugated to Alexa 350 ( blue ) . Coverslips were mounted with Prolong Gold antifade reagent containing DAPI . Slides were examined by epifluorescence microscopy to define the orientation of the parasite relative to the host cell . For those parasites that were actively invading , their orientation was defined based on the position of the RON4-ring; i . e . apical , middle , posterior . Additionally , parasites that had not formed a junction were classified as peripherally attached with no ring . Parasites were scored from 5 random fields on 3 separate coverslips . Values were expressed as a percent of the total parasite population and represent means ± SEM of 3 independent experiments . Freshly harvested tachyzoites were resuspended in Ringer's Media ( 155 mM NaCl , 3 mM KCl , 2 mM CaCl2 , 1 mM MgCl2 , 3 mM NaH2PO4 , 10 mM HEPES , 10 mM glucose ) and added to glass bottom culture dishes ( MatTek , Ashland , MA ) that were pre-coated with 50 µg/ml bovine serum albumin ( BSA ) for 30 min at 37°C . The culture dish was placed on a Zeiss Axiovert phase-contrast microscope and heated using a temperature-controlled stage ( Medical Systems Corp . , Greenvale , NY ) at 37°C . Parasites were imaged under extremely low light using an intensified CCD C2400 camera ( Hamamatsu Photonics K . K . , Hamamatsu City , Japan ) at 40× magnification . Time-lapse images were taken with exposure times ranging from 50–100 milliseconds with 1 second between exposures , using the OpenLab software package ( Improvision , Waltham , MA ) . Images were imported into ImageJ and the Particle Tracker 3D plug-in [55] was used to track cell motility . The Cell Counter plug-in ( http://rsbweb . nih . gov/ij/plugins/cell-counter . html ) was used for quantification of the types of motility as assessed by the experimenter based on visual inspection . Percent motility was then calculated from selected videos . For quantitative analysis , a total of 12 videos were recorded for each sample from four independent experiments , split over two separate days . Within each video 40–50 separate parasites tracks were analyzed to determine the percent motility , based on classifications determined by visual examination and assignment of individual tracks to specific categories by the experimenter . The relative speed of movement was calculated from 20–30 individual tracks based on the change in distance over time as calculated in Excel . Prior to averaging the speed , tracks were assigned a beginning and ending frame based on visual inspection by the experimenter . Values represent means ± SEM of 3 or 4 independent experiments . Parasites were grown in the presence of 1 . 5 µg/ml of Atc for 2 lytic cycles , tachyzoites harvested following natural egress and resuspended in HHE . Parasites were added to wells of a 96- well plate , centrifuged at 750g for 5 min and the parasites fixed in 4% paraformaldehyde in PBS for 20 min at 4°C . following antibodies were used to detect parasite proteins: MIC1 was detected with mAb T4-4F8 provided by Jean Francois Dubremetz; MIC2 was detected with 6D10 [22]; MIC3 was detected with mAb T4-283 provided by Jean Francois Dubremetz; MIC4 was recognized with polyclonal rabbit antiserum provided by Dominique Soldati; MIC5 was detected with polyclonal rabbit antiserum provided by Vern Carruthers; MIC6 was detected with polyclonal rabbit antiserum provided by Dominique Soldati , SAG1 was detected with mAb DG52 provided by John Boothroyd , and AMA1 was detected with mAb B3 . 90 provided by Gary Ward . Samples were washed 3 times in PBS/1% normal goat serum , centrifuged as described and blocked in 10% FBS . Samples were incubated in primary antibodies for 1h followed by incubation with Alexa 488 secondary antibodies ( goat anti-mouse or goat anti-rabbit IgG ) for 1h . Samples were analyzed in a Becton Dickinson FACSCanto™ flow cytometer in the FITC channel , measuring up to 10 , 000 events / sample . All samples were done in quadruplicate and the mean fluorescence values were calculated for each sample using FloJo 7 . 4 software ( Tree Star Inc . , Ashland , OR ) . Data was graphed as the mean fluorescence of samples vs . wild type RH strain parasites , which was considered 100% . Values represent means ± SD of 4 samples , from a representative experiment . Shedding of MIC2 into the supernatant was performed as previously described [48] with the following modifications . Parasites were grown up to 96h in 1 . 5 µg/ml Atc , harvested following natural egress , and resuspended in D0 medium ( DMEM , 20 mM HEPES , pH 7 . 5 ) . Tachyzoites were added to equal volumes of D0 , D0+6% FBS or D0+6%FBS / 4% EtOH . Samples were incubated on ice or at 37°C for 15 min and the assay was stopped by placing the tubes on wet ice at 4°C for 10 min . Supernatants were collected after removing the parasites by centrifugation twice ( 1 , 000g , 5 min , at 4°C ) . Proteins in the supernatants were resolved by SDS-PAGE and detected by Western blotting using mAb 6D10 to MIC2 [22] and rabbit anti-actin [56] followed by secondary antibodies conjugated to HRP and ECL Plus detection ( GE Healthcare , Piscataway , NJ ) , and quantified using an FLA-5000 phosphorimager ( Fuji Film Medical Systems , Stamford , Ct ) . Parasites were grown in the presence of 1 . 5 µg/ml Atc for 96h , harvested as described above and maintained at 18°C , unless otherwise stated . Tachyzoites were treated with 0 . 2 µM of A23187 , Ca2+ ionophore ( EMD Chemicals , Gibbstown , NJ ) for 2 min or 15 min at 37°C in DMEM , before being transferred to an equal volume of 2× fixative ( 5% paraformaldehyde , 0 . 04% glutaraldehyde and PBS ) on ice for 15 min . Fixed cells were washed 3 times with PBS , blocked with 5% FBS/5% NGS for 10 min and then incubated for 1h with a mouse monoclonal anti-MIC2 antibody ( 6D10 ) followed by Alexa goat anti-mouse 488 ( green ) secondary antibody . Parasites were then permeabilized with 0 . 05% saponin , incubated with rabbit anit-MIC2 for 1h followed by Alexa goat anti-rabbit 594 ( red ) secondary antibody . Parasite suspensions were incubated on poly-L-lysine coated slides for 10 min and coverslips were mounted using Prolong Gold ( Invitrogen ) containing DAPI . Cells were examined by epifluorescence microscopy and images obtained as described above . Statistical comparisons between means were conducted in Excel using the Student's t-test assuming equal variance , unpaired samples , and using a 2-tailed distribution . | Apicomplexan parasites invade host cells using a multi-step process that depends on regulated secretion of adhesins , attachment to the cell , and active penetration . Coordinating these activities requires control of proper timing and release of surface proteins that mediate adhesion . Parasites like Toxoplasma gondii attach directionally to their host cells due to the selective discharge of adhesive proteins at their apical end . The resulting complexes are then translocated along the long axis of the parasite , thus propelling the parasite into the cell . Completion of cell invasion also requires that these interactions ultimately be severed to allow detachment . Shedding is accomplished by proteolytic cleavage of the adhesive proteins at the point where they span the parasite outer membrane . By disrupting the expression of the intramembrane protease rhomboid 4 ( ROM4 ) , we demonstrate that it is important for shedding of adhesins . In the absence of ROM4 , a subset of surface adhesive proteins was over-expressed on the parasite cell surface . Although ROM4 knockdown parasites bound better to host cells , they lost their ability to do so directionally , and hence were impaired in cell entry . Our findings demonstrate that host cell invasion by apicomplexan parasites relies on constitutive shedding of surface adhesins for efficient infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
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| [
"microbiology/cellular",
"microbiology",
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"pathogenesis",
"microbiology/parasitology"
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| 2010 | Rhomboid 4 (ROM4) Affects the Processing of Surface Adhesins and Facilitates Host Cell Invasion by Toxoplasma gondii |
Japanese encephalitis ( JE ) virus ( JEV ) causes severe epidemic encephalitis across Asia , for which the live attenuated vaccine SA14-14-2 is being used increasingly . JEV is a flavivirus , and is closely related to dengue virus ( DENV ) , which is co-endemic in many parts of Asia , with clinically relevant interactions . There is no information on the human T cell response to SA14-14-2 , or whether responses to SA14-14-2 cross-react with DENV . We used live attenuated JE vaccine SA14-14-2 as a model for studying T cell responses to JEV infection in adults , and to determine whether these T cell responses are cross-reactive with DENV , and other flaviviruses . We conducted a single arm , open label clinical trial ( registration: clinicaltrials . gov NCT01656200 ) to study T cell responses to SA14-14-2 in adults in South India , an area endemic for JE and dengue . Ten out of 16 ( 62 . 5% ) participants seroconverted to JEV SA14-14-2 , and geometric mean neutralising antibody ( NAb ) titre was 18 . 5 . Proliferation responses were commonly present before vaccination in the absence of NAb , indicating a likely high degree of previous flavivirus exposure . Thirteen of 15 ( 87% ) participants made T cell interferon-gamma ( IFNγ ) responses against JEV proteins . In four subjects tested , at least some T cell epitopes mapped cross-reacted with DENV and other flaviviruses . JEV SA14-14-2 was more immunogenic for T cell IFNγ than for NAb in adults in this JE/DENV co-endemic area . The proliferation positive , NAb negative combination may represent a new marker of long term immunity/exposure to JE . T cell responses can cross-react between JE vaccine and DENV in a co-endemic area , illustrating a need for greater knowledge on such responses to inform the development of next-generation vaccines effective against both diseases . clinicaltrials . gov ( NCT01656200 )
Japanese encephalitis ( JE ) virus ( JEV ) is the cause of around 68 000 cases of encephalitis per year in Asia , mostly in children [1] . JEV is a single stranded positive sense RNA virus of the family Flaviviridae , genus Flavivirus . The JEV genome is 11 kb comprising a single 10 . 3 kb open reading frame encoding three structural proteins ( core , C; pre-membrane , prM; envelope , E ) and seven non-structural ( NS ) proteins denoted NS1 , NS2a , NS2b , NS3 , NS4a , NS4b and NS5 [2] . JEV is transmitted naturally among birds and pigs by Culex mosquitoes , with humans infected coincidentally as dead-end hosts . Ecological control of JE is , therefore , unrealistic: vaccination is the only reasonable prospect of preventing disease in humans [3] . JE vaccines are effective , have been available for many decades [4] , and appear to protect through neutralising antibody ( NAb ) against JEV [5] . Early JE vaccines were inactivated; subsequently an infectious attenuated vaccine ( JEV SA14-14-2 ) has been developed which is safe and immunogenic [6–8] . In JE endemic areas a single dose is 94 . 5% to 99 . 3% effective [9 , 10] and gives durable protection for up to five years [11 , 12] . The vaccine was prequalified by the World Health Organisation in October 2013 [13] . JEV co-circulates in many parts of Asia with the related flavivirus dengue virus ( DENV ) , currently the target of several developmental vaccines . T cell and antibody responses to DENV are cross-reactive , with clinically relevant effects , both potentially beneficial and harmful [14] . The sequence of exposure to JEV and DENV may also be relevant; DENV partially protects against JE [15] , whereas JEV may predispose to worse dengue disease [16] . Cross-reactivity between DENV and other flaviviruses is less well studied , though we have recently described highly cross-reactive CD8+ T cell responses between JEV and DENV in South India , associated with asymptomatic exposure to JEV [17] . In addition to their clinical use , live attenuated vaccines may serve as models for viral infection in humans and allow the study of the development of anti-viral immune responses [18 , 19] . Greater knowledge of cellular responses to both JEV and JE vaccine was identified as research priority in a JE vaccine Cochrane review [20] . A protective role for T cell responses against JE is not clearly established , but both animal and human studies suggest a role for the cellular response as well as NAb in protection and/or recovery from JE [17 , 21–23] . In JE endemic areas most of the population are exposed by adulthood [24] . Therefore , live JE vaccination may mimic repeated exposure to wild type JEV in an immune host , giving information on the T cell response to wild type JEV as well as the vaccine . Although the live JE vaccine is predominantly used in children , the repeated blood sampling required makes such studies impractical in this age group . For these reasons we conducted an exploratory study of T cell responses after vaccination of adults with a single dose of JE vaccine SA14-14-2 in South India , a JE endemic area . Because dengue and JE vaccines will ultimately be used together in much of Asia , and South India is also dengue endemic , we also sought to determine whether T cell responses to JE vaccine could cross-react with DENV ( or other flaviviruses ) , and whether there were JEV-specific T cell responses . Here , we report the first description of T cell responses to live attenuated JE vaccine SA14-14-2 in humans .
Healthy adults aged 18 to 50 years were recruited into the study by advertisement and word of mouth and vaccinated at the Indian Institute of Science ( IISc ) or National Institute of Mental Health and Neurosciences ( NIMHANS ) , both in Bengaluru , Karnataka State , India . Any laboratory workers who were being vaccinated because of potential occupational exposure to JEV were eligible . Because of concern that recruitment would be insufficient , an interventional protocol was developed to enrol additional participants . Participants who were being vaccinated on this protocol were screened for anti-JEV NAb before trial entry as JE vaccination was deemed more readily justified if NAb was not detectable . Participants with positive NAb or ELISpot screening assays were included in an observational study [17] . Participants on the interventional protocol also had HIV , hepatitis B and C excluded before entry . Apart from the pre-vaccination screening , both sets of participants followed an identical protocol and were analysed as one group . Exclusion criteria were previous administration of JE vaccine , pregnancy , immunosuppression of any cause , allergy or adverse reaction to a vaccine or component of the investigational vaccine , previous episode of encephalitis , use of any other investigational drug or vaccine within 30 days of vaccination . This was an open label single arm study . The target sample size was 20 , chosen to give a reasonable chance of representing common HLA types in the South Indian population . No power calculation was performed and no comparative analysis was pre-specified . The primary endpoint was a description of the timing , magnitude , specificity and cross-reactivity of the T cell response to JE vaccine SA14-14-2 up to 8 weeks after vaccination . The number of participants seroconverting to the vaccine ( defined as NAb titre > 1:10 if negative pre-vaccine , or a four-fold increase over baseline titre ) , geometric mean NAb titre , the number of adverse events occurring one month after vaccination and number of serious adverse events at any time were secondary endpoints . Data were analysed descriptively; statistics were performed using R version 3 . 1 . 2 ( www . r-project . org ) . Pre-vaccine samples were collected before subcutaneous injection of 0 . 5 ml attenuated JE vaccine SA14-14-2 ( Chengdu Biological Products , China ) over the deltoid by a study physician ( lot numbers 201107C017-1 , 201107C021-2 , 201103C002-2 or 201206C030-2; derived from primary hamster kidney cells ) . Blood ( 40-50ml ) was drawn at 1 , 2 , 4 and 8 weeks and monthly thereafter ( in some participants ) . Peripheral blood mononuclear cells ( PBMC ) and serum were separated and cryopreserved . Safety was assessed actively using weekly symptom diaries for the first four weeks and passively thereafter . Participants were asked about any symptoms at each contact up to six months and were telephoned at this point if face-to-face contact was not possible . Adverse events were graded 1 ( symptoms but no change in behaviour ) , 2 ( symptoms sufficient to interfere with usual daily activities ) , 3 ( symptoms prompting medical consultation ) or 4 ( hospital admission ) . The study was conducted according to the principles of the Declaration of Helsinki . All participants gave written , informed consent separately for screening and then for administration of the vaccine . The protocol was approved by the IISc Institutional Human Ethics Committee ( ref 5/2011 ) . The observational study was also approved by the Liverpool school of Tropical Medicine ethics committee ( ref . 10 . 59 ) . The interventional protocol was registered at clinicaltrials . gov ( NCT01656200 ) . A library of 18 amino acid peptides overlapping by 10 corresponding to the entire JEV SA14-14-2 open reading frame based on the two sequences available in Genbank in 2010 , accession numbers AF315119 and D90195 ( see S1 JEV peptide library ) , was synthesised commercially ( Mimotopes ) . Peptides were dissolved in dimethylsulphoxide ( DMSO ) and pooled according to JEV proteins: C/prM , E ( 2 pools ) , NS1 , NS2a/NS2b , NS3 ( 2 pools ) , NS4a/NS4b , NS5 ( 3 pools ) . For proliferation assays adjacent pools ( except C/prM ) were combined . In cross-reactivity assays , the following peptide sets , obtained through Biodefense and Emerging Infection ( BEI ) Resources , NIAID , NIH , were used: DENV1 Singapore/S275/1990 E ( NR4551 ) , NS1 ( NR2751 ) , NS3 ( NR2752 ) ; DENV2 New Guinea C ( NGC ) prM ( NR506 ) , E ( NR507 ) , NS1 ( NR508 ) , NS3 ( NR509 ) ; DENV3 Philippines/H87/1956 NS1 ( NR2753 ) , NS3 ( NR2754 ) ; DENV4 Dominica/814669/1981 E ( NR512 ) , DENV4 Singapore/8976/1995 NS1 ( NR2755 ) , NS3 ( NR2756 ) ; West Nile virus NY99-flamingo382-99 prM ( NR433 ) , M ( NR434 ) , E ( NR435 ) , NS1 ( NR436 ) , NS3 ( NR439 ) . JEV infected cell lysate was prepared from Vero cells which were infected with JEV P20778 ( MOI 5 ) , fixed with 0 . 025% glutaraldehyde ( Sigma ) , washed with phosphate buffered saline , suspended in MEM/10% FCS and sonified in a Branson cup-horn sonifier ( Model 450 , Branson Ultrasonics , Danbury , CT ) as previously described [25] . The antigen preparation was diluted to a stock containing 4 μg/ml of JEV E protein and used at a final concentration of 80 ng/ml . Interferon-gamma ( IFNγ ) enzyme linked immunospot ( ELISpot ) assays were conducted as previously described [17] , using 2 x 105 fresh PBMC in triplicate , with peptides at 3 μg/ml and a final DMSO concentration of 0 . 5% ( pools ) , or 3 μg/ml and DMSO <0 . 001% ( individual peptides ) . PBMC were cultured in 100 μl RPMI supplemented with 2mM L-glutamine , 100U/ml penicillin , 0 . 1mg/ml streptomycin ( sRPMI ) and 10% fetal calf serum ( FCS , R10 ) . The cut-off for a positive ELISpot was at least 50 spot forming cells ( SFC ) /106 PBMC and twice the background count . Proliferation assays used cryopreserved PBMC which were thawed , then rested overnight before labelling with Carboxyfluorescein succinimidyl ester ( CFSE ) as previously described [17] . Briefly , PBMC were labelled at 5–10 x 106 cells/ml of pre-warmed phosphate buffered saline ( PBS ) /1μM CFSE at 37°C for 10 minutes , followed by quenching with five volumes of ice cold R10 and two washes . After eight days in culture with 3 μg/ml JEV peptide pools , cells were stained with near-infra-red ( IR ) viability dye ( molecular probes ) , anti-CD3-AF700 ( clone UCHT1 ) , anti-CD4-PE ( clone RPA-T4 ) , anti-CD8-APC ( clone RPA-T8 ) and anti-CD38-PE-Cy7 ( clone HIT2 ) fluorescent antibodies ( all from BD biosciences ) for flow cytometry . Peptide epitope mapping was done either by ELISpot , using an additional blood sample if the volunteer was available , or by expanding short term T cell lines ( TCL ) using left over PBMC . For cross-reactivity assays , short-term T cell lines ( TCL ) were always used for reasons of consistency . PBMC ( 2 x 106 ) were cultured with 5 μg/ml JEV peptide pools , or 10 μg/ml individual peptide , to which responses had been detected in ELISpot assays , in 1 ml sRPMI supplemented with 10% human serum , 10% Natural T cell growth factor/IL2 ( “T-stim , ” Helvetica Healthcare ) and 20 ng/ml recombinant human IL7 ( R&D systems ) . TCLs were expanded for 7–10 days in culture , rested overnight in R10 without peptide , then stimulated with peptides for six hours in the presence of 10 μg/ml brefeldin A ( Sigma ) . Intracellular cytokine staining ( ICS ) assays were done using whole blood , TCL ( 6 hours ) or PBMC ( overnight ) , stimulated with JEV peptides ( 3–10 μg/ml ) , peptide pools ( 3 μg/ml ) , JEV infected cell lysate , or approximately 103 . 4 to 104 . 4 plaque forming units ( PFU ) of JEV SA14-14-2 in the presence of 10 μg/ml brefeldin A during the stimulation . Following stimulation ( and red cell lysis in the case of assays using whole blood ) , cells were stained with near infrared viability dye ( Invitrogen ) at room temperature in the dark for 20 minutes , fixed with 2% formaldehyde at room temperature for 20 minutes , and cryopreserved at -80°C in PBS/1% bovine serum albumin/10% DMSO . Later , cells were incubated in FACS perm/wash buffer ( BD ) at room temperature for 20–30 minutes followed by staining in perm/wash buffer for 30 minutes at 4°C . Antibody clones used for anti-CD3 , CD4 and CD8 were as above . Antibodies used for TCL ICS were: anti-CD3-FITC , anti-CD4-PerCP-Cy5 . 5 , anti CD8-APC , anti-IFNγ-PE or PE-Cy7 ( clone 4S . B3 ) , anti-IL2-PE ( clone 5344 . 111 ) and anti-TNFα-PE-Cy7 ( clone MAb11 ) . Antibodies used for ex-vivo ICS were: anti-CD3-AmCyan , anti-CD4-PerCP-Cy5 . 5 , anti-CD8-Horizon v450 , Anti-CD14-APC-Cy7 ( clone MφP9 ) , anti-IFNγ-PE-Cy7 , anti-TNFα-APC , anti-IL2-PE and anti-MIP-1β-FITC ( clone D21-1351 ) . MIP-1β was from R&D systems , all other antibodies were from BD . Flow cytometry was performed using a BD Canto ( TCL ICS and proliferation assays ) or Canto II ( ex-vivo ICS ) cytometer . Ex-vivo ICS responses were considered positive if the responding population was at least 0 . 02% of the parent gate and double the negative control value . Proliferation responses were considered positive if the CFSElo/CD38hi responding subset were at least 1% of the parent gate and double the negative control value . Analysis of T cells stained for >2 cytokines was done using Simplified Presentation of Incredibly Complex Evaluations ( SPICE ) software version 5 . 35 with pre-processing in Pestle version 1 . 7 [26] . Screening assays before vaccination measured the ability of heat inactivated sera at two fold dilutions from 1:4 to 1:32 to prevent destruction of a monolayer of PS cells infected with 100 plaque forming units of JEV P20778 . Fifty percent plaque reduction neutralisation titres ( PRNT50 ) were measured using LLC-MK2 cells for all study samples together at the end of the study using the same batch of cells and JEV stock to minimize variation in the assay . For PRNT50 , sera were heat inactivated and assayed according to the method of Russell et al [27] . Viruses used were JEV SA14-14-2 ( expanded by three passages in C6/36 cells ) , DENV1 16007 , DENV2 16681 , DENV3 16562 , DENV4 C0036/06 . PRNT50 values were calculated by probit regression .
Seventeen participants were recruited into the study ( Table 1 ) ; nine participants were vaccinated for occupational reasons and seven were vaccinated on the interventional protocol . Median age was 25 years , range 20–39 years . One participant withdrew after a week , a second donated 5ml per sample so had limited assays performed; both remained in the study for safety . Therefore 17 participants were evaluated for safety , 16 for seroconversion , 15 for T cell immunogenicity over 8 weeks , and nine for immunogenicity beyond 8 weeks . Six adverse events in three participants were reported in total , in the first 4 weeks two grade 1 and two grade 2 adverse events occurred , two further grade 2 events occurred >4 weeks after vaccination ( Table 1 ) . Four were febrile illnesses ( two within 4 weeks ) , the others were dizziness with , and without , headache . All adverse events recovered spontaneously and no serious adverse events occurred . Two participants vaccinated for occupational reasons were JEV seropositive ( PRNT50 >1:10 ) before vaccination . In addition , despite negative screening neutralizing antibody assays using PS cells , two participants on the interventional protocol were found to be seropositive when neutralising antibody measurements were repeated using PRNT50 after vaccination . Because NAb at baseline was allowed in the occupational vaccinees , these participants remained in the analysis . Ten out of 16 participants ( 62 . 5% ) seroconverted to PRNT50 >1:10 or >4-fold increase over baseline ( Fig 1A and S1 Fig ) . Of the four seropositive participants at baseline , two seroconverted with 7 . 2 and 9 . 7 fold increases in PRNT50 . Therefore , 12 volunteers were sero-protected after vaccination ( 75% ) . Reciprocal geometric mean titre ( GMT ) at week 4 was 18 . 5 overall but 51 among those who seroconverted . The reciprocal GMT of maximal responses among seroconverters was 61 . 5 . PRNT50 waned after vaccination and , of the nine participants with data 16–26 weeks after vaccination , only four ( 44% ) had PRNT50 >1:10 . Participant VA001/1 had a positive IFNγ-ELISpot assay ( NS3 ) at baseline ( but was vaccinated because of PRNT50 1:6 and laboratory work with JEV ) , but made additional responses after vaccination . In total , 13 out of 15 participants tested ( 87% ) developed new IFNγ-ELISpot responses , peaking two weeks after vaccination with significant increases over baseline at weeks 1 , 2 and 4 ( Fig 1B ) . However , there was variation amongst the participants with some mounting the peak response later ( S1 Fig ) . Proliferation responses were available for 13 participants for at least two time points . Interestingly , T cell proliferation responses were detected in most participants before vaccination , despite negative ELISpot assays and/or PRNT50 . Although five volunteers appeared to make new T cell proliferation responses over the course of the study ( S2 Fig ) , proliferation responses were variable and overall there was no significant difference from baseline values at any time point ( Fig 1C ) . Example flow cytometry data of CFSE assays over the course of the study are shown in supplementary figure S3 Fig . The function and characteristics of ex-vivo T cell responses were investigated further by intracellular cytokine staining ( ICS ) and flow cytometry in 13 participants with positive ELISpot assays; responses were detected in five ( ELISpot is a more sensitive technique than ICS ) . CD4+ T cell responses were detected in three participants; CD4+ and CD8+ T cell responses in two . Flow cytometry data showing CD4+ and CD8+ T cell responses throughout the study from participant VA019/3 are shown in Fig 2 , though sample limitation meant that we could not perform all these assays on every participant . Cytokine responses were small ( Fig 3A ) and polyfunctional T cell responses to JE vaccine were rare , in contrast to our recent findings in natural exposure and recovered JE [17] . In CD4+ T cell responses , IFNγ+ or IL2+ only populations dominated ( Fig 3B ) , indicating that IFNγ-ELISpot alone may not detect all such responses . Participant VA001/1 ( who showed a CD8+ T cell response at baseline ) was an exception; we identified a polyfunctional ( IFNγ/TNFα/IL2 triple positive ) CD4+ T cell response at week 16 after vaccination . Most participants made IFNγ responses to >1 peptide pool ( median five ) and all viral proteins were targeted . The total magnitude of the ELISpot response correlated with the number of responding pools ( Spearman’s R = 0 . 78 , p = 0 . 0005 , Fig 4A ) indicating that there were no strongly immunodominant pools . Normalised to protein size , NS1 elicited responses most frequently ( Fig 4B ) . In six participants , further experiments were conducted to identify some of the epitopes recognised after vaccination with JEV SA14-14-2 . Responding peptide pools were either mapped ex-vivo by ELISpot , or short-term T cell lines ( TCL ) were expanded to peptide pools showing responses in ex-vivo assays . The peptide pools were then deconvoluted first into “mini-pools” of 6–10 peptides which were used to stimulate short term TCL in ICS assays ( also allowing determination of the responding subset ) , followed by mapping down to individual peptides . Fifteen peptides were mapped ( Fig 4C and Table 2 ) and a further six 46 to 90 amino acid regions ( Fig 4D and Table 2 ) eliciting IFNγ responses were identified ( participant VA023/1 had a response mapped to mini-pools only ) . In participant VA019/3 an antigenic region corresponding to amino acids 214–303 , in prM , overlapped with peptide TRTRHSKRSRRSVSV , amino acids 209–223 . The response to amino acids 214–303 was larger than the response to 209–223 , making it unlikely the response to 214–303 was accounted for by only the 10 amino acid overlap . The peptides identified were mostly in the prM , NS1 and NS3 proteins ( Fig 4C ) with one identified in E protein . Table 2 shows all the peptides and antigenic regions identified . All four DENV serotypes were detected in Karnataka State in the two years prior to this study ( S1 Data ) . Therefore , in some participants , we investigated whether the IFNγ responses identified cross-reacted with DENV . Two participants , VA012/3 and VA020/1 , had responses that were not present before vaccination mapped to individual peptides by ex-vivo ELISpot assays . Partial peptide libraries from DENV serotypes 1 , 3 and 4 and complete libraries from DENV2 and WNV were available ( see methods ) . To test for cross-reactive responses , short term T cell lines ( TCL ) were expanded by culturing PBMC from the volunteers in the presence of “T-Stim” ( IL2 ) , IL7 and the relevant JEV peptide at 10 μg/ml for 7–10 days . The same TCL were then stimulated with JEV peptide alongside variant peptides from DENV or WNV selected on the basis of a ClustalW protein alignment . Because the cells specific for the JEV peptide had expanded in culture ( typically from around 0 . 1% ( Fig 3A ) to 5% ( Fig 5 ) ) , a response to the variant peptide of equivalent magnitude to the JEV peptide indicates that it is likely to be the same cells being triggered by the variant peptide and the response is therefore cross-reactive . Participant VA012/3 had high levels of NAb to DENV4 and intermediate levels of NAb to DENV2 and DENV3 at baseline , and no detectable JEV NAb . This participant developed a CD8+ T cell response to peptide TAVLAPTRVVAAEMAEVL ( NS3 ) , subsequently mapped to APTRVVAAEM ( S4 Fig panel A ) , very similar to a previously described partially mapped HLA B*07 restricted DENV4 epitope , LAPTRVVAAEME [28] . A short-term T cell line recognised the very close DENV1 sequence APTRVVASEM equally well ( Fig 5A ) ; the sequence in DENV2-4 is identical to JEV . Interestingly , this subject did not seroconvert to JE vaccine . Participant VA020/1 , who had the highest titre of NAb to DENV1 with lower levels to DENV2 and DENV3 and no JEV NAb at baseline , developed a CD8+ T cell response to GATWVDLVL ( E ) , based on overlapping peptide ELISpot assays and confirmed by flow cytometry with truncated peptides ( S4 Fig panel B ) . This was confirmed by expanding a short-term TCL ( Fig 5B ) ; the epitope cross-reacted with a variant peptide conserved in DENV1 and DENV3 . In a subsequent experiment variant peptides from DENV2 and DENV4 were not recognised , consistent with the serology assays suggesting DENV1 exposure ( S4 Fig panel C ) . These responses represent either priming by JE vaccine that cross-reacts with DENV , or priming by DENV , boosted by JE vaccine , that cross-reacts with additional DENV serotypes because of close sequence similarity . Participant VA019/3 was DENV exposed ( DENV2 PRNT50 1:538 ) , but also had JEV NAb ( titre 1:123 ) before vaccination . This participant had several CD4+ responses mapped by expanding short term TCL to responding pools in the ELISpot assays using 5 μg/ml equivalent concentration of each peptide . The short term TCL were then stimulated with smaller pools ( “mini-pools” ) followed by deconvolution down to individual peptides ( Table 2 ) , Finally , the same TCL were then tested with variant peptides from DENV and WNV . As before , because TCLs were expanded with JEV peptides , responses to variant peptides indicate cross-reactivity , though in this case all but one of the responses were JEV specific ( Table 3 ) . Participant VA001/1 carries the HLA B*15:01 allele and had high levels of DENV2 NAb and intermediate levels to other serotypes at baseline ( Fig 6A ) . Peptide ALRGLPVRY was mapped before vaccination , and a cross-reactive response was identified to the previously described HLA-B*15 restricted peptide ALRGLPIRY from DENV2/4 and WNV [29] . The corresponding vaccine library peptide VVAAEMAEVLRGLPVRY had a modest Val for Ala substitution corresponding to position 1 of the 9-mer ( the same position as the NS3 peptide TAVLAPTRVVAAEMAEVL recognised by participant 012/3 ) , and is predicted to bind the same HLA allele as the wild type peptide with slightly lower affinity ( IEBD . com [30 , 31] ) . The ex-vivo IFNγ response to the pool containing this peptide did not change after vaccination or with seroconversion ( Fig 6B ) , nor did the responses to the wild type and DENV peptides at week 16 ( Fig 6C ) , though other responses developed . The SA14-14-2 peptide produced a smaller IFNγ response , though the response was still detectable ( Fig 6C left panel ) . Short-term T cell lines expanded with JEV wild type peptide before and after vaccination ( Fig 6D & 6E ) confirmed that IFNγ responses were smaller to the vaccine peptide ( Fig 6E ) . However , when analysed for additional cytokines , the total number of responding cells was similar , with the difference mostly accounted for by MIP-1β single positive cells ( Fig 7A & 7B ) . MIP-1β may have a lower triggering threshold than other cytokines [32] , suggesting that the vaccine variant epitope is less efficient than the wild type in this case . This represents an example where responses to DENV2/4 and JEV are highly cross-reactive with each other , but were less efficiently cross-reactive with the SA14-14-2 variant . Together , these data show that T cell responses induced to JEV vaccine SA14-14-2 can recognise wild type JEV , and that responses primed to natural flavivirus infection can cross-react with JEV vaccine SA14-14-2 in some instances . These data are consistent with our recent finding that CD8+ T cell responses to JEV are highly cross-reactive , whereas CD4+ responses are much less so [17] , although the small number of participants in the present study prevents generalisation of these findings .
We have shown that , in participants resident in a JE endemic area , T cell IFNγ responses are detectable after vaccination with JEV SA14-14-2 , are modest in magnitude , peak within eight weeks of vaccination , and return to baseline levels by 4–6 months . Determination of epitope specificity and cross-reactivity in four participants showed that some responses to JE vaccine can cross-react with DENV , and in one case a variant epitope of SA14-14-2 was recognised less efficiently than the JEV wild type peptide by memory CD8+ T cells from a pre-existing response . Our study found a seroconversion rate of 62 . 5% , sero-protection rate of 75% and GMT of 18 . 5 four weeks after vaccination with a single dose of JEV SA14-14-2 . This is lower than studies of single dose JEV SA14-14-2 in children , where the seroconversion rate is 80–99% and GMTs 4 weeks after vaccination range from 56–370 [7 , 8 , 33]; or even higher after previous JEV exposure or vaccination in a DENV non-endemic area ( Korea ) [34] . However , our findings are consistent with a recently published clinical trial using SA14-14-2 in India in which 57 . 7% of participants seroconverted at 4 weeks , including 54 . 9% of adults aged 18–50 , a similar age group to this study [35] . In the study of Singh et al . , seroconversion in children aged 1–6 years was 58 . 8% , suggesting it is the environment and not age driving this effect . The greater number of participants showing T cell responses than NAb , and the presence of proliferation responses at baseline , indicate that measuring NAb alone may not be the only potential test of JE-immunity . NAb titres to JE vaccines can fall below “protective” levels after vaccination in the presence of protection and rapid recall responses [36 , 37] , a feature also observed following HBV vaccination , where B cell memory pools may be detected [38] . DENV circulates in South India where this study was conducted . Four participants in whom DENV PRNT50 were measured all showed neutralising antibody to DENV , indicative of past infection , as did a larger group recently recruited in Karnataka State , South India [17] . One possibility is that immune interference by DENV may account for the lower seroconversion rate after JEV SA14-14-2 in Indian adults . Experiments are underway to investigate the possibility of interference by DENV exposure . So far , five more participants have been tested for DENV3 NAb post JE vaccine , including two participants who did not seroconvert . Preliminary data suggest our study population is highly DENV exposed , and we have so far not identified any evidence of an increase in DENV NAb titres after JE vaccine , or an original antigenic sin type response . Cross-reactive T cells against DENV are readily detectable after natural infection or attenuated tetravalent dengue vaccine ( TV003 ) [32 , 39] and have been associated with pathology [40] but also protection [41 , 42] . One hypothesis for the disappointing results with the new dengue vaccine , Dengvaxia , in some studies [43 , 44] is that , because the vaccine is a chimera based on yellow fever 17D , the appropriate T cell responses are not elicited [14] . In this study , as well as in our earlier work [17] , we have observed a high degree of T cell cross-reactivity between JEV and dengue viruses in adults in this JE/dengue co-endemic area . This may reflect priming by multiple flavivirus exposures , whereby the most conserved epitopes , which receive the largest number of re-stimulations , are the most readily detectable . This is consistent with observations in a humanised animal model and following tetravalent vaccination [39 , 45] . However , this is not always seen in dengue endemic areas , where a sizeable fraction of the T cell response is directed against serotype specific epitopes even in populations with multiple DENV exposures [46] . Conservation between JEV and DENV epitopes and the cross-reactivity observed in most individuals who were tested here suggests that a better understanding of how such responses develop and whether they are protective would be of benefit . There may be strategies that could include such epitopes in next generation vaccines , such as a chimeric JEV/DENV vaccines [47 , 48] , or heterologous prime/boost immunisation schedules with dengue and JE vaccines . The ex-vivo T cell cytokine profiles after vaccination in this study were different from those seen in our recent study of circulating memory T cell responses to JEV [17] . In our previous study , 75% of responding CD8+ T cell responses in healthy , JEV exposed people made two or more cytokines; CD4+ T cell responses were very infrequent . Compared with recovered JE patients , where CD4+ T cell responses were much more frequent , CD4+ T cell responses in this study showed many fewer TNFα secreting cells and cells making two or more cytokines; instead IFNγ and IL2 single positive cells dominated the response . High TNFα levels have been linked to mortality in JE [49] , and this finding provides further support for our earlier observations linking CD4+ T cell derived TNFα with pathogenesis in JE . JE vaccination is effective even if the vaccine virus is relatively genetically distant from circulating virus [5] and protection may be long lasting even in the absence of NAb [50] . In our study , 10 individuals were NAb negative at baseline and had proliferation data available . Nine had T cell proliferative responses at baseline , reflective of ‘central’ memory ( which presumably also make cytokines , though we did not measure this ) rather than IFNγ-ELISpot assays , which , by virtue of their much shorter incubation period , are biased towards ‘effector’ memory [51] . The combination of proliferation in the absence of NAb and ELISpot responses may reflect a common , but hitherto unrecognised , state of long term immunity , mediated by long lived memory T cells [52] . In summary , here we have given the first description of T cell responses to JE vaccine SA14-14-2 in adults in a flavivirus endemic area , most of whom have evidence of prior adaptive immunity against JEV . We have shown that ( i ) T cell responses were detected in most volunteers after vaccination and cross-react with other flaviviruses; ( ii ) seroconversion after vaccination of adults with single dose JEV SA14-14-2 in an endemic area is relatively poor and ( iii ) T cell proliferative responses were detectable before vaccination in most volunteers , even if ELISpot and NAb assays were negative . T cell proliferation is worthy of investigation , in JEV naïve subjects , as an additional immunologic measure of response to vaccine [53] . To what extent prior exposure to related flaviviruses and associated T cell cross reactivity could influence vaccine responsiveness , especially in the B cell compartment , requires further study . Some of the questions surrounding the nature of the response to JEV SA14-14-2 can best be addressed in populations without extensive prior flavivirus exposure . | The Flavivirus genus member Japanese encephalitis ( JE ) virus ( JEV ) , causes severe brain disease in tens of thousands of children across Asia every year . JE is vaccine preventable , and the immune response to JEV plays a major role in disease outcome . However , the response to JEV is hard to study as JE affects young children in rural areas . Related flaviviruses , such as dengue virus ( which has no good vaccine ) , can influence the outcome of JE , probably due to cross-reactive immune responses . T cells ( a subset of white blood cells ) respond to virus infections , but we know little about the timing and nature of T cell responses to JEV after infection and whether T cells are protective against JEV . We used the live JE vaccine SA14-14-2 as a model to study the immune response to JEV . We found T cell responses frequently after JE vaccination . In this small group of volunteers , many of whom were exposed to dengue virus , most of the T cell responses tested cross-reacted between JEV and dengue virus . However , only about two thirds of people made antibody responses to the vaccine . Studying these responses could help design new vaccines for use against JE and dengue in Asia . | [
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| 2017 | Cellular Immune Responses to Live Attenuated Japanese Encephalitis (JE) Vaccine SA14-14-2 in Adults in a JE/Dengue Co-Endemic Area |
p53 , which regulates cell-cycle arrest and apoptosis , is a crucial target for viruses to release cells from cell-cycle checkpoints or to protect cells from apoptosis for their own benefit . Viral evasion mechanisms of aquatic viruses remain mysterious . Here , we report the spring viremia of carp virus ( SVCV ) degrading and stabilizing p53 in the ubiquitin-proteasome pathway by the N and P proteins , respectively . Early in an SVCV infection , significant induction was observed in the S phase and p53 was decreased in the protein level . Further experiments demonstrated that p53 interacted with SVCV N protein and was degraded by suppressing the K63-linked ubiquitination . However , the increase of p53 was observed late in the infection and experiments suggested that p53 was bound to SVCV P protein and stabilized by enhancing the K63-linked ubiquitination . Finally , lysine residue 358 was the key site for p53 K63-linked ubiquitination by the N and P proteins . Thus , our findings suggest that fish p53 is modulated by SVCV N and P protein in two distinct mechanisms , which uncovers the strategy for the subversion of p53-mediated host innate immune responses by aquatic viruses .
The tumor suppressor p53 is a crucial cellular stress sensor that triggers apoptosis , cell-cycle arrest , and a series life biology processes by responding to environmental stresses such as DNA damage , hyperproliferative signals , and hypoxia [1 , 2] . The corresponding cellular responses mediated by p53 depend on its transcriptional factor role to induce particular target genes [3 , 4] . The activity of p53 demands tight limitations to the cell’s stabilization and the protein level of p53 is low in normal cells [5–7] . Previous studies have indicated that p53 participates in the defense against viral infection depending on its capacity to activate cell-cycle arrest or apoptosis via the transcription of target genes [8–10] . p53-dependent apoptosis has been identified as a powerful control to restrict virus infection , such as by limiting the infections of vesicular stomatitis virus ( VSV ) , influenza A virus ( IAV ) , herpes simplex virus ( HSV ) , and poliovirus [11–16] . A putative explanation is that early apoptosis would be harmful to the virus as they should use the host’s resources for replication , thus impairing the production of newly formed viral particles [17] . However , viruses have evolved strategies to handle host p53 activity and thus facilitate viral replication and proliferation . Two pathways are invariably chosen by a virus for its own benefit: 1 . Use p53 activity; p53 is employed by human cytomegalovirus ( HCMV ) , respiratory syncytial virus ( RSV ) , adenovirus , encephalomyocarditis virus ( EMCV ) , and parainfluenza virus to promote viral replication [13 , 18–20] . Moreover , p53 as a transcription factor transcripts the HCMV L44 protein required for virus replication , and 21 binding sites of p53 have been found in the virus genome [18] . 2 . Counteract p53 activity . Kaposi’s sarcoma-associated herpesvirus ( KSHV ) ORF K8 interacts with p53 to inhibit its activity; the adenovirus E4-ORF6 protein degrades p53; HPV E7 suppresses p53 transcriptional activity; KSHV vIRF1 decreases p53 phosphorylation and promotes its ubiquitylation; the polyoma virus blocks the p53-mediated signaling pathway [21–24] . Thus , combat between the host’s innate immune response and viruses regarding p53 is complicated and pivotal , and although multiple correlative research studies have been accomplished in multiple species , this remains unclear for fish and fish virus . Spring viremia of carp virus ( SVCV ) is an aquatic virus that belongs to the genus Vesiculovirus of the Rhabdoviridae family and causes remarkable mortality in common carp ( Cyprinus carpio ) [25] . It contains a ~11 kb negative-sense , single-stranded RNA and encodes five viral proteins in the following order: 3’-nucleoprotein ( N protein ) , phosphoprotein ( P protein ) , matrix protein ( M protein ) , glycoprotein ( G protein ) , and viral RNA-dependent RNA polymerase ( L protein ) -5’ [26] . The exact mechanisms of these viral proteins functioning on SVCV replication and proliferation are poorly known . According to the studies that examined rhabdovirus , N protein interacts with viral RNA to construct a nucleocapsid for assembly , M protein is involved in the budding formation , phosphorylated P protein associates with L protein to form an activated viral polymerase that interacts with the RNA template , and G protein participates in viral endocytosis [27] . Previous studies by our lab have reported the strategies of SVCV to evade the host IFN response . As a decoy of TBK1 , SVCV P protein leads to decreased IRF3 phosphorylation and the N protein degrades MAVS via the K48-ubiquitin-proteasome pathway; thus , both these proteins significantly reduce the host IFN transcription to facilitate viral proliferation [28 , 29] . We further explore the strategies of aquatic virus innate immune evasion pathways here by reporting fish p53 as an identical target that is modulated by SVCV N and P proteins to respectively decrease and stabilize with decreasing and increasing K63-linked ubiquitination at the K358 site . These findings shed light on the mechanisms of p53 regulated by aquatic viruses in lower vertebrates .
Viral infection invariably leads to a changed cellular statement , including cell-cycle arrest and apoptosis [1] . The effect of stimulating fish cells with fish virus remains unclear . We identified the influence of SVCV infection on fish cell-cycle progression by infecting ZFL cells from the liver of zebrafish with different doses and times as indicated and we analyzed the cell-cycle progression by flow cytometry . At 24 hpi , the cell population of the S phase of SVCV-infected cells in ( MOI = 10 ) was close to 2 . 3-fold higher than that among mock-infected cells , and the groups of MOI = 1 and MOI = 0 . 1 were obscure ( Fig 1A ) . At 48 hpi , the S phase accumulations of the three infection groups were respectively increased by 1 . 8- , 2 . 3- , and 2 . 2-fold compared to the mock infection cells ( Fig 1B ) . EPC cells are the usual infection cell line model for SVCV and are effective at SVCV infection . At 24 hpi , there is no obvious difference in S phase accumulation between the mock and infected cells , while SVCV infection triggers a 1 . 8-fold cell-cycle progression that delays the S phase at 48 hpi ( Fig 1C and 1D ) . These results demonstrated that SVCV leads fish cells to a remarkable S phase accumulation . A series of studies have suggested that p53 expression interference is a pivotal mechanism for multiple viruses to force host cells to enter their replicative S phase , favoring virus replication [30] . Since the above study means that the significantly increased fish cell population in the S phase is infected by SVCV , the expression of p53 needs to be clarified . In mock-infected cells , p53 was increased from 12 hpi to 24 hpi to present a stable expression , while this was decreased significantly in SVCV-infected cells at 12 hpi and 24 hpi ( Fig 2A ) . Then , stimulated with a dose-dependent infection of SVCV , p53 expression was inhibited under high-dose viral infection and unchanged in the low-dose group ( Fig 2B ) . We illustrated whether the decline of p53 was caused by mRNA or protein suppression by cloning the ORF of p53 into the eukaryotic expression vector with a Myc tag . Upon transfection with p53-Myc and infection with SVCV , the expression of p53 was attenuated dose-dependently ( Fig 2C ) . Then , the p53-stably expressed ZFL cell line was established and infected with SVCV , the result was consistent with the above ( Fig 2D ) . These results demonstrated that SVCV interferes with the host p53 protein level expression . As p53 is blunted by SVCV infection , the regulation relationship between p53 and SVCV proteins should be identified . First , the expression level of viral protein was monitored . The N and P proteins were expressed at 24–96 hpi ( Fig 2E ) . Subsequently , the co-overexpression of SVCV N protein with Flag tag ( N-Flag ) and p53-Myc , the anti-Flag antibody ( Ab ) -immunoprecipitated protein complexes containing N protein were recognized by the anti-Myc Ab , meaning that N protein is associated with p53 ( Fig 2F ) . Therefore , according to the above data , the N protein of SVCV is associated with p53 . Based on that result , the SVCV N protein interacts with p53 and the effect of p53 expression or stability can be examined . Upon co-transfection of p53-Myc and N-Flag , the cell lysate was checked by immunoblot ( IB ) with specified Abs . Compared with the control group , overexpression of the N protein had obviously declined p53 expression ( Fig 2G ) . Consistent with these observations , we focus on how functional N protein had decreased the p53 expression . Upon overexpression with different doses of N protein , the result from IB showed that p53 was abrogated dose-dependently ( Fig 2H ) . Subsequent infection with SVCV revealed an up to about 180-fold increase of virus titer in N protein-transfected cells ( Fig 2I ) . A previous study demonstrated that the N protein also restricted IFN expression; therefore , the enhanced virus proliferation should be mediated by the multifunction of the N protein . These data demonstrated that SVCV N protein subverted the host’s p53 protein expression . In the above results , the protein level expression of p53-Myc , which contained a strong promoter ( CMV promoter ) , was disrupted by N protein; therefore , we speculate that N protein could regulate p53 at the protein level . N protein and p53 were overexpressed and the cells were then treated with one of three agents: MG132 ( an inhibitor for the proteasome pathway ) , NH4Cl ( a lysosomal inhibitor ) , or 3-MA ( an inhibitor for the autophagosome pathway ) , with DMSO treatment as the control group . In addition to the above result , the expression of p53 protein co-expressed with N protein was weaker than its co-expression with the empty vector , and such attenuation was significantly rescued by MG132 ( Fig 3A ) . Subsequently , the rescue experiment was performed with different doses of MG132 and the declination of p53 regulated by N protein was rescued by MG132 dose-dependently ( Fig 3B ) . These observations indicated that p53 was negatively regulated by N protein in Ub-proteasomal degradation . We further clarified whether p53 was decreased in the Ub-proteasome pathway by co-transfecting p53-Myc and Ub-HA and then infecting with SVCV , then immunoprecipitating p53-Myc; IB result demonstrated that SVCV suppressed the ubiquitination of p53 ( Fig 3C ) . Subsequently , p53-Myc , N-Flag , and Ub-HA were co-transfected in either the presence or absence of MG132 . The result of IB demonstrated that the N protein suppressed the ubiquitination of p53 ( Fig 3D ) . Following the expression of N protein in several doses , the ubiquitination of p53 presented in a dose-dependent manner ( Fig 3E ) . K48 and K63 , which are lysines at positions 48 and 63 of ubiquitin and linked with polyubiquitin chains , represent the two canonical polyubiquitin chain linkages . Many studies have suggested that while target proteins were degraded by K48-linked polyubiquitin chains in a proteasome-dependent manner , they were stabilized by K63-linked polyubiquitin chains . Herein , whether the attenuation of p53 was mediated by the increase of K48-ubiquitination or the decrease of K63-ubiquitination should be identified . Upon co-transfection with p53 , N protein , Ub-K48O , or Ub-K63O with corresponding tags , the K48-linked ubiquitination of p53 was unchanged under N protein regulation; however , the K63-linked ubiquitination of p53 was significantly impaired in the same condition ( Fig 3F ) . As noted above , the observation suggested that p53 was negatively regulated by SVCV N protein via the attenuation of K63-ubiquitination . Meanwhile , in stimulation with SVCV for indicated time points , the RNA of ZFL cells were employed to examine the transcripts . At 12 hpi , the p53 mRNA expressions were nearly equal between the infected and mock-infected groups , whereas it was swiftly down-regulated , and lower by about 5 . 2-fold and 7 . 3-fold compared to the control group ( Fig 4A ) . As it is a key transcript factor , several downstream genes such as p21 and cyclin G1 responded to p53 . Therefore , the mRNA abundance of p21 and cyclin G1 were also detected . In addition to the expression pattern of p53 , the p21 transcripts amount in SVCV-infected cells and mock-infected cells were similar at 12 hpi and lower in the infected group by about 4 . 6-fold and 6 . 1-fold compared to the mock group at 24 hpi and 48 hpi , respectively ( Fig 4B ) . Furthermore , the expression of cyclin G1 was lower by 3 . 5-fold and 4 . 8-fold than the control group when infected with SVCV at 24 hpi and 48 hpi ( Fig 4C ) . Subsequently , the result of infection with different doses of SVCV demonstrated that the p53 mRNA level was blunted by SVCV dose-dependently ( Fig 4D ) . Then , the expression of p53 at 72 hpi and 96 hpi of SVCV infection was also assayed , and found to be lower than that in the control groups ( Fig 4E ) . These data suggest that the mRNA of p53 and related downstream genes were down-regulated in SVCV infection . Subsequently , the mRNA level of p53 regulated by N protein was also monitored . There , small interfere RNA ( siRNA ) ( si-N 1# , si-N 2# , and si-N 3# ) that were designed for N protein were transfected into EPC cells and were then infected with SVCV . In contrast with the control group , the groups of si-N 2# and si-N 3# interfered with the SVCV n gene expression efficiently ( Fig 4F ) . si-N 2# and si-N 3# were chosen for the following assessment . The cells’ overexpression of si-N 2# or si-N 3# and infection with SVCV in real-time PCR assay manifested that p53 was strikingly upregulated when the expression of the SVCV n gene was interfered ( Fig 4G ) . Next , the expression of p21 and cyclin G1 under the disruption of N protein during SVCV infection was also confirmed . Similar to the result for p53 , remarkable increases of p21 and cyclin G1 expression at the mRNA level were observed in the siRNA group ( Fig 4H and 4I ) . Overall , these results indicate that SVCV N protein negatively regulated p53 expression at both the protein and mRNA level . For the virus , host cell apoptosis is favorable for the assembled viron release , and p53 has been revealed as having a crucial role in the progress of apoptosis [31] . Therefore , we expect that cell apoptosis and p53 expression were regulated by SVCV at the late stage . Upon SVCV infection , cells underwent nuclear fragmentation and significant cell apoptotic bodies were observed at 72 hpi while the mock infection group was unaffected ( Fig 5A ) . Then , the expression of p53 at the late stage of SVCV infection was monitored by IB . Intriguingly , consistent with the above results , although p53 was down-regulated for 24–48 hpi , it was up-regulated remarkably at 72–96 hpi ( Fig 5B ) . With the subsequent co-transfection of P-Flag and p53-Myc , IB displayed that the anti-Flag Ab-immunoprecipitated protein complexes including P protein were also recognized by the anti-Myc Ab , which suggests that P protein interacted with p53 ( Fig 5C ) . Moreover , a significant enhancement of p53 was improved by P protein dose-dependently ( Fig 5D ) . Subsequently , the ubiquitination of p53 was assayed under treatment with MG132 and the ubiquitination of p53 was increased in the presence or absence of MG132 ( Fig 5E ) . Furthermore , the ubiquitination of p53 was elevated by P protein dose-dependently ( Fig 5F ) . Considering the different functions of K48-ubiquitination and K63-ubiquitination , Ub-K48O or Ub-K63O was co-expressed in the process of P protein regulated p53 , and the K63-linked ubiquitination of p53 was notably increased when P protein was overexpressed ( Fig 5G ) . Taken together , these data demonstrated that p53 was stabilized by K63-linked ubiquitination mediated by SVCV P protein . In humans , the six lysines in the C-terminal of p53 are the major lysine residues that are ubiquitinated by E3 ligases [32] . Although there is little research about the ubiquitination site of zebrafish p53 , zebrafish p53 is highly similar to mammalian p53 in both structure and function , and it has a 48% identical amino acid sequence to human p53 [33] . Therefore , according to the conserved amino acid sequence between human p53 and zebrafish p53 and professional ubiquitination site prediction ( www . ubpred . org ) , the three lysines in the C-terminal of zebrafish p53 were mutated for ubiquitination site identification ( Fig 6A ) . First , the stabilization of wild-type and mutant p53 were analyzed under the overexpression of N protein; the wild-type p53 was degraded compared to the control group , while the K358R of p53 was significantly rescued ( Fig 6B ) . The results indicated that the K358 might be the ubiquitination site for N protein-mediated K63-linked ubiquitination . Meanwhile , K358R was remarkably maintained at a low expression level by stimulation with overexpressed P protein , which means that the K358 of p53 should be the functional site for P protein-mediated K63-linked ubiquitination ( Fig 6C ) . Subsequently , to confirm the K63-linked ubiquitination of K358R p53 , the immunoblot results during the overexpression of N protein demonstrated that the Ub-K63O of K358R p53 was higher than that of the wild-type p53 and nearly equaled to that of the empty vector group ( Fig 6D ) . Next , the extent of the K63-linked ubiquitination of K358R p53 in the context of overexpressed P protein was monitored . The K63-linked ubiquitination of K358R p53 was lower than the wild-type p53 treated with the overexpression of P protein and approximated the empty vector group level , which demonstrated that the K358 of p53 was indispensable for P protein-mediated K63-linked ubiquitination ( Fig 6E ) . These data suggested that the K358 of p53 was crucial for N protein- and P protein-mediated K63-linked ubiquitination .
The battles between aquatic viruses and fish or other lower vertebrates have remained mysterious to date because knowledge about the molecular mechanisms regarding viral invasion , viral infection , host immune responses , and so on is insufficient . This study reports an aquatic virus called SVCV that employs two distinct manners to regulate the host key factor p53 expression , lowering p53 with N protein and increasing p53 with P protein to promote viral infection . Interestingly , our previous studies showed that SVCV N protein degraded host MAVS to blunt IFN production , and P protein acted as a decoy of TBK1 interfering with IRF3 phosphorylation to abrogate IFN transcription [28 , 29] . Combined with these studies , SVCV N and P proteins play multidimensional roles in controlling cell fate and antagonizing the IFN system . Actually , considering that they are viral non-structure proteins , SVCV N and P proteins should also participate in viral genome transcription and replication like in other rhabdoviruses [27] . This information indicates that two of the five viral proteins of SVCV possess multiple functions . That might be because , unlike DNA viruses , RNA viruses are usually composed of only a few proteins ( e . g . SVCV only contains five proteins ) ; hence , only efficient and multifunctional viral proteins are capable of accomplishing viral proliferation in host cells . The major purpose of this study was to focus on p53 regulation by SVCV N and P proteins at protein level; only a minor space regarded the mRNA regulation of p53 by N protein . In a viral infection , fish IFN is boosted to stimulate antiviral gene transcription [34] . Previous studies have shown that p53 mRNA is stimulated by type I IFN , which leads to the early upregulation of downstream target proteins to launch early apoptosis to inhibit viral replication [8 , 35] . In our early study , the SVCV N protein degraded MAVS through the ubiquitin-proteasome pathway to block the host’s IFN production . Combined with these data , the downregulated p53 mRNA by the overexpression of SVCV N protein in the current study might because that the expression of IFN was reduced . This mechanism should be one strategy of the virus to improve the infection . In addition , the inhibition of host target genes and even of the whole gene transcription level mediated by viral proteins is crucial for viral replication and proliferation [36–38] . The M protein of VHSV , which is an aquatic rhabdovirus , has been reported to decline the host transcription to escape the host immune response [39] . A similar function of SVCV M protein was observed in a series of preliminary experiments at our lab: the overexpression of M protein significantly inhibited the transcription level of both the endogenous genes and exogenous transfected genes . Since the promoters in the expression plasmids are strong ones , these data indicate that the transcriptional suppression mediated by rhabdovirus M protein targets RNA polymerase , but not specific host genes . Therefore , host transcription regulation plus the controlling of RNA polymerase should be crucial for viral invasion , in addition to protein-level modulation . Upon virus infection , p53 is a pivotal regulator of cell-cycle arrest and cellular apoptosis , which are employed by host cells to defend against viruses . Thus , viruses possess evolutionary reasons to encode p53 antagonistic proteins . Usually , cell-cycle arrest is beneficial for viral early infection to plunder a host’s resources for viral replication , and late apoptosis is preferred for viron release . As examples , cell-cycle arrest , as mediated by p53 , to delay apoptosis has been identified as a feasible mechanism to consequently enhance EMCV replication; regarding the regulation of apoptosis , adenovirus E1B-55K and E4-ORF6 proteins , human papillomavirus ( HPV ) E6 , EBV EBNA-5 , and KSHV vIRF4 , as well as the vaccinia virus ( VV ) B1R kinase all induce the degradation of p53 . Other proteins such as Adenovirus E1A and HPV E7 maintain p53 stability [40 , 41] . The strategies utilized by viruses to modulate cellular p53 to affect host apoptosis should finally promote viral replication and proliferation . In this study , the SVCV N protein induces cell-cycle arrest , and the P protein facilitates apoptosis by stabilizing p53 , which indicates the strategies that might be employed by a virus at different time points of infection .
Human embryonic kidney ( HEK ) 293T cells were provided by Dr . Xing Liu ( Institute of Hydrobiology , Chinese Academy of Sciences ) and were grown at 37°C in 5% CO2 in Dulbecco’s modified Eagle’s medium ( DMEM; Invitrogen ) supplemented with 10% fetal bovine serum ( FBS , Invitrogen ) . Zebrafish liver ( ZFL ) cells ( American Type Culture Collection , ATCC ) were cultured at 28°C in 5% CO2 in Ham’s F12 nutrient mixture medium ( Invitrogen ) supplemented with 10% FBS . p53 stably-expressed ZFL cells were transfected with GFP or GFP-p53 and were subjected to G418 selection to enrich GFP positive cells . Epithelioma papulosum cyprini ( EPC ) cells were obtained from China Center for Type Culture Collection ( CCTCC ) and were maintained at 28°C in 5% CO2 in medium 199 ( Invitrogen ) supplemented with 10% FBS . Spring viremia of carp virus ( SVCV ) was propagated in EPC cells until cytopathic effect ( CPE ) was observed , then the culture medium with cells was harvested and stored at -80°C until needed . The ratio of cells in each phase of the cell cycle was determined by DNA content using propidium iodide ( PI ) staining followed by flow cytometric analysis . The cells plated at a density of 1 × 106 cells/flask were treated with the indicated multiplicity of infection ( MOI ) of SVCV for the indicated times as described in the figure legends . The cells were trypsinized , washed twice with PBS , and fixed with 70% ice-cold ethanol at −20°C overnight . Fixed cells were washed with cold PBS and resuspended with PI staining solution containing 50 μg/mL PI ( Sigma-Aldrich ) , 100 μg/mL RNase A ( TIANGEN Biotech ) , 0 . 02% Triton X-100 , and incubated in the dark for 30 min . The samples were analyzed using a flow cytometer ( BD Biosciences ) . The open reading frame ( ORF ) of zebrafish p53 ( GenBank accession number NM_001271820 . 1 ) and p53 mutants was generated by PCR and then cloned into pCMV-Myc ( Clontech ) . The expression plasmids for Flag-tagged N , and Flag-tagged P were described previously [42 , 43] . All constructs were confirmed by DNA sequencing . MG132 was purchased from Sigma-Aldrich and used at a final concentration of 20 μM/ml . Transient transfections were performed in EPC cells seeded in 6-well or 24-well plates or ZFL cells seeded in 6-well plates by using X-tremeGENE HP DNA Transfection Reagent ( Roche ) according to the manufacturer’s protocol . For the antiviral assay using 24-well plates , EPC cells were transfected with 0 . 5 μg Myc-N or the empty vector . At 24 h post-transfection , cells were infected with SVCV ( MOI = 10 ) . After 2 or 3 d , supernatant aliquots were harvested for detection of virus titers , the cell monolayers were fixed by 4% paraformaldehyde ( PFA ) and stained with 1% crystal violet for visualizing CPE . For virus titration , 200 μl of culture medium were collected at 48 h post-infection and used for plaque assay . The supernatants were subjected to 4-fold serial dilutions and then added ( 100 μl ) onto a monolayer of EPC cells cultured in a 96-well plate . After 48 or 72 h , the medium was removed , and the cells were washed with PBS , fixed by 4% PFA and stained with 1% crystal violet . Results are representative of three independent experiments . EPC cells were seeded into 6-well plates overnight and transfected with 100 nM siRNA of N or the negative control ( siNC by using the X-tremeGENE HP DNA transfection reagent ( Roche ) according to the manufacturer’s protocols . siRNA of N and siCon were obtained from GenePharma ( Shanghai , China ) . The following sequences were targeted for SVCV N: siN#1 ( GCAUGUGAAUGCUUAUCUATT ) , siN#2 ( GCCAAAUCACCAUACUCAATT ) , and siN#3 ( GCCUCGCGAUAACUCAGUUTT ) . Total RNA was extracted by the TRIzol reagent ( Invitrogen ) . First-strand cDNA was synthesized by using a GoScript reverse transcription system ( Promega ) according to the manufacturer’s instructions . qPCR was performed with Fast SYBR green PCR master mix ( Bio-Rad ) on the CFX96 real-time system ( Bio-Rad ) . PCR conditions were as follows: 95°C for 5 min and then 40 cycles of 95°C for 20 s , 60°C for 20 s , and 72°C for 20 s . β-actin gene was used as an internal control . The relative fold changes were calculated by comparison to the corresponding controls using the 2-ΔΔCt method . Three independent experiments were conducted for statistical analysis . The following gene-specific primer sequences were utilized for the qPCR: Drp53 , 5’-CCCGGATGGAGATAACTTG-3’ and 5’-CACAGTTGTCCATTCAGCAC-3’; Drp21 , 5’-GACCAACATCACAGATTTCTAC-3’ and 5’-TGTCAATAACGCTGCTACG-3’; DrCyclinG1 , 5’-CATCTCTAAAAGAGGCTCTAG-3’ and 5’-CACACAAACCAGGTCTCCAG-3’; Drβ-actin , 5’-CACTGTGCCCATCTACGAG-3’ and 5’-CCATCTCCTGCTCGAAGTC-3’; SVCV-N , 5’-CCTACAACAGCCGCAGAGAC-3’ and 5’-GCACTCAACCACAGCATCCA-3’ . The HEK 293T cells or EPC cells seeded into 10 cm2 dishes overnight were transfected with a total of 10 μg of the plasmids indicated on the figures . At 24 h post-transfection , the medium was removed carefully , and the cell monolayer was washed twice with 10 ml ice-cold PBS . Then the cells were lysed in 1 ml of radioimmunoprecipitation ( RIPA ) lysis buffer [1% NP-40 , 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM NaF , 1 mM sodium orthovanadate ( Na3VO4 ) , 1 mM phenyl-methylsulfonyl fluoride ( PMSF ) , 0 . 25% sodium deoxycholate] containing protease inhibitor cocktail ( Sigma-Aldrich ) at 4°C for 1 h on a rocker platform . The cellular debris was removed by centrifugation at 12 , 000 × g for 15 min at 4°C . The supernatant was transferred to a fresh tube and incubated with 30 μl anti-Flag affinity gel or anti-Myc affinity gel ( Sigma-Aldrich ) overnight at 4°C with constant agitation . These samples were further analyzed by immunoblot ( IB ) . Immunoprecipitated ( IP ) proteins were collected by centrifugation at 5000 × g for 1 min at 4°C , washed three times with lysis buffer and resuspended in 50 μl 2 × SDS sample buffer . The immunoprecipitates and whole cell lysates were analyzed by IB with the indicated antibodies ( Abs ) . For prokaryotic expression , the ORF of N or P protein was cloned into the EcoR I and Xho I sites of pET28a ( + ) vector . Prokaryotic expression plasmids were transformed into Escherichia coli BL21 ( DE3 ) -CodonPlus-RIL competent cells ( Stratagene ) . Recombinant proteins were expressed by induction with isopropyl-β-D-thiogalactopyranoside ( IPTG ) and purified from the supernatants of cell lysates under native condition . The His-tagged recombinant N or P was bound to a Ni-NTA Superflow resin ( Qiagen ) by rocking at 4°C overnight . The resin was in turn washed with wash buffer 1 ( 50 mM NaH2PO4 , 300 mM NaCl , pH 8 . 0; containing 20 mM imidazole ) , wash buffer 2 ( containing 40 mM imidazole ) , and wash buffer 3 ( containing 60 mM imidazole ) . The affinity-purified proteins were further purified by gel filtration using a Superdex 75 column on an AKTA FPLC system ( GE Healthcare ) . Purity and concentration of the purified recombinant proteins were determined by SDS-PAGE and BCA Protein Assay ( Thermo Scientific ) . Recombinant proteins were applied to immunize white rabbits to raise polyclonal antibodies against SVCV N or P protein , respectively . Immunoprecipitates or whole cell lysates were separated by 10% SDS-PAGE and transferred to polyvinylidene difluoride ( PVDF ) membrane ( Bio-Rad ) . The membranes were blocked for 1 h at room temperature in TBST buffer ( 25 mM Tris-HCl , 150 mM NaCl , 0 . 1% Tween 20 , pH 7 . 5 ) containing 5% nonfat dry milk , probed with the indicated primary Abs at an appropriate dilution overnight at 4°C , washed three times with TBST , and then incubated with secondary Abs for 1 h at room temperature . After three additional washes with TBST , the membranes were stained with the Immobilon Western chemiluminescent horseradish peroxidase ( HRP ) substrate ( Millipore ) and detected by using an ImageQuant LAS 4000 system ( GE Healthcare ) . Abs were diluted as follows: anti-β-actin ( Cell Signaling Technology ) at 1:1 , 000 , anti-Flag/HA ( Sigma-Aldrich ) at 1:3 , 000 , anti-Myc ( Santa Cruz Biotechnology ) at 1:2 , 000 , and HRP-conjugated anti-mouse IgG or anti-rabbit IgG ( Thermo Scientific ) at 1:5 , 000 . Results are representative of three independent experiments . Data are expressed as mean ± standard deviations ( SDs ) which obtained by measuring each sample in triplicate . The p values were calculated by one-way analysis of variance ( ANOVA ) with Dunnett’s post hoc test ( SPSS Statistics , version 19; IBM ) . A p value < 0 . 05 was considered statistically significant . | Upon viral infiltration , host cells employ p53 to defend against infection . Thus , viruses need to inhibit these antiviral surveillance mechanisms in the host to efficiently spread to new hosts . To date , the evasion mechanisms against fish p53 remain unclear . In this study , we reveal that SVCV modulates host p53 expression by two distinct mechanisms . Through a series of experiments , we show that SVCV N protein bound and degraded host p53 through suppressing the K63-linked ubiquitination; SVCV P protein interacted with and stabilized p53 while enhancing the K63-linked ubiquitination; lysine residue 358 was the key site for p53 ubiquitination by the N and P proteins . Our findings shed light on the special evasion mechanisms of fish virus and expand our knowledge of the virus–host interactions that are responsible for regulating p53 in lower vertebrates . | [
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| 2019 | Spring viraemia of carp virus modulates p53 expression using two distinct mechanisms |
Defining the precise cellular mechanisms of neutralization by potently inhibitory antibodies is important for understanding how the immune system successfully limits viral infections . We recently described a potently inhibitory monoclonal antibody ( MAb E16 ) against the envelope ( E ) protein of West Nile virus ( WNV ) that neutralizes infection even after virus has spread to the central nervous system . Herein , we define its mechanism of inhibition . E16 blocks infection primarily at a post-attachment step as antibody-opsonized WNV enters permissive cells but cannot escape from endocytic compartments . These cellular experiments suggest that E16 blocks the acid-catalyzed fusion step that is required for nucleocapsid entry into the cytoplasm . Indeed , E16 directly inhibits fusion of WNV with liposomes . Additionally , low-pH exposure of E16–WNV complexes in the absence of target membranes did not fully inactivate infectious virus , further suggesting that E16 prevents a structural transition required for fusion . Thus , a strongly neutralizing anti–WNV MAb with therapeutic potential is potently inhibitory because it blocks viral fusion and thereby promotes clearance by delivering virus to the lysosome for destruction .
Neutralizing antibodies can inhibit virus infection by impeding one of several critical steps of the virus lifecycle . These include blocking attachment to the cell surface , interaction with host factors required for internalization , and structural transitions on the virion that drive membrane fusion ( reviewed in [1] , [2] ) . Antibodies can independently neutralize virus infection by promoting virus aggregation , destabilizing virion structure , and blocking budding or release from the cell surface ( reviewed in [3] ) . Historically , many of the most potently neutralizing antibodies inhibit infection by interfering with required interactions between viruses and obligate cellular receptors ( e . g . , rhinovirus and ICAM-1 , HIV and CD4 or CCR5 , and poliovirus and CD155 ) . West Nile virus ( WNV ) is a mosquito-borne positive polarity RNA virus of the Flavivirus genus within the Flaviviridae family . Similar to other Flaviviruses , such as Dengue ( DENV ) , yellow fever , and Japanese encephalitis viruses , WNV has an ∼11 kb RNA genome that encodes three structural ( C , prM/M and E ) and seven non-structural ( NS1 , NS2a , NS2b , NS3 , NS4a , NS4b , and NS5 ) proteins that are generated by cleavage from a single polyprotein [4] , [5] . WNV has spread globally and epidemic outbreaks of encephalitis now occur annually in the United States . Infection with WNV causes syndromes ranging from a mild febrile illness to severe neuroinvasive disease and death [6] , [7] . There is currently no approved vaccine or therapy for WNV infection . Structural analysis of the WNV and DENV virions by cryo-electron microscopy [8] , [9] reveals a ∼500 Å mature virion with a smooth outer surface . The 180 copies of the E glycoproteins lay relatively flat along the virus surface as anti-parallel dimers in three distinct symmetry environments . Following exposure to low pH in the endosomal compartment , the E proteins rearrange from homodimers to homotrimers , exposing a fusion peptide , which interacts with the endosomal membrane and allows uncoating and nucleocapsid escape into the cytoplasm [10] . The atomic structure of the surface E glycoprotein has been defined by X-ray crystallography for DENV , WNV , and tick-borne encephalitis virus ( TBEV ) [11]–[15] , revealing three conserved domains . Domain I ( DI ) is a 10-stranded β-barrel and forms the central structural architecture of the protein . Domain II ( DII ) consists of two extended loops projecting from DI and contains the putative fusion loop ( residues 98–110 ) , which participates in a type II fusion event [10] , [16] , [17] . In the mature virus , the fusion loop packs between two anti-parallel dimers and is solvent inaccessible , protecting the virus from premature fusion and inactivation . Domain III ( DIII ) is located on the opposite end of DI , forms a seven-stranded immunoglobulin-like fold , and has been suggested as a receptor binding site [18]–[20] . The humoral immune response controls WNV pathogenesis as mice lacking B cells are highly vulnerable to lethal infection [21] . During infection with flaviviruses , most neutralizing antibodies are directed against the E protein , although a subset binds the prM protein [22] , [23] . To better understand the structural basis of antibody protection against WNV , we recently generated a large panel of monoclonal antibodies ( MAbs ) against WNV E protein [24] . One antibody , E16 , was observed to block WNV infection in vitro and in vivo and was effective as a post-exposure therapy even 5 days after infection [24] , [25] . Potent E16 neutralization occurs with strikingly low stoichiometric requirements , as a virion occupancy of ∼25% is sufficient to inhibit infection [26] . Herein , we determine the mechanism by which this therapeutic MAb neutralizes WNV infection . E16 traffics with WNV particles into permissive target cells , and is strongly inhibitory because it blocks pH-dependent fusion , a critical step in the entry pathway of this virus .
A common mechanism of antibody-mediated neutralization of viral infection is to prevent attachment and entry into target cells . Previously published studies suggested that E16 did not dramatically reduce WNV binding to Vero cells but instead inhibited at a post-attachment step [27] . To gain further insight as to how E16 inhibits infection , WNV was pre-incubated with Alexa-488 conjugated E16 or E53 , a second inhibitory MAb that binds to the fusion loop in DII , prior to a cell binding assay at 4°C . Subsequently , cells were washed at 4°C , fixed and visualized by confocal microscopy . At 4°C , enveloped viruses , including flaviviruses , remain on the cell surface and are not internalized [28]–[30] . As expected , in the absence of WNV , labeled E16 and E53 were not visualized on the surface or interior of cells ( data not shown ) . When Alexa-488-E53-WNV complexes were added , no fluorescence signal was observed on the surface of Vero cells ( Figure 1A , panels F and H ) , suggesting that E53 , as hypothesized previously [27] , primarily inhibits WNV attachment to Vero cells . Similar results were obtained with Alexa-488 conjugated E60 , a MAb that binds to a similar epitope as E53 in DII ( data not shown ) . In contrast , staining was apparent on the surface of cells incubated with labeled Alexa-488-E16-WNV complexes . Thus , despite saturating and neutralizing concentrations ( 100 μg/ml ) of E16 MAb , WNV binding to Vero cells still occurred ( Figure 1A , panels B and D ) . Analogous results were obtained with the strongly neutralizing DIII-specific E24 MAb ( data not shown ) . To determine if the E16 MAb restricted virus entry , Vero cells were warmed to 37°C after MAb-WNV complex pre-binding at 4°C , and again visualized by confocal microscopy . As anticipated , Alexa-488-E53-WNV complexes were not detected inside cells ( Figure 1A , panels N and P ) . In contrast , Alexa-488-E16-WNV complexes readily entered cells and accumulated in acidic vesicles that were identified with a pH sensitive dye ( Figure 1A , panels J and L ) . Even after several hours of incubation , E16-WNV complexes remained localized in these acidic cellular compartments ( Figure 1B , panels B–D ) , whereas E53-WNV complexes were not detected within the cells ( Figure 1B , panels F–H ) . In contrast , in the absence of neutralizing antibodies , WNV infection progresses rapidly as demonstrated by the accumulation of E protein in the cell over time ( Figure S1 ) . Because E16-WNV complexes co-localized with an acidified intracellular compartment for several hours , we hypothesized that this MAb prevented virus fusion with endosomal membranes . Because WNV infection requires a pH-dependent structural rearrangement of E proteins for fusion , we evaluated whether concanamycin A1 , a vacuolar-ATPase inhibitor [31] , blocked WNV infection at a similar cellular stage as did E16 . Vero cells were infected at a high multiplicity of infection ( MOI ) in the presence of 10 nM concanamycin A1 or humanized E16 ( hu-E16 , 100 μg/ml ) or a media control for 3 h or 24 h at 37°C . Cells were washed , fixed , and stained for WNV using an oligoclonal pool of mouse MAbs against the E protein . Samples treated with hu-E16 were also stained with an anti-human IgG secondary antibody to confirm that hu-E16 co-localized with the virus . In the absence of concanamycin A1 or hu-E16 , infected Vero cells showed strong staining of E protein at 3 h that was markedly increased at 24 h ( Figure S1 ) . Treatment with 10 nM concanamycin A1 resulted in a punctate pattern of E protein staining at 3 and 24 h , suggesting that WNV localized to and likely remained sequestered in endocytic compartments ( Figure 2 , panels A and D ) . Analogous to treatment with concanamycin A1 , hu-E16-opsonized WNV showed a similar staining pattern up to 24 hours after infection ( Figure 2 , panels B and E ) . As co-staining of oligoclonal mouse anti-E protein and hu-E16 was observed over time , it is likely that E16 was still bound to WNV , and these virus-MAb complexes accumulated in endosomal/lysosomal compartments ( Figure 2 , panels C and F ) . Of note , in Figure 2C , only a subset of the blue spots ( which indicates the presence of the virion ) co-stain with hu-E16 . This is likely a sensitivity of detection issue as E16 neutralizes infection at both low ( ∼25% or 30 copies per virion ) and high occupancy [26] . Because of the high MOI used , some viruses will be more completely decorated ( and thus fluorescent ) , whereas others will bind fewer antibodies yet still be neutralized . Virions that bind fewer E16 antibodies yet still are neutralized may co-stain less brightly in this microscopic assay . The ability of E16 to block WNV egress from endosomes suggested that this MAb directly inhibited the pH-dependent fusion step . Initially , to test this , we used a surrogate plasma membrane fusion infection assay that has been validated for alphaviruses and flaviviruses [32] , [33] . Normally , flaviviruses enter cells via receptor-mediated endocytosis , with fusion occurring from within acidic endosomes [29] , [34] , [35] . However , flaviviruses also can be induced to fuse directly with the plasma membrane , at low efficiency , when cell-bound virus is exposed to an acidic solution [32] . To assess the effects of E16 on virus-plasma membrane fusion , WNV was pre-bound to Vero cells at 4°C , and subsequently incubated on ice with saturating concentrations of E16 IgG , E16 single chain Fv ( scFv ) , E60 IgG , or no MAb . Cells were warmed to 37°C in pH 5 . 5 media ( or pH 7 . 5 media as a negative control ) to induce virus-plasma membrane fusion and analyzed at 24 hours for level of infection by flow cytometry . In all experiments , 10 nM concanamycin A1 was added to inhibit infection via the canonical receptor-mediated endocytic pathway . As expected , in the absence of antibody , addition of media at neutral pH ( 7 . 5 ) did not promote productive infection ( ∼0 . 7% WNV antigen+ cells , Figure 3A and 3B ) . Exposure of cell bound WNV to media at pH 5 . 5 resulted in a ∼7 fold increase in infection ( ∼5 . 1% WNV antigen+ cells , P<0 . 0005 , Figure 3A and 3B ) . The addition of E60 following viral attachment did not appreciably affect virus-plasma membrane fusion ( P = 0 . 4 ) , confirming earlier results that this MAb does not inhibit Vero cell infection at a post-attachment step [27] . In contrast , both E16 IgG and scFv efficiently blocked WNV-plasma membrane fusion ( 0 . 15% and 0 . 08% WNV antigen+ cells , respectively; Figure 3A and 3B , P<0 . 0001 ) . To confirm that E16 blocks membrane fusion of WNV , we evaluated the fusogenic properties of WNV in a model liposome system . To this end , WNV particles were metabolically labeled with pyrene hexadecanoic acid and purified by density gradient centrifugation . Subsequently , pyrene-labeled virions were pre-incubated with various concentrations of E16 , E60 or E111 ( a DIII-specific non-neutralizing control MAb [24] ) and mixed with liposomes . The mixture was acidified to pH 5 . 4 and fusion was measured on-line in a fluorimeter at 37°C as a function of the decrease in pyrene excimer fluorescence . WNV fuses rapidly and efficiently with liposomes . In contrast , no membrane fusion activity was measured with saturating concentrations of E16 ( Figure 4A ) . Inhibition of membrane fusion by E16 was dose-dependent as decreasing concentrations of E16 blocked fusion to a lesser degree ( Figure 4A and 4B ) . E111 did not influence the membrane fusion properties of WNV as efficient fusion was measured at all antibody concentrations tested . MAb E60 was observed to induce a dose-dependent inhibition of membrane fusion activity , although a complete inhibition of fusion was not achieved ( Figure 4B ) . Previous studies have shown that exposure of WNV or other flaviviruses to acidic ( pH<6 ) media in the absence of target membranes results in E protein rearrangement , premature exposure of the fusion loop , virus aggregation , and rapid irreversible inactivation of fusion competence [36]–[38] . We reasoned that if E16 neutralized WNV infection by directly blocking the pH-dependent fusion event it should prevent adventitious inactivation in solution after exposure to acidic pH . To test this , WNV ( 3×103 PFU ) was pre-incubated with saturating ( 100 μg/ml ) concentrations of E16 , E60 , or E9 ( a DIII non-neutralizing MAb [24] ) Fab fragments . Although the E60 MAb did not appear to enter cells or potently neutralize WNV infection [39] , we included this fusion loop-specific Fab as a control because it partially inhibited pH-catalyzed virus fusion in the liposome assay . Excess buffered media at pH 7 . 5 or pH 5 . 5 was added to the virus-Fab complexes and incubated at 37°C for 15 min . The solution was normalized after dilution with a 25-fold excess of pH 7 . 5 media and added to Vero cells for 1 h at 37°C to allow infection as the monovalent Fab fragments detached . As expected , exposure to a pH 7 . 5 solution did not change WNV infectivity , as the monolayer contained ∼3 . 9×103 PFU ( Figure 5 ) . In contrast , treatment with a pH 5 . 5 solution inactivated WNV and reduced infectivity ( P<0 . 0001 ) below the limit of detection ( ∼20 plaques ) . The E9 Fab failed to protect the virus from low pH inactivation , whereas neutralizing concentrations of E16 and E60 Fabs at pH 5 . 5 partially protected WNV from pH-induced inactivation as 2 . 2 and 8 . 2×102 PFU were detected , respectively ( Figure 5; P<0 . 05 and P<0 . 0001 ) . Because less infectious virus was detected with E16 compared to E60 treatment following pH normalization and dilution , we hypothesized that even a small number of bound E16 Fab could still inhibit infectivity since this MAb requires a low fractional occupancy for efficient neutralization [26] . Conversely , even detachment of a few E60 Fabs could significantly increase infectivity because virtually complete occupancy is required for neutralization by this MAb [40] . Experiments were repeated and excess recombinant E protein DIII ( 0 . 4 mg/ml ) was added at the time of pH normalization and dilution to compete off additional bound E16 Fab . The addition of excess recombinant DIII further increased WNV infectivity by ∼4 fold ( data not shown ) , presumably by lowering the number of bound E16 Fab on some virions below the threshold for neutralization . Overall , these experiments show that saturating concentrations of both E16 and E60 Fabs at least , partially prevent irreversible pH-dependent inactivation of WNV in the absence of target membranes .
Antibody neutralization is essential for protection against infection by many viruses . A greater understanding of the mechanism ( s ) by which the most strongly neutralizing antibodies act could facilitate strategies for generating targeted vaccines and immunotherapies . To establish the mechanism of action of E16 , a strongly neutralizing anti-WNV MAb with therapeutic potential , we performed a series of cellular and biochemical experiments . Cell biology studies demonstrate that E16 blocks WNV infection at a post-entry stage by sequestering the virus in acidic compartments and preventing its egress into the cytoplasm . Biochemical experiments demonstrate that E16 neutralizes WNV by directly blocking the pH-dependent fusion process . Thus , the inhibitory activity of E16 against WNV in vivo is likely defined by its ability to block viral fusion and nucleocapsid penetration into the cytoplasm where replication occurs . Analysis of the crystal structure of E16 Fab bound to WNV E protein led to a hypothesis that E16 blocked the structural rearrangement required for fusion at low pH [27] . Indeed , E16 engages a large solvent-exposed surface of DIII , a domain that is positioned distinctly in the pre- and post-fusion E protein conformations [10] . The biochemical data presented here demonstrating that E16 Fab blocks the pH-dependent inactivation of WNV in solution is consistent with a direct inhibition of the structural transition of E protein that occurs during fusion . Nonetheless , definitive evidence of this structural mechanism awaits solution of the E16-WNV structure by cryo-electron microscopy in media at acidic pH . In surface plasmon resonance ( SPR ) binding studies , E16 bound DIII of the WNV E protein with similar affinity across a range of pH values from pH 5 to pH 8 ( B . S . Thompson , M . S . Diamond and D . H . Fremont , unpublished data ) . This explains why the binding and neutralizing activity of E16 is not altered as the virus-MAb complex transits through the endosomal compartments . Indeed , the confocal microscopy experiments showed co-localization of E16 and virus through acidic compartments into the lysosome . Our investigations with MAbs are consistent with an earlier study showing a strongly neutralizing polyclonal serum against WNV inhibited at a post-attachment step [41]; the authors of that study speculated but did not show that the most potently inhibitory antibodies block viral fusion . One reason why antibody blockade of fusion may be particularly potent in vivo for flaviviruses is because it acts downstream of an increasing number of cellular attachment factors ( e . g . , DC-SIGN , DC-SIGNR , heparin sulfate , Fc-γ receptors , and αvβ3 integrin [42]–[45] ) . The confocal microscopy experiments also suggest that E16-opsonized WNV is retained in acidic compartments that are ultimately targeted for degradation . Antibodies like E16 that block fusion may be particularly potent at clearing viral infection in vivo because in addition to directly limiting transit to and replication in the cytoplasm they effectively convert permissive cells into ones that target virus for destruction . This feature of E16 , along with its ability to disrupt transneuronal spread [46] , high affinity , and capacity to neutralize at low virion occupancy [26] , begins to explain its single-dose potent post-exposure therapeutic activity in animals [24] , [47] . The mechanistic analysis of E16 and WNV is supported by recent studies with MAbs against DIII of TBEV , some of which also blocked fusion of pyrene-labeled virus with liposomes [48] . Nonetheless , it remains unclear if the DIII MAbs against TBEV have equivalent neutralizing capacity and bind the same structural epitope as E16 . The TBEV study also showed that DII-fusion loop MAbs were effective at blocking liposomal fusion . Although we also observed efficient dose-dependent inhibition of membrane fusion with E60 , approximately one-third of the virus particles remained fusion competent even under conditions of antibody excess . This data is consistent with our observation that E53 and E60 are less strongly inhibitory MAbs against WNV [39] and that heterogeneity of WNV particles with respect to their state of maturation ( mostly immature , partially mature , or fully mature ) affects the ability of fusion loop MAbs to bind and neutralize infection [40] . As the fusion loop epitope is poorly accessible on the mature WNV virion [13] , [40] , [49] , E53 and E60 MAbs require a relatively high fractional occupancy to inhibit infection [40] . Indeed , they may not achieve sufficient MAb concentration in the endosomes to neutralize by this mechanism . Instead , at least for Vero cells , our data with E53 and E60 suggests that antibodies of this class block at a proximal attachment step [27] . Based on these observations , we have developed a model for how the DII-fusion loop and DIII-lateral ridge MAbs neutralize WNV infection ( Figure 6 ) . Blockade of viral fusion by antibodies or pharmacologic agents is usually considered as a therapeutic strategy for viruses that fuse with the plasma membrane . For example , enfuvirtide ( Fuzeon™ or T-20 peptide ) effectively inhibits entry of HIV at the plasma membrane of CD4+ T cells by interfering with the requisite structural transition that brings viral and cell surfaces into proximity for fusion ( reviewed in [50] ) . In contrast , there have been relatively few descriptions of antibodies that neutralize flaviviruses by blocking endosomal fusion . Butrapet et al described an anti-Japanese encephalitis virus antibody ( MAb 503 ) that inhibited fusion-induced syncytia of infected insect cells and virus internalization into Vero cells . Although they concluded that this MAb functioned at a step post-attachment , they did not clearly demonstrate that it directly blocked fusion [51] . Similarly , the mechanism of action of the potently neutralizing anti-DENV2 MAb , 3H5-1 [52] , has been speculated . Whereas He et al , showed that 3H5-1 blocked attachment of DENV2 to Vero cells [53] , Se-Thoe et al , using LLC-MK2 cells , concluded that 3H5-1 primarily blocked the DENV2 fusion at the plasma membrane [54] . We recently localized the epitope of 3H5-1 of DENV2 to residues in the N-terminal region and FG loops of the lateral ridge of DIII , in an analogous position to that for E16 and WNV DIII [55] . Although further studies are necessary , based on structural localization and functional potency , we speculate that 3H5-1 and other strongly neutralizing DIII lateral ridge MAbs inhibit flavivirus infections , at least in part through similar fusion blocking mechanisms . In summary , our experiments define the mechanism of action of a potently inhibitory therapeutic antibody against WNV . E16 prevents egress of WNV from endosomes , leading to retention in progressively acidic compartments and likely destruction in the lysosome . Vaccines that skew the immune response towards production of antiviral antibodies that inhibit fusion may improve protection against challenge . For highly promiscuous viruses like flaviviruses , targeting of the endosomal fusion step may be particularly relevant given the discovery of increasing numbers of distinct entry pathways on mammalian cells [42] , [43] .
Vero cells were used for confocal microscopy experiments , the plasma membrane fusion assay , and to titrate infectious virus by plaque assay . Vero cells were grown in Dulbecco's modified eagle's medium ( DMEM ) supplemented with 10% FBS , 10 mM HEPES and 1% penicillin/streptomycin , as described [56] . WNV ( strain 3000 . 0259 , New York , 2000 ) [57] was propagated in C6/36 Aedes albopictus cells , aliquotted , and frozen at −80°C . Pyrene-labeled WNV was isolated from the medium of infected BHK21 cells that was cultured in the presence of 15 μg/ml of 16- ( 1-pyrenyl ) -hexadecanoic acid ( Invitrogen , Breda , The Netherlands ) , essentially as described before for alphaviruses [58] , [59] . BHK21 cells were infected at a MOI of 4 . At 24 h post-infection , the supernatant was harvested and pyrene-labeled WNV particles were pelleted by ultracentrifugation ( Beckman type 19 rotor; 15 hr at 48 , 500×g at 4°C ) . Subsequently , the virus particles were purified on an Optiprep ( Axis-Shield , Oslo , Norway ) density ( 15–55% w/v ) gradient by ultracentrifugation ( Beckman SW41 rotor; 18 hr at 100 , 000×g at 4°C ) . The infectivity of the virus preparation was determined by titration on BHK21-15 cells . Protein concentration was determined by micro-Lowry analysis . Large unilamellar vesicles were prepared by a freeze/thaw extrusion procedure as described [59] . Liposomes consisted of phosphatidylcholine ( PC ) from egg yolk , phosphatidylethanolamine ( PE ) prepared by transphosphatidylation of egg PC , and cholesterol in a molar ratio of 1:1:2 . Liposomes were prepared with an average size of 200 nm . All lipids were obtained from Avanti Polar Lipids ( Alabaster , AL ) . The anti-WNV antibodies E9 , E16 , E24 , E53 , E60 , and E111 have been previously described [24] , [27] , [39] . Fab fragments were generated by papain digestion and purified by protein A affinity and size exclusion chromatography as described [27] . The generation and purification of the E16 scFv will be described in detail elsewhere ( B . Kauffman , S . Johnson , D . Fremont , M . Diamond , and M . Rossmann , manuscript in preparation ) . Direct conjugation of MAbs to fluorochromes was performed using an Alexa Fluor® 488 ( or 647 ) MAb labeling kit ( Invitrogen , Carlsbad , CA ) according to the manufacturer's instructions . Both anti-human and anti-mouse secondary antibodies conjugated to fluorochromes were purchased ( Invitrogen ) and used at a 1:200 dilution for confocal microscopy and flow cytometry . Flow cytometric analysis was performed using a BD FACS Calibur and BD Cellquest Pro™ software ( Becton Dickinson , San Jose , CA ) . Vero cells were plated at ∼7 , 500 cells/well in 8-well Lab-Tek chambered slides ( Nunc , Rochester , NY ) and incubated overnight . The cells were infected with WNV ( MOI of 100 ) in the presence or absence of Alexa-488 conjugated antibodies at the indicated temperature and time , washed with PBS , and fixed with 2% paraformaldehyde in PBS for 30 min at room temperature . Acidified endosome and lysosome compartments were identified with Lysotracker red ( Invitrogen ) by adding the dye ( 50 nM ) to the cells for the last 30 min of the incubation prior to fixation . To assess whether blockade of endosomal acidification mimics treatment with E16 , Vero cells were infected at an MOI of 100 in the presence of 10 nM concanamycin A1 or 100 μg/ml hE16 for 3 h or 24 h , fixed with 2% paraformaldehyde , and permeabilized with PBS supplemented with 0 . 1% saponin . Cells were stained with a pool of Alexa-488 conjugated mouse anti-E MAbs and in some experiments , Alexa-647-conjugated goat anti-human IgG . After extensive washing and fixation , cells were analyzed by confocal microscopy using a Zeiss LSM510 META Laser Scanning Confocal Microscope ( Carl Zeiss Inc . , Thornwood , NY ) as described [60] . Images were analyzed using the LSM510 software suite and Volocity™ software package ( Improvision Inc . , Waltham , MA ) . The assay for plasma membrane fusion of flaviviruses has been described previously [32] . We adapted the protocol to test the effects of MAbs on WNV fusion at the plasma membrane . Briefly , Vero cells were plated in 12 well plates at 5×104 cells per well and incubated for 24 h at 37°C . The cells were then pre-incubated with 10 nM concanamycin A1 for 30 min . WNV ( MOI of 100 ) was complexed with 100 μg/ml E16 IgG , E16 scFv , E60 IgG or control medium for 30 min at 4°C and bound to Vero cells for 2 h on ice . Subsequently , cells were washed twice with iced PBS and pre-warmed DMEM ( buffered to pH 5 . 5 or pH 7 . 5 ) was added at 37°C for ∼7 min . The cells were then washed with PBS and incubated for 24 h at 37°C in DMEM containing 10 nM concanamycin A1 , which blocks virus fusion after receptor mediated entry pathways . The cells were washed twice in PBS and fixed in PBS with 2% paraformaldehyde , permeabilized with 0 . 1% saponin and stained with an oligoclonal pool of Alexa Fluor-488-labeled anti-WNV MAbs . Samples were processed by flow cytometry and data was analyzed using the Cellquest Pro™ software . WNV ( ∼3×103 PFU ) was incubated alone or with 100 μg/ml E16 Fab , E60 Fab or E9 Fab in DMEM at neutral pH for 30 min at 4°C . The reactions were then diluted 5-fold in DMEM supplemented with 20 mM succinic acid ( pH 5 . 5 ) or 20 mM HEPES ( pH 7 . 5 ) and incubated at 37°C for 15 min . Each reaction was subsequently neutralized by a 25-fold dilution in DMEM at pH 7 . 5 and added to a monolayer of Vero cells in a 6 well plate for 1 h at 37°C . Following this incubation , the cells were overlaid with 2% low melting agarose and a standard plaque assay was performed . In some experiments , recombinant DIII ( 0 . 4 mg/ml ) purified from E . coli [27] was added at the time of 25-fold dilution to compete bound Fabs . Fusion of pyrene-labeled WNV with PE/PC/cholesterol ( molar ratio of 1:1:2 ) liposomes was monitored continuously in a Fluorolog 3–22 fluorometer ( BFi Optilas , Alphen aan den Rijn , The Netherlands ) , at excitation and emission wavelengths of 345 nm and 480 nm . Pyrene-labeled WNV ( 0 . 35 μg protein; corresponds to 1 . 5×1010 particles ) and an excess of liposomes ( 140 nmol phospholipid; corresponds to 3×1010 liposomes ) was mixed in a final volume of 0 . 665 ml in 5 mM HEPES pH 7 . 4 , 150 mM NaCl , and 0 . 1 mM EDTA . The content was stirred magnetically at 37°C . At t = 0 sec , the pH of the medium was adjusted to 5 . 4 by addition of 35 μl 0 . 1 MES , 0 . 2 M acetic acid , pre-titrated with NaOH to achieve the final desired pH . The fusion scale was calibrated such that 0% fusion corresponded to the initial excimer fluorescence value . The 100% value was obtained through the addition of 35 μl 0 . 2 M octaethyleneglycol monododecyl ether ( Fluka Chemie AG , Buchs , Switzerland ) to achieve an infinite dilution of the probe . The extent of fusion was determined 60 seconds after acidification . To analyze the influence of E16 , E60 , and E111 on WNV fusion , pyrene-labeled WNV was incubated with increasing concentrations of MAbs for 1 hr at 20°C prior to mixing with liposomes . | Antibodies are essential components of the immune response against many pathogens , including viruses . A greater understanding of the mechanisms by which the most strongly inhibitory antibodies act may influence the design and production of novel vaccines or antibody-based therapies . Our group recently generated a highly inhibitory monoclonal antibody ( E16 ) against the envelope protein of West Nile virus , which can abort infection in animals even after the virus has spread to the brain . In this paper , we define its mechanism of action . We show that E16 blocks infection by preventing West Nile virus from transiting from endosomes , an obligate step in the entry pathway of the viral lifecycle . Thus , a strongly inhibitory anti–West Nile virus antibody is highly neutralizing because it blocks fusion and delivers virus to the lysosome for destruction . | [
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| 2009 | A Therapeutic Antibody against West Nile Virus Neutralizes Infection by Blocking Fusion within Endosomes |
The Fanconi Anemia ( FA ) pathway is a multi-step DNA repair process at stalled replication forks in response to DNA interstrand cross-links ( ICLs ) . Pathological mutation of key FA genes leads to the inherited disorder FA , characterized by progressive bone marrow failure and cancer predisposition . The study of FA is of great importance not only to children suffering from FA but also as a model to study cancer pathogenesis in light of genome instability among the general population . FANCD2 monoubiquitination by the FA core complex is an essential gateway that connects upstream DNA damage signaling to enzymatic steps of repair . FAAP20 is a key component of the FA core complex , and regulated proteolysis of FAAP20 mediated by the ubiquitin E3 ligase SCFFBW7 is critical for maintaining the integrity of the FA complex and FA pathway signaling . However , upstream regulatory mechanisms that govern this signaling remain unclear . Here , we show that PIN1 , a phosphorylation-specific prolyl isomerase , regulates the integrity of the FA core complex , thus FA pathway activation . We demonstrate that PIN1 catalyzes cis-trans isomerization of the FAAP20 pSer48-Pro49 motif and promotes FAAP20 stability . Mechanistically , PIN1-induced conformational change of FAAP20 enhances its interaction with the PP2A phosphatase to counteract SCFFBW7-dependent proteolytic signaling at the phosphorylated degron motif . Accordingly , PIN1 deficiency impairs FANCD2 activation and the DNA ICL repair process . Together , our study establishes PIN1-dependent prolyl isomerization as a new regulator of the FA pathway and genomic integrity .
The Fanconi anemia ( FA ) DNA repair pathway resolves DNA interstrand cross-links ( ICLs ) and other obstacles encountered during DNA replication [1 , 2] . Germ-line mutation of at least 22 genes involved in this pathway not only causes a childhood blood disorder of bone marrow failure , FA , but also predisposes affected children to a variety of cancers , highlighting the role of the FA pathway as a tumor suppressor mechanism that preserves genomic integrity [3 , 4] . Central to this pathway is FANCD2 activation , triggered by its monoubiquitination via a multi-subunit ubiquitin E3 ligase , the FA core complex , which targets the FANCD2-FANCI heterodimeric complex to DNA lesions to recruit structure-specific nucleases and initiate nucleolytic incision of cross-linked DNA [5 , 6] . Thus , FANCD2 monoubiquitination by the FA core complex constitutes an essential gateway to connect the DNA damage response ( DDR ) to enzymatic steps of DNA ICL repair . Monoubiquitinated FANCD2 is also required for maintaining DNA replication fork integrity independently of DNA ICL processing [7 , 8] . The FA core complex consists of at least eight FA gene products associated with several accessory proteins and exhibits modular features to promote the activity of the catalytic E3 ligase core [9–11] . Each subunit is under the control of numerous posttranslational modifications , implicating multiple layers of regulation in response to DNA damage and replication checkpoint [12] . The FANCA-FANCG-FAAP20 subcomplex constitutes a structural module to maintain the integrity of the FA complex and supports its localization to the sites of DNA lesions [10 , 11] . The 20 kD FA-associated protein FAAP20 directly interacts with FANCA and promotes its stability [13–15] . In the absence of FAAP20 , the degron motif of FANCA is exposed to undergo SUMO-dependent proteolytic degradation , leading to the loss of the FA core complex integrity and thus a defect in FANCD2 monoubiquitination [16] . Accordingly , a patient-derived mutation that disrupts the FANCA-FAAP20 interaction causes FA-like phenotypes [16] . The dynamics of the FANCA-FAAP20 interaction is regulated by FAAP20 degradation , which is mediated by the SKP1-CUL1-F-Box/FBW7 ( SCFFBW7 ) ubiquitin E3 ligase complex [17] . Specifically , glycogen synthase kinase 3β ( GSK3β ) -dependent phosphorylation of FAAP20 at the Cdc4 phospho-degron ( CPD ) motif is recognized by FBW7 to trigger polyubiquitination and proteasome-dependent FAAP20 degradation [17] . Hence , phosphorylation-dependent ubiquitin signaling plays an essential role in regulating the FANCA-FAAP20 interaction and FA pathway activation . Nevertheless , the upstream signaling that governs FAAP20 phosphorylation status and its detailed mode of action for FAAP20 degradation remain uncharacterized . The reversible phosphorylation-dependent ubiquitin-proteasome system ( UPS ) is a fundamental regulatory mechanism for protein degradation . As exemplified by FBW7-dependent FAAP20 degradation , phosphorylation of the phospho-degron motif allows proteins to be recognized by a ubiquitin E3 ligase and delivered to the proteasome . Meanwhile , given the rapid phosphorylation-dephosphorylation event mediated by kinases and phosphatases , catalysis by peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 ( PIN1 ) is often considered a key rate-determining step in controlling phosphorylation-dependent signaling [18] . PIN1 specifically recognizes a phosphorylated Ser or Thr residue preceding a Pro ( pSer/Thr-Pro ) . By catalyzing the Pro cis-trans isomerization that converts substrates into a conformation that is favorable or refractory to downstream signaling , PIN1 acts as a molecular switch to control diverse cellular functions , including proteolysis [19] . Accordingly , previous studies have established the role of PIN1 in regulating the stability of oncoproteins and tumor suppressors in multiple cellular processes , including transcriptional regulation ( c-Jun , c-Myc , p53 ) , cell cycle ( Cyclin D1 & E ) , and cell death ( MCL-1 ) [20–27] . Notably , many of these substrates are also substrates for SCFFBW7 , implying a complex interplay at the PIN1-SCFFBW7 phospho-dependent ubiquitin signaling axis to modulate substrate ubiquitination and degradation . Interestingly , a recent study has revealed the role of PIN1 in promoting degradation of CtIP , a mediator of double-strand break ( DSB ) repair , thereby connecting PIN1 signaling to DNA repair processes [28] . However , the molecular details of how PIN1 regulates its substrates associated with DNA repair are only beginning to be understood . Here , we identify PIN1 as a new regulator of the FA pathway . We provide evidence that FAAP20 is a new substrate of PIN1 and that PIN1 antagonizes proteolytic signaling of FAAP20 degradation mediated by SCFFBW7 , thus promoting the integrity of the FA core complex and FANCD2 activation . Together , our study uncovers a new role for the prolyl isomerase PIN1 in governing the DNA ICL repair process and genomic integrity . Given that PIN1 is deregulated in many human cancers , our findings also provide insights into how the disruption of FA pathway signaling may be connected to the genome instability of PIN1-related cancers .
FANCA and FAAP20 interact and stabilize each other in the FA core complex [13 , 15 , 16] . We and others have previously shown that the N-terminal region of FAAP20 is required for the FANCA interaction ( Fig 1A ) [13 , 14] . While further charactering the FAAP20-FANCA interaction via extensive mutagenesis , we serendipitously found that one FAAP20 mutant ( W40A , L44Q , R45A; hereinafter WLR ) , which fails to interact with FANCA , exhibits an additional , slower-migrating upper isoform during SDS-PAGE ( Fig 1B; lane 8 ) . This additional band was specific to the WLR mutant , and not seen with the CPD degron mutant ( Fig 1C ) . This drastic mobility shift may reflect a conformational change of the proline backbone , which often persists even under denaturing conditions , as previously seen in the PIN1 substrate ATR [29] . Notably , mass spectrometry analysis of both Flag-FAAP20 WLR isoforms revealed that the upper form is phosphorylated at Ser48 adjacent to Pro49 ( S1A–1C Fig ) . Since the phosphorylated Ser-Pro motif is known to be a target for PIN1-catalyzed cis-trans isomerization , we determined whether pS48-P49 is responsible for the structural change of the FAAP20 WLR mutant . Indeed , mutations either in the phosphorylated residue Ser48 or in the isomerizing residue Pro49 abolished the upper form ( Fig 1D ) . Overexpression of PIN1 increased the ratio of the upper versus lower form as well as overall FAAP20 WLR levels , while PIN1 knockdown decreased the levels of the upper form ( S1D Fig ) . FAAP20 contains two pSer-Pro motifs , one of which we previously defined as a degron motif that is recognized by FBW7 [17] . Some of the CPD of FBW7 substrates have previously been shown to undergo isomerization by PIN1 , thereby directly affecting signaling centered on the CPD [22] . Nevertheless , unlike the Pro49 mutation , disruption of the Pro at the CPD did not abolish the upper form of the FAAP20 WLR ( Fig 1E ) . Interestingly , the FAAP20 WLR mutant was much more stable than wild-type ( WT ) upon inhibition of nascent protein synthesis by cycloheximide , despite its inability to interact with its protective partner FANCA , suggesting that downstream proteolytic signaling is impaired ( Fig 1F ) . Together , these data indicate that the FAAP20 WLR mutation leads to a structural change specific to the pS48-P49 motif and influences FAAP20 stability . Our data from the FAAP20 WLR mutant raises the possibility that the pS48-P49 motif of WT FAAP20 could be a physiological target of PIN1 . Thus , we determined whether FAAP20 interacts with PIN1 through the pS48-P49 motif . To this end , we purified GST-tagged PIN1 from E . coli and incubated with in vitro transcribed and translated ( IVTT ) Flag-tagged FAAP20 . GST-PIN1 directly interacted with Flag-FAAP20 WT , whereas the PIN1 W34A substrate-binding mutant [30] failed to do so ( Fig 2A ) . GST-PIN1 also pulled down endogenous FAAP20 from cell lysates ( S2A Fig ) . Similarly , treatment with lambda protein phosphatase also decreased the interaction , arguing for the requirement of FAAP20 phosphorylation for the PIN1 interaction ( Fig 2A ) . Indeed , the non-phosphorylatable FAAP20 S48A mutant was unable to interact with PIN1 in vitro , whereas the WT or the phospho-mimic S48D mutant retained the interaction ( Fig 2B ) . Moreover , Flag-FAAP20 WT , but not S48A , immunoprecipitated HA-PIN1 from cell lysates ( Fig 2C ) . Notably , GST-PIN1 interacted with the WLR mutant stronger than with WT FAAP20 in vitro and induced the formation of the upper form while pulling down the FAAP20 WLR , indicating that enhanced PIN1 interaction renders the FAAP20 WLR more susceptible to the action of PIN1 and subsequent isomerization ( Fig 2D ) . In contrast , the IVTT FAAP20 WLR itself did not exhibit its shifted isoform when it was immunoprecipitated alone in vitro in the absence of PIN1 , indicating that the isoform directly results from the structural change induced by PIN1 , rather than representing a posttranslational modification that may have occurred during incubation ( S2B Fig ) . We also showed that the FAAP20 WLR point or deletion mutants interact more strongly with PIN1 in the cells in comparison to WT , suggesting that increased affinity of the FAAP20 WLR mutant to PIN1 , perhaps due to the change of conformation near the pS48-P49 motif caused by the disruption of the adjacent WLR region , allows enhanced isomerization and appearance of two isoforms ( Fig 2E ) . To further support the idea that FAAP20 is a substrate of PIN1 , we monitored the conformational change of FAAP20 catalyzed by PIN1 using NMR spectroscopy . To this end , we synthesized FAAP20 peptides either non-phosphorylated or phosphorylated at Ser48 ( Fig 3A ) . Each peptide was incubated with PIN1 , and the cis-trans conformational exchange of the pSer and Glu residues flanking the Pro residue in the peptide was monitored by 1H-1H ( 2D ) ROESY ( rotating frame Overhause effect spectroscopy ) [31] . In this experiment diagonal-peaks corresponding to the amide protons of pSer7 and Glu9 in their cis ( cc ) and trans ( tt ) conformations were studied . Cross-peaks that have the same sign as the diagonal peaks indicate conformational exchange between two distinct conformations , but cross-peaks that have the opposite sign indicate an NOE ( Nuclear Overhauser effect ) . In the absence of PIN1 , no exchange cross-peaks were detected , suggesting that conformational exchange between the cis and trans conformations was too slow to be detected ( Fig 3B , bottom left ) . In contrast , exchange cross-peaks were observed in the phospho-peptide upon PIN1 incubation , indicating evidence of conformational exchange ( Fig 3B , bottom right ) . As a control , incubation of a non-phosphorylated peptide with PIN1 did not generate any notable exchange cross-peaks ( Fig 3B , top ) . These data suggest that substantially greater conformational exchange occurs in the presence of PIN1 , and that PIN1 specifically recognizes the phosphorylated S48-P49 motif of FAAP20 to catalyze its isomerization . We also determined both forward ( Kctcat ) and reverse ( Ktccat ) rate constants for the two-state conformational exchange process by analyzing the ratio of the Itc cross-peak intensity to the Itt diagonal-peak intensity ( S3A and S3B Fig ) . Our analysis indicates that the average value of Kctcat is almost 9-fold greater than that of Ktccat , which is consistent with previous reports showing that the forward rate of cis-to-trans is greater than the reverse rate of trans-to-cis , and that the trans conformation is predominant over the cis conformation ( Fig 3C ) [32–34] . In addition , we further demonstrated that limited proteolysis of IVTT FAAP20 by trypsin is attenuated when FAAP20 was pre-incubated with recombinant PIN1 , indicating that full-length FAAP20 adapts a different conformation upon PIN1-induced isomerization in vitro ( Fig 3D ) . Together , these data suggest that PIN1 catalyzes the isomerization of FAAP20 . PIN1-dependent cis-trans isomerization often exerts a profound impact on the stability of phosphorylated proteins by affecting the ubiquitin signaling required for proteasomal degradation [19] . Given that we previously identified FAAP20 as a substrate of SCFFBW7 , which participates in the UPS known to be modulated by PIN1 [30] , we determined whether PIN1 affects the stability of FAAP20 . Knockdown of PIN1 with two independent siRNAs facilitated the degradation of endogenous FAAP20 upon cycloheximide chase ( Fig 4A ) , and the reduced FAAP20 levels were rescued by proteasome inhibition ( Fig 4B ) , indicating that PIN1 promotes FAAP20 stability in a physiological manner . To further substantiate our findings , we generated PIN1 knockout ( KO ) U2OS human osteosarcoma cell lines by CRISPR-Cas9 ( Fig 4C ) . Independent KO clones demonstrated that the half-life of FAAP20 degradation is dramatically shorter in the absence of PIN1 ( Fig 4D and S4A Fig ) . Reduced levels of exogenous FAAP20 expression were rescued by proteasome inhibition , suggesting that PIN1 antagonizes FAAP20 degradation via the proteasome ( Fig 4E and S4B Fig ) . Accordingly , mutations in the Ser48 or Pro49 residues of FAAP20 accelerated FAAP20 degradation , further supporting the idea that FAAP20 isomerization by PIN1 promotes FAAP20 stability ( Fig 4F and 4G ) . The residues in the catalytic PPIase domain of PIN1 , including Lys63 , Arg68 , and Arg69 , form a positively charged phosphate-binding loop to coordinate the pSer/Thr of the substrate [35] . Importantly , reconstitution of the catalytically dead PIN1 mutant into PIN1-/- cells failed to restore the FAAP20 levels reduced by PIN1 deletion , indicating that PIN1 activity is required for stabilizing cellular FAAP20 levels ( Fig 4H and S4C Fig ) . Moreover , Ni-NTA pull-down of ubiquitinated proteins demonstrated that the polyubiquitination of FAAP20 increases in the FAAP20 P49A mutant and in the absence of PIN1 , suggesting that FAAP20 is susceptible to degradation without PIN1-induced isomerization ( Fig 4I and S4D Fig ) . Collectively , these data support the idea that PIN1 is required for maintaining FAAP20 stability . Phosphorylation at the CPD of FAAP20 is prerequisite for FAAP20 degradation [17] . Thus , we next sought to determine the elements that control the FAAP20 degradation regulated by CPD phosphorylation and PIN1-induced isomerization . Protein phosphorylation is antagonized by phosphatases , and protein phosphatase 2A ( PP2A ) is a major proline-directed Ser/Thr phosphatase known to regulate diverse cellular processes by counteracting kinase signaling [36] . PP2A has been shown to mediate dephosphorylation of several PIN1 substrates , preferentially recognizing a specific conformation [37] . Hence , we explored the possibility that PP2A is involved in regulating the FAAP20 phosphorylation at the CPD . We had previously generated a FAAP20 antibody that specifically recognizes the pS113 of the CPD , which was used to monitor the CPD phosphorylation status [17] ( S5A Fig ) . Incubation of cells with the PP2A inhibitor okadaic acid ( OA ) [38] increased the pS113 of FAAP20 ( Fig 5A ) . The PP2A holoenzyme exists as a heterotrimeric complex composed of a catalytic C , a scaffolding A , and a diverse group of regulatory B subunits , which is further classified into four distinct families ( B55 , B56 , B′′ , and B′′′ ) , each of which have several isoforms such as α , β , γ , δ , and ε [36] . Specific knockdown of the PP2A catalytic subunit α-isoform ( PP2Ac; encoded by PPP2CA ) with two independent siRNAs also elevated pS113 levels , indicating that the enzymatic activity of PP2A antagonizes FAAP20 phosphorylation at the CPD ( Fig 5B and S5B Fig ) . Conversely , overexpression of PP2A was sufficient to decrease pS113 levels ( Fig 5C ) . To further explore the direct role of PP2A in FAAP20 dephosphorylation , we determined the interaction of FAAP20 with B56α , the α-isoform of the largest B regulatory subunit family B56 ( B′/PR61 ) , which interacts with a substrate and thus confers substrate specificity toward the PP2A holoenzyme [36] . Intriguingly , serial deletion mutagenesis of FAAP20 revealed that the N-terminal amino acid 1–30 residues are required for the interaction with B56α ( Fig 5D ) . As expected , the comparison between the FAAP20 ΔN30 and ΔN48 mutants showed that the amino acid 31–48 residues encompassing the WLR region were necessary for the interaction of FAAP20 with endogenous FANCA . Together , these data suggest that the N-terminal region of FAAP20 is a platform that serially mediates the interactions with multiple regulatory proteins involved in FAAP20 proteolysis , including B56α , FANCA , and PIN1 ( Fig 5E ) . This also indicates that the affinity and activity of PP2A toward FAAP20 may be regulated by the FAAP20 isomerization , which occurs at the adjacent pSer-Pro motif by PIN1 . Our results thus far raise the possibility that PIN1-mediated FAAP20 isomerization may increase the PP2A holoenzyme association with FAAP20 , thereby promoting dephosphorylation of the CPD , which would prevent its interaction with FBW7 and subsequent degradation . Consistent with this idea , we observed that the FAAP20 WLR ( i . e . isomerization-prone ) mutant binds stronger to B56α in comparison to WT ( Fig 6A ) . On the other hand , the interaction of FAAP20 WT or mutant with GSKβ was largely unaffected , indicating that dephosphorylation may be a rate-limiting step for determining CPD phosphorylation status ( S6A Fig ) . Regarding CPD phosphorylation , the FAAP20 WLR mutant exhibited lower pS113 levels compared to WT , and exogenous expression of PIN1 further decreased pS113 levels ( Fig 6B ) . Importantly , the ratio of pS113 signals in the upper and lower isoforms of FAAP20 was lower than that of the total FAAP20 immunoblot signal , and was further decreased following PIN1 expression , indicating that the isomerized FAAP20 ( i . e . upper isoform ) is less prone to CPD phosphorylation , and because of its high affinity to PIN1 , is more susceptible to dephosphorylation . This idea was further supported by the result showing that CPD phosphorylation was elevated when mutations were introduced to the pS48-p49 motif of the WLR mutant in comparison to the WLR mutation only ( Fig 6C ) . Similarly , exogenous expression of PIN1 WT , but not the catalytically dead mutant , was sufficient to decrease the pS113 levels of WT FAAP20 ( Fig 6D ) . Conversely , the isomerization-defective mutant , FAAP20 P49A , exhibited an increased interaction with FBW7 , indicating that the increase in CPD phosphorylation , caused by reduction of PIN1 and PP2A activity , promotes SCFFBW7-dependent ubiquitin signaling ( Fig 6E ) . To further support this idea , we examined the interplay among PIN1 , PP2A , and FBW7 toward FAAP20 degradation . Co-immunoprecipitation demonstrated that exogenous expression of PIN1 decreased the interaction of FAAP20 with FBW7 , which was restored by the inhibition of PP2A activity by OA , indicating that PIN1 counteracts SCFFBW7 proteolytic signaling by promoting dephosphorylation of the CPD motif , mediated by PP2A ( Fig 6F; compare FBW7 lanes 7 & 8 ) . Accordingly , decreased FAAP20 levels in the absence of PIN1 were rescued by FBW7 depletion , suggesting that PIN1 restricts SCFFBW7 activity to promote FAAP20 stabilization ( Fig 6G and S6B Fig ) . Collectively , these data support our model that PIN1-induced conformational change of FAAP20 promotes FAAP20 dephosphorylation at the CPD by PP2A , thereby preventing SCFFBW7-dependent FAAP20 degradation . Disruption of FAAP20 stability impairs the integrity of the FA complex , leading to a defect in FANCD2 activation required for the initiation of DNA ICL repair [13] . Thus , we hypothesized that PIN1 is an unidentified regulatory component of the FA core complex that controls FANCD2 monoubiquitination and examined the role of PIN1 in FA pathway signaling . PIN1 depletion using two independent siRNAs decreased the levels of FANCD2 monoubiquitination induced by a DNA cross-linking agent mitomycin C ( MMC ) , which is visualized by the more slowly migrating , modified FANCD2 ( FANCD2-Ub ) in an immunoblot , indicating that PIN1 is required for promoting FANCD2 activation ( Fig 7A ) . Under the prolonged treatment of cycloheximide , PIN1 knockdown resulted in accelerated degradation of FANCA , a direct interaction partner of FAAP20 in the FA core complex , which was antagonized by exogenous expression of the FAAP20 CPD mutant that is refractory to degradation by SCFFBW7 but proficient for FANCA interaction ( S7A Fig ) . This suggests that defective FA pathway activation in PIN1-deficient cells results primarily from the compromised FA core complex caused by destabilization of FAAP20 . Accordingly , cytometry-based quantification of γH2AX , a marker for replication-associated DSBs , revealed that PIN1 knockdown significantly increases the cells with positive γH2AX signals upon MMC treatment when compared to control ( Fig 7B and 7C ) . Furthermore , a comet assay demonstrated that PIN1 depletion increases the levels of DNA breaks upon MMC treatment , together suggesting that defects in the FA pathway caused by PIN1 deficiency results in persistent DNA damage and impaired resolution of DNA lesions ( Fig 7D ) . Accordingly , cells depleted of PIN1 were hypersensitive to MMC , indicating that PIN1 dictates the progression of DNA ICL repair and cellular survival ( Fig 7E and S7B Fig ) . To further substantiate the specific role of FAAP20 isomerization in the FA pathway , we reconstituted siRNA-resistant FAAP20 WT or isomerization-defective mutants in FAA20-depleted cells and examined MMC sensitivity . While FAAP20 WT could complement MMC hypersensitivity of FAAP20-depleted cells , the FAAP20 S48A or P49A mutants failed to do so despite their comparable or higher expression than endogenous FAAP20 , indicating that FAAP20 isomerization is required for the function of FAAP20 in the FA pathway ( Fig 7F ) . On the other hand , the WLR mutant , which cannot interact with FANCA despite its increased stability , was not able to complement the FAAP20 deficiency ( S7C Fig ) . The CPD mutant could not fully complement the FAAP20 deficiency either , since FANCA turnover dynamics during DNA ICL repair , which is regulated by FAAP20 phosphorylation and degradation , is also a determinant for DNA ICL repair outcome as previously described [17] . Together , the results of the WLR and CPD mutants further highlight the notion that the effect of PIN1-induced FAAP20 isomerization on DNA ICL repair is largely mediated through the FANCA interaction and the FA core complex . Modulation of FA pathway activity is closely associated with the chemotherapeutic efficacy of DNA cross-linking cytotoxic chemotherapy [39] . Thus , based on our findings , we determined whether pharmacological inhibition of PIN1 is sufficient to disrupt the FA pathway via FAAP20 destabilization in breast cancer , where high levels of PIN1 have been correlated with aggressiveness and chemoresistance [40] . Treatment of MDA-MB-231 triple negative breast cancer ( TNBC ) cell lines with the recently identified PIN1 inhibitor all-trans retinoic acid ( ATRA ) , which binds to the PIN1 active site and degrades PIN1 [41] , decreased the levels of FAAP20 in a dose-dependent manner , as well as the levels of AKT , a known target of PIN1 , without significantly affecting cellular viability [42] ( Fig 8A and S7D Fig ) . FAAP20 degradation was accelerated in the presence of ATRA , further confirming that PIN1 activity is required for FAAP20 stability ( Fig 8B ) . Accordingly , MDA-MB-231 cells treated with ATRA exhibited less damage-induced FANCD2 monoubiquitination and reduced localization of monoubiquitinated FANCD2 to chromatin , indicating that signaling of FANCD2 activation by the FA core complex is disrupted ( Fig 8C and 8D ) . Collectively , these data allude to the potential of pharmacological PIN1 inhibition as a method for enhancing the chemotherapeutic response of cross-linking regimens by destabilizing FAAP20 and thus disrupting the FA pathway .
Despite the critical roles of PIN1 in regulating numerous cellular processes , its connection to DNA ICL repair and genome maintenance pathways has remained uncharacterized . Here , we have identified PIN1 as a new regulatory component of the FA core complex in the FA pathway and established the first direct link between PIN1-SCFFBW7-mediated proteolysis and DNA ICL repair . Our results propose a model wherein PIN1 maintains the integrity of the FA core complex via phosphorylation-dependent FAAP20 isomerization ( Fig 8E ) . Dissociation of FAAP20 from FANCA in the FA core complex subjects FAAP20 to GSK3β-dependent phosphorylation at the CPD , leading to SCFFBW7-dependent polyubiquitination and proteasome delivery . PIN1 antagonizes this process by acting as a molecular switch to catalyze the isomerization of the phosphorylated S48-P49 motif of FAAP20 and induce its conformational change , which enhances its interaction with PP2A , subsequently decreasing CPD phosphorylation and SCFFBW7 interaction . Importantly , our FAAP20 WLR mutant turned out to be a valuable separation-of-function mutant . It has lost its interaction with FANCA and is thus subject to degradation; however , it has also become more susceptible to PIN1-induced isomerization . This unique property allowed us to specifically address the PIN1-PP2A signaling that antagonizes SCFFBW7-dependent degradation of FAAP20 when dissociated from FANCA . Since the WLR-deletion mutant still strongly interacts with PIN1 and exhibits two isoforms , we do not believe that the WLR region directly mediates the PIN1 interaction ( Fig 2E ) . Rather , the WLR region may be antagonistic for PIN1 access to the adjacent pS48-P49 motif , and disruption of this region may alter the local conformation of FAAP20 , enhancing PIN1 targeting to FAAP20 . We propose that the increased FAAP20 stability would allow FAAP20 to favorably associate with the FA core complex , thereby promoting the integrity of the FA core complex and FANCD2 activation upon damage . In other words , cellular levels of FAAP20 that are available to interact with FANCA , even the pool of FAAP20 that may transiently dissociate from FANCA , may be positively maintained by PIN1 in order to sustain FANCA and the FA core complex . PIN1-dependent FAAP20 isomerization may occur during translation before FAAP20 is incorporated into the FA core complex , and PIN1 counteracts the degradation process of FAAP20 to keep adequate levels of FAAP20 available for the interaction with FANCA . Alternatively , PIN1-induced isomerization also antagonizes degradation of FAAP20 while interacting with FANCA by preventing the access of SCFFBW7 , thus further promoting the stability of the FA core complex . In this regard , PIN1 activity is critical for dictating the outcome of DNA ICL repair processes by modulating integrity of the FA core complex . It is also tempting to speculate that PIN1-induced FAAP20 isomerization regulates the FAAP20-FANCA interaction dynamics during DNA ICL repair , and thus fine-tunes the activity of the FA core complex . We previously showed that FBW7 depletion alongside prolonged accumulation of FAAP20 impairs DNA ICL repair , indicating that spatiotemporal removal of FANCA-FAAP20 , or the FA core complex as a whole , from DNA lesions is critical for the completion of DNA ICL repair [17] . Downregulation of PIN1 activity may trigger degradation of FAAP20 , thus facilitating the clearance of the FA core complex to suppress FANCD2 monoubiquitination in the later stage of repair . Indeed , various post-translational modifications of PIN1 , including phosphorylation and SUMOylation , affect PIN1 activity , and PIN1 is known to be phosphorylated in a DNA damage-dependent manner [35 , 43–46] . Thus , elucidating how modification of PIN1 in response to the DNA damage checkpoint regulates PIN1 activity will be an important future direction to more clearly understand how PIN1 contributes to genomic integrity . Interestingly , we observed that the P49A mutation of FAAP20 does not disrupt its interaction with FANCA , indicating that conformational change of FAAP20 by isomerization per se does not directly regulate the FANCA interaction ( S7E Fig ) . In contrast , we consistently notice that the S48A mutation more or less impairs FANCA interaction , indicating that phosphorylation of FAAP20 may regulate its association with the FA core complex independently of isomerization , although its exact role remains to be determined . Our study provides important mechanistic insights into how phosphorylation-dependent ubiquitin-proteasome signaling is regulated by PIN1-catalyzed isomerization . Here , we show that PIN1 accelerates conformational changes of phosphorylated FAAP20 , which affects its interaction with regulatory proteins , including PP2A and FBW7 , thus changing the fate of the protein . This result highlights a complex interplay among prolyl isomerization , phosphorylation , and ubiquitin signaling , which is in agreement with previous studies of known PIN1 substrates . Proline-directed phosphatase PP2A is conformation-specific , and PIN1-induced prolyl isomerization is known to allow PP2A to interact with and dephosphorylate the pSer/Thr-Pro motif of Cdc25C [37] . PIN1 also interacts with the pThr231-Pro motif of tau , which facilitates PP2A-dependent dephosphorylation of hyperphosphorylated tau , restoring its function in microtubule assembly [37 , 47] . Moreover , PIN1-induced isomerization at the pThr58-Pro motif of c-Myc has been shown to enhance PP2A function to dephosphorylate Ser62 , which promotes c-Myc degradation [25] . These studies support the notion that PIN1-induced isomerization is an important regulatory mechanism for controlling PP2A-mediated protein dephosphorylation , which determines the kinetics of substrate degradation and modulates its function . Interestingly , a recent study reported that conformational changes of the epigenetic modulator BRD4 by PIN1 not only prevent its degradation , but also increase its interaction with the downstream transcriptional regulator CDK9 and thus BRD4’s transcriptional activity [48] . By directly visualizing the CPD phosphorylation status that dictates FAAP20 degradation , we were able to provide a mechanism in which a conformational change of FAAP20 by PIN1 modulates the dynamic interaction between PP2A and FBW7 with FAAP20 . Currently , it is not clear how a specific conformation of FAAP20 favors the interaction with PP2A or whether PIN1-induced isomerization at the pS48-P49 motif also influences the conformational change at the CPD . Interestingly , we showed that a defined N-terminal region of FAAP20 preceding the pS48-p49 motif is responsible for interacting with the substrate-binding subunit of PP2A and FANCA . This suggests that a local conformational change at the pS48-P49 motif by PIN1 can readily influence the association of FAAP20 with PP2A , although we do not exclude the possibility of FANCA that binds FAAP20 through the WLR region directly affecting the interaction of FAAP20 to PP2A . A detailed structural analysis of this region would be required to reveal how PIN1-induced isomerization affects the dynamic interactions of FAAP20 with its regulatory proteins . Besides to the pathogenesis of FA , the mechanistic principle developed in our studies has important clinical implications to cancer , since exploiting deregulation of PIN1 activity in the FA pathway could alter the response of cancer cells to cytotoxic chemotherapy or poly ADP-ribose polymerase ( PARP ) inhibitors [49] . Numerous efforts have been made to develop small molecule inhibitors to modulate reversible monoubiquitination of FANCD2 and inhibit the FA pathway , thereby augmenting the sensitivity of cancer cells to cytotoxic chemotherapy regimens , including platinum [12 , 50 , 51] . Intriguingly , PIN1 is widely overexpressed in many human cancers and is associated with poor clinical outcomes [52 , 53] . In particular , increased activity is often observed in the majority of human breast cancers , and PIN1 is considered to be an essential factor for breast tumorigenesis , as well as cancer stem cells [24 , 40 , 54 , 55] . Accordingly , PIN1 inhibition has been considered as an attractive strategy for cancer therapy [56] . Our data indicate that PIN1 inhibition and subsequent disruption of the FA pathway can potentially function as a chemosensitizer for DNA cross-linking cytotoxic chemotherapy . This may be particularly relevant to the TNBC subtype of breast cancer , which shares similar molecular features to the tumors arising from BRCA1/FANCS and BRCA2/FANCD1-associated DNA repair deregulation [57] . A recent study also proposed a role of PIN1 in suppressing CtIP , and thus homologous recombination ( HR ) , which may increase error-prone repair and promote tumorigenesis , indicating that PIN1 inhibition could be a general strategy to supplement chemosensitization or exploit the synthetic lethality of PARP inhibition in PIN1-upregulated tumors [28] . Future studies to characterize a comprehensive regulatory network that governs PIN1-PP2A-SCFFBW7 signaling will provide important mechanistic insights into the proteolytic control of the FA pathway in preserving genomic integrity and allow for the development of therapeutic strategies to exploit aberrant DNA repair in cancer cells caused by deregulated phosphorylation-dependent ubiquitin signaling .
U2OS and 293T cell lines were acquired from the American Tissue Culture Collection ( ATCC ) . MDA-MB-231 was a kind gift from Jun Chung ( Stony Brook Medicine ) . Cells were cultured in Dulbecco’s Modified Eagle’s Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin , following standard culture conditions and procedures . FAAP20 , FBW7 , and GSK3β constructs were previously described [17] . Plasmids encoding GST-PIN1 was a gift from Michael Yaffe ( Addgene plasmid #19027 ) , His-PIN1 from Dustin Maly ( Addgene plasmid #40773 ) , V245 pCEP-4HA-B56α from David Virshup ( Addgene plasmid #14532 ) , and pBABE-zeo PPP2CA from William Hahn ( Addgene plasmid #10689 ) . PIN1 cDNA was subcloned into modified pcDNA3-HA or pMSCV-Flag-HA vectors ( Invitrogen ) . Point or deletion mutations were introduced using the QuikChange II XL Site-Directed Mutagenesis ( SDM ) kit ( Agilent Technologies ) and confirmed by DNA sequencing ( SBU DNA sequencing facility ) . Stable cell lines were generated by retroviral transduction of pMSCV-Flag-HA-PIN1 constructs using 8 μg/mL polybrene ( Sigma-Aldrich ) , followed by selection with 2 μg/mL puromycin . Viruses were generated from 293T cells that were co-transfected with pMSCV-Flag-HA-PIN1 , pCMV-Gag/Pol and pCMV-VSV-G . Transient plasmid transfection was performed using GeneJuice ( Millipore ) according to the manufacturer’s protocols . siRNA duplexes were transfected at 25 nM using Lipofectamine RNAiMAX ( Invitrogen ) . The following DNA sequences were targeted by siRNA: Control: 5′-CAGGGTATCGACGATTACAAA-3′; FAAP20: 5′-CACGGTGAGCCCGGAGCTGAT-3′; PIN1-1: 5′-CGGCTACATCCAGAAGATCAA-3′; PIN1-2: 5′-CAGGCCGAGTGTACTACTTCA-3′; FBW7-1: 5′-GTGGAATGCAGAGACTGGAGA-3′; FBW7-2: 5′-CGGGTGAATTTATTCGAAATT-3′; BRCA2: 5′-TTGAAGAATGCAGGTTTAATA-3′ ( Qiagen ) . siRNA sequences for hPP2Ac are 5′-GAACTTGACGATACTCTAAtt-3′ ( #1; s10959 ) and 5′-CCAAACUAUUGUUAUCGUUtt-3′ ( #2; s10957 ) and synthesized from Ambion , Thermo Fisher . Generation of siRNA-resistant FAAP20 was previously described [58] . Antibodies used in this study included: FAAP20 ( HPA038829 , Sigma-Aldrich ) , Flag ( F1804 , Sigma-Aldrich ) , c-Myc ( 9E10 , Sigma-Aldrich ) , α-Tubulin ( sc-8035 , Sigma-Aldrich ) , FANCD2 ( FI-17 , Santa Cruz ) , PCNA ( PC-10 , Santa Cruz ) , PIN1 ( A302-316A , Bethyl ) , FANCA ( A301-980A , Bethyl ) , MCL-1 ( A302-715A , Bethyl ) , γ-Tubulin ( A302-631A , Bethyl ) , HA ( 6E2 , Cell Signaling ) , ubiquitin ( P4D1 , Cell Signaling ) , β-Actin ( 4967 , Cell Signaling ) , p97 ( 2648 , Cell Signaling ) , pCHK1 S345 ( 2341 , Cell Signaling ) , γH2AX ( 2577 , Cell Signaling ) , AKT ( 9272 , Cell Signaling ) , Histone H4 ( 07–108 , Millipore ) , BRCA2 ( OP95 , Millipore ) , and pS113 FAAP20 ( in house; Genscript ) . Mitomycin C ( M5030 ) , Z-Leu-Leu-Leu-al ( MG132; C2211 ) , and cycloheximide ( C4859 ) , were purchased from Sigma-Aldrich . Okadaic acid ( 459620 ) was from EMD Millipore and all-trans retinoic acid ( ATRA ) /Tretinoin ( S1653 ) was from Selleckchem . Drugs were used at the concentrations indicated in the figure legends . Cells were lysed in NETN300 buffer ( 1% NP40 , 300 mM NaCl , 0 . 1 mM EDTA , and 50 mM Tris [pH 7 . 5] ) supplemented with protease inhibitor cocktail ( Roche ) and halt phosphatase inhibitor cocktail ( Thermo Fisher ) , resolved by SDS-PAGE , transferred onto PVDF membranes ( Millipore ) , and antibodies were detected using an enhanced chemiluminescence method . Some of immunoblot images were acquired by iBright CL1000 imaging system ( Thermo Fisher ) . For co-immunoprecipitation , 293T cells were lysed in NETN150 buffer ( 1% NP40 , 150 mM NaCl , 0 . 1 mM EDTA , and 50 mM Tris [pH 7 . 5] ) in the presence of protease and phosphatase inhibitor cocktails and were centrifuged at 15 , 000 rpm for 10 min at 4 °C . Cell lysates were incubated with anti-Flag M2 affinity gel ( A2220 , Sigma-Aldrich ) or anti-c-Myc agarose affinity gel ( A7470 , Sigma-Aldrich ) for 4 h followed by five washes with NETN150 buffer . Resins were boiled in 2X Laemmli sample buffer and subjected to SDS-PAGE . Subcellular fractionation was performed as previously described [59] . Briefly , cells were lysed using cytoskeleton ( CSK ) buffer ( 10 mM Tris [pH 6 . 8] , 100 mM NaCl , 300 mM sucrose , 3 mM MgCl2 , 1 mM EGTA , 1 mM EDTA , and 0 . 1% Triton X-100 ) for 5 min on ice . After centrifugation at 1 , 500 g for 5 min , the supernatant ( S ) was separated from the pellet ( P ) , and pellets were sequentially lysed in PBS and 2X boiling lysis buffer ( 50 mM Tris [pH 6 . 8] , 2% SDS , and 850 mM β-mercaptoethanol ) . After separation via SDS-PAGE and coomassie blue staining , excised gel pieces were destained , reduced , alkylated , and digested with trypsin gold ( Promega , V5280 ) , essentially as previously described with minor modifications [60] . The resulting peptide extract was dried and dissolved in a solution of 2% acetonitrile ( ACN ) , 0 . 1% formic Acid ( FA ) ( buffer A ) for analysis by automated microcapillary liquid chromatography-tandem mass spectrometry . Fused-silica capillaries ( 100 μm inner diameter—i . d . ) were pulled using a P-2000 CO2 laser puller ( Sutter Instruments , Novato , CA ) and packed with 10 cm of 5 μm ProntoSil 120-5-C18H ( Bischoff Chromatography , Leonberg , Germany ) using a pressure bomb . The samples were loaded via an Eksigent NanoLC Autosampler . The column was installed in-line with an Eksigent Nano2D High Performance Liquid Chromatography ( HPLC ) pump running at 300 nL min-1 . The peptides were eluted from the column by applying a 115 min gradient from 2% buffer B ( 98% ACN , 0 . 1% FA ) to 40% buffer B . The gradient was switched from 40% to 80% buffer B over 3 min and held constant for 3 min . Finally , the gradient was changed from 80% buffer B to 2% buffer B over 0 . 1 min , and then held constant at 2% buffer B for 29 more minutes . The application of a 2 . 2 kV distal voltage electrosprayed the eluting peptides directly into an LTQ Orbitrap XL ion trap mass spectrometer ( Thermo Fisher ) equipped with a nano-liquid chromatography electrospray ionization source . Full mass spectra ( MS ) were recorded on the peptides over a 400 to 2000 m/z range at 60 , 000 resolution , followed by top-five MS/MS scans in the ion-trap . Charge state dependent screening was turned on , and peptides with a charge state of +2 or higher were analyzed . Mass spectrometer scan functions and HPLC solvent gradients were controlled by the Xcalibur data system ( Thermo Fisher ) . MS/MS spectra were extracted from the RAW file with ReAdW . exe ( http://sourceforge . net/projects/sashimi ) . The resulting mzXML data files were searched with The GPM X ! Tandem and MaXQuant Andromeda search engines against a custom database composed of the Uniprot human proteome with added sequences for common contaminants . The non-phosphorylated and phosphorylated FAAP20 peptides were synthesized from Genscript . All NMR experiments were performed on a Bruker 850 MHZ Avance III spectrometer at 25 °C equipped with a cryoprobe . NMR samples contained 2 mM peptide in 10 mM sodium phosphate pH 6 . 5 , and 10% D2O , in the absence or presence of 0 . 03 mM PIN1 . Total Correlation Spectroscopy ( TOCSY ) data for the peptide in the absence of PIN1 was collected with 4096 ( TDF2 ) and 256 data points ( TDF1 ) , a spectral width of 10 ppm ( 8503 Hz ) x 10 ppm ( 8503 Hz ) , 80 ms mixing time , and 10417 Hz spinlock frequency . ROESY data for the peptide in the presence of PIN1 were acquired with similar data points and spectral width as described for the TOCSY experiment but with different mixing times of 30 , 50 , 70 , 90 , 110 ms , and 4310 Hz spinlock frequency . ROESY data for the substrate peptide in the presence and absence of PIN1 were also acquired with 300 ms mixing time . All NMR spectra were processed with Topspin and analyzed with CcpNmr Analysis . The Itc/Itt ratios ( peak intensity ratio of the conformational exchange trans-to-cis cross-peak to the trans conformation diagonal-peak ) for pSer7 and Glu9 , which precedes and follows Pro8 respectively , depend on the forward ( Kctcat ) and reverse ( Ktccat ) rate constants for the two-state cis-to-trans conformational exchange process . In order to determine Kctcat and Ktccat , tc/tt ratios were fitted to the equation below using KaleidaGraph ( Synergy software ) . Itc/Itt = Ktccat[exp ( Kextm ) -1]/ [Kctcatexp ( Kextm ) + Ktccat] , where tm is the mixing time , while Kex is the sum of Kctcat and Ktccat GST pull-down was performed as previously described [59] . Briefly , for the interaction between GST-PIN1 and Flag-FAAP20 in vitro , GST or GST-PIN1 was expressed using E . coli BL21 ( DE3 ) expression strain induced with 0 . 5 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG , Sigma-Aldrich ) at 30 °C . Cells were lysed in PBS with lysozyme , sonicated , and further incubated with 1% Triton X-100 . Cell lysates were recovered by centrifugation at 15 , 000 rpm at 4 °C for 15 min and incubated with glutathione-sepharose beads ( GE Healthcare ) . After washing , the beads were incubated with in vitro transcribed and translated ( IVTT ) proteins in NETN150 buffer for 3 h at 4 °C followed by three washes . For IVTT , a total of 250 ng of pcDNA3 plasmids were incubated with 10 μL of TnT T7 Quick Coupled Transcription/Translation Master Mix ( Promega ) at 30 °C for 70 min to produce proteins . For the purification of recombinant PIN1 for NMR analysis , His-PIN1 was expressed using BL21 ( DE3 ) cells in 2xYT medium with 0 . 5 mM IPTG at 18 °C overnight . Cells were resuspended and crushed in NiA buffer ( 20 mM Tris [pH 7 . 5] , 500 mM NaCl , 5% glycerol , and 5 mM imidazole ) using the EmulsiFlex-C3 homogenizer ( Avestin ) . The soluble protein was loaded onto Ni-NTA resin , washed , and eluted with NiB buffer ( 20 mM Tris [pH 7 . 5] , 500 mM NaCl , 5% glycerol , and 1 M imidazole ) . The eluate was dialyzed against 20 mM Tris [pH 7 . 5] , 500 mM NaCl , 5% glycerol , and stored at -80 °C . In vivo ubiquitin assays were performed under denaturing conditions . MG132-treated cells were resuspended with PBS/1% SDS , snap-frozen in liquid nitrogen , and boiled for 15 min . Cell lysates were diluted 10-fold with PBS and centrifuged at 15 , 000 rpm for 15 min at 4 °C . Lysate aliquots ( 4% ) were saved for input , and lysates were incubated with HisPur Ni-NTA Resin ( Thermo Fisher ) in the presence of 10 mM imidazole ( Sigma-Aldrich ) at 4 °C for 3 h , followed by five washes with PBS/0 . 1% SDS , 10 mM imidazole . Resins were boiled in 2X Laemmli sample buffer and subjected to SDS-PAGE and Western blotting . RNA was isolated using TRIzol ( Invitrogen ) . cDNA synthesis was performed using a high-capacity cDNA reverse transcription kit ( Applied Biosystems ) according to the manufacturer’s protocols . Real-time quantitative PCR was performed using Fast SYBR Green Master Mix ( Applied Biosystems ) and a StepOnePlus Real-Time PCR system ( Applied Biosystems ) . GAPDH mRNA levels were used as a control for normalization . The following primers were used for cDNA amplification: PP2Ac forward 5′-CAGCTAGTGATGGAGGGATA-3′; PP2Ac reverse 5′-TGGGTCAAACTGCAAGAAA-3′; FBW7 forward 5′-CACTCAAAGT GTGGAATGCAGAGAC-3′; FBW7 reverse 5′-GCATCTCGAGAACCGCTAACAA-3′; GAPDH forward 5′-CAACTACATGGTTTACATGTTC-3′; GAPDH reverse 5′- GCCAGTGGACTCCACGAC-3′ . A pair of oligos containing the human PIN1 sgRNA targeting sequence was designed using crispr . mit . edu . The forward oligo sequence is 5′-CACCGATGCGCTTCTCCCAGCCGGG-3′ and the reverse oligo sequence is 5′-AAACCCCGGCTGGGAGAAGCGCATC-3′ . Annealed oligos were cloned into pSpCas9 ( BB ) -2A-Puro ( pX459; a gift from Feng Zhang , Addgene plasmid #48139 ) and transfected into U2OS cells using GeneJuice ( Millipore ) . Control cells were transfected with a pX459 empty vector . Twenty-four hours after transfection , cells were selected with 2 μg/mL puromycin . After recovery from selection , cells were seeded onto 96-well plates , in medium without puromycin , for clonal selection . Selected clones were subsequently analyzed by Western blotting , using anti-PIN1 antibody , to confirm successful knockout . Cells in 6-well plates were transfected with siRNA oligos and seeded on 96-well plates the next day . Cells were treated with increasing doses of MMC in duplicates at 48 h after transfection , and cell viability was determined using the CellTiter-Glo luminescent cell viability assay ( Promega ) 4–5 days after continuous drug treatment . Luminescence was measured using a GloMax Navigator microplate luminometer ( Promega ) . Mean values were analyzed for statistical significance using paired Student’s t-test . Single-cell gel electrophoresis for the detection of MMC-induced DNA breaks was performed using the CometAssay kit ( 4250-050-K , Trevigen ) according to the manufacturer’s protocol . Twenty-five μL of a cell suspension at 2 x 105 cells per mL were combined with 225 μL of low-melting agarose ( 1:10 ratio , vol/vol ) , and 50 μL were spread on Comet slides ( Trevigen ) . After solidification , the slides were immersed in cold lysing solution at 4 °C for 45 min and placed in freshly prepared alkaline unwinding solution ( 200 mM NaOH , 1 mM EDTA ) for 20 min at RT . Electrophoresis of unwound DNA was performed at 21 V for 30 min . The slides were washed with dH2O for 5 min , dehydrated with 70% ethanol for 5 min , dried , and stained with SYBR Gold ( Thermo Fisher ) . Comet tails were examined using a Nikon Eclipse E600 fluorescence microscope and analyzed by OpenComet [61] . Per group , up to 300 individual nuclei were evaluated . The olive tail moment was calculated as a measure of DNA damage and presented as the product of the DNA % ( tail intensity ) and the distance between the intensity-weighted centroids of a head and a tail ( DNA migration ) . Difference between mean values was tested for statistical significance using two-tailed unpaired Student’s t-test . For quantification of γH2AX-positive cells , cell pellets were pre-extracted with PBS/0 . 5% Triton-X for 5 min , fixed with 4% paraformaldehyde for 15 min , and incubated with γH2AX Alexa Fluor 488 ( 1:100; CR55T33 , Thermo Fisher ) in Foxp3/transcription factor staining buffer ( Thermo Fisher ) for 1 h . Cells were washed once and suspended in 500 μL 7-AAD viability staining solution ( Thermo Fisher ) supplemented with 200 μg/mL PureLink RNase A , stained for 30 min at 37 °C , and analyzed by Attune NxT acoustic focusing cytomether and Attune NxT software v2 . 7 ( Thermo Fisher ) . Student’s t-test was used to assess the statistical significance of our results , using Prism ( GraphPad ) . | Fanconi anemia ( FA ) is a devastating disease of children that leads to birth defects , bone marrow failure , and a variety of cancers early in their lives . Germ-line mutations in FA genes disrupt the DNA repair process , namely the FA pathway , resulting in genome instability and clinical features of FA patients . Thus , understanding the molecular mechanisms by which the FA pathway is regulated is critical for alleviating the burden of children suffering from FA and related cancer . A critical step in this pathway is the monoubiquitination of FANCD2 by a multi-subunit ubiquitin E3 ligase called the FA core complex , and the FAAP20 subunit is required for its functional integrity . Here , we show that proline-directed structural change of FAAP20 catalyzed by the PIN1 prolyl cis-trans isomerase is essential for the FAAP20 stability by counteracting phosphorylation-dependent proteolytic signaling of FAAP20 and thus promotes FANCD2 activation and DNA repair . Our findings reveal how PIN1-mediated phosphorylation signaling cascade and proteolysis preserves genomic integrity and how its deregulation is associated the pathogenesis of FA . Our knowledge on a new regulatory mechanism governing FA pathway activation may lead to the development of a new target for FA and FA-related malignancy . | [
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| 2019 | Prolyl isomerization of FAAP20 catalyzed by PIN1 regulates the Fanconi anemia pathway |
Because of the parallels found with human language production and acquisition , birdsong is an ideal animal model to study general mechanisms underlying complex , learned motor behavior . The rich and diverse vocalizations of songbirds emerge as a result of the interaction between a pattern generator in the brain and a highly nontrivial nonlinear periphery . Much of the complexity of this vocal behavior has been understood by studying the physics of the avian vocal organ , particularly the syrinx . A mathematical model describing the complex periphery as a nonlinear dynamical system leads to the conclusion that nontrivial behavior emerges even when the organ is commanded by simple motor instructions: smooth paths in a low dimensional parameter space . An analysis of the model provides insight into which parameters are responsible for generating a rich variety of diverse vocalizations , and what the physiological meaning of these parameters is . By recording the physiological motor instructions elicited by a spontaneously singing muted bird and computing the model on a Digital Signal Processor in real-time , we produce realistic synthetic vocalizations that replace the bird's own auditory feedback . In this way , we build a bio-prosthetic avian vocal organ driven by a freely behaving bird via its physiologically coded motor commands . Since it is based on a low-dimensional nonlinear mathematical model of the peripheral effector , the emulation of the motor behavior requires light computation , in such a way that our bio-prosthetic device can be implemented on a portable platform .
The complex motor behavior originating the rich vocalizations of adult oscine birds results from the interaction between a central pattern generator ( the brain ) and a nonlinear biomechanical periphery ( the bird's vocal organ ) [1] , [2] . The fact that this complex behavior is learned , together with the parallels between the physical mechanisms of birdsong and human speech production , make birdsong an ideal model to study how a complex motor behavior is acquired , produced and maintained [3] . In an effort to understand what gives rise to complexity in this behavior , a part of the birdsong community has set focus on the capabilities of the periphery to produce vocalizations owning a diverse set of nontrivial acoustic features [2] . The avian vocal organ , comprised mainly by the respiratory system , the syrinx and the vocal tract , is a highly nonlinear biomechanical device . The complexity of its dynamics leaves traces in the sounds that can be produced in it . In this way , several acoustic features found in vocalizations can be related to nonlinear phenomena occurring in the syrinx [4] , [5] or introduced by acoustic interactions between the syrinx and the tract [6]–[9] . In all these cases , the complexity of the behavior does not require a complex motor pattern to drive the vocal organ , but rather simple , smooth gestures . Through a combination of experimental observations and theoretical analysis , low-dimensional mathematical models have been proposed that account for the physical mechanisms of sound production in the avian vocal organ [10] , [11] . In particular , a model based on Titze's proposed flapping mechanism for oscillations in human vocal folds [12] was recently used to synthesize the song of the Zebra finch ( Taeniopygia guttata ) [13] , [14] . This model captures the nonlinear dynamics of the folds oscillating to produce sound , in a way that a variety of complex vocalizations are generated by the tuning of parameters related to physiologically observable motor gestures elicited by the bird . Part of the appeal of counting with this model is the prospect of applying it to the construction of a bio-prosthetic device . In this scenario computation is relatively inexpensive because of the low dimension of the mathematical model . In addition , the physical description of the peripheral effectors led to the identification of a set of smoothly varying parameters that determine the behavior [13] . By recording the physiological activity related to the parameters and feeding it to a device that solves the equations of the model in real-time , vocal behavior can be emulated by a prosthesis controlled by a subject via its motor instructions . The usual strategy of BCIs and BMIs ( Brain Computer Interfaces and Brain Machine Interfaces ) is to decode motor commands from recordings of physiological activity in the brain and use this activity to control bio-mimetic devices [15]–[17] . In [17] , for instance , multi-electrode recordings of tens to hundreds of neurons in different cortical areas of primates are used to drive a robotic arm . In recent work , Cichocki et . al . discuss the perspectives of using electroencephalographic ( EEG ) recordings to generate noninvasive BCI solutions [16] . In these examples ( as well as in many other BCI implementations ) , the crucial problem is the classification of the features of the large data set which correspond to a determined set of motor tasks . The feature extraction is performed by different techniques which include linear decomposition in a diversity of vector spaces and machine learning algorithms [15] , [16] , [18] . In this way , accurate control of bio-mimetic effectors is achieved for a finite number of specific tasks , such as grasping or cursor moving . Our current understanding of the biophysics of the avian vocal organ , particularly our capacity to identify the dynamical mechanisms by which complex behavior occurs when the peripheral systems are driven by low dimensional , smooth instructions , allows us to propose an example of a different kind of bio-prosthetic solution . The model predicts a diversity of qualitatively different solutions to the system for continuous paths in a parameter space . Not only is this parameter space suggested by the model , but it is also physiologically pertinent . We present a device that is driven by a freely behaving Zebra finch to produce realistic , synthetic vocalizations in real-time . The device is based on the real time integration of the mathematical model of the vocal organ on a Digital Signal Processor ( DSP ) . It is controlled by the bird's subsyringeal air sac pressure gesture , which is transduced , digitized and fed to the DSP to provide the model with the appropriate path in parameter space . The work is organized as follows . In the Methods section we describe the Zebra finch vocal organ and the mathematical model that accounts for its dynamics . We also discuss its applicability to the construction of a model-based bio-prosthetic device , and introduce the physiological motor gestures that relate to parameters of the model . We present the steps leading to the real-time implementation of the model on a device controlled by a spontaneously singing bird . In the Results section we show the example of a successful case . Finally , we summarize the results and discuss the impact of this device as an example of a kind of bio-prosthetic device enabled by the low-dimensional dynamical model of the peripheral effector .
All experiments were conducted in accordance with the Institutional Animal Care and Use Committee of the University of Utah . One of the most studied species of songbirds is the Zebra finch . Its song presents a set of diverse acoustic features , which can be accounted for by the dynamics displayed by the mathematical model of its vocal organ [5] , [11] . This low-dimensional mathematical model , when driven by the appropriate gesture in parameter space , is capable of producing realistic , synthetic birdsong . By implementing this model on a Digital Signal Processor , we are able to construct a bio-prosthetic vocal organ . The mathematical model for the vocal organ , the reduction of the system ruling the dynamics of the sound source , and the identification of the pertinent parameters accounting for its motor control , they all make way for the construction of a bio-prosthetic device . The parameters determining acoustic properties of vocalizations in the normal form ( 5 ) are physiologically meaningful and the set of differential equations is easy enough to compute in a portable platform such as a Digital Signal Processor . By fitting the parameters and integrating the system in real-time , synthetic song can be produced in a device controlled by the motor instructions elicited by a freely behaving bird . In many bio-prosthetic solutions , the physiological motor gestures used to drive the device are degraded respect to those recorded in the intact subjects [15] . One application of this device is the performance of altered auditory feedback experiments [32]–[34] . In experiments enabled by this device , the bird's own auditory feedback can be replaced by synthetic birdsong computed in real-time . Since the synthetic feedback is produced by the integration of the model when fed with actual physiological motor gestures elicited by a freely behaving bird , alterations of the feedback are possible that are consistent with alterations in the motor gestures intended to produce them . Here , the prosthetic vocal organ is driven by a bird that is muted via the insertion of a cannula through its inter-clavicular air sac . Phonation is prevented as the airflow is bypassed away from the syrinx . As a side effect , the pressure pattern registered on the muted bird differs from that recorded in the intact animal . The device reconstructs the intended motor gesture from this degraded pressure gesture to trigger integration of the model . When the pressure pattern corresponding to the syllables comprising a motif are identified , the mathematical model for the vocal organ is computed with the appropriate paths in parameter space , to produce the corresponding synthetic output .
The device succeeds in synthesizing song online when driven by the pressure gesture of a muted bird . From the altered motor gesture , the algorithm infers the segment of a motif intended by the bird and computes the model to produce the vocalizations . An example is illustrated in Fig . 5 . The upper panels of the figure display the recorded subsyringeal pressure and sonogram of a segment of a bout with its preceding introductory call . A song bout of this bird ( B06 ) is composed of a number of introductory notes ( O ) and the repetition of a simple motif containing two syllables ( A and B ) , indicated by different colors and opacity of shading in Fig . 5 . An initial segment of syllable A ( marked with clearer shading in the figure ) is used to detect the intention to elicit a motif in the pressure gesture of the muted bird . The part that is produced upon triggering of the synthesis appears in a darker shade of the same color . These recordings were used to fit the parameters of the model to produce synthetic vocalizations showing a match in fundamental frequency and spectral content . The bird is then muted and placed in the setup to drive the electronic vocal organ with its pressure gesture . In the lower panel of Fig . 5 we show the pressure gesture of the muted bird and the sonogram of the sound recorded by a microphone placed close ( about ) to the bird and the speaker . It can be noted in the sonogram that no sound is produced during the bird's attempts to phonate an introductory note . When the pressure motor gesture corresponds to the first syllable in the bout ( syllable A ) , the instruction is recognized and the corresponding song is synthesized and played through the speaker . In the first bout , the bird only elicits the gesture corresponding to the first syllable , and then stops . In this case the algorithm detects the interruption and turns off the integration . In the second bout , the bird continues with the second syllable and drives the electronic syrinx to the end of the motif . This example illustrates how this device works , and shows that it is successful in synthesizing the song motif as the bird drives it . We evaluate its success by counting the times the motif was properly detected and synthesized , and how many times a false trigger occurred . During a session of hours ( the second day after the muting took place ) , a muted bird elicited about calls , out of which less than generated false triggering of synthetic song . In most cases the false trigger event was recognized by the algorithm and silenced after less than . The rate of success in detecting the beginning of a bout was of about in attempted bouts elicited by the bird . Despite the variability of the altered pressure gesture in the subsequent days ( days after the muting ) , a brief calibration before each daily session allowed the rates of success and false triggers to be maintained . To do this calibration , the pressure gesture was recorded during and these data were used to re-set the cross-correlation thresholds , while keeping the test segment of the intact pressure gesture previously selected . We show here that realistic vocal behavior is synthesized in real-time by our device , as it is controlled by the spontaneous behavior of a muted bird , a physiological signal ( its air sac pressure ) that is degraded in respect to the one recorded in the intact bird . The computing platform is a low cost , portable processor , and the initial rate of success is high . This is an encouraging example of the plausibility of a kind of interface between the central motor pattern generator and the synthetic , bio-mimetic behavior . DSP technology is being implemented in a variety of biologically inspired problems , and together with Field Programmable Gate Array technology ( FPGA ) is likely to become a standard solution for a variety of bio-mimetic applications [15] , [35] . Brain computer and brain machine interfaces ( BCI and BMI ) typically read physiological data and attempt to decode motor instructions that drive peripheral devices in order to produce synthetic behavior [15] , [17] , [35] , [36] . Because we have a physical model of the peripheral effector , the origins of the complexity of the behavior were linked to smooth paths in a low dimensional parameter space . Following the identification of the pertinent parameters and their physiological link to the pattern generator ( activity of the ventral syringeal muscle and sub-syringeal air sac pressure ) , a further simplification of the system was carried out , on dynamical grounds , by eliminating irrelevant nonlinear terms ( performing a reduction of the model to its normal form ) . This led to the possibility of implementing our bio-prosthetic device on a programmable electronic platform . Since the computing capabilities of the platform are greatly enhanced by the low costs of our implementation , technological advances in this front will have great impact on the complexity of the peripheral biomechanical system that can be emulated .
We have built a device that emulates complex motor behavior when driven by a subject by its actual ( yet degraded ) physiological motor gestures . It successfully reproduces the result of the stereotyped motor gesture that leads to the behavior , i . e . , the diverse and complex set of sounds comprising the bird's song bout . The realistic synthetic vocalizations are produced in real-time , by computing a mathematical model of the vocal organ on a portable Digital Signal Processor . The relative computational and technological simplicity of the device relies on the current level of understanding of the peripheral biomechanical effector [1] , [2] , [12] , [24] . We have been able to construct a physical model of the syrinx that presents the adequate level of description and converges to a low dimensional ( as low as two dimensions ) dynamical system . The deterministic model of the vocal organ on which our device is based is not a statistical attempt to capture causal relationships between motor commands and behavior; instead , it is a hierarchization of the interactions within the biomechanical periphery and with the pattern generator . It aims to identify the dynamical mechanisms by which the behavior is produced . Furthermore , exploration of the model leads to the finding that much of the diversity and complexity of the behavior can be explained in terms of the dynamical features of this nonlinear system [5] , [28] , requiring only simple instructions of the nervous system to produce a rich variety of vocalizations . Just as it is identified that a low dimensional system reproduces the main features of the complexity of the vocal organ , it can also be concluded that the control parameters are few and their behavior is simple ( i . e . , the physiological motor gestures linked to the paths in space are smooth ) . In this way , the parameter space of the model not only suggests the pertinent physiological instructions determining the main properties of the output but also how they are expected to behave . Paths in parameter space reconstructed in order for the model to produce vocalizations matching experimental recordings are indeed effective in predicting the physiological motor gestures [14] . In addition , knowledge of nonlinear dynamics allows us to find the simplest system with equivalent oscillatory behavior . The reduction of the low dimensional mathematical model for the syrinx to its normal form reduces the computational requirements and makes way for the implementation on a real-time computing solution , such as a DSP . Realistic vocal behavior is synthesized online , controlled by the motor gesture of a freely behaving muted bird , which is a physiological signal that is degraded respect to the one recorded in the intact bird . This was achieved by computing in real time a mathematical model describing the mechanisms of sound production in the interface between the motor pattern generator and the behavior , the highly nonlinear vocal organ . The computing platform is a low cost , portable processor . This successful avian vocal prosthesis is an encouraging example of the plausibility of a kind of interface between the central motor pattern generator and the synthetic , bio-mimetic behavior . An advance towards models in which certain complex features of the motor behavior are understood in terms of the underlying nonlinear mechanisms of the peripheral effectors has the potential to enhance solutions of brain-bio-mimetic effector interfaces in many ways . | Brain Machine Interfaces ( BMIs ) decode motor instructions from neuro-physiological recordings and feed them to bio-mimetic effectors . Many applications achieve high accuracy on a limited number of tasks by applying statistical methods to these data to extract features corresponding to certain motor instructions . We built a bio-prosthetic avian vocal organ . The device is based on a low-dimensional mathematical model that accounts for the dynamics of the bird's vocal organ and robustly relates smooth paths in a physiologically meaningful parameter space to complex sequences of vocalizations . The two physiological motor gestures ( sub-syringeal pressure and ventral syringeal muscular activity ) , are reconstructed from the bird's song , and the model is implemented on a portable Digital Signal Processor to produce synthetic birdsong when driven by a freely behaving bird via the sub-syringeal pressure gesture . This exemplifies the plausibility of a type of synthetic interfacing between the brain and a complex behavior . In this type of devices , the understanding of the bio-mechanics of the periphery is key to identifying a low dimensional physiological signal coding the motor instructions , therefore enabling real-time implementation at a low computational cost . | [
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| 2012 | Prosthetic Avian Vocal Organ Controlled by a Freely Behaving Bird Based on a Low Dimensional Model of the Biomechanical Periphery |
Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides , refolding and degradation of misfolded proteins , and activation of a wide range of client proteins . The prokaryotic heat-shock protein DnaK is the E . coli representative of the ubiquitous Hsp70 family , which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides . Accurate prediction of DnaK binding sites in E . coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins . In order to map DnaK binding sites in protein sequences , we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling . We show that this combination significantly outperforms either single approach . The final predictor had a Matthews correlation coefficient ( MCC ) of 0 . 819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives . To test the robustness of the learning set , we have conducted a simulated cross-validation , where we omit sequences from the learning sets and calculate the rate of repredicting them . This resulted in a surprisingly good MCC of 0 . 703 . The algorithm was also able to perform equally well on a blind test set of binders and non-binders , of which there was no prior knowledge in the learning sets . The algorithm is freely available at http://limbo . vib . be .
Hsp70 molecular chaperones are part of the quality control machinery that functions to assist protein folding . Members of the Hsp70 family have been implicated in refolding of misfolded proteins , folding of newly synthesized polypeptide chains , disassembly of larger aggregates and translocation of proteins in organelles [1] . Hsp70 molecules also enable cell survival during stress or heat-shock conditions that are characterized by an increased concentration of ( partially ) denatured polypeptides . These chaperones recognize and bind misfolded or aggregation-prone peptide stretches through exposed hydrophobic regions which are normally buried in the protein core . Such exposed regions are typical for non-native proteins [2] , [3] . Hsp70 molecular chaperones consist of two distinct domains , an N-terminal ATPase domain [4] and a C-terminal peptide binding domain [5] . Hsp70 function is dependent on an ATP-regulated cycle of substrate binding and release [6] . With ATP bound , substrate affinity is low and Hsp70 resides in an open state , ready to receive a suitable substrate . Once the substrate is bound , ATP is hydrolyzed to ADP and Hsp70 undergoes a conformational change to a high affinity state , subsequently trapping the substrate . The co-chaperone Hsp40 ( DnaJ in E . coli ) binds Hsp70 and stimulates the ATPase function , causing retention of the substrate . Hsp40 also recognizes hydrophobic stretches and may serve as a substrate delivery chaperone to Hsp70 [6] , [7] . Upon exchange of ADP for ATP , Hsp70 returns to a low affinity state , enabling binding of another substrate or providing another refolding cycle for the same substrate if necessary . The crystallisation of the archetypical and well characterized E coli Hsp70 DnaK bound to a peptide reflects a heptameric substrate binding motif requiring a hydrophobic core region and preferably basic flanking residues that complement the overall negatively charged DnaK surface [5] . This preference was later confirmed in the seminal work of Bukau and co-workers by binding studies of DnaK to cellulose-based peptide libraries and a DnaK binding profile was derived [3] . Contrary to these previous studies on DnaK binding motif profiling which utilised only sequence information , we complement the experimental binding information on a set of peptides with structural data from homology modelling to obtain an accurate predictor . Similar dual based approaches have already been shown successful to predict other peptide signatures . Prediction of binding of endogenous antigenic peptides to MHC class I molecules was aided by adding structural information from molecular models to the sequence data [8] . Branetti et al used structural data from various SH3/ligand complexes and sequence information from phage libraries to predict preferred ligand binding to different SH3 domains [9] . Recently , an algorithm to predict amylogenic regions in protein sequences profited greatly from the combination of sequence based data and structural information from amyloid fibers crystallographic studies ( Maurer-Stroh et al , unpublished data ) . In this article we introduce such a dual based method for profiling DnaK binding sequences . We combine sequence based information from experimental binding assays with structural information from molecular modelling via the FoldX force field [10] . We present a DnaK binding prediction algorithm that , under cross-validated conditions , performs strikingly accurate .
We screened the ability of DnaK to bind cellulose-immobilised peptides by detecting the bound DnaK via a specific monoclonal antibody ( see materials and methods ) , according the method previously developed by Bukau and co-workers [3] . Figure 1 shows the outcome of a typical experiment . The peptides were selected in groups by different criteria . Group 1 peptides were selected using the statistical thermodynamics algorithm TANGO for the prediction of cross-beta protein aggregation [11] , on the assumption that the Hsp70 chaperone family binds to exposed sequences that can nucleate protein aggregation . An initial crude DnaK predictor was then constructed on the basis of these peptides . The E coli proteome was then scanned with this predictor to search for relatively short proteins harbouring more than one predicted DnaK binding sites . Seven proteins that met these requirements were randomly picked for a DnaK binding peptide scan and were named Group 2 . Such a peptide scan consists of subsequent decapeptides overlapping by five residues , spanning the whole protein sequence . Two additional putative DnaK binding peptides from the known DnaK binding RNA-polymerase sigma-32 factor [3] , [12] were included in the binding experiments ( Group 3 ) . All analyzed peptide sequences are listed in Table S1 of the Supplemental data . A separate experiment was carried out where the peptide membrane was drenched with only the monoclonal antibody but not DnaK . A significant chemiluminescence signal would be the result of antibody-peptide binding and thus a false positive . A total of 16 peptides with a false positive signal higher than half of the DnaK-peptide signal were removed from the sets for any further analysis . The false positive signal of the rest of the peptides was subtracted from their DnaK-interaction signal to correct for any antibody-peptide binding that might occur . To build the learning sets of sequences ( see below ) , the peptides were grouped as either binders or non-binders . Setting a single cut-off value of relative binding would create a twilight zone of peptides that show neither clear binding nor clear non-binding . Therefore we set a high and a low cut-off value per membrane/experiment . Peptides with a signal above the high cut-off are considered as binders , peptides with a signal below the low cut-off are considered as non-binders . Out of a total of 172 peptides , there were 53 binders and 119 non-binders . A fraction of 15% from each set was randomly selected to constitute an independent validation set . Sequences from this validation set were never implemented in any training sets of the predictor to allow an independent validation assessment . This set consisted of 9 binders and 19 non-binders . The remaining 144 peptide sequences will be referred to as ‘benchmark’ sets . The binders and non-binders groups are less then 90% redundant in sequence identity . A sequence based profile or position specific scoring matrix ( PSSM ) can only be derived from properly aligned learning sets of sequences of the same length . As the aim of our DnaK predictor was to evaluate stretches of heptapeptides for DnaK binding affinity , we fragmented both positive ( binders ) and negative ( non-binders ) benchmark sets into heptapeptides to create the final learning sets . To generate the negative learning set we assumed that for peptides longer than 7 residues , each heptapeptide fragment of the peptide is a true non-binder . Therefore the final negative learning set consisted of all possible heptapeptides from the full length non-binding peptides . Generating a heptapeptide learning set from the true binder peptides is less trivial as one or more heptapeptide stretches must be identified from each DnaK binding peptide that is assumed to occupy the DnaK substrate binding groove . To this end , we used the FoldX force-field [10] to identify such potential stretches within each full peptide . Each peptide from the binders set was threaded through the DnaK binding pocket using the crystal structure of DnaK bound to a substrate peptide [5] . In this way , the binding energy of each possible heptapeptide from every full peptide was calculated with FoldX ( see material and methods ) . The best binding heptapeptide from each full peptide was selected to fill the positive learning set of heptapeptides . To allow multiple potential heptapeptide DnaK binders per full peptide , we also added those heptapeptides with a binding energy within 0 . 5 kcal/mol of the binding energy of the best binding heptapeptide . The final negative and positive learning sets contained 443 and 56 heptapeptides , respectively . The performance of the intermediate and final predictor PSSMs was assessed by receiver operating characteristic ( ROC ) curves in which , for a certain score threshold , the percentage of false positive predictions is plotted against the percentage of true positive predictions ( see material and methods for detailed explanation of data point generation ) . The performance of each PSSM was measured against the aforementioned benchmark sets of known true positive binders and true negative binders . The calculation of ROC curves allows finding a predictor with high specificity , i . e . to find a score threshold above which the amount of false positives remains acceptable , while still finding a large amount of true positives . In this study we aim at specificity between 90–100% , i . e . 0–10% false positives . The overall performance for a certain score threshold is calculated via the Matthews correlation coefficient ( MCC ) ( see material and methods for formula ) . Hence , we will use the highest MCC in the 90–100% specificity area as a predictor performance marker . Two separate PSSMs were constructed from the positive and negative learning sets using the log odd based method , similar to a recently developed amyloid prediction algorithm ( Maurer-Stroh et al , unpublished data ) . For each position in the heptapeptides , the residue frequency was calculated by normalising the number of residue occurrences by the total number of sequences in each learning set . The logarithm of the ratio of the observed frequency over the expected frequency was calculated and used as the PSSM value for each position of the heptapeptide ( see material and methods for formula ) . The expected frequency is the occurrence of residues in large databases , for which we used the Swiss-Prot database frequencies [13] . The log odd score based PSSM has advantages over the frequency based PSSM; it accounts for the chance of randomly finding a specific residue and inherently puts more weight on ( DnaK ) motif specific residues . The non-binders PSSM now represents a sequence profile which is unfavorable for DnaK binding , whereas the binders PSSM reflects residue preferences for binding . To incorporate both types of data , we generated the final sequence-based PSSM by subtracting the scores of the non-binders PSSM from the scores of the binders PSSM , hereby reaching a consensus profile from the experimental data . To generate the structure based residue profile of DnaK substrate binding , we used the high resolution crystal structure published by Zhu et al as a template structure [5] . A heptapeptide with the sequence NRLLLTG , identified in a phage-display library screen [14] , was co-crystallized and showed a substrate recognition motif of DnaK for a minimum of 7 residues arranged in an extended peptide conformation ( Figure 2 ) . To address the contribution of every possible amino acid at each of the seven positions in the heptapeptide , we used the FoldX force-field to perform a position scan on the heptapeptide . Firstly , the heptapeptide was mutated to poly-alanine . Next , each position was sequentially mutated to all other 19 amino acids , while the other 6 residues remained as alanine . The binding energy for each residue at every position was calculated and subtracted from the binding energy of alanine at the same position , resulting in the corresponding ΔΔG for each residue and position . The more negative the ΔΔG , the better the residue fits DnaK binding . To convert each ΔΔG into a PSSM score , we took the negative of each ΔΔG and filled the structure-based PSSM accordingly . Peptide backbone variation could influence the quality of the resulting PSSM . Therefore we assessed multiple backbone conformations of the entire structure by generating a ROC curve for PSSMs originating from different structures and calculating the MCC in the high specificity area . Structures of DnaK in complex with a substrate peptide were gathered from an NMR ensemble ( PDB code 1Q5L ) [15] and from different crystal structures ( PDB codes 1DKX and 1DKY , of which the latter is a DnaK dimer with monomers A and B ) [5] . The MCC of structure 1DKX was the highest as compared to 1DKY ( A and B ) and was above the MCCs of three randomly picked NMR structures from the ensemble 1Q5L ( Table 1 and Figure 3 ) . Moreover , the overall performance of the NMR structures was much lower than that of any X-ray structure , as shown in the ROC curves . Therefore we continued with the PSSM of the crystal structure 1DKX ( See suppl . Table S2b for PSSM ) . The performance of the sequence based matrix and the combination of the sequence- and structure-based matrices was also assessed by means of ROC curves . To test the robustness of the learning set , each predictor was subjected to a simulated cross-validation and a cross-validation against the validation peptide set . . During the simulated cross-validation we excluded sequences and their close homologs sequentially from the learning sets and calculated the rate of repredicting them . The MCC of the sequence-based predictor was 0 . 792 , but this dropped to 0 . 708 after a simulated cross-validation assessment . While these MCC values are acceptable , the MCC of this predictor against the validation set was only 0 . 106 , indicating that the current learning sets of peptides are not suitable to predict DnaK binding of peptides of which there is no prior knowledge in the learning sets . Next , we added the structure-based matrix scores to the sequence-based matrix scores . Before cross-validation , the MCC had a value of 0 . 756 , reduced to 0 . 626 upon simulated cross-validation and 0 , 375 when the validation set was assessed . Although the not cross-validated and simulated cross-validation MCC had a slight performance setback ( but still acceptable ) upon combining sequence and structure-based PSSMs , the validation set MCC improved remarkably ( Figure 4 for the ROC curves ) . It seems thus that adding structural information broadens the generality of the predictor . Although the heptapeptides in the learning sets were selected on a methodologically acceptable basis , inconsistencies in the learning set selection could not be excluded . We recently developed a training algorithm in which learning set sequences are sequentially removed and the effect on the cross-validation is assessed ( Maurer-Stroh et al , unpublished data ) . Only when the Matthews correlation coefficient in the high specificity area improved , removal of the learning set sequence was accepted . The algorithm removed 8 sequences from the learning sets when the sequence-based PSSM was used to evaluate the MCC . The final not cross-validated MCC became 0 . 919 , whereas simulated cross-validation showed an MCC of 0 . 651 and the validation set had an MCC of 0 . 662 , a huge improvement compared to the starting MCC values for the sequence-based PSSM ( See suppl . Table S2a for PSSM ) . Next , we trained the algorithm with the combined sequence- and structure-based PSSM . Here , 6 of the 499 learning set sequences were removed . The not cross-validated MCC reached 0 . 819 and upon cross-validation we were able to obtain an MCC of 0 . 703 . This optimized sequence and structure based predictor had an MCC of 0 . 759 on the validation set sequences ( Figure 5 for the ROC curves and suppl . Table S2c for PSSM ) ) . The accuracy of the non cross-validated predictor was 92 , 4% in which 77 , 3% of the experimentally verified binding peptides were correctly predicted ( true positives ) with only 1% false positives ( or 99% specificity ) . It is important to note that a higher rate of true positive detections can be achieved ( 93 , 2% instead of 77 , 3% ) but at the cost of a lower specificity ( 91% ) . These settings can easily be customized in the online version of the predictor . When ran over the validation set , the highest obtainable accuracy was 89% with 66 , 7% true positives and no false positives . The performance appears to be stable when analyzing benchmark sets of variable redundancy ( Suppl . Table S3 , Suppl . Figure S1 ) . The evolution of the best MCC at high specificity for the various predictors is listed in Table 2 . Our DnaK binding profile confirms previous observations of a hydrophobic core with basic flanking residues [3] , albeit not as strict as once thought . We are able to define specific residue preferences and disfavors of each of the seven peptide positions ( numbered N-terminally from 1 to 7 ) . Positions 2 , 3 , 4 , 5 and 6 score high for hydrophobic residues , with all but position 4 having also preference for the aromatic resdues F and Y ( W also at position 6 ) . The basic residue preference is most apparent at position 7 , where R is the most preferred residue , after the aromatic F . Positions 2 , 3 and 5 also allow basic residues ( preferably R ) , but this feature is not as pronounced as at position 7 . Position 4 is the most restricted position , where only L , I , M and to a lesser extent T and V are observed . Most other amino acids are not allowed at this central position . Positions 2 and 3 are also very restricted in their residue preference . The majority of amino acids is not observed in binding sequences at these positions . Hydrophobicity ( mainly L ) , aromaticity ( mainly F , Y ) and positive charge ( R ) seem to be the requirements here . The large aromatic residue W is strongly preferred at the first and sixth position . Overall there seems to be an underrepresentation of C and D . E is strongly disfavoured at the second position , but shows a positive score at the very first position . A graphical representation of the profile is given in Figure 6 ( See Suppl . Table S2c for PSSM ) .
From a dataset of 144 non-redundant experimentally tested peptide sequences , we were able to build a predictor for DnaK binding protein motifs by adding structural information obtained through molecular modelling . The final predictor performs well under cross-validated conditions . The success rate was dependent on two main variables . Firstly , the training set was well optimised since the training set optimisation algorithm rectifies to a certain extent anomalies from in silico analysis . Our initial positive learning set was , in part , generated from in silico threading; from each full peptide the heptapeptide with the best binding energy was selected and also those heptapeptides with a binding energy within 0 . 5 kcal/mol of the heptapeptide with the best binding energy . The latter step is prone to some misclassification , since we allow multiple DnaK binding sites per peptide and this might not always be the case in vitro . However , our learning set training algorithm was able to remove 6 heptapeptides that appeared ‘noisy’ to allow acceptable cross-validated prediction of the benchmark peptide set . The second and most profound variable is the initial perspective of this study , that combined sequence and structural data will complement each other to detect protein-protein interaction motifs . The ROC curves in Figure 5 illustrate the power of merging both approaches: the overall performance of the dual based predictor exceeds the individual sequence- and structure-based predictors ( see also Table 2 ) . Each approach must therefore , to a certain extent , contribute information that the other lacks . The sequence learning set or alignment of both binders and non-binders must inherently provide information on the ability of seven consecutive residues to adopt the extended conformation , since this represents the DnaK binding mode of a substrate peptide . The structural approach serves to further refine the sequence information by detecting direct peptide binding preferences based on amino acid sidechain volume , charge , hydrophobicity , etc . The major improvement in the cross-validation upon adding the structural information could also be explained as the ability of the structural data to cancel out some positional residue bias or any missing residue information inside the sequence data set . It should be noted however , that the structural template was chosen according to its stand-alone MCC and ROC performance with the experimental benchmark sequence set ( Figure 3 and Table 1 ) . Structure profiles based on NMR structures were unable to come close to the performance obtained with the X-ray structure . Moreover , the fact that out of three crystal structures the one with the highest resolution performed best should stress the use of high quality crystal structures to obtain a reliable structure-based profile . Our dual approach DnaK profile confirmed in part the previously observed residue preferences for basic residues flanking a hydrophobic core region [3] . We extended this to show that positions 1 and 3 do not totally disfavour the negatively charged glutamate . Aspartate on the other hand is strongly rejected in all positions . The central position 4 prefers the aliphatic hydrophobic amino acids leucine , isoleucine and methionine . It is the most restricted position , which is not remarkable since the residue must fit in a well defined hydrophobic pocket of DnaK [5] . Aromatic residues , although not always evenly distributed , are very abundant at all sites , except at the central position which is neutral for phenylalanine and the last position which disfavors tryptophan . The second and third positions show a clear spectrum of aliphatic , aromatic and basic residues . The absolute last position prefers arginine , and phenylalanine ( Figure 6 ) . Overall , phenylalanine is the only residue not to have a clear disfavored site . We have compared our method with a previously published predicting algorithm [3] ( see supplementary Table S2d for PSSM ) . To test the generality and possible overprediction of sequences that are not known DnaK binders , we added 200 randomly selected 15-mer peptides from real proteins to the negative benchmark set . Additionally , the peptides were selected with the prerequisite to have less than 50% sequence identity to the learning set sequences . In this task , both methods perform comparably well at relevant levels of high specificity of 90 . 3% ( Figure 7A ) . As can be seen in the ROC curve for this specificity , our method reaches a sensitivity of 68 . 2% where the previously published algorithm has a sensitivity of 54 . 5% . For the same amount of false positives , we predict thus more true positives compared to the algorithm of Rüdiger et al . in this experiment . We also compared the performance of both predictors on the validation set of peptide sequences ( Figure 7B ) and conclude that our method performs slightly better in terms of true positive predictions ( sensitivity ) . Of note , the algorithm of Rüdiger et al . was modified from a 13-mer scoring matrix to a 7-mer scoring matrix to score heptapeptides ( see supplementary Table S2d for PSSM ) . In summary , we have shown that the combination of sequence and structural data can be combined to generate a validated DnaK-substrate binding predictor using experimentally tested peptide sequences . Given a well sampled sequence dataset and one or more high quality crystal structures , the same methodology can be applied to generate other protein-protein interaction predictors . The algorithm is freely accessible at http://limbo . vib . be
For DnaK-peptide binding determination peptides where chemically sensitized on cellulose beta-alanine membranes ( PepSpot membranes , JPT Peptide Technologies , GmbH ) . The membranes were washed in 100% MeOH for ten minutes , and three times for twenty minutes in 20 mM Tris buffer pH 7 . 5 plus 150 mM NaCl ( TBS buffer ) . The membranes were blocked in blocking buffer ( BSA 2% w/v in TBS buffer ) for one hour . The blocked membranes were incubated in 100 nM DnaK ( Axxora ) in 31 mM Tris pH 7 . 5 , 170 mM NaCl , 6 . 4 mM KCl , 5% Sucrose , 0 . 05% Tween-20 for one hour . Subsequently membranes were washed three times for ten minutes in TBS buffer and incubated for one hour with mouse monoclonal anti-DnaK antibody ( clone 8E2/2 , Stressgen ) diluted 1∶2000 in blocking buffer plus 0 . 05% v/v Tween20 . Membranes were washed three times for ten minutes with TBS buffer plus 0 . 05% v/v Tween20 and incubated for 30 minutes with anti-mouse HRP-conjugated antibody ( Promega ) diluted 1∶10000 in blocking buffer plus 0 . 05% v/v Tween20 . The membranes were finally washed two times for ten minutes in TBS buffer plus 0 . 05% Tween20 , one time for ten minutes in TBS buffer and subjected to chemiluminescent reaction using SuperSignal West Dura substrate ( Pierce ) , detected by CCD camera connected to the ChemiDoc XRS image acquire and process system ( BioRad ) . All benchmark peptides were scored with every intermediate and final scoring matrix as described in the results section of this article . The range between the minimum and maximum score was calculated and divided in 30 equally sized bins . Each bin threshold was compared to the score of the individual peptides . When the peptide score was above a certain threshold , we consider the peptide as a predicted binder . If the peptide was indeed an experimentally verified DnaK binder , the number of predicted true positives was increased by one . When the predicted binder was shown to be an experimentally verified non-binder , the number of predicted false positives was increased by one for that threshold . Each tuple of false positives and true positives for such a threshold becomes one data point in the ROC curve . The Matthews correlation coefficient ( MCC ) is a measure of quality of binary classifications and is given by the formula:Where tp = true positives , tn = true negatives , fp = false positives , fn = false negatives . An MCC value of 1 is for a perfect prediction , 0 for a completely random assignment and −1 for the worst possible prediction . For both the alignments of the binding and non-binding heptapeptides the number of observed residue occurrences nobs at each position was counted . Next , this value was normalised by the number of aligned sequences in each learning set , which resulted in the positional residue frequency fobs:The final matrix score S was calculated by taking the logarithm of the ratio of the observed frequency fobs over the experimental ( database ) residue frequency fex:Some residues do not occur at all at certain positions in the heptapeptide alignment which leads to the logarithm of zero . To circumvent this issue , we implemented so-called pseudocounts: whenever a zero count occurred , it was substituted by 0 . 001 . We employed FoldX version 2 . 7 to model mutants of the peptide bound to DnaK in the crystal structure ( PDB code: 1DKX ) . To this end , the peptide was first reduced to poly-Alanine . Then , all possible natural amino acids were systematically introduced at each position , while keeping the remainder of the peptide as alanines . Energy estimates were calculated with FoldX as the ΔG difference ( ΔΔG ) to the reference poly-Alanine . This method reduces the sequence space to be covered by the modelling dramatically , but ignores any dependencies between the positions . Given the extended conformation of the peptide bound to DnaK , this assumption seems reasonable . | Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides , refolding and degradation of misfolded proteins , and activation of a wide range of client proteins . This variety of functions results from the existence of multiple chaperones with different structures . Chaperones bind to exposed regions of proteins to fulfil their function . The chaperone must hereby recognise a certain signal sequence on the substrate protein . The nature of the sequence that is exposed will determine the types of chaperones that can interact with it , and in the end will also determine the fate of the substrate protein: refolding , translocation , degradation or activation . Knowledge of the binding sequence determinants of molecular chaperones will shed more light on the mechanism of how each chaperone contributes to the cellular protein quality control system . In this study we have made an algorithm which accurately predicts binding sites for the well studied E . coli Hsp70 chaperone , DnaK , which is implicated in folding efficiency and prevention of aggregation . The ability to detect and design high-affinity DnaK binding sites enhances our understanding of chaperone-substrate recognition and opens great opportunities to enhance protein solubility using protein-DnaK binding motif fusions . | [
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| 2009 | Accurate Prediction of DnaK-Peptide Binding via Homology Modelling and Experimental Data |
Atopic dermatitis ( AD ) is a common chronic inflammatory skin disorder and a major manifestation of allergic disease . AD typically presents in early childhood often preceding the onset of an allergic airway disease , such as asthma or hay fever . We previously mapped a susceptibility locus for AD on Chromosome 3q21 . To identify the underlying disease gene , we used a dense map of microsatellite markers and single nucleotide polymorphisms , and we detected association with AD . In concordance with the linkage results , we found a maternal transmission pattern . Furthermore , we demonstrated that the same families contribute to linkage and association . We replicated the association and the maternal effect in a large independent family cohort . A common haplotype showed strong association with AD ( p = 0 . 000059 ) . The associated region contained a single gene , COL29A1 , which encodes a novel epidermal collagen . COL29A1 shows a specific gene expression pattern with the highest transcript levels in skin , lung , and the gastrointestinal tract , which are the major sites of allergic disease manifestation . Lack of COL29A1 expression in the outer epidermis of AD patients points to a role of collagen XXIX in epidermal integrity and function , the breakdown of which is a clinical hallmark of AD .
Atopic dermatitis ( AD ) is a chronic inflammatory skin disease that is characterized by intensely itchy skin lesions . AD is one of the most common chronic diseases in childhood affecting 10%–20% of children in industrialized societies [1] , with a steady increase over the past decades [2 , 3] . Along with asthma and hay fever , AD is commonly associated with an abnormal immune response and the formation of allergy antibodies ( IgE ) against innocuous environmental allergens . AD is often the first clinical manifestation of allergic disease . The onset of disease is typically observed during the first two years of life [4] . For the majority of affected children , AD heralds a lifetime of allergic disease . A susceptible child commonly passes a characteristic sequence of transient or persistent disease stages that is known as the “atopic march” which begins with AD and food allergy in the young infant and continues with the development of allergic airways disease later in childhood and adulthood [5] . The close familial and intra-individual association of the allergic disorder strongly suggests shared genetic etiology . A strong genetic component in allergic disorders has been recognized almost a century ago . Cooke and van der Veer first reported that the relatives of patients are at significantly increased risk of developing allergic disease [6] . Even today , a positive family history for allergic disorders is the single strongest predictor for the development of AD [7] . Additional evidence for the importance of genetic factors in atopic disease comes from twin studies . The concordance rate for AD among monozygotic twins of about 80% far exceeds the concordance rate of 20% observed among dizygotic twins [8 , 9] . These data clearly indicate that the genetic contribution to the expression of AD is substantial . Furthermore , studies on the vertical transmission of AD and atopic disease show that children are more likely to inherit these disorders if the mother is affected ( parent-of-origin effect ) [10] . The predominance of maternal inheritance may be due to environmental factors such as uterine milieu or breast feeding , but they may also arise due to genetic mechanisms such as parent-specific gene expression ( genomic imprinting ) [11] . AD and atopic disorders are regarded as multifactorial conditions , the onset and severity of which are influenced by both genetic and environmental factors . The data are consistent with an immune etiology shared by all allergic diseases and a congenital target organ defect , the penetrance of which is modified by multiple environmental factors during early childhood . The identification of genes underlying AD and allergic disorders has the capacity to define primary physiologic mechanisms , thereby clarifying disease pathogenesis , identifying pathways and targets for therapeutic intervention . Although several genome-wide linkage screens for AD have been conducted [12–14] , there was no substantial overlap between the identified regions of highest linkage , and the underlying genes remained elusive [15] . We have previously mapped a major susceptibility locus for AD on Chromosome 3q21 [16] . Here we report the identification and characterization of a novel epidermal collagen gene as the underlying disease gene .
To narrow the candidate region spanning 12 . 75 centiMorgans ( cM ) ( 13 . 5 Mb ) on Chromosome 3q21 ( Figure 1A ) , 96 additional microsatellite markers at an average distance of 140 kb were genotyped in 199 affected sibling families with AD from the original linkage scan . Linkage analysis yielded a 1-lod support interval of 5 . 4 Mb between the markers M3CS075 and M3CS233 ( Figure 1B ) . Subsequently , we conducted an association scan of the 5 . 4-Mb region using 212 single nucleotide polymorphisms ( SNPs ) at an average distance of 25 . 5 kb ( Table S1 ) . The SNPs were selected primarily to cover known and predicted genes . Because we had observed a strong maternal effect in the linkage study [16] , we chose a family-based association analysis that allowed us to search for risk alleles preferentially transmitted from the mother [17] . Two adjacent SNPs , rs5852593 ( p = 0 . 0079 ) and rs1497309 ( p = 0 . 016 ) , located 36 kb apart , were associated with AD ( Table S1 and Figure 1B ) . To define the critical region , we typed 16 additional SNPs . Eight of these markers showed association with AD and a maternal transmission pattern to affected children , which was consistent with our previous linkage results ( Table 1 ) . The strongest association with a single marker was observed for rs4688761 ( pall = 0 . 0016 , pmaternal = 0 . 0006 ) . We selected eight markers spanning 96 kb that were associated with AD and carried nonredundant information based on the linkage disequilibrium ( LD ) in the region , and we performed haplotype analysis which confirmed the results ( transmissions ( T ) all:non-transmissions ( NT ) all = 222:168 , pall = 0 . 0076 , Tmat:NTmat = 105:68 , pmaternal = 0 . 0070 ) . In addition , we assessed the significance of the difference in maternal-versus-paternal haplotype over transmissions empirically using the permutation procedure for parent-of-origin transmission disequilibrium test ( TDT ) implemented in PLINK ( P_POOmaternal emp = 0 . 014 ) [18] . Next , we investigated whether the observed association accounted for the linkage in the region . We used the marker that had shown the strongest association , rs4688761 , to identify 73 of the 199 families from the original linkage scan in whom the disease-associated allele had been transmitted to affected offspring . Nonparametric linkage analysis in the 73 associated families yielded significant evidence for linkage ( Zall = 4 . 18 versus 4 . 31 in the complete cohort ) , demonstrating that the majority of the linkage signal was attributable to these families . The significance of this finding was assessed by performing nonparametric linkage analysis in 10 , 000 random selections of 73 families . An empirical significance level was calculated as the proportion of replicates for which the maximum Zall score was equal or greater than that obtained in the actual analysis . The probability of obtaining a Zall score of ≥4 . 18 by chance in a random selection of 73 families was estimated by 10 , 000 simulations to be 0 . 027 . To confirm the association result , we used a large independent replication set consisting of 292 complete nuclear families including 481 children with AD . We genotyped the selected eight SNPs covering an interval of 96 kb that were associated with AD in the discovery dataset . We confirmed the association with AD across all markers with the strongest association with AD observed for marker A36637742 ( p = 0 . 00038 ) , which also showed a significant overtransmission of the maternal allele ( p = 0 . 0013 ) ( Table 2 ) . The association remained significant after correction for multiple testing . For each marker , it was the more common allele that was overtransmitted . Haplotypes were constructed over the region , which confirmed the association with AD and showed that this phenotype is associated with the overtransmission of the most common haplotype ( haplotype frequency 65% , Table 3 ) , and of the maternal allele ( pmaternal = 0 . 000025 for AD , P_POOmaternal emp = 0 . 033 ) . Next , we compared the AD status among the parents: in the discovery cohort , significantly more mothers ( n = 63 ) than fathers ( n = 19 ) suffered from AD ( odds ratio [OR] 4 . 39 , 95% confidence interval [CI] 2 . 51 – 7 . 68 , p = 5 . 46 × 10−8 ) . Similarly , in the replication cohort , mothers ( n = 83 ) were significantly more frequently affected with AD than fathers ( n = 55 ) ( OR 1 . 71 , 95% CI 1 . 16 – 2 . 52 , p = 8 . 3 × 10−3 ) . To assess whether the parent-of-origin effect that was observed originated from the discrepancy in AD prevalence among the parents , we compared haplotype transmissions from affected and unaffected mothers . In the discovery cohort , the excess in maternal transmissions was not predominantly attributable to affected mothers ( T:UT = 24:21 , p = 0 . 14 ) compared to unaffected ones ( T:UT = 81:47 , p = 0 . 0059 ) . Similarly , affected mothers ( T:UT = 25:17 , p = 0 . 14 ) of the replication cohort did not contribute a larger transmission excess than the unaffected ones ( T:UT = 70:29 , p = 0 . 00013 ) . We conclude that the observed maternal overtransmission pattern in both cohorts was not due to the higher AD prevalence among mothers . A database search within the associated 96-kb interval revealed a single predicted gene , FLJ35880 , extending 11 . 6 kb into the associated region . No other expressed sequence tag was detected . To identify any additional transcripts , we used putative exons predicted with The National Center for Biotechnology Information ( NCBI ) Modelmaker within and bordering the critical region to perform rapid amplification of cDNA ends ( RACE ) from human skin mRNA . We thus identified a single transcript of 9226 bp that consisted of 42 exons ( Figure 1D ) and included all eight exons of FLJ35880 . The corresponding gene spanned 139 kb of genomic sequence and completely encompassed the associated region . Pairwise LD measures ( D′ ) of the genotyped markers indicated that the gene was contained within one 170-kb region of increased LD ( Figure 2 ) , whereas the neighboring genes , LOC440978 and LOC131873 , were located in separate blocks . The LD structure was consistent with the data for the European population in the HapMap database ( Figure S1 ) [19] . The size of the transcript was confirmed by Northern blot hybridization with an FLJ35880-specific probe detecting a single transcript of 9 . 6 kb in human skin mRNA ( unpublished data ) , which was in good agreement with the RACE experiments . The open reading frame yielded a protein of 2614 amino acids with an estimated molecular weight of 289 . 9 kDa . The predicted protein contained a collagenous domain in the central part and was therefore classified as a new member of the collagen superfamily , collagen XXIX . A BLAST search revealed the human collagen VI alpha 3 chain as its closest neighbor ( 32% identity ) . Homology with collagen VI alpha 3 was further strengthened by a similar domain architecture consisting of six N-terminal and three C-terminal von Willebrand factor–type A domains ( vWAs ) flanking a short collagen triple helix ( Figure 1E ) , and an 18–amino acid secretion signal [20] . Sequence analysis of all 42 exons in 46 unrelated children with AD and 2 unaffected individuals revealed 13 common and six rare sequence variations ( frequency <2% ) predicted to cause nonsynonymous amino acid substitutions ( Table S2 ) . Four variants were located within the triple helix repeat of a collagen domain , however none of them changed the first glycine residue in the repeating sequence patterns ( Gly-X-Y ) , which is critical for triple helix formation [21] . In addition , we performed in silico comparisons of the six variants located within a vWA with the crystal structure of the human vWF A3 domain . One variant , V669G , affects an amino acid within a highly conserved stretch of eight amino acids . The mutation of an adjacent amino acid within this conserved region of a vWA in the homologous gene COLVIA1 has been reported to cause the monogenic muscle disorder Bethlem myopathy [22] . This variant , however was rare ( allele frequency 1 , 3% ) . One additional variant ( E455K ) is located in a region predicted to be important for integrin–collagen interaction , and another one ( M56T ) is located in a helix near a rather conserved region that may also affect protein–protein interaction . All coding SNPs were genotyped in the discovery cohort . Four of them showed a positive association with AD that did , however , not account for the observed haplotype association ( Table S2 ) . Gene expression analysis in human tissues revealed a tissue-specific expression pattern of COL29A1 . The highest expression was observed in the skin , but also in the lung , small intestine , colon , and testis ( Figure 3A ) . Overall , COL29A1 expression is moderately low compared to more abundant epidermal transcripts such as keratin 10 ( unpublished data ) . To specify the expression sites in the layers of the skin and to assess the role of collagen XXIX in AD , we performed in situ hybridization using a COL29A1-specific cRNA-probe on skin biopsies of five patients with AD and five healthy patients ( controls ) . In normal skin , COL29A1 was exclusively expressed in the epidermis with the strongest staining in the suprabasal viable layers . In contrast , the skin of patients with AD revealed a striking absence of COL29A1 mRNA staining in the most differentiated upper spinous and granular layers ( Figure 3B and Figure S2 ) . No significant difference in the expression of COL29A1 was observed comparing patients with AD to controls ( 1 . 28- ± 0 . 53-fold down-regulation in AD patients , p = 0 . 41 ) using quantitative Taqman reverse-transcriptase ( RT ) -PCR . These results indicate that while differences in mRNA quantity were not detected , AD patients show a distinct abnormal cellular distribution pattern of COL29A1 expression in the differentiated outer epidermis . We generated a polyclonal antibody to visualize the collagen XXIX protein in the skin of five patients with AD and five normal controls , including four and three of each group , respectively , in whom in situ analysis was performed . Consistent with the in situ findings , we observed collagen XXIX staining in the differentiated suprabasal layers of the epidermis in normal human skin and a remarkable absence of staining in the most differentiated upper spinous and granular layers ( Figure 4 and Figure S3 ) .
In a whole-genome linkage scan for AD , we previously identified a susceptibility locus on human Chromosome 3q21 . The candidacy of this chromosomal region was further supported by Bradley et al . , who mapped a locus for AD severity in close proximity ( 3q14 ) in a Swedish population [13] . By positional cloning , we have now identified the disease-causing gene , COL29A1 , which encodes a novel epidermal collagen . The disease gene was located in a two-staged investigation consisting of systematic linkage and association scanning of the region and subsequent confirmation of the association in a large , independent , replication dataset . In the first stage , we genotyped additional microsatellite markers in the candidate region , which narrowed the initial 12 . 75-cM linkage interval to 5 . 4 Mb . The association analysis with an average marker distance of 25 . 5 kb , finally , revealed an association that was confined to a haplotype block of 170 kb , which included a single gene , COL29A1 . Pairwise LD measures indicated LD across the entire gene and defined two subblocks of increased LD . The strongest association was observed within the 96-kb subblock encoding the collagenous domain and the C terminus of collagen XXIX . A rapid decay of LD at the borders of the COL29A1 haplotype block and lack of association of the SNPs located within the neighboring genes clearly limited the association to COL29A1 . In addition , we demonstrated in the discovery dataset that the families that contributed to the association of SNP rs4688761 with AD also accounted for most of the linkage signal . This finding corroborated that variants in COL29A1 explained the previously reported linkage of AD to 3q21 [16] . Finally , we confirmed association in a large independent family cohort , making COL29A1 the first AD susceptibility gene , to our knowledge , that is identified by positional cloning . Consistent with the linkage analysis , we found a maternal transmission pattern to affected offspring in both family cohorts . Although the sexes were equally represented among the affected children of both cohorts , we observed a marked maternal preponderance in AD status among the parents in both cohorts . This finding clearly supports the notion of a maternal effect . It may , however , also raise the question whether the maternal overtransmission pattern observed for the COL29A1 haplotype was due to the different prevalence of AD in mothers and fathers . This is unlikely to be the case , because the analytical tools used in this study , nonparametric linkage and TDT , do not take into account the parental phenotype . Furthermore , we showed that the observed maternal effect was not predominantly attributable to transmissions from affected rather than unaffected mothers . Although parent-of-origin effects have not previously been reported for genes from Chromosome 3q21 , they have been observed at other loci influencing allergic disease [23] . Tissue-specific imprinting of genes encoding extracellular matrix ( ECM ) proteins has been reported in the mouse [24] , and their disruption has been shown to impair skin structure and function [25] . Interestingly , COL29A1 is expressed in human placenta , an organ of embryonic origin . Apart from classic genomic imprinting mechanisms , maternal effects may be due to an interaction of the child's genotype with the maternal environment during prenatal life . Sequencing of the exons and promoter region revealed 19 nonsynonymous coding SNPs , six of which were located within a vWA . In silico analysis of these variants revealed only one rare mutation altering a highly conserved amino acid , but all of them may affect protein–protein interaction . None of the nonsynonymous coding SNPs explained the observed association on its own . It has been demonstrated in other complex diseases that multiple independent variants may occur in a single disease gene [26] . It is therefore possible that several variants or combinations thereof which are associated with the most common haplotype of COL29A1 are involved in the disease pathogenesis . The functional influence of the associated variants remains to be determined . Involvement of COL29A1 in AD is further supported by its tissue- and cell-specific expression pattern . Like COL29A1 , a growing number of collagens recently identified show a restricted expression pattern . These are not mainly found in fibrous connective tissue , but rather in the ECM of more specialized tissue structures pointing to a distinctive function of these proteins [27] . Highest COL29A1 expression was observed in the skin , but also in other epithelial tissues such as the lung , small intestine , and colon , which are the main manifestation sites of allergic disorders , including asthma and food allergies . This gene expression pattern might indicate a role of collagen XXIX in a wider spectrum of allergic diseases and suggests a molecular link between AD , respiratory airways disease , and food allergies , which are epidemiologically closely associated [28 , 29] . In human skin , collagen XXIX was detected throughout the viable layers of the epidermis with an increase toward the differentiated cells of the granular layer . Comparative expression analysis of COL29A1 by in situ hybridization and immunohistochemistry in skin biopsies of patients with AD and healthy controls revealed a distinct lack of COL29A1 mRNA and protein in the outer viable layers of the epidermis . The process of epidermal stratification is tightly regulated by an increasing gradient of extracellular Ca2+ concentration and a specific special and temporal expression pattern of transcriptional regulators [30] . Our findings indicate that the specific cellular milieu acquired during terminal epidermal differentiation affect the regulation or degradation of COL29A1 mRNA in the outer epidermis . However , our findings do not allow us to distinguish between these two processes . Lack of collagen XXIX in the outer epidermis of AD patients indicates that a defective ECM may give rise to the disease , proposing a new pathomechanism for AD . Collagens are the most abundant ECM proteins in vertebrates and play a crucial role in maintaining tissue integrity . Their importance for tissue function has been highlighted by the wide spectrum of human diseases caused by mutations in collagen genes [31] . Although a large number of collagens in the connective tissue–rich dermis have been characterized , little is known about collagens in the ECM of the epidermis [32 , 33] . Collagen XXIX belongs to the vWA containing collagens . They form filaments with globular domains containing the vWA motifs , which are involved in protein–ligand interactions for the organization of tissue architecture and cell adhesion [34] . It is therefore conceivable that collagen XXIX plays an important role in keratinocyte cohesion . Lack of collagen XXIX may facilitate antigen penetration through the skin , which may explain the association found between COL29A variants and allergic sensitization , a common feature in AD patients [35] . Recent findings indicate that structural and functional integrity of the epidermis is a key factor in the development of AD [36] and in the disease progression to allergic airways disease [37] . Furthermore , ECM collagens influence the migration of epidermal antigen-presenting Langerhans cells and T cells [38 , 39] and may thus play an important role in the initiation and maintenance of cutaneous immune responses . In addition , ECM collagens participate in immune regulation by binding to inhibitory immune receptors [40] , rendering collagen XXIX an interesting novel susceptibility gene for AD . Impairment of the immune defense function of the skin is a clinical hallmark of AD . Patients with AD show a diminished resistance against microbes resulting in the colonization of nonlesional skin with Staphylococcus aureus in nearly 90% of patients and an increased susceptibility to bacterial and viral skin infections [41] . Our findings led to the identification of collagen XXIX as a novel component of the epidermal ECM and propose a new disease mechanism in the etiology of atopic dermatitis and allergies .
The diagnosis of AD was made according to standard criteria , as previously described [16] . Recruitment was restricted to patients with an age of onset below 2 y and moderate to severe disease expression . Total IgE levels and levels of specific IgE against 12 common environmental allergens were determined using ImmunoCAP ( Phadia AB; http://www . phadia . com/ ) . Allergic sensitization was defined as either the presence of specific IgE to at least one allergen ( detection limit 0 . 35 kU/l ) or a total serum IgE level elevated above the age-specific norm . The institutional review boards of the participating centers approved the study protocol and informed consent was obtained from all probands or their legal guardians . One discovery study sample and one replication sample were investigated . All families were of European origin . The discovery data set consisted of 199 complete affected sibling families composed of 427 children with AD that were studied in the original genome scan . [16] The replication set consisted of 292 families including 481 children with AD with an age of onset ≤2 y and moderate to severe disease expression . Among AD patients in the discovery and the replication cohorts , the proportion of boys was 52% and 50 . 9% , and the proportion of children with allergic sensitization was 74% and 72 . 1% , respectively . Punch biopsies of human skin were obtained from six patients with AD and 7 healthy donors aged 24–55 y with written informed consent . Specimens were prepared for in situ hybridization and immunohistochemistry as described below . In the first stage , fine mapping with microsatellite markers was performed in the discovery dataset . 96 short tandem repeat markers were selected within the interval between D3S1303 ( 126 . 07 cM ) and D3S1292 ( 138 . 82 cM ) from the Genethon ( http://www . genethon . fr/ ) and Marshfield ( http://research . marshfieldclinic . org/genetics/ ) databases , or were identified by screening human genome sequence data for short tandem repeats . Fluorescence-based semi-automated genotyping was performed as previously described [16] . Primer sequences , amplification conditions , and allele size are available on request . We performed an association scan of the 5 . 4-Mb region using 212 SNPs at an average distance of 25 . 5 kb . The SNPs were selected from the NCBI database ( http://www . ncbi . nlm . nih . gov/projects/SNP/ ) to cover known and predicted genes . To determine the allele frequency of the polymorphisms , we amplified 600 to 800 bp surrounding each SNP by PCR for resequencing in 31 unrelated Caucasian individuals . Markers with a minor allele frequency ( MAF ) of >5% were selected for genotyping . To identify functional variants in collagen XXIX , we sequenced all 42 exons including the exon–intron boundaries and 5 . 1 kb of the promoter region in 46 unrelated patients with AD and two controls . Sequencing was performed on an ABI3730 DNA sequencer ( Applied Biosystems; http://www . appliedbiosystems . com ) using standard procedures . We carried out SNP genotyping using TaqMan allelic discrimination , with probes and primers designed and synthesized by the supplier ( Applied Biosystems ) , or by pyrosequencing using PSQ HS 96A ( Pyrosequencing AB; http://www . biotagebio . com/ ) . The averages genotyping success rate was 97 . 9% . All primer and probe sequences are available on request . We performed linkage analysis of the microsatellite data using Genehunter V . 2 . 1 [42] . Each SNP was checked for compliance with Hardy-Weinberg equilibrium ( HWE ) in the parent population using a χ2 test , and those markers that were not in HWE were excluded from the analysis . We calculated pairwise LD between each marker pair using the D′ statistic . In view of the strong imprinting effect at our locus , we conducted family-based association tests , because this strategy allows us to determine the parental origin of an associated allele . In the affected sibling families , we used the sib_TDT of the ASPEX software that performs a permutation procedure to calculate empirical p-values that are independent of linkage within families [17] . Furthermore , to assess the significance of the maternal effect , we calculated empirical p-values for the difference in maternal versus paternal haplotype transmissions using the parent-of-origin TDT implemented in PLINK [18] . The sib_TDT was also used in the analysis of eight markers in the replication dataset . The significance level of the replication results was assessed empirically . Using all pedigrees and all genetic markers used in the actual analysis , we generated 10 , 000 replicates using Allegro V1 . 2c [43] and conducted an association analysis as in the original dataset . An empirical significance level was calculated as the proportion of replicates for which the maximum χ2 score was greater than that obtained in the real dataset . All p-values are two-sided , significance was defined as statistical evidence expected to occur 0 . 05 times at random in the analysis . For multipoint analysis , we used the FBAT tools package [44] to generate haplotypes and performed family-based association tests for five marker haplotypes using the empirical variance option to adjust for correlation among sibling genotypes . To evaluate parent-of-origin effects in the multipoint analysis , we estimated haplotypes using MERLIN , recoded the haplotypes as alleles , and performed the sib_TDT using ASPEX . Protein sequences were retrieved from the UniProt ( http://www . uniprot . org ) and Ensembl ( http://www . ensembl . org ) databases . The domain architecture of the collagen XXIX protein was retrieved from the NCBI conserved domain search website ( http://www . ncbi . nlm . nih . gov:80/Structure/cdd/wrpsb . cgi ) . The following domains were found in collagen XXIX and analyzed further: cd01472 ( vWA_collagen ) , cd01450 ( vWFA_subfamily_ECM ) , cd01470 ( vWA_complement_factors ) , cd01465 ( vWA_subgroup ) , and Pfam01391 ( collagen triple helix repeat ) . To predict the 3D structure of the vWF protein domains in collagen XXIX , we explored the structure prediction results returned by the web servers GenTHREADER ( http://bioinf . cs . ucl . ac . uk/psipred/ ) and FFAS03 ( http://ffas . ljcrf . edu ) . Based on their very similar predictions , the human vWF A3 domain was chosen as the structural template to analyze the position of COL29A1 SNPs in 3D and to predict their potential effect on protein function . A 2447-bp sequence from a human testis cDNA library which covered eight exons was the starting point for the characterization of COL29A1 . Using rapid amplification of cDNA ends together with the Model Maker of NCBI , we identified a total of 42 exons and determined the transcription start site as well as the 3′ end of the COL29A1 transcript in cDNA from human skin . The complete sequence of the transcript was confirmed by cloning and sequencing of the full-length cDNA . To explore the potential gene function of COL29A1 , the protein sequence was predicted ( http://us . expasy . org/tools/dna . html ) , a domain search was performed ( http://www . sanger . ac . uk/Software/Pfam/search . shtml ) [45] , and the presence and location of signalling peptides was analyzed ( http://www . cbs . dtu . dk/services/SignalP/ ) [46] . We examined tissue-specific expression of COL29A1 using oligo ( dT ) -primed cDNA of 17 different human tissues . cDNA samples of 16 tissues were from the human MTC Panels I and II , which are standardized for the expression of GAPDH ( BD Biosciences; http://www . bdbiosciences . com ) . In addition , human skin poly ( A ) + RNA ( BD Biosciences ) was transcribed into cDNA using the Transcriptor First Strand cDNA Synthesis Kit ( Roche Diagnostics; http://www . roche . com ) . PCR was performed using COL29A1-specific primers 5′-GTTCTAACCAGAATGTATAATCATC ( forward ) and 5′-TAATTCCCAAGAACATCTCTGGT ( reverse ) , yielding a product of 694 bp , and the GAPDH control primers supplied with the MTC panels . For in situ hybridization , we generated a plasmid by cloning a COL29A1-specific PCR product amplified from human skin cDNA [5′-ACCTTAGGAGACAGGGTTGCT ( forward ) ; 5′-AGTTCCAATCTGGCTTGTGG ( reverse ) ] into the pCRII vector ( Invitrogen; http://www . invitrogen . com ) . We synthesized antisense and sense digoxigenin-labeled riboprobes using the Dig RNA Labeling Kit ( Roche Diagnostics ) . Punch biopsies of human skin were obtained from five AD patients and five healthy donors , immediately fixed in 4% paraformaldehyde for 4 h , cryoprotected in 30% sucrose overnight , and embedded in Tissue-Tek ( Sakura; http://www . sakura . com ) for cryosectioning . 10 μm cryosections were mounted on slides and dried for 15 min at 50 °C . Sections were postfixed in 4% paraformaldehyde for 7 min at 4 °C and acetylated with 0 . 25% acetic acid for 10 min . Sections were prehybridized for 3 h and hybridized overnight at 50 °C with digoxigenin-labeled riboprobes . After hybridization , sections were washed twice with 2 × SSC at 53 °C and once with 0 . 1 × SSC at 58 °C . For detection of the hybridized probe , slides were incubated with BCIP/NBT staining solution ( Roche Diagnostics ) for 4 d according to the manufacturer's recommendations . To quantify gene expression in skin specimens , total RNA was isolated from 160 μm cryosections of skin biopsies using the RNeasy Mini Kit ( Qiagen; http://www . qiagen . com ) . RNA was reverse transcribed into cDNA with random hexamer primers using the Transcriptor First Strand cDNA Synthesis Kit ( Roche Diagnostics ) . Taqman real-time PCR was performed with iTaq SYBR Green ( BioRad; http://www . biorad . com ) and gene-specific PCR products were detected on the ABI PRISM 7900 sequence detection system ( Applied Biosystems ) . All measurements were performed in duplicate . COL29A1 expression was normalized for 18S rRNA expression . Differences in gene expression were calculated using the ΔΔct method and were expressed as fold change . Gene-specific primers were as follows; COL29A1-forward , 5′-CCACCCTCTGGATCATCACT , COL29A1-reverse , 5′-GTTTTCTGTGCCACCGTTCT , KRT10-forward , 5′-CTGAAACCGAGTGCCAGAAT , KRT10-reverse , 5′-GCCTCCGGAACTTCCCTCT , 18S rRNA-forward , 5′-GGATGCGTGCATTTATCAGA , 18S rRNA-reverse , 5′-GATCAGCCCGAGGTTATCTA . The sizes of the PCR products were confirmed by gel electrophoresis and the specificity of the reaction was confirmed by melting curve analysis of the PCR products . For statistical analysis , the unpaired t-test was used . A polyclonal antibody against human collagen XXIX protein was raised by immunizing rabbits with a collagen XXIX specific peptide ( SLGSTRKDDMEELAC , residues 2115–2128 ) ( Eurogentec; http://www . eurogentec . com ) . The specificity of the antibodies purified by affinity chromatography was tested by comparing their reactivity against recombinant proteins by Western blotting and by blocking experiments . For immunohistochemistry , freshly isolated skin specimens from five AD patients and five healthy individuals were embedded in Tissue-Tek . Cryosections of 5 μm thickness were prepared and fixed with acetone . Sections were incubated with anti-collagen XXIX antibodies followed by dextran-coupled anti-rabbit antibody , an alkaline phosphatase labelled amplification polymer ( DAKO EnVision System; http://www . dako . com ) and detection with fuchsin ( DAKO ) . Nuclei were counterstained with Mayer's hematoxylin solution ( Sigma-Aldrich; http://www . sigmaaldrich . com ) . Omission of primary antibody and preincubation with equimolar amount of peptide used for generation of the antibody in rabbits were used as negative controls for parallel sections . The results were consistent among the AD patients on one hand , and among the control biopsies on the other .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/index . html ) accession numbers for genes and proteins discussed in this paper are: AK093199 ( FLJ35880 ) , NM 153264 ( FLJ35880 ) , and NP 476507 ( human collagen VI alpha 3 chain ) , The Protein Databank ( http://www . pdb . com ) accession number for the human vWF A3 domain is 1atz . | Atopic dermatitis ( AD , eczema ) is a common chronic inflammatory skin disorder and a major manifestation of allergic disease . Typically , AD first occurs in early childhood , often preceding the onset of allergic airways disease , such as asthma and hay fever . A family history of allergic disorders is the single strongest predictor for AD , showing that genetic factors play a major role in the disease development . We have previously mapped a disease locus for AD on Chromosome 3q21 , Now we have used a dense map of microsatellite markers and single nucleotide polymorphisms ( SNPs ) to find the underlying disease gene . We identified genetic markers in a subregion that showed association with AD , and replicated this finding in a large independent family cohort . The associated region contained a single gene , COL29A1 , which encodes a novel collagen . We demonstrate that AD patients lack COL29A1 expression in the outer epidermis , implicating collagen XXIX in epidermal integrity and function . The gene expression pattern of COL29A1 in other organs , including the lung and the gut , suggests that this gene could have a role in a wider spectrum of allergic diseases and may provide a molecular link between AD and respiratory airways disease and food allergies . | [
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| 2007 | Variants in a Novel Epidermal Collagen Gene (COL29A1) Are Associated with Atopic Dermatitis |
Children of mothers infected with soil-transmitted helminths ( STH ) may have an increased susceptibility to STH infection . We did a case-control study nested in a birth cohort in Ecuador . Data from 1 , 004 children aged 7 months to 3 years were analyzed . Cases were defined as children with Ascaris lumbricoides and/or Trichuris trichiura , controls without . Exposure was defined as maternal infection with A . lumbricoides and/or T . trichiura , detected during the third trimester of pregnancy . The analysis was restricted to households with a documented infection to control for infection risk . Children of mothers with STH infections had a greater risk of infection compared to children of uninfected mothers ( adjusted OR 2 . 61 , 95% CI: 1 . 88–3 . 63 , p<0 . 001 ) . This effect was particularly strong in children of mothers with both STH infections ( adjusted OR: 5 . 91 , 95% CI: 3 . 55–9 . 81 , p<0 . 001 ) . Newborns of infected mothers had greater levels of plasma IL-10 than those of uninfected mothers ( p = 0 . 033 ) , and there was evidence that cord blood IL-10 was increased among newborns who became infected later in childhood ( p = 0 . 060 ) . Our data suggest that maternal STH infections increase susceptibility to infection during early childhood , an effect that was associated with elevated IL-10 in cord plasma .
Soil-transmitted helminths ( STHs , also known as geohelminths or intestinal helminths ) are estimated to infect 2 billion people worldwide [1] . These parasites , of which Ascaris lumbricoides and Trichuris trichiura are the most prevalent , are a major cause of morbidity related to malnutrition and reductions in childhood growth [2] . In addition , STHs are considered to have potent immune modulatory effects , and may have an impact on the epidemiological distribution of other diseases . For example , A . lumbricoides and T . trichiura infections may contribute to an accelerated progression of HIV [3] , a greater susceptibility to tuberculosis [4] , and modify the development of allergy [1] . We still do not understand all of the factors involved in susceptibility to STH infections or the causes of the characteristic overdispersed or aggregated distributions of parasites [5] . Heterogeneity in exposure to infective stages may be relevant , but host factors must also be involved , as suggested by individuals re-acquiring heavy infections after treatment [5] . Understanding these factors is likely to help us develop novel interventions to complement mass drug administration ( MDA ) with anthelmintic drugs , and make MDA more efficient thereby reducing rates of re-infection . Maternal infection with STH – and in-utero programming of the fetal immune response – may be one factor that contributes to disparities in infection outcomes . Previous studies have provided evidence that maternal infections with filiarial and schistosome helminths may affect the development of immune responses and susceptibility to infection during childhood [6] , [7] , [8] . Evidence for effects of maternal infection with STH on susceptibility to STH infections in offspring , in comparison , is more limited . A swine model of ascarid infection suggests that maternal infections affect susceptibility and parasite burden in piglets [9] . Additionally , it has been shown that exposure to maternal ascariasis is associated with evidence of immunologic sensitization to Ascaris antigens in newborn humans [10] . To our knowledge , there is no published evidence that maternal infections with STH have an effect on susceptibility to infection during childhood or on the modulation of systemic immune responses in childhood , despite the high prevalence of these parasites worldwide . To determine if maternal infections with A . lumbricoides and/or T . trichiura affect susceptibility or intensities of infection with these parasites in early childhood , we compared parasitological data from children of uninfected and infected mothers to three years of age in a case-control study nested in an ongoing birth cohort study . We also looked for effects of maternal infection on induction of immune tolerance by measuring levels of IL-10 in newborn plasma .
Mothers and their children were selected from the ECUAVIDA birth cohort study , an ongoing birth cohort study in the rural District of Quinindé in Esmeraldas Province , Ecuador . All pregnancies were uncomplicated vaginal deliveries , and follow-up evaluations including the collection of single stool samples were done at 3 , 7 , 13 , 18 , 24 , 30 , and 36 months . A detailed description of the aims and methods of this study is described elsewhere [11] . The design was case-control nested in a birth cohort . Cases were defined as children between 7 months and 3 years of age with A . lumbricoides and/or T . trichiura infection . Only these two STH infections were considered because the prevalence of hookworm and Strongyloides stercoralis was less than 2% among the children in our study , and thus these data were unlikely to give meaningful results . Controls were defined as children between 7 months and 3 years of age with no STH infection . Exposure was defined as the presence of maternal infection with A . lumbricoides and/or T . trichiura detected in a stool sample collected in the third trimester of pregnancy . Inclusion into the study required data collected from stool samples . Stool samples were examined using the modified Kato-Katz and/or formol-ether concentration methods [12] . All cases and controls were selected from households where there was a previously documented infection with A . lumbricoides or T . trichiura determined by examination of stool samples collected within two weeks of birth from all household members . Households without a documented infection with A . lumbricoides or T . trichiura were excluded from the analysis because of probable limited risk of infection in such children . At the time this study was conducted , there was no program in place for the treatment of STH infections or malaria during pregnancy . IL-10 levels in plasma from cord blood were measured in 90 individuals , 30 cases and 60 controls . Cases were children who had an infection with STH at 13–18 months of age while controls were children with no STH infection before 13–18 months of age . Maternal infection data had previously been collected during the third trimester of pregnancy for all cases and controls . IL-10 was measured in undiluted plasma , stored at −70°C using a commercial ELISA assay ( Invitrogen ) following the manufacturer's instructions . The limit of detection for this assay is 0 . 2 pg/ml . We estimated that a sample of 400 each of cases and controls would allow us to detect an OR of 1 . 5 with a maternal STH prevalence of 35% , an α of 0 . 05 , and a power of 80% . For the purposes of analysis , three parasitological outcomes were considered: 1 ) susceptibility to STH infection – defined by a documented infection with A . lumbricoides or T . trichiura during the first 3 years of life; 2 ) susceptibility to high STH parasite burdens ( defined by WHO-recommended thresholds of >50 , 000 eggs per gram [epg] of stool for A . lumbricoides and >10 , 000 epg for T . trichiura [13] ) – based on egg counts of A . lumbricoides and T . trichiura from a stool sample collected at 3 years of age; and 3 ) aggregation – defined by the degree to which the estimated negative binomial parameter k tends to zero at 3 years of age for each of A . lumbricoides and T . trichiura . To evaluate differences in susceptibility between children of infected and uninfected mothers , we used logistic regression with adjustment for age , gender , maternal educational level , monthly income , number of household electric appliances , crowding ( defined as number of persons per bedroom ) , bathroom facility , and water source . We also adjusted for the number of stool samples collected given that the more stool samples examined , the more likely an infection would be detected . All variables were included in the final model because precision of the estimated odds ratios did not change after removal of non-significant confounders [14] . The primary analysis examined effects of any maternal infection with A . lumbricoides and or T . trichiura on childhood susceptibility to infection with any STH infection ( defined by an infection with A . lumbricoides and or T . trichiura ) . Secondary analyses looked at: 1 ) parasite-specific effects ( A . lumbricoides vs . T . trichiura ) on childhood susceptibility to any STH infection; 2 ) the effects of maternal co-infections on childhood susceptibility to any STH infection . Intensities of infection with A . lumbricoides and T . trichiura were recorded as eggs per gram of stool . Stool egg counts were overdispersed ( A . lumbricoides , mean 2 , 832 epg , variance 1 . 5×108; T . trichiura , mean 317 epg , variance 4 . 8×106 ) , so a negative binomial model was used to fit the data , and potential confounders were controlled in multivariate analysis . The parameter k was also estimated and reported with Wald confidence intervals . For analysis of cord blood levels of IL-10 , two associations were evaluated: 1 ) the relationship between IL-10 and maternal infection status; and 2 ) the relationship between IL-10 and childhood infection status among children at 13–18 months of age . Geometric means were calculated , and Mann-Whitney U tests were used to compare IL-10 levels between groups . All analyses were done in STATA ( version 10 . 0 ) or in R ( version 2 . 4 . 1 ) . The ECUAVIDA study protocol was approved by the Ethical Committees of the Hospital Pedro Vicente Maldonado and the Universidad San Francisco de Quito . Appropriate treatment was given to each individual with positive stool tests for STH infections as required by the study protocol .
Data from 1 , 004 mother-child pairs ( 410 cases and 594 controls ) were included in the analysis out of a total of 1888 mother-child pairs with stool data collected to at least 7 months of age for each child eligible ( 884 pairs were excluded because of no documented infection with A . lumbricoides and T . trichiura among family members ) . Of the mothers included in the analysis , 23 . 6% ( n = 237 ) were infected only with A . lumbricoides , 22 . 7% ( n = 228 ) were infected only with T . trichiura , 18 . 6% ( n = 187 ) were co-infected , and 35 . 1% ( n = 352 ) were not infected . Geometric mean infection intensities with ascariasis and trichuriasis among all mothers were 7 ( range: 0–130 , 970 ) and 5 ( range: 0–20 , 510 ) , respectively . The proportion of cases ( 38% ) and controls ( 32% ) with at least one infected household member was not significantly different . Summary statistics for cases and controls are provided in Table 1 . Children whose mothers were infected with STHs were 2 . 61-times more likely to be infected with STHs than children whose mothers were not infected ( Table 2 ) . Effects of maternal ascariasis and trichuriasis infection on childhood susceptibility to any STH infection were similar . Unique maternal ascariasis ( i . e . in the absence of T . trichiura ) raised the risk of STH infection in children by 2 . 19-fold ( 95% CI: 1 . 46–3 . 29 , p<0 . 001 ) . Likewise , unique maternal trichuriasis raised the risk of STH infection for children by 1 . 89-fold ( 95% CI: 1 . 26–2 . 81 , p = 0 . 002 ) . The risk of childhood STH infection was even greater when mothers were co-infected . Children whose mothers were co-infected had 5 . 91-fold ( adjusted 95% CI: 3 . 55–9 . 81 , p<0 . 001 ) increased risk of being infected with STHs than children whose mothers had no STH infection . This is compared to a 2 . 08-fold increased risk for children of mothers with single infections ( i . e . either A . lumbricoides or T . trichiura only ) ( Table 2 ) . There seemed to be little association between paternal infection and childhood infection using data available from 531 fathers . No evidence was found for an association between paternal STH infections and child STH infections . There was also no evidence for associations between paternal infection with A . lumbricoides or T . trichiura and childhood susceptibility to general STH infection . There was , however , a significant relationship between paternal co-infection and childhood infection outcomes . Children of co-infected fathers were 2 . 54-fold more likely to be infected with STHs ( 95% CI: 1 . 25–5 . 19 , p = 0 . 01 ) . No significant relationship was observed between maternal infection and childhood infection intensity after adjusting for confounding factors ( Table 3 ) . Univariate estimates for T . trichiura were unreliable because of low egg counts - thus the wide confidence interval . Significantly greater levels of plasma IL-10 were also observed in cord blood from newborns of infected compared to uninfected mothers ( geometric means: 1 . 32 pg/mL vs . 0 . 75 pg/mL , p = 0 . 033 ) ( Figure 1 ) . Subjects infected with A . lumbricoides or T . trichiura in later childhood ( 13–18 months of age ) had a trend of greater levels of IL-10 in cord blood plasma than children who were not infected between 13–18 months of age ( geometric means: 1 . 20 pg/mL vs . 0 . 77 pg/mL , p = 0 . 060 ) . Interestingly , two of three outlying values were from co-infected mothers with the highest infection intensities . Outliers did not affect the non-parametric analysis used .
This study provides evidence that children whose mothers are infected with the STH parasites , A . lumbricoides and T . trichiura , have an increased risk of infection compared to children of uninfected mothers . This increased risk was most pronounced for children of mothers infected with both parasites . We did not observe an effect of maternal STH infection on the intensities of infection or distributions of parasites in offspring– an observation that likely reflects the relatively low parasitic infection intensities observed at 3 years of age . It is important to remember that STHs do not multiply within the host , but instead are acquired over time by continual exposure to embryonated eggs in the environment [15] . We believe that the observed increase in susceptibility could be the result of in utero immune modulation that induces greater neonatal and childhood tolerance . This is predicated upon findings that maternal ascariasis and trichuriasis alter newborn Th1 and Th2 anti-parasite cytokine responses [10] . Furthermore , there is strong evidence of in utero sensitization and induced tolerance to other helminths [7] , [8] . The precise mechanism by which the fetus is sensitized is unclear , but helminth antigen may cross the placenta because Ascaris antigen has been found in peripheral blood [16] . Trans-placental infection seems highly unlikely because we found no eggs in stool samples collected at 3 months of age . Further , to our knowledge there is no published evidence showing that A . lumbricoides and T . trichiura infections can be transmitted in utero . We hypothesized that the increased susceptibility to infection through having an infected mother could be mediated by IL-10 , an immune modulatory cytokine that is produced by regulatory cells [21] . Increased production of IL-10 is a feature of chronic STH infections and is reflected in elevated plasma levels [11] and greater constitutive production by peripheral blood leukocytes ( PBLs ) [17] , [18] , [19] , [20] . The treatment of ascariasis has been associated with a decline in levels of plasma IL-10 [11] . Similarly , children of mothers infected with filarial parasites have an increased susceptibility to filarial infection , their PBLs produce elevated levels of IL-10 constitutively [6] , and they show impaired T-cell responses [8] . Our observation of elevated levels of IL-10 in cord blood plasma of the infants of infected mothers and in cord blood plasma of infected children provides a potential mechanism by which maternal infections may subsequently increase susceptibility to infection in children . Elevated levels of IL-10 may have important effects in the modulation of inflammatory responses to the parasite that prevent potentially damaging pathology but increase susceptibility to infection [18] . Such non-specific effects on the host systemic immune response would be predicted to have effects on other inflammatory conditions such as allergy in which IL-10 is considered to have an important modulatory role [21] . In support are observations from recent studies that indicate that maternal STH infections may protect against infantile eczema [22] and modify immune responses to tuberculin [23] . There is a complex relationship between exposure to parasites and the resulting distributions of parasites and susceptibility to infection among hosts , so there may be other explanations for household clustering of infection other than in utero antigen exposure . For example , the observed maternal effect may have been a product of residual confounding ( i . e . that which is not removed or accounted for by our analysis ) . To minimize such a bias , our study attempted to control for demographic , socioeconomic , geographic , and behavioral risk factors . Our inclusion criteria , for one , required a documented infection in at least one family member at the time of the child's birth to try to control for the risk of exposure . Moreover , in the analysis we adjusted for six potential confounding variables that are closely linked to the presence of helminth infections [24] . Three of these – crowding , bathroom facility , and water source – are behaviorally related , and have been previously found to be risk factors for STH infections in pregnancy and in infants [25] , [26] . Alternatively , children of infected mothers may have been genetically predisposed to STH infection as evidence has been found for a genetic contribution toward susceptibility [27] , [28] , [29] . However , the weak association between paternal and childhood infection status argues against this explanation and is consistent with findings from previous studies that have shown weak or no associations between paternal helminth infection and childhood susceptibility to infection [6] , [30] . Finally , we cannot exclude the possibility that light infections were missed although we employed a combination of parasitologic methods to maximize sensitivity . In conclusion , our data provide evidence that maternal infections with the STH parasites , A . lumbricoides and T . trichiura , are associated with an increased susceptibility to infection in children during the first 3 years of life . One possible mechanism by which this effect may be mediated is plasma IL-10 that was elevated in cord plasma of newborns of infected mothers and in cord plasma of children who later became infected with STHs . If maternal infection does induce long-lasting tolerance in children and such an effect is associated with elevated plasma IL-10 from birth , our data may provide an explanation for the apparent protective effect of maternal helminth infections against childhood allergy and their possible deleterious effects on other infectious diseases such as HIV , TB , and malaria [22] , [23] , [31] . | Soil-transmitted helminths ( intestinal worms ) are among the most common childhood infections worldwide and are a significant cause of morbidity particularly among poor populations living in developing countries . The potent immune modulatory effects of these parasites have been suggested to be a determinant of the epidemiological distributions of other infectious diseases ( e . g . , HIV and tuberculosis ) and allergy . There is strong epidemiological evidence that some individuals have an increased susceptibility to re-infection after treatment and the mechanisms underlying this are not well understood . A possible explanation is that in utero exposure to maternal STH infections may increase the risk of infection during childhood , but , as far as we are aware , no published study has addressed this hypothesis for STH infections in humans . In this study , we evaluated whether children of mothers infected with STH infections have a greater risk of infection when compared to children of uninfected mothers . We also examined whether this increased susceptibility to infection might occur through the tolerogenic effects of increased levels in the systemic circulation of the immune regulatory cytokine IL-10 , in early life . Our data provide evidence that maternal STH infections predispose children to infections with STH parasites , and this effect was associated with elevated levels of IL-10 in newborn blood . | [
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| 2012 | Maternal Geohelminth Infections Are Associated with an Increased Susceptibility to Geohelminth Infection in Children: A Case-Control Study |
Dengue remains the most prevalent arthropod-borne viral disease in humans . While probing for blood vessels , Aedes aegypti and Ae . albopictus mosquitoes transmit the four serotypes of dengue virus ( DENV1-4 ) by injecting virus-containing saliva into the skin . Even though arthropod saliva is known to facilitate transmission and modulate host responses to other pathogens , the full impact of mosquito saliva on dengue pathogenesis is still not well understood . Inoculating mice lacking the interferon-α/β receptor intradermally with DENV revealed that mosquito salivary gland extract ( SGE ) exacerbates dengue pathogenesis specifically in the presence of enhancing serotype-cross-reactive antibodies—when individuals already carry an increased risk for severe disease . We further establish that SGE increases viral titers in the skin , boosts antibody-enhanced DENV infection of dendritic cells and macrophages in the dermis , and amplifies dendritic cell migration to skin-draining lymph nodes . We demonstrate that SGE directly disrupts endothelial barrier function in vitro and induces endothelial permeability in vivo in the skin . Finally , we show that surgically removing the site of DENV transmission in the skin after 4 hours rescued mice from disease in the absence of SGE , but no longer prevented lethal antibody-enhanced disease when SGE was present . These results indicate that SGE accelerates the dynamics of dengue pathogenesis after virus transmission in the skin and induces severe antibody-enhanced disease systemically . Our study reveals novel aspects of dengue pathogenesis and suggests that animal models of dengue and pre-clinical testing of dengue vaccines should consider mosquito-derived factors as well as enhancing antibodies .
During the transmission of arthropod-borne diseases , blood-feeding insects deposit pathogen-containing saliva into the skin [1] . How factors in arthropod saliva impact pathogen replication , host responses , and disease outcome is an area of active research . The four serotypes of dengue virus ( DENV1-4 ) cause the most prevalent mosquito-borne viral disease , with up to 390 million infections and 96 million cases of dengue annually [2] . The first ( 1° ) DENV infection usually causes dengue fever or subclinical disease , and individuals generate memory immune responses that protect against infection with the same DENV serotype . In contrast , sequential infection with a different DENV serotype can induce severe and potentially fatal disease [3] . Severe dengue is characterized by high fever , low platelet count , hemorrhagic manifestations , systemic vascular leak , and/or circulatory shock [4] . The memory immune response generated during a previous DENV infection can exacerbate a subsequent infection with a different serotype via a phenomenon known as antibody-dependent enhancement ( ADE ) and/or a cytokine storm produced by cross-reactive T cells [5 , 6] . During ADE , antibodies that were generated during the 1° infection and cross-react with the next infecting DENV serotype do not neutralize but instead enhance infection of Fcγ receptor-bearing cells , such as macrophages ( MΦs ) and monocytes [7–10] . Whereas monocytes circulate in the blood and enter inflamed tissues , MΦs and classical dendritic cells ( cDCs ) reside in steady-state tissues . After systemic dissemination , DENV infects monocytes in the blood [11] , as well as MΦs and DCs in lymph nodes ( LNs ) , spleen , intestine , and liver [12–16] . While probing for blood vessels in the skin , arthropods induce a host response that includes narrowing of blood vessels , blood clotting , and inflammation . To allow efficient blood feeding , arthropods inject saliva that contains various molecules that counteract this response [1] . Pathogens have co-evolved within their vector to optimize transmission [17] . Plasmodium [18 , 19] and Leishmania parasites [20 , 21] efficiently establish infections experimentally only when transmitted with vector saliva , and mosquito saliva enhances West Nile virus ( WNV ) infection [22 , 23] . Female Aedes aegypti and Ae . albopictus mosquitoes transmit DENV , Zika virus , yellow fever virus , and Chikungunya virus and are thus important vectors of disease . Nonetheless , the role of mosquito saliva in dengue pathogenesis is not well understood . Ae . aegypti saliva increased DENV serum viremia in mice lacking interferon ( IFN ) regulatory factors 3 and 7 ( IRF3/7 ) [24] and prolonged dengue viremia in “humanized” mice xeno-transplanted with human hematopoietic stem and progenitor cells [25] . In contrast , Ader et al . found that mosquito saliva inhibits DENV infection of human monocyte-derived DCs ( moDCs ) in vitro [26] . Even though dermal cDCs and MΦs are the initial targets for DENV replication [27 , 28] , the impact of mosquito saliva on immune cells in DENV-infected skin has not been studied . Here we examined the impact of Ae . aegypti salivary gland extract ( SGE ) on dengue pathogenesis , the host response , and DENV infection in the skin . We used mice lacking the IFN-α/β receptor ( Ifnar–/– ) that are susceptible to intradermal ( i . d . ) DENV infection [28] and display key features of human disease , such as lethal vascular leak [29–31] . In infected human cells , DENV proteins block IFN-α/β signaling [32–34] . In contrast , wild-type mice do not support DENV replication [35] because DENV proteins cannot block IFN signaling in mice [36] . While DENV inoculation of naïve Ifnar–/–mice mimics 1° infection conditions , inoculation of mice previously injected with sub-neutralizing levels of DENV-specific antibodies models ADE [14 , 15 , 31] . We needle-inoculated Ifnar–/–mice i . d . in the presence or absence of enhancing antibodies with DENV alone or DENV mixed with SGE from female Ae . aegypti mosquitoes—a model to examine the role of vector-derived factors in pathogen transmission that is more broadly accessible and easier to standardize than the use of live mosquitoes [37–40] . We establish here that only the combined presence of SGE and enhancing antibodies augments dengue pathogenesis , DENV infection of DCs and MΦs in the dermis , and immune responses in skin-draining LNs . In addition , we show that SGE increases endothelial permeability in vitro and induces vascular leak in the skin . Finally , removing the site of DENV infection in the ear after 4 hours ( h ) rescued mice from severe disease in the absence of SGE , but this rescue was lost when SGE was present . Mosquito-derived factors thus augment antibody-enhanced dengue pathogenesis in the skin and beyond .
To determine the role of mosquito-derived factors on dengue pathogenesis , we inoculated Ifnar–/–mice i . d . with 105 PFU of the mouse-adapted virulent DENV2 strain D220 [31] ( hereafter termed “DENV” ) in PBS or mixed with SGE ( equivalent to one salivary gland of a female Ae . aegypti mosquito ) . SGE had no effect on dengue pathogenesis in the absence of enhancing antibodies ( 1° infection ) , which induced mild or no disease ( Fig 1A ) . In contrast , the presence of SGE significantly increased morbidity ( Fig 1B ) and induced lethal disease in 54% of the mice ( Fig 1C ) under antibody-enhanced conditions ( ADE ) . The presence of enhancing antibodies mildly increased morbidity compared to 1° infection in the absence of SGE only on day 7 ( p<0 . 05 ) but significantly increased morbidity on days 4 , 5 , 6 ( p<0 . 001 ) and 7 ( p<0 . 05 ) in the presence of SGE ( Fig 1A and 1B ) . Severe dengue disease resulting from 105 PFU DENV is thus dependent upon the combined presence of mosquito-derived factors in SGE and enhancing antibodies . Due to the mild morbidity under 1° conditions that were observed after inoculation of 105 PFU , we further tested the effect of SGE at a 10-fold higher DENV dose of 106 PFU . Unexpectedly , SGE diminished disease severity under 1° conditions and did not further augment pathogenesis during antibody-enhanced DENV infection at 106 PFU ( S1 Fig ) , possibly because ADE of 106 PFU already causes lethal disease . Because Culex mosquitoes transmit a median dose of 105 PFU of the related Flavivirus , WNV [41] , and a higher virus dose might override the enhancing effect of SGE on dengue pathogenesis , we subsequently used 105 PFU DENV . In endemic areas , exposure to non-infected mosquitoes regularly occurs . In order to test whether pre-existing immunity to SGE would alter the effect of SGE on dengue pathogenesis , we inoculated Ifnar–/–mice three times i . d . with SGE alone ( at ≥2 weeks intervals ) . Subsequent i . d . antibody-enhanced DENV infection in the presence of SGE augmented pathogenesis similarly to that observed in SGE-naïve mice ( S1 Fig ) . We hypothesized that SGE synergizes with enhancing antibodies to impact DENV replication in the skin . In fact , SGE alone and to a greater degree the combination of SGE and enhancing antibodies significantly increased DENV titers in the skin at 14 h post-inoculation , as measured by qRT-PCR ( Fig 2A and 2B ) . We thus dissected DENV infection of cell subsets in the skin via intracellular staining for DENV non-structural protein NS3 and structural protein E in flow cytometric analysis . We gated dermis-resident CD11b+ cDCs ( CD45+ MHCIIhigh ( hi ) Langerin-CD11b+ Ly6C- ) and MΦs ( CD45+ MHCIIlow/–Langerin-CD11bhi Ly6G- Ly6Clow/–SSCint-hi ) as previously established ( [28] and S2 Fig ) . Dermis-resident CD11b+ cDCs and MΦs were the initial targets for DENV infection 14 h after i . d . inoculation of 105 PFU ( Fig 2C–2G ) , similar to inoculation of 106 PFU , as reported previously [28] . While DENV infection was low in the absence of SGE or enhancing antibodies , the combined action of SGE and enhancing antibodies significantly increased DENV infection of dermal CD11b+ cDCs ( Fig 2D and 2E ) and MΦs ( Fig 2F and 2G ) . While myeloid cells in the dermis expressed various levels of Fcγ receptors ( CD16/32 ) , only MΦs and , to a minimal degree , CD11b+ cDCs expressed mannose receptor ( CD206 , S2 Fig ) . These dermal cell surface molecules may serve to mediate DENV attachment in the presence ( Fcγ receptors ) [8 , 42] or absence ( mannose receptor for MΦs ) of enhancing antibodies [43] . No cells in the epidermis , such as CD45+ MHCII+ Langerin+ Langerhans cells , contained intracellular DENV proteins after 14 h . The combined presence of mosquito-derived factors and enhancing antibodies thus augmented DENV infection of CD11b+ cDCs and MΦs as initial targets for DENV replication in the dermis . We next examined the recruitment of immune cells to the dermis . We gated de novo-recruited cell populations in the dermis as previously established via adoptive transfer of labeled monocytes in the absence of SGE [28] . Ly6G expression identified neutrophils , and Ly6C expression separated recruited Ly6Chi monocytes from resident Ly6Clow/–MΦs among MHCIIlow/–CD11b+ cells ( Figs 3A and S2 ) . Whereas inoculation of 105 PFU DENV under 1° conditions led to a modest recruitment of inflammatory neutrophils ( Fig 3B ) and monocytes to the dermis ( Fig 3C ) , SGE significantly amplified the recruitment of neutrophils and monocytes in the presence of enhancing antibodies . Ly6C expression further separated Ly6C+ moDCs from resident Ly6C- CD11b+ cDCs among MHCII+ Langerin-CD11b+ cells ( S2 Fig ) . During ADE , SGE augmented the number ( Fig 3D ) and DENV infection ( Fig 3E and 3F ) of de novo-recruited moDCs in the dermis . In contrast , few monocytes ( <0 . 5% ) and no other cell types ( e . g . , DCs or MΦs ) were infected with DENV in the dermis 14 h after inoculation . SGE also increased the percentage of monocytes and moDCs in the dermis ( S3 Fig ) . Together , these data show that the presence of SGE and enhancing antibodies augmented innate responses to DENV infection in the skin via the recruitment of neutrophils , monocytes , and moDCs and increased the infection of de novo-recruited moDCs . We hypothesized that SGE may further modulate immune responses beyond the skin . While activated cDCs efficiently migrate from the skin to draining LNs ( MigDCs ) via expression of the chemokine receptor CCR7 [44 , 45] and prime naïve T and B cells , MΦs and recruited moDCs mostly remain within the inflamed skin to phagocytose pathogens and locally stimulate effector or memory T cells [46 , 47] . After 14 h , skin-draining LNs contained significantly more and a higher frequency of MigDCs with a CD11cint MHCIIhi CCR7+ migratory phenotype ( Figs 4A and S4 ) when DENV was inoculated with SGE in the presence of enhancing antibodies ( Fig 4B ) . The combined presence of SGE and enhancing antibodies further boosted the recruitment of CD11bhi Ly6G+ neutrophils ( Fig 4C ) and MHCII-CD11b+ F480+ SSClow monocytes to LNs ( Fig 4D ) , likely from the blood . For neutrophils , this effect was due to decreased recruitment during 1° infections when SGE was present . In contrast , SGE did not significantly change the number of CD11chi MHCIIint cDCs or cells with a CD11clow MHCIIint CD11b+ F480+ SSClow phenotype that likely consist of moDCs in LNs after 14 h ( S4 Fig ) . We further examined the activation of naïve T cells during antibody-enhanced DENV infection . Even though 82% of CD8+ and 58% CD4+ T cells in the spleen and 34% of CD8+ and 21% CD4+ T cells in LNs draining the site of i . d . inoculation acquired an activated CD44+ CD62L- phenotype within 5 . 5 days , no significant differences were detectable in the presence or absence of SGE ( S5 Fig ) . Along the same lines , 22% of CD8+ T cells and 13% of CD4+ T cells produced IFN-γ in the spleen regardless of whether SGE was present or not ( S5 Fig ) . However , a non-significant trend towards decreased proliferation of T cells in the presence of SGE was observed ( S5 Fig ) . Consequently , the presence of SGE during antibody-enhanced DENV infection increases the migration of DCs and monocytes to skin-draining LNs . Nevertheless , whether augmented innate responses subsequently affect adaptive immune responses and thereby influence dengue pathogenesis remains to be determined . Endothelial cells lining blood vessels separate the skin from systemic blood circulation . We hypothesized that factors contained in SGE may disrupt endothelial barrier function to enable blood feeding , which might also facilitate cell migration . To determine the direct effect of SGE on the vascular endothelium , we measured trans-endothelial electrical resistance ( TEER ) of human dermal microvascular endothelial cell ( HMEC-1 ) monolayers growing on Transwell inserts ( Fig 5A ) . Monolayers displayed high TEER values and thus low permeability in steady state . Addition of SGE to either the basolateral side of the monolayer that corresponds to the side facing the dermis ( Figs 5B , 5C and S6 ) or the apical side facing the blood ( S6 Fig ) significantly decreased TEER values , thus increasing endothelial permeability , in a dose-dependent manner . TNFα served as a positive control for endothelial permeability and decreased TEER values with similar dynamics as SGE ( S6 Fig ) . We next tested whether SGE also affected endothelial barrier function at the site of inoculation in the mouse ear . Our results demonstrated that i . d . inoculation of SGE disrupted the barrier function of the microvasculature in the mouse ear and increased the leak of plasma into the dermis , visualized via fluorescently-labelled dextran that was inoculated intravenously ( Fig 5D ) . The vasculature in the PBS-inoculated control ear of the same animal showed significantly lower leak of plasma , and untouched ears of steady-state animals showed only minor endothelial permeability ( Fig 5D and 5E ) . The signal at the base of the ear was due to autofluorescence of cartilege and was excluded from quantification at the site of inoculation ( white circles , Fig 5D ) . Consequently , SGE disrupts endothelial barrier function at the site of virus transmission in the dermis and may thus be one factor that contributes to systemic dengue pathogenesis . Finally , to test whether the effects of SGE in the skin impacted systemic dengue pathogenesis , we surgically removed the site of DENV transmission in the presence or absence of SGE after 4 h ( Fig 6A ) . In the presence of SGE , removing the site of DENV transmission did not have a protective effect on the morbidity and survival of mice after i . d . inoculation with an antibody-enhanced DENV inoculum of 105 PFU ( Fig 6B and 6C ) . Fifty percent of mice succumbed to infection regardless of whether the control side or the site of inoculation was removed . In contrast , if no SGE was present , removing the site of DENV transmission completely protected mice from morbidity after inoculation with an antibody-enhanced dose of 105 PFU ( Fig 6D and 6E ) . Even more striking was the significant protective effect on morbidity and survival of the mice observed after inoculation with an antibody-enhanced high DENV dose of 106 PFU ( Fig 6F and 6G ) . These data show that the effect of mosquito-derived factors on dengue pathogenesis rapidly spreads beyond the skin . The effect of SGE on endothelial barrier function and the immune response thus influence dengue pathogenesis .
We establish here that Ae . aegpyti SGE exacerbates systemic dengue pathogenesis during ADE . We determined that SGE in the presence of enhancing antibodies increases DENV infection of cDCs and MΦs in the dermis , recruitment of monocytes to the dermis and draining LNs , and DC migration from the skin to LNs . We also showed that SGE acts directly on endothelial cells in vitro , disrupts endothelial barrier function , and induces plasma leak in the mouse ear . Further , surgical removal of the site of DENV transmission after 4 h rescued mice from enhanced disease in the absence but not in the presence of SGE , indicating that the effect of mosquito-derived factors on dengue pathogenesis spreads rapidly beyond the skin . Previous studies have demonstrated that mosquitoes inoculate saliva that contains pathogens into the dermis while probing for blood vessels . Anopheles mosquitoes inoculated saliva with Plasmodium parasites into the dermis of mice [48] . Similarly , in studies of mosquitoes feeding on mouse tails , >99% of inoculated WNV was recovered from the skin section that infected Culex mosquitoes probed upon [41] . Although Culex mosquitoes inoculated WNV directly into the blood circulation when obtaining a successful blood meal [41] , the amount of WNV that was deposited into the bloodstream remained low at ~100 PFU , which was only 0 . 1% of the total i . d . -transmitted median dose of 105 PFU of WNV [41] . We show that removing the site of i . d . DENV infection after 4 h rescued mice from disease in the absence of SGE but , in the natural setting , DENV is always transmitted in the saliva of infected mosquitoes . Studies have shown that survival of mice was prolonged when the distal third of the tail was removed 5 min to 6 h after mosquitoes inoculated with St . Louis encephalitis [49] or Rift Valley fever viruses [50] . Similarly , mice were protected from blood parasitemia when the site of inoculation was removed up to 5 min but no later than 15 min after Anopheles-transmitted Plasmodium infection [51] . Our data that removing the site of DENV transmission rescues mice in the absence but not in the presence of SGE establishes that DENV infection in the skin early after inoculation is critical for subsequent dengue pathogenesis and that SGE modulates the dynamics of this process . The endothelial barrier of capillaries separates the skin from systemic blood circulation . Mosquito saliva [1 , 18] or innate immune recognition of DENV [52 , 53] can increase endothelial cell permeability via activation of mast cells and release of inflammatory mediators , such as histamine , TNFα , or proteases . Our in vivo results of SGE inducing vascular leak in the ear skin may thus be a combined effect of SGE on mast cells and endothelial cells . In contrast , our in vitro results of SGE decreasing the TEER of endothelial cell monolayers demonstrate that SGE can act directly on endothelial cells to increase permeability . In the absence of SGE , Luplertlop et al . showed that DENV-infected DCs induce vascular permeability in the skin via production of metalloproteinases that degrade endothelial cell adhesion molecules [54] . Serine proteases in Ae . aegypti SGE were shown to degrade the extracellular matrix of embryonic fibroblasts [40] . We thus speculate that SGE disturbs the integrity of blood vessels through degradation of extracellular matrix proteins or disturbance of tight/adherens junctions between endothelial cells . Factors in SGE that disrupt endothelial barrier function may have evolved to help the mosquitoes penetrate blood vessels or feed from a pool of blood in the dermis after injury to a blood vessel . Previous studies examined the effect of mosquito-derived factors on DENV infection only in the absence of enhancing antibodies [24 , 25 , 40] . In our hands , SGE did not augment dengue pathogenesis after inoculation of 105 PFU under 1° conditions and even reduced disease severity after inoculation at a 10-fold higher dose of 106 PFU . In contrast , SGE augmented dengue pathogenesis during antibody-enhanced infection with 105 PFU . Numerous reports show that ADE enhances DENV infection of human monocytes [8 , 55] and MΦs [9] in vitro . In mice , intestinal monocytes/MΦs and liver sinusoidal endothelial cells are particularly susceptible to antibody-enhanced DENV infection [15 , 30] . We speculate that , via disruption of endothelial barrier function in the skin , SGE may augment access of DENV to enhancing antibodies in the serum , increase the spread of infectious virus-antibody complexes , enhance infection within systemic tissues , and thus exacerbate dengue pathogenesis during ADE . Our results thus suggest that animal models of dengue and pre-clinical validation of dengue vaccine candidates should be evaluated in the combined presence of mosquito saliva and enhancing antibodies . As one possibility , SGE may augment systemic dengue pathogenesis by disrupting endothelial barrier function and thus facilitating DENV dissemination . In humans , higher DENV titers at peak viremia correlate with more severe disease [5] . Accelerated early DENV dissemination may subsequently increase virus titers and thus the risk for severe disease . In mice , feeding of uninfected Culex mosquitoes at the site of needle inoculation or concurrent inoculation with SGE augmented WNV serum viremia after 24–48 h [22] . Likewise , feeding of uninfected Ae . aegypti at the site of needle inoculation with WNV subsequently increased mortality compared to needle inoculation with virus alone [23] . Similarly , mosquito transmission of DENV or needle-inoculation with concurrent feeding of uninfected mosquitoes prolonged viremia and increased erythema ( redness of the skin ) in “humanized” mice [25] and increased DENV titers around peak viremia in mice lacking IRF3/7 [24 , 56] . When needle-inoculated into the foot pad , mosquito SGE further increased DENV titers in LNs of Ifnar–/–mice after 24 h [40] . Together , these data show that mosquito-derived factors increase DENV titers 24 h to several days post-inoculation , which could be due to increased virus dissemination and/or systemic replication . Our ear removal data show that factors that augment dengue pathogenesis spread beyond the skin within 4 h in the presence of SGE . Nevertheless , the precise impact of mosquito-derived factors on early DENV dissemination prior to the first round of DENV replication , which takes place within ~12 h [57] , remains unknown . Our data also show that SGE boosts the recruitment of innate immune cells during acute DENV infection . Previous studies revealed that monocytes and neutrophils enter the DENV-infected dermis in the absence of SGE [28] . Several studies have confirmed the recruitment and phenotype of inflammatory monocytes ( MHCII-Langerin-CD11b+ Ly6G- Ly6Chi ) and moDCs ( MHCIIhi Langerin-CD11b+ Ly6C+ ) to the dermis during allergic reactions [58] , or infection with Leishmania major [59] or DENV [28] via adoptive transfer of genetically- or fluorescently-labeled monocytes . The phenotype remained consistent for 7 days , before monocyte-derived cells down-regulated Ly6C expression [58 , 59] . During DENV infection , monocyte recruitment and differentiation to moDCs increased the number of targets for virus replication in the dermis [28] . Sterile injury [60] or transmission of Leishmania via sandfly feeding triggered the recruitment of neutrophils to the skin , where they then served as a “Trojan horse” reservoir for parasite infection [61] , and abrogated vaccine-induced protection by suppressing parasite-specific CD4+ T cell responses [62] . Few studies have addressed the role of neutrophils in dengue pathogenesis . In one study , the serum of dengue patients contained higher levels of IL-8 and neutrophilic elastase than healthy controls , suggesting activation and degranulation of neutrophils [63] . Previous studies have documented the exit of epidermal Langerhans cells [64 , 65] and dermal DCs [27] from DENV-infected skin and their migration to LNs in the absence of SGE . We establish here that the combined presence of enhancing antibodies and SGE boost the recruitment of monocytes and neutrophils to the DENV-infected dermis and draining LNs , as well as DC migration from the skin to LNs . SGE disruption of endothelial barrier function in the dermis may facilitate both transmigration of monocytes and neutrophils through blood vessels to enter the dermis and migration of skin DCs to LNs via increased drainage of plasma that leaks into the skin . Because removal of the site of co-inoculation of DENV with SGE after 4 h did not rescue mice from systemic antibody-enhanced pathogenesis , rapid spread of inflammatory mediators may contribute to increased disease severity . As we did not observe significant differences in T cell activation in the presence versus absence of SGE 5 . 5 days after DENV inoculation , further studies are needed to determine whether SGE affects systemic immune responses . Along the same lines , Cox et al . determined that increased viremia and prolonged fever occurred even when mosquito saliva and DENV were inoculated at distant sites [25] , supporting the view that mosquito-derived factors can impact dengue pathogenesis systemically . We confirm here the recent discovery that dermal cDCs and MΦs are the initial targets for DENV replication in the skin of mice and humans in the absence of SGE [27 , 28] , but at a 10-fold lower virus dose in vivo . Only one in vitro study has examined the impact of mosquito saliva on human immature moDCs , and it found that Ae . aegypti saliva inhibits DENV infection under 1° conditions [26] . In our study , SGE led to a small but significant increase in DENV titer in the skin under 1° conditions , as measured via qRT-PCR 14 h after inoculation . Under 1° conditions , however , flow cytometric analysis did not detect significant differences in the infection of dermal cDCs , moDCs , and MΦs , which were the only DENV-infected cells in the skin at this time-point . We establish here that the combined presence of SGE and enhancing antibodies significantly enhanced DENV infection of cDCs and MΦs in the dermis . While monocytes , MΦs , and mature moDCs are known targets for enhanced DENV infection in vitro [8 , 9 , 42] , this is the first report that ADE boosts DENV infection of cDCs in the presence of SGE . In contrast , ADE alone did not significantly enhance infection of cDCs and MΦs in the dermis in the absence of SGE ( Fig 2 and [28] ) . Whereas enhancing antibodies may have limited access to the skin in the absence of SGE , the increased leak of plasma into the skin that we found in the presence of SGE may allow more enhancing antibodies to enter the skin and mediate antibody-enhanced infection of cDCs and MΦs in the dermis . Although DCs are key for priming adaptive T and B cell responses , DENV infection can impair DC function [10] . In addition , Ae . aegypti saliva suppresses host responses , such as cytokine production and T cell proliferation at the feeding site [66 , 67] , and skews systemic CD4+ T cell responses from Th1- towards Th2-type by decreasing IFN-γ and increasing IL-4 expression [56 , 68 , 69] . Even though our ear removal experiments show that dengue pathogenesis of the ongoing infection occurs beyond the skin when SGE and enhancing antibodies are present , the increased DENV infection of skin DCs may affect priming of memory responses that protect or enhance pathogenesis during subsequent DENV infections . In endemic regions , exposure to non-infected arthropod vectors regularly occurs and could modulate the effect of vector saliva . For malaria , exposure of mice to the bite of non-infected Anopheles mosquitoes protected them from subsequent mosquito-transmitted Plasmodium infection [70] . Similarly , vaccination with sand fly SGE or exposure to the bites of non-infected sand flies protected mice or non-human primates against subsequent Leishmania challenge plus SGE [71–74] . However , immunization with two distinct sand fly salivary proteins , PpSP15 or PpSP44 , either protected or increased susceptibility to Leishmania infection , respectively [75] . Similarly , pre-exposure of mice to bites of non-infected Ae . aegypti mosquitoes exacerbated WNV lethality [76] , but immunization with the recombinant protein D7 from Culex saliva enhanced pathogenesis during subsequent mosquito-transmitted WNV infection [77] . One study reported an association of higher serum reactivity to Ae . aegypti salivary proteins in Thai children with severe secondary dengue disease compared to not-infected individuals , but it remains unclear whether this correlation was due to prior exposure or reactivity to DENV or non-infected mosquitoes [78] . In our hands , antibody-enhanced DENV infection in the presence of SGE induced lethality in a comparable proportion of mice that were pre-exposed three times to SGE versus SGE-naïve animals . Further studies are needed to determine the effect of anti-Ae . aegypti immunity on dengue pathogenesis , and the potential use of mosquito-derived components in vaccine formulations against dengue requires thorough evaluation . Our study introduces in vitro and mouse models to study the impact of mosquito-derived factors on endothelial cells and dengue pathogenesis , respectively . DENV suppresses the IFN response , replicates , and causes disease in humans but not wild-type mice [28 , 35 , 36] . Ifnar–/–mice are readily available and more immunocompetent than other DENV infection models due to intact IFN-γ receptor signaling . After i . d . inoculation , DENV infects the same cells ( i . e . , dermal cDCs and MΦs ) in Ifnar–/–mice [28] and human skin explants [27] , cross-validating the reliability of these models . Studies using transmission via DENV-infected live mosquitoes are most physiologically relevant but naturally cannot control for the presence of mosquito-derived factors or the virus dose delivered , as mosquitoes transmit varying doses of virus ( e . g . , WNV [41] ) . A study design ( “spot feeding” ) where uninfected female Ae . aegypti mosquitoes deposit saliva into mouse skin followed by i . d . needle inoculation of virus mimics the secretion of saliva while mosquitoes probe for blood vessels , can control for the presence or absence of mosquito-derived components , delivers a defined dose of virus , and has successfully been used to study DENV infection in vivo [24 , 25] . Live mosquitoes , however , inoculate varying amounts of saliva [41] depending on how fast they locate and penetrate a blood vessel; thus , salivary dose cannot be controlled or monitored in this model . The simplified model that we use here , i . e . , needle inoculation of SGE from non-infected mosquitoes with a defined dose of pathogen , has proven useful in assessing the role of vector-derived factors for infection with DENV [40] , WNV [23] , Sindbis virus [69] , Rift Valley fever virus [39] , and Leishmania [38] . Similarly , WNV transmission via infected Culex mosquitoes , deposition of saliva via uninfected mosquitoes at the site of needle inoculation , or concurrent i . d . needle inoculation of WNV with SGE all induced earlier and higher viremia compared to needle inoculation of WNV alone [22][79] . While not precisely replicating DENV transmission via live mosquitoes , the SGE model is more broadly applicable to future immunological analyses and pre-clinical testing of dengue vaccines , as the variables can be controlled , unlike mosquito-transmitted DENV . Mosquito saliva can be collected artificially in a sucrose solution in a capillary tube into which the mosquito proboscis is inserted , or through a membrane . However , such saliva preparations differ qualitatively and quantitatively from mosquito saliva that is inoculated into the host during a natural blood meal [79 , 80] . Furthermore , the mosquito does not replicate normal probing and secretion of saliva during artificial feeding . Needle inoculation i . d . of DENV with or without SGE is thus the model that is easiest to standardize between assays and between research groups . The dose of 1 SGE that we have used in vivo contained 0 . 37–0 . 60 μg total protein and lies within the range for which Moser et al . determined a dose-dependent increase in WNV serum viremia in the presence of SGE from Culex mosquitoes [79] . In addition , Wasserman et al . determined that a single Ae . aegypti mosquito inoculates approximately 0 . 35 salivary gland pairs ( equivalent to 0 . 7 SGE ) [81] . We have thus inoculated mice with SGE that is likely equivalent to the bite of 1–2 mosquitoes . We further introduce an in vitro system measuring TEER as a practical tool to assess the direct effect of vector-derived factors on endothelial barrier function that is independent of specific pathogens and can thus be extrapolated to other viruses carried by Ae . aegypti mosquitoes . Using the TEER system , we show that 0 . 2 to 2 SGE increased vascular permeability in a dose-dependent manner , indicating that 1 SGE is a non-saturated dose but lies within the range of a dose-response curve . In summary ( Fig 7 ) , our study establishes that mosquito-derived factors exacerbate dengue pathogenesis in individuals that carry enhancing antibodies and thus already have an increased risk for severe disease . Consequently , safety and efficacy of vaccine and therapeutic candidates against dengue should be tested pre-clinically in models that consider both the mosquito vector as well as enhancing antibodies . We establish that SGE disrupts endothelial barrier function in the skin , induces vascular leak , and , in combination with enhancing antibodies , increases dendritic cell migration to skin-draining LNs . Because antibody-enhanced dengue pathogenesis occurs beyond the site of DENV-SGE co-inoculation in the skin , future studies are needed to determine whether SGE alters immune responses and thereby augments systemic dengue pathogenesis . Increasing DENV infection of cDCs and MΦs in the dermis could further modulate the priming of memory responses that determine pathogenesis during subsequent DENV infections . The role of mosquito-derived factors in dengue pathogenesis warrants further studies , and our findings call for additional research on arthropod-borne pathogens whose vectors share similar blood-feeding strategies and may also impair endothelial barrier function .
Mice were bred and maintained in the University of California Berkeley Animal Facility , and experiments were performed strictly following guidelines of the American Veterinary Medical Association and the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The University of California Berkeley’s Animal Care and Use Committee pre-approved all experiments ( protocol AUP-2014-08-6638 ) . Trained laboratory personnel performed anesthesia of mice via isoflurane inhalation and euthanasia of mice using exposure to CO2 followed by cervical dislocation . Wild-type C57BL/6 mice were purchased from the Jackson Laboratory . C57BL/6 mice deficient in the IFN-α/β receptor-1 ( Ifnar1tm1Agt , here termed Ifnar–/– ) were provided by Dr . Daniel Portnoy ( University of California , Berkeley , CA ) [82] . After DENV inoculation , mice were monitored following the standardized cumulative morbidity scale from 1 ( healthy ) to 5 ( moribund ) [28 , 31]: 1 , healthy; 2 , mild signs of lethargy; 3 , fur ruffling , hunched posture; 4 , increased lethargy , limited mobility; 5 , moribund with minimal mobility and inability to reach food or water . Moribund mice were euthanized immediately , assigned a score of 5 , and excluded from the mean morbidity calculations on subsequent days . To mimic DENV transmission from an infected mosquito to the mammalian host , we inoculated Ifnar–/–mice intradermally ( i . d . ) with DENV produced in C6/36 mosquito cells . We used the DENV2 strain D220 , which was derived via 20 passages of the Taiwanese clinical isolate PL046 between C6/36 cells and serum of 129/Sv mice deficient in IFN-α/β and -γ receptors , acquiring defined mutations that increased its virulence in mice [29 , 31] . D220 stocks ( hereafter termed DENV ) were propagated in C6/36 cells ( obtained from Dr . Paul Young , University of Queensland , Brisbane , Australia ) at 28°C in M199 medium containing 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin/streptomycin , and GlutaMax ( all from LifeTechnologies ) . T175 flasks containing 80% confluent C6/36 cells were infected with DENV at a multiplicity of infection of 0 . 05–0 . 1 in serum-free medium for 2 h and then supplemented with 2% FBS . Supernatants were harvested between days 5 and 8 post-infection and concentrated using Amicon Ultra-15 Centrifugal Filter Units with a 100 kDa molecular weight cut-off ( Millipore ) before storage at -80°C . DENV titers were determined via plaque assay using BHK-21 clone 15 cells , which were maintained at 37°C and 5% CO2 in α-MEM medium with 5% FBS , 100 U/ml penicillin/streptomycin , and GlutaMax . BHK-21 cells in 12-well plates ( Becton Dickinson ) at 60% confluence were infected with 150 μl DENV in 10-fold serial dilutions for 2 h before overlaying with 10% low-melting agarose ( Cambrex ) in fully supplemented α-MEM medium . BHK-21 cells were fixed 7 days later using 10% buffered formalin phosphate ( Fisher Scientific ) and stained with 2 . 5% crystal violet in 30% ethanol . DENV titers were calculated as plaque forming units ( PFU ) /ml . Salivary gland extracts ( SGE ) were obtained from non-infected wild-caught Nicaraguan ( DENV infections in vivo ) or colonized Panamanian female Ae . aegypti mosquitoes ( vascular permeability assays in vitro and in vivo ) . Mosquitoes were immobilized with CO2 , dipped into 70% ethanol , and placed on glass slides in sterile PBS . The mosquito head was pulled away from the body , and the exposed salivary glands were dissected from the head or thorax under aseptic conditions and placed into fresh PBS ( 2 salivary glands/10 μl ) . Salivary gland membranes were disrupted via sonication in ice water at 100 mV for 3 bursts of 20 sec with 1 min of cooling time between bursts . Extracts were centrifuged at 5 , 000 g for 10 min at 4°C , and SGE was collected as supernatant . The protein concentration of the SGE stocks was 0 . 37–0 . 60 μg/ml , as estimated via NanoDrop ( Thermo Scientific ) . SGE stocks had a neutral pH of 7 . 0 and were free of endotoxin or contained very low levels that were well below the FDA-approved limit for injection solutions of ( <5 endotoxin units per kg per hour ) as tested using the endpoint chromogenic Limulus Amebocyte Lysate kit ( Lonza ) . Ears of anesthetized Ifnar–/–mice were immobilized using cover slip forceps . A sterilized needle ( 30-gauge , 25-mm length , and 10°-12° bevel ) used with a 25-μl reusable glass microinjection syringe ( Hamilton ) was inserted ~3 mm into the ventral side of the ear skin at a flat angle with the bevel pointing up , and DENV diluted in 20 μl PBS was slowly inoculated . To model the presence of mosquito-derived factors , we co-injected DENV mixed with the equivalent of 1 salivary gland from Nicaraguan mosquitoes in 20 μl PBS . Control ears were left untouched or were inoculated with SGE diluted in PBS or PBS alone . Naïve mice served to model 1° infections , whereas ADE conditions consisted of inoculation with a subneutralizing dose of 5 μg anti-DENV protein E monoclonal antibody 4G2 ( ATCC ) intraperitoneally in 200 μl PBS 24 h prior to DENV infection . In certain experiments , mice were anesthetized once again 4 h after i . d . DENV infection , and the inoculated site of the ear skin or an equivalent piece from the non-inoculated ear was surgically removed using sterile scissors . Bleeding was stopped immediately by applying pressure via sterile gauze , and mice were monitored closely for any direct adverse effects of the procedure . Only occasional scratching for less than 2 h was observed . Organs of euthanized mice were collected on ice . Ears were incubated for 5 min at room temperature with hair removal cream ( Nair ) , washed in PBS , and split into dorsal and ventral halves using tweezers ( TDI ) . Skin samples were digested floating with the epidermal side up for 90 min with 2 U/ml Dispase II in HBSS without Ca2+/Mg2+ ( LifeTech ) in 5% CO2 at 37°C . The epidermis was then separated as one sheet from the dermis and each was placed into a tube containing RPMI 1640 medium with 10% FBS . Using scissors , the epidermal and dermal layers were cut into pieces of 1–2 mm size , PBS containing a final concentration of 1 . 6 mg/ml collagenase type-1 ( LifeTech ) and 10 U/ml DNase 1 ( Roche ) was added , and digestion of the epidermis dermis was allowed to proceed for 45 min and 80 min , respectively , at 37°C while shaking at 220 rotations per min . Homogeneous cell suspensions were generated via pipetting and filtering through 100 μm nylon meshes . Cervical LNs or spleens were crushed in medium under nylon meshes and digested in a final concentration of 1 mg/ml collagenase type-1 and 10 U/ml DNase 1 for 30 min at 37°C and 5% CO2 before generating single cell suspensions via pipetting . Spleen samples were treated with Red Blood Cell Lysis Buffer ( eBioscience ) . Total numbers of live cells in LNs were determined via manual count and trypan blue exclusion . Cells were stained with Zombie Aqua ( BioLegend ) in PBS to exclude dead cells . Cell surface markers were then stained in PBS without Ca2+/Mg2+ with 2% FBS and 2 mM EDTA ( LifeTech ) using the following monoclonal antibodies ( from BioLegend or BD Pharmingen ) to identify cell types: CD3 ( 17A2 ) , CD4 ( GK1 . 4 ) , CD8 ( 53–67 ) , CD11c ( N418 ) , CD11b ( M1/70 ) , CD44 ( IM7 ) , CD45 ( 30-F11 ) , CD62L ( MEL-14 ) , CD103 ( 2E7 ) , F4/80 ( BM-8 ) , Ly6C ( AL21 ) , Ly6G ( 1A8 ) , MHCII ( I-A/I-E , M5/114 . 15 . 2 ) , CCR7 ( 4B12 ) or isotype control ( RTK2758 ) , and FcγR ( 2 . 4G2 ) or isotype control ( A-95-1 ) . The monoclonal antibodies were conjugated to PacificBlue , Brilliant Violet 605 , PE , PE-CF594 , PE-Cy7 , Alexa Fluor 700 , APC-Cy7 , or biotin . Cells stained with biotinylated antibodies were visualized using PE-Cy7- or Brilliant Violet 605-conjugated streptavidin ( BioLegend ) . Cells were fixed with 2% formaldehyde ( Ted Pella ) for 10 min at room temperature , and membranes were permeabilized with 0 . 1% saponin ( Sigma ) in PBS containing 2% FBS , 2 mM EDTA , and 1% normal mouse serum to block remaining fixative . To identify DENV-infected cells , cells were stained intracellularly with monoclonal antibodies directed against DENV non-structural protein NS3 ( clone E1D8 , [14] ) and structural protein E ( clone 4G2 , ATCC ) that were conjugated to Alexa Fluor 488 or Alexa Fluor 647 , respectively , using protein-labeling kits ( LifeTech ) . Intracellular staining for mannose receptor ( C068C2 ) or isotype control ( RTK2758 ) further dissected cell populations . Spleen cells were harvested 5 . 5 days post-i . d . DENV inoculation and were stained intracellularly for Ki-67 ( SolA15 ) or isotype control ( kit , eBiosciences ) or after in vitro restimulation for 4 hours in RPMI 1640 medium with 10% FBS , 20 nM phorbol 12-myristate 13-acetate ( Sigma ) , and 1 μM ionomycin ( Sigma ) , with 5 μg/ml brefeldin A ( eBiosciences ) added during the final 2 hours were stained for IFN-γ ( XMG1 . 2 ) or isotype control ( RTK2071 ) . Data were recorded using an LSR Fortessa cell analyzer ( BD Biosciences ) with 405 , 488 , 561 , and 632 nm laser excitation lines and data analyzed using FlowJo 8 . 8 . 7 software ( TreeStar ) . Gating FSC-A/SSC-A , SSC-H/SSC-W , FSC-H/FSC-W , and Zombie Aqua negative defined single , live-cell populations . The total number of cells in the dermis was determined via flow cytometer , while acquiring all cells for each sample and calculating total acquired cell counts . To determine DENV titers , a piece of less than 20 mg of tissue stored in RNAlater ( Ambion ) was homogenized for 1 min with 1-mm zirconia-silica beads using a Mini-Beadbeater-8 ( BiospecProducts ) , and RNA was extracted after centrifugation using the RNeasy Mini Kit ( Qiagen ) . RNA was extracted from 20 μl serum stored at -80°C using the QIAamp Viral RNA Mini Kit ( Qiagen ) . RNA samples were eluted in a total volume of 50 μl , and 2 μl was used per qRT-PCR reaction in duplicate . DENV2 NS5 primer and probe sequences were as follows [83]: forward primer 5’-ACA AGT CGA ACA ACC TGG TCC AT , probe 5’-Fam TGG GAT TTC CTC CCA TGA TTC CAC TGG Tamra-Q , reverse primer 5’-GCC GCA CCA TTG GTC TTC TC ( synthesized by Eurofins WMG Operon ) . DENV RNA and Gapdh mRNA ( TaqMan Rodent Gapdh Control Reagents , ThermoFisher ) were amplified in separate reactions using Verso one-step qRT-PCR Kits ( ThermoFisher ) . A 7300 Real-Time PCR System ( Applied Biosystems ) was set to the following thermal cycler profile: reverse transcription at 50°C for 30 min , hot start at 95°C for 12 . 5 min , and for amplification and data acquisition , 40 PCR cycles consisting of denaturation at 95°C for 15 sec and annealing/extension at 60°C for 1 min . Trans-endothelial electrical resistance ( TEER ) was used to measure endothelial permeability of confluent single-cell monolayers of human microvascular endothelial cells ( HMEC-1 ) in Transwell Permeable Supports ( Corning ) using a voltohmmeter ( World Precision Instruments ) after adding 0 . 2 , 0 . 5 , 1 or 2 salivary gland equivalents to 300 μl medium in the apical or 500 μl in the basolateral compartment of the transwell insert . Adding 10 ng/ml of TNFα ( R&D Systems ) to HMEC-1 cells served as a positive control for increased endothelial permeability . After 24 h , 50% of the medium in the upper and lower chambers was replaced with fresh medium . Relative TEER was calculated as percent resistance in Ohms of the experimental condition divided by non-treated cells . To measure vascular permeability in vivo , wild-type mice were inoculated intravenously with 200 μg Dextran ( 10 kDa molecular weight ) labeled with Alexa Fluor 680 . After circulating for 5 min , one salivary gland equivalent from Panamanian mosquitoes was inoculated i . d . into mouse ears in 15 μl PBS . Control ears were inoculated with PBS alone or were left untouched . Mice were euthanized 30 min after i . d . inoculation , and ears were scanned using the Odyssey CLx Infrared Imaging System ( Licor ) to quantify fluorescence of a 3300 pixel size area . Fluorescence values of steady-state control ears were subtracted from values of ears inoculated with SGE or PBS . Numerical data were tested for statistically significant differences between experimental groups using unpaired parametric t-tests for flow cytometry data and DENV tissue viral load . A paired t-test was used to test for statistically significant differences between the fluorescence of the two ears of one animal injected with either PBS or SGE . Unpaired Mann-Whitney tests were used for non-parametric data , such as morbidity . Survival data were tested for statistically significant differences using the Log-rank ( Mantel-Cox ) test . Experimental groups were considered significantly different at p<0 . 05 and are marked on graphs as * for p<0 . 05 , ** for p<0 . 01 , *** for p<0 . 001 , and **** for p<0 . 0001 , or non-significant ( n . s . ) . Data were plotted and statistically analyzed using Prism 6 . 0 software ( GraphPad ) . Data deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . 4rd14 [84] . | Mosquitoes inject saliva into the skin while probing for blood vessels . Saliva facilitates blood feeding and can contain pathogens when the mosquito is infected . In tropical regions , Aedes mosquitoes transmit the four serotypes of dengue virus ( DENV1-4 ) and infect almost 400 million humans every year . DENV causes severe disease especially in people who have already been exposed to a different serotype . During antibody-dependent enhancement , antibodies that were generated during the first infection bind , but do not neutralize , DENV , and instead enhance infection of immune cells . We injected mouse ears with DENV alone or with extracts from mosquito salivary glands to study the impact on disease . We found that saliva induced severe disease and death only during antibody-enhanced infection . Saliva increased DENV infection in the dermis , immune cell migration to skin and lymph nodes , and permeability of endothelial cells that line blood vessels . Removing the site of DENV inoculation in the skin rescued mice from severe disease , but this protective effect was lost when saliva was present . Our study reveals that mosquito saliva affects dendritic cell migration , increases endothelial permeability , and augments dengue disease severity . Mosquito saliva and enhancing antibodies thus need to be considered when developing vaccines and drugs against dengue . | [
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| 2016 | Mosquito Saliva Increases Endothelial Permeability in the Skin, Immune Cell Migration, and Dengue Pathogenesis during Antibody-Dependent Enhancement |
Bacterial cells maintain sophisticated levels of intracellular organization that allow for signal amplification , response to stimuli , cell division , and many other critical processes . The mechanisms underlying localization and their contribution to fitness have been difficult to uncover , due to the often challenging task of creating mutants with systematically perturbed localization but normal enzymatic activity , and the lack of quantitative models through which to interpret subtle phenotypic changes . Focusing on the model bacterium Caulobacter crescentus , which generates two different types of daughter cells from an underlying asymmetric distribution of protein phosphorylation , we use mathematical modeling to investigate the contribution of the localization of histidine kinases to the establishment of cellular asymmetry and subsequent developmental outcomes . We use existing mutant phenotypes and fluorescence data to parameterize a reaction-diffusion model of the kinases PleC and DivJ and their cognate response regulator DivK . We then present a systematic computational analysis of the effects of changes in protein localization and abundance to determine whether PleC localization is required for correct developmental timing in Caulobacter . Our model predicts the developmental phenotypes of several localization mutants , and suggests that a novel strain with co-localization of PleC and DivJ could provide quantitative insight into the signaling threshold required for flagellar pole development . Our analysis indicates that normal development can be maintained through a wide range of localization phenotypes , and that developmental defects due to changes in PleC localization can be rescued by increased PleC expression . We also show that the system is remarkably robust to perturbation of the kinetic parameters , and while the localization of either PleC or DivJ is required for asymmetric development , the delocalization of one of these two components does not prevent flagellar pole development . We further find that allosteric regulation of PleC observed in vitro does not affect the predicted in vivo developmental phenotypes . Taken together , our model suggests that cells can tolerate perturbations to localization phenotypes , whose evolutionary origins may be connected with reducing protein expression or with decoupling pre- and post-division phenotypes .
The localization of proteins is highly regulated throughout all kingdoms of life . In eukaryotic cells , asymmetric distributions of proteins contribute to a diverse set of processes including cell-shape determination and motility [1] , embryonic development [2] , stem-cell maintenance [3] , and the structural establishment of neurons and cilia [4] . Spatial organization is often dynamic , particularly throughout the cell cycle with the eventual generation of protein compositions that differ across the two halves of the cell . When this occurs , two distinct daughter cell types can be created by the post-cytokinesis segregation of the differential protein populations . The number of localized proteins in bacteria , and the biological processes in which they are engaged , are now known to be extensive; in the model bacterium Caulobacter crescentus , at least 10% of all proteins are non-uniformly localized and this subset covers all manners of function [5] . In many cases , localization is consistent with protein function , such as the coordination of cytokinesis by proteins that localize specifically to the division plane . Moreover , synthetic biology applications have begun to feature engineered systems utilizing protein localization to achieve specific functions such as increased metabolic pathway output [6] . However , relatively little is known regarding whether precise localization of the components of a complex system is required for achieving cellular functions , due to both the challenge of creating novel localization mutants and the absence of quantitative models for interpreting the mechanisms underlying changes in phenotype . In Caulobacter , each round of the cell cycle involves an intricate program of spatially regulated developmental events that leads to the formation of two different cell types: a sessile , stalked cell and a motile , swarmer cell . During the cell cycle , the swarmer cell progresses through phases in which the flagellum is activated , shed , and replaced first with pili and then a stalk and holdfast . Development is controlled by several two-component signaling systems , which usually consist of a histidine kinase and its cognate response regulator . Histidine kinases can act either as a kinase or a phosphatase , to phosphorylate or dephosphorylate the response regulator , respectively [7] . Central to development is the master cell-cycle response regulator CtrA , which controls morphogenesis , DNA methylation , and many essential cell-cycle events [8] , [9] . In recent work , we demonstrated that Caulobacter generates a spatial gradient of the active , phosphorylated form of CtrA that directly regulates DNA replication [10] . Employing a combination of mathematical modeling , single-cell microscopy , and genetic manipulation , we determined that this gradient is produced by asymmetric polar localization of the phosphorylation and dephosphorylation of CtrA by the bifunctional enzyme CckA . Our data indicated that cells robustly establish the asymmetric replicative fates of daughter cells before cell division effects physical compartmentalization . Importantly , localization of the phosphorylation or dephosphorylation activity alone is sufficient to establish and maintain the asymmetry . The freely diffusing response regulator DivK regulates CtrA transcription and , with the polar-bound histidine kinases DivJ and PleC , controls the flagellar pole development ( FPD ) program in Caulobacter . DivJ plays a role in cell division and stalk development , and phosphorylates DivK in vitro and in vivo [11]–[13] . PleC is necessary for cell motility [14] and dephosphorylates DivK-phosphate [13] , [15] , [16] . Throughout this work , we will refer to the unphosphorylated and phosphorylated forms of DivK as K and , respectively . Interestingly , DivJ and PleC are spatially regulated throughout the cell cycle [9] , [13] , [17]–[19] . At the time of cell division , DivJ is localized to the stalked pole while PleC is localized to the opposite swarmer pole ( Fig . 1A ) [13] . Cytokinesis leads to compartmentalization of PleC separate from DivJ , which is thought to dramatically reduce the levels of in the swarmer cell and permit FPD . DivK accumulates at both poles during growth , and the presence of swarmer pole-bound DivK alone is sufficient to inhibit FPD [8] , [20]; DivK mutants that exhibit normal phosphorylation and dephosphorylation dynamics but impaired polar binding develop at the flagellar pole independent of cell division [8] , [20] . The presence of the PleC phosphatase at the swarmer pole and the DivJ kinase at the stalked pole has motivated a “Ping-Pong” model for the passive dynamic polar localization of DivK in which the rapid diffusion of K and between the two poles maintains a steady-state level of at the flagellar pole , thereby inhibiting FPD [20] . Adding to the complexity of this model is recent evidence that can act as an allosteric regulator to switch PleC from a phosphatase to an autokinase [21] . Although the dynamic regulation of DivK phosphorylation by DivJ and PleC is clearly affected by their localization at opposite poles , it remains unclear whether their polar localization is strictly necessary for FPD . Previous genetic studies have indicated that a reduction in the levels of at the flagellar pole is a major requirement for FPD; non-phosphorylatable mutants of DivK do not localize to the swarmer pole [16] , and in this case FPD takes place independent of cell division [20] . In wild-type cells , the asymmetric localization of DivJ and PleC at opposite poles means that their antagonistic kinase and phosphatase activities on DivK are separated by cytokinesis , leading to different developmental outcomes in the post-divisional stalked and swarmer cells . It has been suggested that localization of DivJ and PleC to opposite poles produces a pre-divisional gradient of that helps to regulate FPD [22] . However , three lines of evidence argue that this simple model of spatiotemporal dynamics is insufficient to account for the regulation of FPD . First , the phosphatase activity of PleC would actually create a minimum in the pre-divisional distribution at the swarmer pole , potentially permitting development pre-cytokinesis . Second , the mutant does not bind to the flagellar pole , yet it remains phosphorylatable and hence subject to potential gradient establishment by DivJ and PleC; nevertheless , cells expressing do not require cell division for FPD [8] , [20] . Third , suppresses the defects of a pleC::Tn5 mutant , allowing FPD in the absence of PleC dephosphorylation , indicating that the phosphatase activity of PleC is not required for FPD despite its necessity for gradient formation [8] , [20] . Taken together , these observations suggest that the progression of FPD is dictated primarily by the levels of localized and bound to the flagellar pole , with higher levels inhibiting FPD . In the pre-divisional cell , high levels of are maintained by DivJ , leading to enhanced polar binding and prevention of premature FPD . Cell division then segregates DivJ from the swarmer cell , leading to a reduction in both cellular and polar-bound levels via PleC-mediated dephosphorylation and allowing FPD to progress in the swarmer cell ( Fig . 1 ) , while the stalked cell has increased levels due to DivJ activity [20] . Although this model is qualitatively consistent with the developmental trajectory of wild-type cells , a quantitative framework is required to assay whether other strategies could lead to FPD independent of cytokinetic compartmentalization . Here , we used experimental measurements of DivK fluorescence profiles to constrain potential models of K and dynamics . Using this data , we developed and validated a reaction-diffusion model for the DivJ-DivK-PleC system . We used this model to make predictions regarding the developmental phenotypes of various localization mutants and discuss how these strains could provide insight into the importance of localization . We show that the localization of PleC and DivJ at opposite poles is likely to give rise only to very shallow gradients insufficient to produce asymmetry in response-regulator activity in the cytoplasm; instead , our simulations support the hypothesis that binding at the swarmer pole suffices to create functional polarity . Moreover , we show that in strains with stalked-pole mislocalization of PleC , the predicted lack of FPD can be rescued by over-expression of PleC , suggesting that development can be robust to changes in protein localization . Therefore , polarity may have evolved to counter the costs of high protein expression and to expand the phenotypic repertoire by dissociating the phenotypes of daughter cells .
In previous work , we used modeling to demonstrate that kinase and phosphatase kinetics are the dominant factors in the establishment of gradients of protein activity [10] . We found that spatial heterogeneities in other processes such as synthesis and degradation are unlikely to overcome rapid rates of diffusive mixing , or perturb the steepness of gradients formed by rapid kinase/phosphatase activity . Therefore , to analyze the distribution of in the DivJ-DivK-PleC system , we focused on the biochemical kinetics of DivJ and PleC . Fluorescence loss in photobleaching experiments have established that DivK cycles between the poles within a 5-sec time scale , indicating rapid kinetics [20] . However , unlike the uniform fluorescence profile expected from a system relying purely on kinase and phosphatase activity , DivK-GFP levels increase at the poles [17] . These polar accumulations suggest that DivJ-mediated phosphorylation must actually be treated as a two-step process involving ( i ) binding of K to DivJ and ( ii ) subsequent release of . Furthermore , they also imply that phosphorylation kinetics are slow compared to the rate of binding , such that DivK spends a significant amount of time bound to DivJ at the pole; a similar conclusion applies for PleC-mediated dephosphorylation . Thus , we defined separate rate constants for binding and for catalysis ( which we assumed is immediately followed by release ) : and for binding and phosphorylation by DivJ , respectively , and and for binding and dephosphorylation by PleC , respectively . The magnitudes and spatial dependencies of these rates are regulated by the abundances and membrane distributions of DivJ and PleC and thus factor into the binding rates and ; that is , where DivJ is localized and 0 elsewhere , and where PleC is localized and 0 elsewhere . While polar accumulation may be achieved solely by direct binding of K or to DivJ and PleC , evidence that DivK-GFP accumulates at the swarmer pole even in the absence of PleC [20] suggests that DivK also binds to the swarmer pole in a PleC-independent manner that we will refer to as “polar binding . ” To account for this behavior , we defined additional binding and unbinding rate constants ( , respectively ) that are nonzero only at the swarmer pole . Given that the non-phosphorylatable mutant does not exhibit polar binding [16] , we assumed that the DivK binds to the flagellar pole only in its phosphorylated state . Likewise , a divJ mutant does not exhibit polar localization of DivK-GFP , indicating that DivJ-mediated phosphorylation is essential for DivK accumulation at the stalked pole [17] . Based on this evidence , we developed a minimal reaction-diffusion model for a pre-divisional cell considering two freely diffusing cytoplasmic species ( K and ) , and three non-diffusing , membrane-bound species: K bound to DivJ prior to phosphorylation ( K ) , bound to PleC prior to dephosphorylation ( ) , and bound to the flagellar pole independent of PleC ( ) . In the interest of keeping the number of parameters small , we assumed that the reverse reactions involving release of from DivJ without phosphorylation ( and release of from PleC without dephosphorylation ) are slow compared to the rates of phosphorylation and dephosphorylation , respectively , and therefore can be ignored . We modeled the distributions of these species along the length of a one-dimensional cell as shown schematically in Fig . 1A and represented mathematically as where at the swarmer pole and 0 elsewhere . We assumed that diffusion is not affected by the phosphorylation state of DivK . Table 1 outlines the parameters that were used in all simulations . Experimental measurements of the DivK-GFP distribution in wild-type and a variety of mutant cells were used to estimate the magnitudes of the six unknown catalytic and binding rates in our model . To determine the relative amounts of DivK protein bound to different regions of the cell , we synchronized a wild-type population and quantified the polar and midcell concentrations of DivK-GFP in late pre-divisional cells . From Eq . 3 , we expected that the steady-state ratio of polar , DivJ-bound K to freely diffusing K in the vicinity of the stalked pole to satisfy , indicating that should be larger than in order to observe polar accumulation . Although our fluorescence measurements could not distinguish between the two phosphorylation states K and , we estimated the pole-to-mid cell fluorescence ratio in wild-type cells as ( Fig . 1C ) . In simulations based on our model , we found that and achieved a similar pole-to-midcell ratio of DivK protein ( blue curve in Fig . 2 ) . Levels of DivK-GFP at the stalked and swarmer poles are roughly equivalent in pre-divisional cells; although it was not always possible to determine which was the stalked pole , a random ordering of the poles to determine the ratio gave a mean value of 0 . 9 and standard deviation 0 . 3 ( Fig . 1B ) . Therefore , we set the parameters associated with PleC kinetics equal to those of DivJ ( ) . Furthermore , DivK-GFP levels at the swarmer pole of a pleC::Tn5 mutant are comparable to those in wild-type cells [17] . This observation required for significant levels of protein to be bound to the pole in the absence of PleC . For DivK molecules to be able to cycle quickly between the poles , we set and . Given a diffusion constant m2/s , this choice of parameters ensured that the vast majority of the DivK population in a cell would be bleached in less than 5 seconds by a laser focused on the stalked pole ( Fig . S1 ) , as was previously measured experimentally [20] . Since the rates of phosphorylation and dephosphorylation cannot be considerably faster than diffusion and still result in polar accumulation [10] , we do not expect an appreciable midcell gradient of K and . Indeed , the steady-state solutions to Eqs . 1–5 for both K and have a nearly flat midcell distribution , with a peak in the total DivK distribution at the poles due to DivJ , PleC , and flagellar-pole binding ( blue curve in Fig . 2 ) . The peak in DivK protein at the swarmer pole is due to increased levels of , which would inhibit FPD in the pre-divisional cell . To validate our model , we altered the PleC/DivJ spatial distributions and/or kinetics to mimic several previously characterized DivJ and PleC mutants ( Fig . 2 ) [16] , [17] . For each mutant , we used our computational prediction of the spatial distribution of DivK to simulate a typical DivK-GFP fluorescence image using the software package BlurLab ( Methods ) , which generates simulated fluorescence microscopy data from 3D positions and intensities of fluorescent molecules [23] . In all cases , the predicted DivK-GFP distributions closely matched the corresponding experimental data ( Fig . 2 ) [16] , [17] , [20] . Thus , our model successfully integrates the activities of PleC , DivJ and polar binding and provides a framework for evaluating the potential developmental consequences of changes in protein localization and expression . Mathematical models provide a useful tool for predicting how genetic changes that affect localization and catalytic activity can be used to infer the spatial distribution of response regulator phosphorylation [10] . Here , we applied our model for the DivJ-DivK-PleC system to estimate the spatial distribution of in strains with altered DivJ kinase or PleC phosphatase activity . We considered scenarios that reflect three potential phenotypes of mutations in DivJ or PleC: ( 1 ) concentrated at opposite poles ( wild-type localization ) , ( 2 ) localized to the wrong pole ( “mislocalized” ) , or ( 3 ) uniformly distributed throughout the cell membrane ( “delocalized” ) . In addition to localization phenotypes , we also considered the effects of changes in expression levels , specifically to determine whether the predicted phenotype of a localization mutant could be altered by over-expression . In each case , we mimicked a mutant phenotype by changing the spatial distribution and magnitude of and in Eqs . 1–5 ( Table 1 ) . We explored seven specific hypothetical strains: ( 1 ) wild-type , ( 2–4 ) PleC mislocalized to the stalked pole at 1× , 5× , or 25× wild-type expression levels , ( 5 ) delocalized PleC , ( 6 ) DivJ mislocalized to the swarmer pole , and ( 7 ) delocalized DivJ . All of these strains are experimentally realizable . PleC has been shown to delocalize in the presence of mutations in podJ [24] , [25] , and DivJ has been delocalized by removing 326 N-terminal residues that do not contain the catalytic domain [26] . Furthermore , the localization domain of DivJ could potentially be used to create a chimera with the DivJ stalked-pole localization sequence and the phosphatase catalytic domain of PleC that would constitute a mislocalized PleC; a similar strategy could be employed to construct a mislocalized DivJ . Given that rapid kinetics are required to produce the wild-type fluorescence profile with significant polar accumulations of DivK-GFP , all species in our model reached their steady-state distributions on a time scale ( 10 sec ) much faster than the cell cycle . We therefore used our model to determine the steady-state distributions for each of the seven hypothetical localization mutants . We focused on both the expected distribution of DivK-GFP and the levels of polar-bound , before cytokinesis in the pre-divisional cell and after in the swarmer cell . Simulated fluorescence profiles based on our numerical predictions ( Fig . 3 , Methods ) can be directly compared with experimental measurements , while levels of bound at the flagellar pole can be used to predict the developmental phenotype of pre- and post-divisional cells . Specifically , numerical solutions of our model for a given set of parameters can be used to determine whether a change in localization or expression will cause an increase or decrease in the level of polar-bound relative to wild-type , and hence whether a given strain should exhibit FPD independent of cytokinesis , only after cytokinesis , or not at all . In some cases , simple physical considerations predict general trends in the distributions across the various strains independent of our choice of parameters . For the wild-type phenotype , cell division separates the source ( DivJ ) and the sink ( PleC ) , eliminating the polar accumulation of and thereby allowing FPD ( Figs . 3 , S2 ) [22] . When PleC is mislocalized , PleC and DivJ compete to determine the phosphorylated fraction of DivK at the stalked pole , and the rest of the cell maintains constant levels of K and due to diffusion ( Fig . S2 ) . levels can thus be modulated by the expression level of mislocalized PleC , with lower and levels for higher PleC levels ( Fig . S2 ) . After cytokinesis , the uniform midcell distribution of remains unchanged in both daughter cells since DivJ and PleC are not separately compartmentalized ( Fig . S2 ) . Thus , when PleC is mislocalized , we expect the FPD phenotype to be unaffected by cytokinesis , and sufficiently high levels of PleC expression should reduce the levels at the swarmer pole enough to initiate premature FPD prior to cytokinesis . Mislocalization of DivJ has a similar behavior with uniform levels of K and throughout the cytoplasm ( Fig . S2 ) . Delocalization of PleC alters the level of polar-bound in the pre-divisional cell , but upon cytokinesis the swarmer cell again has PleC without DivJ , which should eliminate the polar and allow FPD to progress similar to the wild-type case ( Figs . 3 , S2 ) . In contrast , delocalization of DivJ increases the levels of at the swarmer pole , since the source of DivK phosphorylation has been shifted closer to the swarmer pole . For this strain , we also predict a significant reduction in the total levels of DivK-GFP at the stalked pole , resulting in a more uniform cytoplasmic distribution . After cytokinesis , the swarmer cell has both a sink and a source of , and hence the level of polar-bound should depend on the relative catalytic activities of DivJ and PleC . Using numerical simulations of our reaction-diffusion model , we quantified the overall DivK distribution and the polar-bound concentration ( ) in each mutant . Figure 3 shows simulated DivK-GFP fluorescence intensities for the strains described above ( Methods ) ; details and quantification of the respective concentration distributions can be found in the Supplemental Information ( Fig . S2 ) . Even without experimental quantification of the total DivK protein levels , we can interpret the levels relative to wild-type , with higher levels more unlikely to exhibit FPD . Based on the levels of before and after cytokinesis ( Fig . 3B ) , we predict that in the pre-divisional cell , only PleC mislocalized and over-expressed by 5× ( ) and 25× ( ) are likely to exhibit FPD , while the post-divisional wild-type ( wt ) and delocalized PleC ( ) cells will develop normally ( Fig . 3B ) . Although the parameters we used in our model were motivated by experimental data ( Figs . 1 , 2 ) , we were interested to determine whether the predicted ordering of levels across our hypothesized localization/expression strains ( Fig . 3B ) , and therefore the predicted FPD phenotypes , would be robust to changes in the kinetic parameters . To do so , we perturbed the rates ( ) over a wide physiological range and numerically determined the steady-state levels of pre- and post-division . For a subset of the strains , the relative ordering of could be readily inferred from physical considerations ( Figs . S2 , S3 ) . For instance , we predict that a strain with PleC over-expressed 25× will always have lower than a wild-type cell: over-expressing the sink will deplete the cell of , and thus the cell will always be more likely to initiate FPD , independent of cytokinesis . However , the relative ordering for many strains is nontrivial due to the competition among DivJ , PleC , polar binding , and diffusion; hence our mathematical model was critical for quantitatively analyzing the system kinetics . We explored the parameter space by scaling each rate constant by a random factor selected logarithmically between 0 . 25 and 4 and calculated the levels of for all hypothesized localization/expression phenotypes , before and after cytokinesis . Although the absolute levels of varied substantially due to changes in the overall kinase or phosphatase rates , the specific ordering of polar-bound levels in pre-divisional cells was identical to that in Fig . 3B for each of 10 , 000 random parameter sets ( Fig . S3 ) . Therefore , given that wild-type cells do not exhibit FPD when cytokinesis is blocked [20] , we predict that all hypothesized strains with higher than wild-type levels of ( delocalized DivJ , 1× mislocalized PleC , and mislocalized DivJ ) will also not undergo FPD before the completion of cytokinesis . Furthermore , simulations of the delocalized PleC mutant resulted in levels very close to wild-type ( for this strain , 50% of the parameter sets yielded levels within 10% of wild-type ) , and thus we also predict that these cells will not undergo FPD pre-cytokinesis . On the other hand , we predict that sufficiently over-expressed , mislocalized PleC strains will have at a low enough level to allow FPD pre-division . For 99 . 98% of the parameters sets , the levels in the mutants with PleC mislocalized and over-expressed 25× harbor levels less than 25% of wild-type . The ordering of levels in simulations of the swarmer cell after compartmentalization is even more pronounced , with wild-type and delocalized PleC strains exhibiting the lowest levels , followed by mutants with 25× and 5× mislocalized PleC . We predict that cells with delocalized DivJ will have higher levels than all strains but mislocalized DivJ or PleC . As in pre-divisional cells , levels in the mislocalized PleC and mislocalized DivJ mutants have a flat distribution between the poles that is unaffected by division . Given that wild-type cells initiate FPD after cytokinesis , we predict that mutants with delocalized PleC will develop normally . Mutants with over-expressed , mislocalized PleC should show the same developmental phenotype in the absence of cytokinesis , and the extremely low levels of polar-bound in cells with 25× over-expression of mislocalized PleC suggest that such a strain should exhibit FPD independent of cytokinesis . However , the levels of for mutants with either delocalized DivJ or normal expression levels of mislocalized DivJ/PleC are not significantly reduced by cytokinesis to levels lower than pre-divisional wild-type cells , and hence we predict that these mutants will not exhibit FPD . To mimic the effects of PleC and DivJ gene expression noise on our predicted FPD phenotypes , we varied and between 0 . 7 and 1 . 5 times the values used in Fig . 3 and plotted relative to the wild-type levels in the pre-divisional cell before cytokinesis ( Fig . 4 ) . The only strain whose phenotype is likely to be affected by noise is the PleC delocalized mutant , which , if PleC levels are high and DivJ levels are low , could have low enough levels to undergo development pre-cytokinesis . Experimentally , this effect may manifest experimentally as a range of developmental phenotypes in cells from this mutant strain . Taken together , our simulations predict a systematic ordering of levels that is independent of the choice of parameters . This ordering corresponds to predictions of the developmental outcomes for many of the strains considered here . Moreover , our simulations indicate that the creation of a mutant with mislocalized PleC under inducible expression would provide insight into the quantitative relationship between the polar-bound concentration and FPD progression by revealing the largest concentration that still allows FPD . In contrast , we predict that the other strains have levels of that lie at more extreme ends of the developmental spectrum , indicating a more clearcut FPD phenotype . Recent biochemical evidence has suggested that PleC is bifunctional , with acting as an allosteric regulator to switch PleC from a phosphatase to an autokinase incapable of dephosphorylating [21] . To investigate whether this additional regulation is likely to play a role in controlling FPD in vivo , we modified our model to include an additional loop in which a complex of and PleC can result in the switching of PleC into an autokinase with deactivation rate . To ensure that a small number of molecules cannot switch the entire PleC population into autokinases , this deactivated form must reactivate to a phosphatase , which we assumed occurs spontaneously with rate ( Fig . 5A , Table 1 ) . Our modified model ( the “PAR” model ) thus considers the subdivision of the PleC population ( ) into three species: unbound PleC in its active ( ) and deactivated ( ) forms , and active PleC bound to ( ) . DivJ is assumed to always be active as a kinase and is represented by . Whereas in Eqs . 1–5 the expression levels of PleC and DivJ were incorporated into the rate constants , we now explicitly consider the concentrations of PleC and DivJ such that all rate constants represent intrinsic properties of the respective intermolecular interactions ( Fig . 5A ) : When PleC and DivJ levels are comparable to or greater than DivK levels , the system behaves similarly to the non-allosteric model ( compare Figs . S2 and S4 ) , since there is always a pool of active PleC to dephosphorylate . In contrast , when PleC levels are very low , the PleC kinetics are similar to those of a saturated enzyme . Importantly , one effect of decreasing the PleC and DivJ concentrations relative to DivK is the reduction of polar accumulation: PleC concentrations of less than 20% of the total DivK concentration lead to pole-to-midcell ratios less than our experimentally observed values ( Fig . S5 ) . Therefore , to maintain our experimentally observed pole-to-midcell ratio of DivK-GFP , we hereafter study the regime in which overall PleC and DivJ levels are similar to DivK levels . In this regime , our PAR reaction-diffusion model with with and again reproduces the experimental fluorescence data for the strains described in Fig . 2; in particular , our model shows the expected polar accumulation of DivK-GFP relative to midcell in a wild-type cell ( Fig . S6 ) . Next , we explored how the PleC deactivation and reactivation rates affect the accumulation of at the pole . Varying the reactivation rate in the PAR model did not correlate significantly with changes in the wild-type concentration profile ( Fig . S7A ) ; in contrast , increasing the deactivation rate lowered the total amount of and bound to the poles ( Fig . S7B ) . As more PleC became inactive , less was dephosphorylated , decreasing ; as the fraction of PleC that was available to bind dropped , decreased until all PleC ended up inactivated by the large pool ( Fig . S7 ) . Such high levels combined with low polar accumulations and are not consistent with experimental evidence from the fluorescence experiments previously described ( Fig . 2 ) , justifying our selection of the deactivation and reactivation rates at intermediate values of 1/s . In this regime the DivK pole-to-midcell ratio is close to 3 ( Fig . 5B , C ) , as we observed experimentally ( Fig . 1C ) . A higher reactivation rate would not affect the polar ratio; however , in the limit of very high the PAR model is reduced to the Ping-Pong model , as all of the PleC pool would be active , equivalent to having large levels of PleC compared to DivK . Similar to our analysis of the Ping-Pong model , we ordered the levels of each strain in the PAR model to infer the FPD phenotype ( Fig . S8 ) . In the PAR model , the ordering was slightly changed compared to the Ping-Pong model , but our overall conclusions were not affected ( Figs . S8 , S9 ) . We still expect the DivJ delocalized , PleC mislocalized , and DivJ mislocalized mutants to not exhibit FPD either before or after cytokinesis . In contrast to the Ping-Pong model , the PleC delocalized mutant could undergo FPD prior to cytokinesis if the PleC reactivation rate is very fast ( Fig . S10 ) . This scenario causes more PleC to participate in a futile deactivation/reactivation cycle without dephosphorylating DivK , leading to higher overall levels in all mutants . However , the PleC delocalized mutant have the least relative increase in upon a reactivation rate increase among all the strains , since this effect is spread out over the cell instead of being concentrated at the swarmer pole . The opposite effect is predicted to occur when the reactivation rate decreases in the PleC mislocalized and over-expressed mutants , whose levels increase relative to wild-type . In the post-divisional swarmer cell , the relative ordering is very similar to the non-allosteric , Ping-Pong model ( compare Figs . S3 and S9 ) . In the PAR model , we also predict FPD in the PleC delocalized mutant and in the the 25× and 5× PleC mislocalized mutants . Overall , ordering is the same as the Ping-Pong model , except that the PleC mislocalized and over-expressed mutants have increased levels relative to the wild-type as the reactivation rate decreases , which causes the DivJ delocalized mutant to appear lower in the ordering . Similar to the Ping-Pong model , the DivJ delocalized , PleC mislocalized , and DivJ mislocalized mutants did not experience a large reduction in levels after cell division , and hence these strains are not expected to undergo FPD . Our simulations predict that PleC allosteric regulation should have little effect on most conclusions of the Ping-Pong model , with the possible exception of a delocalized PleC mutant that should be informative about the strength of PleC regulation by in vivo .
Sub-cellular localization of histidine kinases in bacteria can give rise to asymmetries in response regulator activities , thereby creating the basis for differential developmental outcomes [10] . Here , we have developed a reaction-diffusion model that predicts that the distributions of the phosphorylated and unphosphorylated DivK species are relatively homogeneous in the pre-divisional cytoplasm of Caulobacter cells . accumulation at the swarmer pole allows for tight prevention of FPD and this inhibition is lost once the cell divides and the phosphatase PleC is left alone to dephosphorylate . A gradient is not required in this system; instead , the asymmetric localization of the kinase DivJ and phosphatase PleC promotes a switch-like behavior . Importantly , although division enhances the asymmetry to ensure switch-like dephosphorylation of , if the levels of are kept low enough by over-expression of PleC , we predict that division is not required to activate FPD . Our computational model reproduces experimental DivK-GFP fluorescence data from seven DivJ , DivK , and PleC mutant strains with a single set of binding and enzymatic rates ( Fig . 2 ) . Given this validation , we applied our model to predict levels for several mutant localization phenotypes . We have shown that our predictions are independent of the choice of rates , indicating that our model makes general predictions regarding the developmental phenotype of each mutant strain . Comparison of the Ping-Pong and PAR models of DivK steady-state levels revealed that the behavior of the DivJ-DivK-PleC system is not significantly affected by the allosteric regulation of PleC as long as PleC deactivation does not dominate the system dynamics . If the PleC deactivation rate is very high ( or conversely , if the PleC reactivation is very low ) , only the PleC delocalized mutant is significantly affected relative to wild-type in the pre-divisional cell ( Figs . S7 , S9 ) . In general , all strains are expected to lack FPD in the pre-divisional cell except for mutants with 25× and 5× over-expressed and mislocalized PleC . On the other hand , in the post-divisional swarmer cell , all strains are expected to develop at the flagellar pole except the PleC mislocalized , DivJ mislocalized , and DivJ delocalized strains . The observation that random variations in model parameters do not affect our conclusions ( Fig . 4 ) indicates that the DivJ-DivK-PleC system is robust to noise and fluctuations in catalytic rates and expression levels . It is possible that allosteric regulation of PleC is required for other downstream reactions during development . In this case , our work then reveals that experiments focusing only on DivK regulation are insufficient to fully explore the origins of PleC localization and regulation . Our analysis also suggests experiments that would illuminate the mechanism underlying regulation of FPD . Tunable expression of mislocalized PleC would allow the characterization of the levels required to switch between development and non-development . We predict that this mutant should not change developmental state upon division , and by titrating the induction levels it should be possible to determine the expression threshold for FPD , with the flagellar-pole-localized levels of varying inversely with expression levels of stalked-pole-mislocalized PleC ( Fig . 6 ) . This result also highlights the non-trivial complementarity of localization and expression levels in this system , via the novel prediction that the phenotype caused by mislocalization of PleC can be rescued by its over-expression . Our study reinforces the importance of mathematical modeling for deconstructing complex biological networks , and raises questions regarding the importance of the simultaneous localization of DivJ and PleC , particularly in light of a potential gene duplication event that may have led to the specialization of these two histidine kinases with 45% amino acid sequence similarity [27] , [28] . Moreover , the robustness of FPD to changes in localization and expression levels would allow the cell to alter kinase localization to address other functions such as stalked-pole development without disrupting swarmer pole events . Given that distributions of similar to wild-type can be achieved when either PleC or DivJ are delocalized ( Fig . 3 , S2 ) , their polar accumulation may indicate that other downstream events and reactions require a high local concentration of these kinases for stalked-pole development; in this study , we have investigated only one aspect of development and PleC and DivJ are known to participate in multiple developmental events . Finally , our simulations suggest that synthetic biological systems with cellular asymmetry could be constructed without requiring the full complexity of their natural counterparts .
C . crescentus CJ403 expressing DivK-GFP was grown as described previously [29] . Cells were synchronized using Percoll density centrifugation [30] . Synchronized swarmer cells were resuspended in peptone-yeast extract ( PYE ) medium to an of 0 . 2–0 . 3 and imaged on 1% agarose pads . Imaging was performed on a Nikon Eclipse Ti-E inverted microscope with a Nikon Plan Apo 100× objective ( numerical aperture of 1 . 4 ) running Manager . Cell boundaries and fluorescence linescans along the longitudinal axis of the cell were determined using MicrobeTracker [31] . In Fig . 1C , for each cell we computed the average fluorescence excluding the poles , and normalized the intensity profile to this mid-cell average . After normalizing position along the midline to the cell length , we computed the mean fluorescence profile along the normalized coordinate from 0 to 1 . The variability was defined as the maximum and minimum normalized intensity at each position along the cell midline . In order to estimate the rate of DivK diffusion , we used Monte Carlo simulations to calculate the minimum diffusion constant required to ensure that a given percentage of a uniformly distributed population would be photo-bleached within 5 seconds , where a particle is considered bleached if it approaches within 300 nm of the left pole of the cell ( Fig . S1 ) . For , 99% of the molecules would be photo-bleached within 5 seconds , similar to experimental observations for DivK-GFP [20]; this rate compares favorably with the experimentally measured diffusion constant of a maltose-binding protein in Escherichia coli [32] . Increasing increases the rate at which K and find their binding partners , thereby increasing the fraction of DivK bound to PleC , DivJ , and to the pole . In the various localization strains , this scenario resulted in increased , particularly for the DivJ delocalized , wild-type , and PleC delocalized strains , but the ordering was maintained . The mislocalized mutants were not affected , since the distribution is flat and hence is not sensitive to changes in . Unless otherwise noted , we use , , and ( Table 1 ) . In Fig . 2 , to simulate the non-catalytic interaction of K and with DivJ and PleC , we set the catalytic rates /s and 0/s , respectively . K and are then released after binding with rate s and s without change in their phosphorylation state . In the PAR model , we include ( Table 1 ) . Furthermore , given that DivK accumulates at both poles in the absence of PleC [17] , we infer the activity of a background phosphatase with rate . We exclude the possibility of a significant background kinase since a DivJ mutant that has no catalytic activity ( DivJ ) does not exhibit DivK-GFP accumulation at the swarmer pole [16] . We have previously shown that background kinase and phosphatase activity does not significantly affect response regulator distributions [10] . In Figs . S5 , S7 , S10 the simulations were carried out varying only the concentrations of DivK ( ) and PleC ( ) , and . Table 1 provides the kinetic parameters and localization profiles in the simulations of each strain . We used the software package BlurLab [23] to generate simulated microscopy images of DivK-GFP from 1D computational distributions . To create the 3D positions of fluorescent molecules within a pre-divisional cell , we modeled a crescent-shaped cell as a bent cylinder 3 . 5 m in length and 0 . 5 m in diameter with a radius of curvature of 1 . 5 m and hemispherical poles , for a total length of 4 m . Swarmer cells were modeled as a bent cylinder 1 . 1 m in length and 0 . 5 m in diameter with a radius of curvature of 1 . 5 m and hemispherical poles for a total length of 1 . 6 m . Within these volumes we positioned 10 , 000 molecules in the pre-divisional cell ( 4 , 000 in the swarmer cell ) so that their spatial distribution matched the 1D concentration profiles from our computational modeling . The coordinates were then used by BlurLab to compute the expected fluorescence distribution utilizing a point spread function for a 100× objective with numerical aperture 1 . 4 . | The development of multicellularity requires specialization and differentiation of individual cells . The process of differentiation requires the breaking of cellular symmetry , which can be achieved via asymmetric localization of proteins; cell division then gives rise to cells with different compositions and hence potentially different fates . However , little is known regarding the classes of changes in protein localization a cell can tolerate without disrupting development . Caulobacter crescentus is a model bacterium that , following cell division , gives rise to two differentiated daughter cells , only one of which is flagellated and motile . This process is regulated by two proteins , PleC and DivJ , located at opposite ends of the mother cell . Using computational modeling , we investigate the robustness of flagellar pole development to protein localization changes . Our analysis suggests that the development of C . crescentus is robust to a wide range of localization changes of PleC and DivJ . Furthermore , certain mutant localization patterns that would disrupt development can nevertheless support it when the mislocalized protein is present at higher abundance relative to wild-type . Our analysis highlights informative localization mutants that can be used to further deconstruct the requirements for C . crescentus development , and addresses the general requirements for protein localization in developmental processes and for engineering differentiation in biological systems . | [
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| 2012 | Interplay between the Localization and Kinetics of Phosphorylation in Flagellar Pole Development of the Bacterium Caulobacter crescentus |
Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data . However , most existing methods do not consider signal integration represented by pathway topology , resulting in enrichment of convergent pathways when downstream genes are modulated . Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes . This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization . Here , we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data . We introduce a new algorithm , Boolean Omics Network Invariant-Time Analysis ( BONITA ) , for signal propagation , signal integration , and pathway analysis . Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity , and propagates binary signals repeatedly across networks . Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search . The rules determine the impact of each node in a pathway , which is used to score the probability of the pathway’s modulation by chance . We have comprehensively tested BONITA for application to transcriptomics data from translational studies . Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation . Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies . Additionally , BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls . Most interestingly , the two highest impact score nodes identified by BONITA included known drug targets . Thus , BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods . BONITA is available at: https://github . com/thakar-lab/BONITA .
Gene set and pathway analysis have become one of the first choices for gaining mechanistic insights from high-throughput sequencing and gene/protein profiling techniques [1] . Typically , gene set analysis uses a set of pathway genes to estimate its modulation and discounts pathway topology . This approach ignores synergy among genes , resulting in enrichment of convergent pathways when downstream genes are modulated . Though none of the existing methods explicitly investigate synergy among genes , current topology-based methods use graph theoretical metrics to weigh pathway nodes based on connectivity before estimating pathway modulation [1–3] . However , it is critical to go beyond this simple characterization in order to identify key regulators from large-scale datasets for systematic prioritization of follow-up experiments . Discrete state network modeling facilitates prioritization of experiments by using simple logic rules such as ‘AND’ or ‘OR’ to explicitly define signal integration , enabling investigation of cross-talk and downstream events as shown in our previous studies . [4–6] . Discrete state network modeling has been used to study high throughput gene and protein profiling data collected across multiple time-points by utilizing two different underlying models of variation [7 , 8] in addition to conventional Boolean modeling . Fuzzy models explain variation in the gene expression levels using multiple states , unlike Boolean models that allow only binary ( on/off ) states . Recently , fuzzy models have been used to study literature-derived prior knowledge networks using a genetic programming algorithm to derive logic rules from time course data by Liu et al . [9] . Probabilistic Boolean Network models assume that variability arises from ambiguity in logic-rules employed rather than in amount of activation [10] , making the counterintuitive assumption that cells randomly employ one of multiple different wirings . Many biological insights have resulted from fuzzy network [11 , 12] and Probabilistic Boolean Network [13 , 14] models , but there remains great potential for improvement in describing variation and improving applicability to cross-sectional datasets . Unlike time course data , cross-sectional data is collected from multiple samples ( and possibly conditions ) at a single time point providing minimal information about interactions between genes . Indeed , cross-sectional sampling is more feasible in translational studies and algorithms that derive discrete state network models from this data type would have greater applicability in translational research . Here , we describe BONITA- Boolean Omics Network Invariant-Time Analysis , to capture cellular heterogeneity , a critical source of variability in transcriptomic data . A portion of variance in gene expression stems from heterogeneity in the activation state of cells in addition to variation in expression levels within each cell . This is demonstrated by gene expression in multiple stem cell types [15] [16] and stimulated bone marrow-derived dendritic cells [17] . BONITA is designed specifically to leverage this bimodality in cell-specific gene expression to perform continuous-valued simulations of molecular networks under assumptions of switch-like behavior in each cell . Hence , BONITA network propagation ( NP ) assumes that the activity of each biomolecule is directly dependent upon the proportion of cells in which that molecule is active or , equivalently , the probability a node is active in an arbitrary cell . The propagation of signals across multiple cells facilitates the application of NP to the cross-sectional data . Since this NP approach should recapitulate steady states in cross-sectional data , BONITA rule determination ( RD ) finds rules that minimally change activities after NP . These logic rules representing synergy between genes from cross-sectional data are utilized in BONITA pathway analysis ( PA ) . Thus , by capturing integration of signals coming from multiple genes , BONITA uncovers differentially regulated pathways . BONITA is currently implemented and tested for application to transcriptomics data , but work is under way to apply it to other types of data including proteomics , metabolomics , and phosphoproteomics . BONITA is rigorously tested using simulated data and is applied to publicly available experimental datasets . In addition , a comparison of BONITA-RD to an existing algorithm for time-course data [9] shows comparable performance for cross sectional data , improving applicability to translational studies . Moreover , comparison of BONITA-PA with state-of-the-art pathway analysis methods CAMERA [18] and CLIPPER [3] shows exceptional Receiver Operating Characteristic ( ROC ) and higher specificity in detecting signaling modulations in validated experimental studies . Finally , when applied to disease specific data from patients vs healthy humans , BONITA impact scores identify known drug targets as key regulators . This suggests that BONITA can be used for drug discovery from large-scale high-throughput datasets .
BONITA network propagation ( NP ) runs on prior knowledge networks obtained from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) using the KEGG API . Activating/inhibiting relationships are inherited from KEGG edge attributes [19] . Edges in KEGG pathways contain edge type annotations; these are exploited to determine activating or inhibitory edges . Hence , all the Boolean functions inferred by BONITA are sign-compatible functions , i . e . , they satisfy positive or negative unateness based on the interaction annotation , as described in Zhou et al [20] . We demonstrate in S1 Text and S1 Fig that BONITA-NP infers these sign-compatible functions in an unbiased manner . BONITA-NP assumes that the mRNA-producing cells are proportional to counts obtained from mRNA-sequencing . To obtain the proportion of cells expressing mRNAs , the RNA-seq data is transformed to [0 , 1] domain using division by the maximum element . This transformed data is used as a starting point to compute a series of Boolean Network simulations using synchronous or asynchronous update algorithms as described in [21] . The ensemble averages of 1000 such repeated runs are used to define activities which are compared with the transformed data to determine fitness value in BONITA-RD below . Comparison of methods for data transformation to [0 , 1] demonstrated that division by maximum was the best method for transforming data and that BONITA-RD , as expected , has better fits than purely Boolean simulation ( S2 Text , S2 Fig ) . In this report , all BONITA-NP simulations were carried out for 100 steps using the synchronous update algorithm . The maximum number of steps necessary to reach the steady state or terminal cycle of the Boolean network is the longest path between any two nodes in the network . The longest shortest path between all pairs of nodes across KEGG networks was 17 ( S3 Text , S3 Fig ) indicating that 100 simulation steps were adequate . The results were reported as average over the last ten steps of the simulation . BONITA-RD implements a combination of a genetic algorithm and a node-wise local search to infer logic-rules . BONITA assumes cross-sectional samples represent steady states and minimizes change after simulation of a network as given by: ∑ i = 1 d 1 n ∑ j = 1 n ( D i , j - O i , j ) 2 ( 1 ) In Eq 1 , d is the number of available samples , n is the number of nodes in the network , Di , j is the value of node j in sample i , and Oi , j is the value of node j in sample i calculated by BONITA-NP . The overall design of the rule determination algorithm is graphically represented in Fig 1 . The genetic algorithm generates new rule sets ( individuals ) either by selecting rules for randomly chosen nodes from their parent rule sets , or by mutating ( altering ) a particular rule and incoming nodes . At later generations , crossover events tend to produce rule sets that have already been tried in earlier generations , leading to a greater probability of mutations . The space of potential rules is extremely large and scales quickly with in-degree . Hence , to reduce the space of potential rules to a region that can be sampled , a maximum of three upstream regulators are selected . This is a compromise between decreasing resolution and increasing search time . The three upstream regulators ( U ) are sampled for nodes with >3 upstream regulators in the genetic algorithm using a probability function P ( U ) = C U , N ∑ U C U , N where CU , N is the Spearman correlation of upstream regulators with the node ( N ) for which the rule is being determined . For all simulations shown in this report , the genetic algorithm was run for 120 generations from a starting population and constant population size of 24 . Thus , 24 new rule sets were generated and tested at each generation . Decreasing errors ( Fig 2a ) with a plateau before 40 generations for networks with varying complexities indicated that 120 generations are appropriate for the genetic algorithm . The genetic algorithm searches the product of the number of possible rules at each node in the network . In order to transform this multiplicative problem into an additive one , a node-level local search strategy was implemented . The local search only considers the error at the node under consideration as given by ∑ i = 1 d ( D i , j - O i , j ) 2 ( 2 ) This exhaustive search only evaluates the possible rules at each node while holding constant all other rules as well as the incoming edges to that node as determined by the genetic algorithm . The node-level local search was initiated with the minimal error rule set from the genetic algorithm and was found to be effective in inferring the rules as shown in the results ( Fig 2b ) . During the local search , rules within a tolerance threshold of this minimal rule are kept as equivalent rules i . e . the equivalent rule set ( ERS ) . This set was constructed to overcome the inability to distinguish between equivalent rules with cross-sectional data . Thus , while local search improves accuracy , it is dependent on the global search performed by the genetic algorithm to resolve the complexity of the networks ( S2 Text , S2 Fig ) . To test BONITA-RD , simulated data representing 5 samples was generated by BONITA-NP with a rule set and initial states determined by a uniform random distribution . Rules determined by BONITA-RD were then compared with the rule-set used to generate the data . BONITA-PA seeks to prioritize nodes that have a large influence over signal flow through the network by assigning node-level impact scores . The impact score , Ig , captures the change induced in the network when the node is perturbed . Ig is given by the difference in network state after knockout and knock-in of g: I g = ∑ i = 1 d ∑ j = 1 n ( ( O i , j - Z i , j ) 2 ) ( 3 ) In Eq 3 , j ranging from 1 to n indicates nodes in the network , i ranging from 1 to d indicates samples , Oi , j and Zi , j are BONITA-NP outputs when g = 0 and g = 1 across all iterations , respectively . The comparison of BONITA-PA’s node impact score with graph theoretical measures of node centrality such as degree centrality , eccentricity , shortest-path betweenness , eigenvector centrality and the hubscore and authority scores obtained from the hyperlink-induced topic search algorithm showed no correlation ( S4 Text , S4 Fig ) . The pathway modulation is measured by taking into account impact score and fold difference in the expression across conditions of interest . Specifically , Mp is calculated as follows: M p = ∑ 1 n l o g ( I g ) * | l o g ( q g ) | * s t d ( g ) ( 4 ) In Eq 4 , qg is the fold difference of g and std ( g ) is the standard deviation of g across all samples . To calculate the p-value , a distribution of nodes with different impact scores having a range of fold differences is generated . Specifically , the distribution of Mp values is generated by weighting impact scores for a specific pathway’s topology with random fold differences that are re-sampled from the gene expression data . Pathways with at least four genes in the transcriptomic data are considered . To compare BONITA-PA with existing pathway analysis approaches , simulated datasets that resembled biological data were constructed . The data was generated using a negative binomial distribution with gene-wise means and dispersions from existing RNA-seq data [22] . To simulate the modulation of pathways , the expression levels of source nodes were multiplied by log2 ( -attenuation ) where attenuation values were 0 . 0 , 0 . 5 , 1 . 0 , 1 . 5 , and 2 . 0 as described in Ihnatova et al [2] . This attenuated signal was propagated by BONITA-NP with random rules to simulate inhibition of the entire network mediated by source node inhibition . To test the performance of BONITA- RD , a subset of networks were obtained by searching the KEGG database for Interferon Gamma ( IFN-γ ) . These 12 networks were used as test networks since they provide an unbiased set of networks with varying complexity , ranging in size from 13 to 346 nodes . Signal attenuation and propagation was performed 10 times each on the 6 test networks with nodes ( genes ) in the RNA-seq data , and analysis performed using CLIPPER , CAMERA , and BONITA . CLIPPER determines modulated pathways based on mean and concentration ( the inverse of the covariance ) matrices . However , for simulation studies , only p-values from CLIPPER comparison of means and not concentration matrices were considered since this improved performance of CLIPPER substantially . BONITA was rigorously assessed using RNA-seq data . First , BONITA was compared with state-of-the-art pathway analysis approaches using data from the public domain . Second , BONITA’s specificity in detecting disease specific pathway from patient data was investigated . Finally , BONITA’s ability to infer rules from a de novo directed network constructed was evaluated . Comparison of BONITA pathway analysis with CLIPPER and CAMERA was performed using previously published RNA-sequencing data measuring IFN-γ signaling modulation in human choriocarcinoma cells [23] and a study representing translational design where peripheral blood mononuclear cells from infants with mild or severe respiratory syncitial virus were assessed by RNA-sequencing [22] . Data was processed using voom [24] for CAMERA or CLIPPER . A set of 37 immunologically relevant KEGG pathways identified in previous studies were utilized because RSV infection and IFN-γ stimulation are expected to modulate these pathways [34] . Furthermore , a priori selection of biologically relevant pathways reduces the requirement for correction for multiple comparisons . Data from all studies were processed in R . To test whether BONITA identifies disease specific pathways , microarray gene expression data from a set of 36 experiments comparing patients to healthy controls in 15 unique diseases was analyzed [2 , 25 , 26] . Previously RMA normalized and log2 transformed microarray data was downloaded and was processed to keep probe ID with highest mean expression for each gene symbol . The data was exponentiated with base 2 before running BONITA . The data was retrieved from Gene Expression Omnibus ( GEO ) using R packages KEGGandMetacoreDzPathwaysGEO and KEGGdzPathwaysGEO [25 , 26] . BONITA was applied to KEGG networks associated with each disease in the data set . Finally , a de novo directed network was generated by application of miic [27] to RSV data . Miic constructs directed networks by inferring a coexpression network using mutual information . Edges with higher cumulative mutual information than alternative paths are retained and directed based on topological characteristics . For edges that remained bidirectional , two edges , one going in each direction , were inserting before running BONITA-RD . BONITA is written entirely in Python and C using genetic algorithms from deap [28] . It has been tested for use with Intel Distribution for Python 2 . 7 . Generation of network representations of rules was performed by modification of previously published code from the Albert Lab [29] . BONITA is designed to be run from the command line by a non-expert user . Code and documentation are available on Github at https://github . com/thakar-Lab/BONITA .
BONITA Network Propagation ( BONITA-NP ) propagates continuous-valued signals across molecular networks with the assumption that bulk transcriptomic measurements are proportional to the number of cells expressing specific genes . The signal propagation depends on the inference of logic rules performed by BONITA rule determination ( BONITA-RD ) , which is optimized to preserve steady states assumed to be represented by the cross-sectional data . The logic rules define integration of signals coming from different genes . To test the performance of BONITA-RD , a subset of networks were obtained by searching the KEGG database for Interferon Gamma ( IFN-γ ) . These 12 networks were used as test networks since they provide an unbiased set of networks with varying complexity , ranging in size from 13 to 346 nodes . Simulated data representing cross-sectional measurements were generated for each test network using BONITA-NP and were used as inputs for BONITA-RD . Rules recovered from BONITA-RD were compared to the rules used to generate simulated data . BONITA recovered exact rules used to generate the simulated data with 50% accuracy across test networks . However , multiple logic rules can result in similar cross-sectional outcomes . Hence , the multiple logic rules that produce equivalent cross-sectional outcomes were treated as ‘equivalent’ rule sets ( ERS ) ( see Methods ) . ERS facilitated evaluation of accuracy of BONITA-RD within the limits of cross-sectional data . BONITA-RD accuracy reached 87—99% when considering ERS among test networks ( Fig 2b ) . The size of the ERS depicting number of rules in the set varied from just 1 rule to all possible rules ( 1 , 15 and 127 for in-degree 1 , 2 and 3 , respectively ) . The size of the ERS was expected to be dependent on signal flow from the shared upstream nodes . Since signal could flow directly or indirectly from such nodes to the node of interest , it is theoretically impossible to distinguish them with cross-sectional data . Consider a network with 3 nodes , A , B , and C , and with edges from A to B , A to C , and from B to C . Under no circumstances will cross-sectional data reveal whether changes in A are propagated to C directly , via B , or both . We wanted to understand whether the size of the ERS was driven by such unsolvable equivalences . To identify these situations , cases where a single node ( like A in the network described above ) could influence two incoming edges were enumerated . To this end , the sum of intersection between nodes influencing the signal along each pair of incoming edges was calculated as the sum of the shared ancestors between pairs of upstream nodes U of the node under investigation . This total ancestor overlap is given by ∑ U 1 ≠ U 2 | A ( U 1 ) ∩ A ( U 2 ) | , for all such pairs of upstream nodes U where A is the set of all ancestors of U . The total ancestor overlap and the size of the ERS were highly correlated ( Fig 2d , Spearman r = 0 . 947 ) , demonstrating that alternative paths that were indistinguishable in cross-sectional data lead to unsolvable rules and consequently larger ERS sizes . The strikingly high accuracy across diverse networks when considering ERS demonstrates that BONITA rule inference can correctly infer rules to the extent they are distinguishable by cross-sectional data . Next , we investigated the impact of network complexity on BONITA-RD . To assess the impact of network size BONITA-RD accuracy was compared with the number of nodes in each test network . Though the test networks have a wide range of sizes , node numbers did not explain differences in accuracy across networks ( Fig 2b ) . To further understand the differences in accuracy , we hypothesized that the accuracy of ERS would be associated with in-degree . Though BONITA-RD restricts the in-degree to 3 , decreasing accuracy with increasing original in-degree ( Fig 2c , Spearman r = -0 . 851 ) was observed . The in-edges are optimized by BONITA-RD , which could compound the inaccuracies introduced by size of the rule space for the nodes with 3 incoming edges . These findings indicate that even when BONITA achieves >80% accuracy , the nodes that are incorrectly inferred have high in-degree in original network . Having established the ability of BONITA-RD to recover rules from large-scale data , we wanted to establish BONITA’s robustness to other important factors in transcriptomic data: sample number and technical noise . Susceptibility of BONITA to technical noise was investigated by adding random noise in the range 1-200% for each node in the network . The BONITA-RD accuracy remains >80% with up to 10% noise in the data ( Fig 3a ) , however accuracy was 65-91% when noise to signal ratio was 50% . Overall , accuracy dropped to 65-88% with addition of 200% noise for larger networks . For sample size analysis , the number of samples were varied from 2 to 15 in the simulated data . Fig 3b shows that accuracy improves from 83-95% with three samples to 91-99% with 15 samples across test networks , but accuracy was preserved to be >80% with just 2 samples . Altogether , these simulations show that BONITA is robust to sample number and technical noise . Typically , pathway topologies available in databases are generalized cases that can lead to false positive edges not relevant to the context of a specific study . Hence , the robustness of BONITA to false positive edges in the prior knowledge network was assessed and compared to the existing algorithm that utilized discrete state modeling [9] . A toy network from [9] was used to generate a dataset and false positive edges were added as multiples of that network’s edge number . The ability of BONITA-RD to retrieve the original network was measured by structural distance . Specifically , structural distance is the number of edges that must be added or removed to obtain the original correct network . BONITA-RD performed substantially better with <0 . 5 times the number of edges added to the prior knowledge network than Liu’s model , but worse when false-positive edges were greater than the number of edges in the prior knowledge network ( Fig 4 ) . Critically , BONITA application to cross-sectional data performs comparably to Liu’s model applied to time course data . Generally , time course data is expected to make rule inference , especially of directional edges , easier . Thus , BONITA-RD performance is robust given the limitations of cross-sectional data in rule inference and relies on reasonably accurate prior knowledge networks . Pathway analysis is the most useful functionality of BONITA-RD . Briefly , nodes of network representing pathway are perturbed in silico to measure network-wide changes and calculate a node-level impact score . This impact score is then used to measure pathway-level modulation in the dataset under study . The performance of BONITA-PA was assessed by comparing its output to previously developed topology based pathway analysis method ( CLIPPER ) and a popular gene-set enrichment method ( CAMERA ) . CLIPPER was chosen since it was the best performing algorithm in a recent comparative analysis of network based pathway analysis techniques [2] . The comparison was performed on the simulated data representing attenuation of pathway source node and downstream events ( details in Methods ) . BONITA was more sensitive than previous methods especially at low levels of source node attenuation ( Fig 5 , refer to box and star ) with the area under the curves ( AUCs ) 0 . 842 , 0 . 832 and 0 . 830 for BONITA , CLIPPER and CAMERA respectively at log2 attenuation of 0 . 5 . All the methods performed well in detecting the number of pathways for induced attenuation >1 in source nodes ( Fig 5b ) . The performance of all the three was excellent ( . 99-1 . 00 AUC ) at log2 attenuation of 2 . 0 . The same results hold when attenuation is not propagated through the downstream nodes of the pathways ( S5 Text , S5 Fig ) . Thus , BONITA-PA outperforms previous state-of-the-art methods at low levels of pathway perturbation . Moreover , even though BONITA-PA performs as well as other methods at high level of signal perturbation , it offers rules for synergy among genes unlike any other methods . BONITA’s excellent performance on simulated data and in modeling pathway modulation calls for verifying its performance in similar experimental setting . RNA-seq data from our previous study investigating Interferon-regulated genes ( IRG ) following stimulation of human choriocarcinoma ( Jar ) cells with IFN-γ with or without pervanadate , a protein tyrosine phosphatase inhibitor , or valproic acid , a histone deacetylase ( HDAC ) inhibitor [23] was used . Human choriocarcinoma cells are hypo-responsive to IFN-γ stimulation due to impaired activation of the JAK-STAT pathway [30 , 31] . This impaired activation could be released by pervanadate and/or valproic acid . We previously showed modulation of certain IRGs by inhibitor alone . Nonetheless , stimulation with both IFN-γ and inhibitors did not reveal elicitation of higher number of pathways using existing tools such as CAMERA in [23] . In this study , BONITA , CAMERA , and CLIPPER were used to assess significance of 37 immune pathways in IFN-γ treatment with or without inhibitors compared to untreated cells ( Fig 6 , S1 Table ) . Both BONITA and CAMERA identified 6 significant pathways ( p <0 . 05 ) when cells are treated with IFN-γ alone . However , BONITA performed better in reproducing IFN-γ induced pathways in joint stimulation with inhibitors than CAMERA . Specifically , BONITA revealed activation of two pathways only upon joint treatment of IFN-γ and either one of the inhibitors as expected from previous studies [30 , 31] . CLIPPER performed poorly in detecting responsiveness to IFN-γ treatment and mostly detected pathways when cells were treated with the inhibitors . Thus BONITA’s ability to detect pathways specifically upon joint stimulation is due to the inference of modulation of downstream events by upstream nodes , rather than only detecting downstream modulations . Detecting specific pathway signals is a major challenge in genome-wide sequencing studies of human samples due to variation across individuals . Previously , we have measured changes in isolated CD4+ T cells from infants with mild and severe respiratory syncytial virus ( RSV ) infection by genome-wide mRNA sequencing [22] . It is well understood that the convalescent time point is critical in understanding antigen-specific long term responses required for resolving infections [32–34] . However , our previous work indicates that the changes at the convalescent time point are attenuated . Interestingly , BONITA-PA and CAMERA , but not CLIPPER , identified several pathways as differentially regulated across mild and severe comparison even at the convalescent visit ( Table 1 ) . Further , BONITA-PA produces helpful network synthesis , including rules , which can be visualized easily in a network viewer such as Cytoscape , as in Fig 7 . This network synthesis was used to investigate the Apoptosis pathway which was detected to be differentially regulated between mild and severe disease by BONITA but not CAMERA or CLIPPER at the convalescent visit . Interestingly , 20 out of 138 nodes obtained high impact score , 5 of which also had >0 . 5 fold difference between mild and severe . These nodes include many well-known upstream regulators such as PDGFB and PIK3CA [35–37] . Thus , BONITA effectively prioritizes pathway modulation by emphasizing upstream regulators in translational studies . To further test BONITA’s specificity , data from Ihnatova et al . was used [2 , 25 , 26] , which consists of 36 microarray experiments comparing patients with 15 unique diseases to healthy controls . Each of the disease conditions represented in our datasets corresponds to one disease pathway in KEGG . BONITA correctly found corresponding disease pathways to be significant in 22/36 datasets ( S2 Table ) . Ihnatova et al describe that CLIPPER found a comparable number ( 24/36 ) to be significant in exactly same comparisons . Further , BONITA has a unique capability to identify nodes with high impact scores , which we hypothesized would be potential drug targets . Drug targets were identified using DrugBank and the targets with indications including the name of the disease pathway ( e . g . ‘acute myeloid leukemia’ ) were retained [38] . Four datasets ( 3 acute myeloid leukemia and 1 chronic myeloid leukemia ) were identified with >1 drug target among high impact nodes designated by BONITA . The enrichment of drug targets among high impact scores was statistically significant ( p<0 . 01 , t-test ) . For example , FLT3 , a critical receptor tyrosine kinase mutated in up to 35% of acute myeloid leukemia ( AML ) cases was found to be one of the three highest impact genes in all three AML datasets . FLT3 is commonly targeted for treatment of AML . Similarly , ABL1 , part of the BCR-ABL target of imatinib , an early immunotherapy , had the top impact score in both datasets with chronic myeloid leukemia . Since DrugBank annotations might not be complete , the top 2 impact score nodes in each disease network with p<0 . 05 in BONITA-PA were manually queried as targets of drugs either under development or approved . This revealed that high impact nodes in each network , except those in Alzheimer disease network , were either targeted by or were the ligand of a receptor targeted by an approved or under development drug ( S2 Table ) . In Alzheimer disease , there are no mechanistic drugs . However , 1/4 datasets revealed TNFRSF1A , the TNF − α receptor , candidacy of which is supported by previous studies [39–41] ( S2 Table ) . Interestingly , ADRB1 , the β − 1-adrenergic receptor was the second highest impact gene for dilated cardiomyopathy , which is often treated with beta-blockers targeting the adrenergic receptor ( S2 Table ) . Thus , not only is BONITA-PA able to detect differences in relevant disease networks between patients and healthy control subjects , but it is also highly effective in identifying promising drug target genes . BONITA connects upstream differences with downstream effects , identifying true cascades depicted by the pathway topology that are highly modulated in comparison of interest . Taken together , these results show the effectiveness of BONITA-PA in prioritizing pathways for further experimental studies following genome-wide transcriptional profiling . One of the applications of BONITA is to define co-operativity in networks inferred from the data . Mutual information-based inductive causation ( miic ) [27] was used to generate a directed network using RSV infection dataset described in the previous section . BONITA was run to obtain logic rules and impact scores . BONITA predicts that absence of TAXBP1 , a gene known to participate in restricting antiviral signaling and YPEL5 , a gene involved in cell cycle progression leads to activation of TRAF3IP3 , which is supported by previous studies [42 , 43] . Finally , MYC and SP100 are hypothesized to activate MX1 together . This is particularly interesting since MX1 is a nuclear factor known to recruit SP100 and involved in antiviral response [44] . Thus , in addition to application of BONITA for pathway analysis , it can have high utility in de novo hypothesis generation .
BONITA is , to our knowledge , the first ever attempt to use discrete-state modeling for pathway analysis and builds upon decades of work to calculate node impacts in Boolean networks , Probabilistic Boolean Networks and fuzzy logic networks [45] . BONITA uses cross-sectional data along with network topology to find node specific impact scores . The impact scores consider both the learned rules defining synergy among genes and the condition-specific distribution of expression values . Since BONITA-RD recovers rules , in silico generation of hypotheses for downstream effects of node perturbations ( knockout , knockdown , knock-in ) are trivial . In this way , BONITA offers an extension to pathway analysis that no other approach affords . BONITA-RD is a novel approach to rule determination for cross-sectional data that offers significant advantages over previous algorithms . Existing software can solve the key problem of Boolean rule determination for large-scale omics datasets by use of genetic [9 , 14] , linear , or nonlinear programming algorithms [12 , 46] . These implementations , however , require time-course data which is infrequent in translational studies , limiting their usability . Indeed , BONITA-RD shows comparable robustness and accuracy to the previous algorithms which were solely developed for time-series data [9] . Moreover , these methods require either strictly Boolean or fuzzy values , missing the cell-based variability arising between on and off states . These limitations have hampered the adoption of discrete state modeling in the analysis of omics data . Approaches to solving networks for cross-sectional data must apply more general optimization solutions because there are no explicit transitions available . Though efficient , genetic algorithms often do not find the best configuration when combinatorial possibilities are high , i . e . , when network topology is complex . BONITA-RD combines an exhaustive node-wise local search with a genetic algorithm and achieves high accuracy in determining rules from simulated data . While local search improves accuracy , it is dependent on an initial global search to resolve the complexity of the networks . BONITA-RD is robust to inaccuracies in prior knowledge networks , noise , and number of samples . This optimization happens relatively rapidly within the genetic algorithm ( Fig 2a ) and the current settings are sufficient for significantly larger and more complex networks than are studied in this report . In addition to making rule determination possible from cross-sectional data , the BONITA-NP algorithm accounts for cellular heterogeneity by explicitly modeling a population of cells with a distribution of on/off starting states , rather than from varying levels of expression in each as modeled by fuzzy models . Not all genes vary in a switch-like manner by cell , but those that vary in a fuzzy manner will be implicitly modeled ( with similar accuracy ) in a pseudo-switch-like manner , because the internal direction of gene activation will remain the same . As expected , this model outperforms purely Boolean approaches in terms of error across pathways ( S2 Text , S2 Fig ) . In the future , BONITA can be extended to group the cells into subpopulations and to derive the estimation of transition across cell states/ subpopulations [20] . However , such inferences from the bulk transcriptomic data are non-trivial and newer techniques such as single-cell transcriptomics would facilitate the development . Rigorous testing of rule inference is a difficult problem . The DREAM challenge provides rigorously validated time-series data sets for evaluation of novel algorithms; however , no such test sets exist for rule inference from cross-sectional data . Hence , a well-controlled study from our collaborator Dr . Shawn Murphy was used to validate BONITA [30 , 31] ( Fig 6 ) . BONITA’s ability to identify pathways specifically in case of joint-stimulation with IFN-γ-valproic acid and IFN-γ-pervanadate corroborates with multiple previously published studies [30 , 31] . Previous methods could not distinguish increased immune signaling with co-treatment of IFN-γ and valproic acid or pervanadate , demonstrating that BONITA can extract useful biological information . Furthermore , rigorous testing of accuracy and robustness to noise , errors in the prior knowledge network and number of samples demonstrates the effectiveness of BONITA-RD in learning rules from cross-sectional data . Interestingly , mutually exclusive pathways were identified by CAMERA and BONITA at the convalescent visit after RSV infection but no pathways identified by CLIPPER . The pathways were most likely mutually exclusive because BONITA has higher sensitivity to detect pathways with upstream changes that are linked to downstream variation whereas CAMERA will detect changes in downstream genes as observed in the glycolysis pathway even in the absence of corresponding changes in upstream regulators of a pathway . Non-signaling networks like glycolysis may have unclear signal flows , as described in the Methods , or may contain many loops . In these cases , BONITA’s performance will be similar to that of other gene-set analysis methods instead of the enhanced performance observed on signaling networks when causation of downstream events can be linked to the upstream changes . BONITA-PA explicitly provides increased impact to upstream nodes in the context of downstream nodes . This quantitative prioritization of upstream signaling and relative modulation highlights nodes and interactions that make pathways most interesting for further exploration . The utility of such an approach is underscored by the effectiveness of BONITA impact scores in identifying drug targets . Thus , BONITA provides a unique perspective and new capabilities to maximize the utility of transcriptomics experiments in guiding future studies . Further , BONITA can be applied to de novo inferred networks ( Fig 8 ) , extending its use to create a complete platform to capture network of interactions from transcriptomic data . Finally , in addition to the applications described here , accurate models of all-or-none behavior in heterogeneous populations like those described by BONITA-NP have broad applicability for diverse types of molecular networks . Thus BONITA offers a novel tool for mechanistic interpretation of transcriptomic data . In conclusion , BONITA introduces a new , useful , and conceptually elegant approach to considering variance in transcriptomic data . BONITA is theoretically applicable to any directed network , including de novo inferred regulatory networks . Future releases of the BONITA software will include interfaces to other pathway databases . Further developments in transcriptomics technology and de novo assembly of directed networks from these rich data sets will enhance the applicability and usefulness of the BONITA approach . | 21st-century biotechnology has enabled measurements of genes and proteins at large scale by RNA sequencing and proteomics technologies . In particular , RNA-sequencing has become a first step of unbiased interrogation . These studies frequently produce a long list of differentially abundant genes , which become interpretable by widely used pathway analysis methods . The pathway topologies frequently include information on how genes interact and influence each other’s expression , but current methods do not utilize this information to estimate signal flow through each pathway . We have developed a model of binary ( on/off ) behavior that accounts for varying expression across samples as different proportions of cells expressing genes . We model signal flow by averaging repeated simulations of individual cells passing binary signals through molecular networks . We use this model to infer regulatory rules explaining gene expression . These rules of signal integration for all nodes in the network are used to identify the most important genes , and to determine if a pathway’s activity is different between two groups . BONITA compares favorably to previous approaches using simulated and real data . Furthermore , application to 36 datasets from 15 different diseases demonstrates BONITA’s exceptional ability to detect drug targets . | [
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| 2019 | Executable pathway analysis using ensemble discrete-state modeling for large-scale data |
RNA silencing or interference ( RNAi ) is a gene regulation mechanism in eukaryotes that controls cell differentiation and developmental processes via expression of microRNAs . RNAi also serves as an innate antiviral defence response in plants , nematodes , and insects . This antiviral response is triggered by virus-specific double-stranded RNA molecules ( dsRNAs ) that are produced during infection . To overcome antiviral RNAi responses , many plant and insect viruses encode RNA silencing suppressors ( RSSs ) that enable them to replicate at higher titers . Recently , several human viruses were shown to encode RSSs , suggesting that RNAi also serves as an innate defence response in mammals . Here , we demonstrate that the Ebola virus VP35 protein is a suppressor of RNAi in mammalian cells and that its RSS activity is functionally equivalent to that of the HIV-1 Tat protein . We show that VP35 can replace HIV-1 Tat and thereby support the replication of a Tat-minus HIV-1 variant . The VP35 dsRNA-binding domain is required for this RSS activity . Vaccinia virus E3L protein and influenza A virus NS1 protein are also capable of replacing the HIV-1 Tat RSS function . These findings support the hypothesis that RNAi is part of the innate antiviral response in mammalian cells . Moreover , the results indicate that RSSs play a critical role in mammalian virus replication .
An important criterion for productive virus infection is that the virus evades host antiviral immune responses . In plants , insects , and nematodes , the basis of these protective immune responses is formed by the RNA interference ( RNAi ) mechanism [1–4] . During virus infection , RNAi against the virus is activated by the production of virus-specific double-stranded RNAs ( dsRNAs ) . These virus-specific dsRNAs are processed into small interfering RNAs ( siRNAs; a 21-nucleotide dsRNA duplex ) by the RNAse III–like endonuclease-denoted Dicer . Subsequently , one strand of the siRNA duplex , the guide-strand , is incorporated into the RNA-induced silencing complex ( RISC ) to target viral mRNAs bearing complementary sequences for destruction . To overcome this antiviral RNAi response , viruses encode RNA silencing suppressors ( RSSs ) [5] . For plant viruses , RSSs were first described as pathogenicity factors that contribute to high virus accumulation and disease . One of the best-characterized suppressors is the tombusvirus-encoded P19 protein . This protein , which suppresses RNAi both in plants and mammalian cells , blocks RNAi by binding siRNAs via its dsRNA-binding domain , thereby sequestering the siRNAs from the RNAi pathway [6] . Another way to block RNAi is via inhibition of Dicer activity . For example , the turnip crinkle virus P83 protein was recently shown to specifically block the activity of the Dicer-like 4 protein [7] . Activation of RNAi in mammalian cells , either by transfection of synthetic siRNAs or by endogenous expression of short hairpin RNAs ( shRNAs ) , is a potent new antiviral tool [8] . These findings support the idea that RNAi is part of the innate immune system in mammals . However , in most cases , virus-specific siRNAs could not be detected in virus-infected mammalian cells [9] . So far , virus-specific siRNAs have only been identified in human cells for human immunodeficiency virus type 1 ( HIV-1 ) and the LINE-1 retrotransposon [10–12] . It has been argued that mammalian cells do not need RNAi-based antiviral responses because they have acquired the interferon ( IFN ) response [13] . However , all other eukaryotes also evolved innate antiviral defence responses . For instance , plants have pattern recognition receptors , and virus recognition leads to apoptosis and the systemic acquired resistance response that is analogous to the IFN response in mammalian cells [14] . Similar to RNAi , the IFN pathway is triggered by cytoplasmic viral dsRNAs and acts as a sensitive and potent antiviral response that is involved in innate and subsequent adaptive immunity . If RNAi has an antiviral function in mammals , then the infecting viruses should encode RSSs as they do in plant and insect viruses . Recently , several mammalian viruses have been shown to encode viral factors that exhibit RSS activity in animal cells . These factors include the influenza A virus NS1 ( NS1 ) , vaccinia virus E3L ( E3L ) , hepatitis C virus Core , primate foamy virus type 1 ( PFV-1 ) Tas , and the HIV-1 Tat proteins , as well as the adenovirus virus-associated RNAs I and II ( VAI and VAII ) [11 , 15–18] . Like plant virus RSSs , these suppressors block induced RNAi against reporter gene constructs . Moreover , NS1 and E3L were able to replace the RNAi suppression function of the b2 protein encoded by Flock house virus and to support virus replication in insect cells [17] . Although most of these viral proteins/RNAs are essential for virus replication , their mode of action is largely unknown . We and others have shown that the adenovirus virus-associated RNAs inhibit RNAi by acting as decoy substrates for Exportin 5 , Dicer , and RISC [16 , 19] . HIV-1 Tat is thought to block Dicer activity , whereas NS1 and E3L may sequester dsRNAs and siRNAs [11 , 17 , 20] . Strikingly , all RSS proteins from mammalian viruses possess IFN or protein kinase R ( PKR ) antagonistic properties , suggesting that RNAi and other innate antiviral responses are interrelated [21–24] . Although there is consensus about the fact that viral factors can indeed block RNAi in mammalian cells , there is an ongoing debate about the significance for viral replication [13] . Because non-viral dsRNA-binding proteins such as the bacterial RNase-III protein can also act as RSSs in plant cells , it is important to determine the contribution of RNAi suppression to virus replication [25] . Here , we show that Tat-mediated RNAi suppression is required for HIV-1 virus production . Using defective Tat-minus HIV-1 mutants , we identified the Ebola virus ( EBOV ) VP35 protein as a potent RNAi suppressor that is functionally equivalent to the HIV-1 Tat RSS function . Furthermore , we show that the E3L and NS1 proteins can also functionally complement the Tat RSS function and rescue virus production of the HIV-1 Tat-minus mutants . These data support the role of RNAi in innate antiviral responses in mammalian cells and the essential role of RNAi suppression in HIV-1 replication .
At present , all RSS proteins identified in mammalian viruses have IFN/PKR antagonistic properties . Previously , it was shown that the EBOV IFN antagonist protein VP35 is capable of restoring the replication of an influenza A virus mutant with a large deletion in the NS1 gene [21 , 26] , raising the possibility that VP35 acts as an RSS in mammalian cells . To investigate the RNAi suppressor activity of EBOV VP35 , we assayed whether this protein is capable of suppressing shRNA-mediated silencing of a luciferase reporter . HEK293T cells were co-transfected with expression vectors for luciferase and firefly luciferase–specific shRNA ( shLuc ) [27] , and with an EBOV VP35 expression plasmid containing the human EF1α promoter . Co-transfection of luciferase with shLuc resulted in a strong decrease of luciferase expression ( Figure 1A ) . Addition of the VP35 expression plasmid suppressed the shRNA-mediated RNAi in a dose-dependent manner and restored luciferase expression . To exclude that the observed effect is caused by the IFN antagonistic properties of VP35 , its RSS activity was also determined in African green monkey kidney Vero cells that have a defect ( IFN− ) in the IFN pathway [28] . As in HEK293T ( IFN+ ) cells , VP35 expression suppressed RNAi in Vero ( IFN− ) cells and rescued luciferase expression ( Figure 1B ) . Next , we compared the RSS activity of VP35 with that of NS1 and E3L , which were previously demonstrated to have RSS activity in insect and plant cells [17 , 20 , 29] . Because insect and plant cells lack the IFN pathway , the RSS activity was determined both in HEK293T ( IFN+ ) and Vero ( IFN− ) cells . E3L suppressed RNAi in HEK293T cells , whereas NS1 protein did not ( Figure 1C ) . However , both E3L and NS1 were able to suppress RNAi in Vero cells . The control expression plasmid encoding green fluorescent protein ( GFP ) did not suppress RNAi-mediated inhibition of luciferase expression ( Figure 1D ) . These data show that VP35 , E3L , and NS1 are able to suppress shRNA-induced RNAi in mammalian cells . Possibly , the cell type–dependent RSS activity of NS1 follows from differences in the expression level of certain cellular RNAi co-factors . VP35 has a dsRNA-binding motif with high similarity to the dsRNA-binding domain of the NS1 protein [30] . Mutational analysis has shown that this VP35 domain is important for suppression of type I IFN responses [30–32] . Because binding of dsRNA is one of the strategies to suppress RNA silencing , we wanted to investigate the importance of this domain in VP35-mediated RSS activity [33 , 34] . The VP35 dsRNA-binding domain has been identified as 304PRACQKSLRPV314 . We tested two mutants containing a single amino acid substitution , K309A and R312A , and a double mutant , K309A/R312A . Furthermore , we tested the C-terminal deletion mutant R300T , which lacks 40 amino acids . Mutants K309A and K312A have a defect in dsRNA binding , whereas R300T lacks the complete dsRNA-binding domain [30 , 31] . The wild-type VP35 ( VP35wt ) and the various mutants were cloned in the expression vector pcDNA3 . 1 containing the cytomegalovirus ( CMV ) promoter . HEK293T cells were co-transfected with expression vectors for firefly luciferase , shLuc , the wild-type EBOV VP35wt , or the various mutants . In addition , a renilla luciferase expression plasmid ( pRL-CMV ) was co-transfected as internal control . Co-transfection of the firefly luciferase with shLuc resulted in a strong decrease of luciferase expression ( Figure 2A ) . Addition of the VP35wt expression plasmid suppressed shRNA-mediated RNAi and restored luciferase expression . Interestingly , all mutants , in particular R312A , K309A/R312A , and R300T , were unable to rescue luciferase expression ( Figure 2A ) . The internal renilla luciferase control was not affected by the various VP35 expression plasmids , and the firefly/renilla luciferase ratio showed a similar trend as the firefly luciferase data . These results indicate that dsRNA binding is essential for VP35-mediated RNAi suppression in the luciferase assay . To ensure that the effect of VP35wt is specific for the silenced luciferase , we tested the effect of VP35wt on luciferase expression in the presence of an inactive shRNA against luciferase , shLucR . In shLucR , the hairpin cassette has been inserted in reversed orientation , making it inactive as RNAi inducer [27] ( Figure 2B ) . Co-transfection of the luciferase expression plasmid with shLucR did not affect luciferase expression , whereas shLuc induced a marked reduction in luciferase expression . Co-transfection of the VP35wt expression plasmid did not stimulate luciferase expression in the presence of shLucR , whereas it did enhance luciferase expression in the presence of shLuc . These results show that VP35-stimulated reporter expression is specific for an RNAi-silenced reporter . RSS proteins derived from plant or insect viruses , but also NS1 and E3L , have been identified by their ability to functionally complement attenuated viruses that lack their own RSS [17 , 35] . Since it has recently been shown that the HIV-1 Tat protein has RSS activity , we wanted to determine whether EBOV VP35 is capable of functionally replacing the Tat RSS activity in HIV-1 . To do this , one needs a Tat-minus virus , but such variants are completely replication impaired due to a transcription defect . However , we previously constructed the HIV-rtTA virus , in which Tat-mediated transcription transactivation is inactivated and replaced by the Tet-On system ( Figure 3A ) [36 , 37] . This virus is fully dependent on doxycycline ( dox ) for gene expression and replication . To test whether the Tat function can be removed in this context , we introduced a frame shift at Tat codon 20 by a single nucleotide deletion in the Tat open reading frame . Virus production was measured in HEK293T cells transfected with the HIV-rtTA molecular clone encoding Tatwt ( HIV-rtTA-Tatwt ) or the frame shift mutant Tatfs ( HIV-rtTA-Tatfs ) . Both constructs produced virus in a dox-dependent manner , but Tatfs produced much less virus than HIV-rtTA-Tatwt ( Figure 3B ) . Because HIV-rtTA is not dependent on Tat-mediated transcriptional activation , this result indicates that Tat is needed for an additional function . To investigate whether the observed production defect of HIV-rtTA-Tatfs is caused by inactivation of Tat RSS activity , we studied rescue of HIV-rtTA-Tatfs by co-transfection of expression plasmids for Tatwt and two Tat mutants , TatF32A and TatY26A [38] . We first determined the transcriptional activity of TatF32A and TatY26A . For this , we used TZM-bl cells that contain the firefly luciferase reporter gene under control of the HIV-1 long terminal repeat ( LTR ) , which is Tat responsive . Only transfection of Tatwt expression plasmid activated luciferase expression , whereas the Tat mutants TatF32A and TatY26A did not stimulate luciferase expression ( Figure 4A ) . We next determined the RSS activity of HIV-1 Tatwt , TatF32A , and TatY26A using the luciferase RNAi assay . Tatwt and TatY26A were capable of suppressing shLuc-mediated silencing of the luciferase expression , but mutant TatF32A did not suppress RNAi ( Figure 4B ) . Because both mutants express stable proteins that lack transactivation capacity [38] , this result indicates that RSS activity can be separated from transcriptional activity , which is in agreement with the results described by Bennasser et al . in 2005 [11] . We next determined whether the production defect of HIV-rtTA-Tatfs in HEK293T cells could be restored by co-expression of Tatwt , TatF32A , or TatY26A . Tatwt and TatY26A , which are capable of suppressing shRNA-mediated silencing of luciferase expression , rescued the production of HIV-rtTA-Tatfs ( Figure 4C ) . However , TatF32A , which did not suppress RNAi , could not restore virus production . These data indicate that the defect in HIV-rtTA-Tatfs virus production is caused by a trans-acting RSS defect , rather than a cis-acting defect , of the frame shift mutation . We subsequently tested whether the HIV-rtTA-Tatfs production defect could also be restored by co-transfection of the EF1α expression plasmids for VP35 , NS1 , or E3L . Indeed , VP35 rescued virus production of HIV-rtTA-Tatfs ( Figure 5A ) . Little RSS activity was measured for E3L and NS1 ( Figure 5A ) , but increasing activity was measured when more RSS DNA was used in the transfection ( Figure 5B ) . In the latter case , a GFP reporter was included as a negative control . To exclude that VP35 , E3L , NS1 , and Tatwt stimulate HIV-rtTA-Tatfs virus production by inducing promoter activity , we transfected cells with a firefly luciferase reporter under transcriptional control of the 8tetO promoter ( the same promoter as in HIV-rtTA-Tatfs ) with an rtTA expression plasmid and with the different RSS vectors . We measured dox-dependent promoter activity resulting in luciferase expression ( Figure 5C ) . Co-expression of Tatwt , E3L , VP35 , NS1 , or GFP did not further increase luciferase expression , indicating that these proteins do no activate the 8tetO promoter in trans . The various RSS proteins also did not activate expression of a luciferase reporter under control of a wild-type HIV-1 LTR promoter in TZM-bl cells ( Figure 5D ) . This shows that the observed rescue of the Tat function by the heterologous RSSs is independent of promoter activation . To confirm that the observed rescue of virus production corresponds with an increase in viral mRNAs , we analysed viral RNA accumulation by Northern blot analyses . First , rescue of HIV-rtTA-Tatfs virus production by Tatwt and VP35 was determined by measuring CA-p24 production in the supernatant ( Figure 6A ) . The amount of full-length genomic RNA of HIV-rtTA-Tatfs accumulating in transfected cells was reduced when compared to that of HIV-rtTA-Tatwt ( Figure 6B ) . In accordance with the CA-p24 values , co-transfection of Tat and VP35 restored the amount of full-length HIV-1 RNA . Next , we wanted to determine whether the VP35 dsRNA-binding capacity is required for the complementation of HIV-rtTA-Tatfs . HEK293T cells were transfected with either HIV-rtTA-Tatwt or HIV-rtTA-Tatfs , and expression plasmids for Tatwt , VP35wt , and the VP35 mutants K309A , R312A , K309A/R312A , and R300T ( Figure 7 ) . Tatwt , VP35wt , and K309A were able to rescue HIV-rtTA-Tatfs production , and co-transfection of mutant R312A partially restored HIV-rtTA-Tatfs virus production . In contrast , mutants K309/R312A and R300T were unable to rescue virus production . These results correlate with the ability of the various VP35 mutants to suppress RNAi in the luciferase assay . Thus , the data confirm the importance of RNAi suppression in HIV-1 production and that the dsRNA-binding capacity of VP35 is essential for this activity .
Although the importance of antiviral RNAi responses in plants , nematodes , and insects is firmly established , it is still under debate whether RNAi has a similar function in mammalian cells . If RNAi is a protective antiviral response in mammals , then virus infection should trigger the production of virus-specific siRNAs; inhibition of RNAi should affect virus replication , and viruses should have evolved factors that suppress RNAi [13] . To date , there are only three examples of virus-specific siRNAs accumulating in infected mammalian cells . The human retrotransposon LINE-1 was shown to be inhibited by transposon-specific siRNAs , which is similar to what was previously described for transposon silencing in Caenorhabditis elegans [10 , 12 , 39] . Furthermore , there is data suggesting accumulation of virus-specific siRNAs during HIV-1 replication [11] . However , virus-specific siRNA accumulation in mammalian cells appears to be relatively low compared to that in plant and insect cells . The reason for this has remained unclear . One explanation could be the lack of host RNA-dependent RNA polymerase ( RdRp ) activity in mammalian cells . In plants and insects , RdRp activity is responsible for amplification of the RNAi signal . In combination with viral RSS activity , the absence of RdRp activity could explain the low siRNA levels in mammalian cells in comparison to those in plant , insect , or nematode cells . Another possibility is that antiviral RNAi in mammalian cells is initiated by cellular microRNAs rather than by de novo production of siRNAs [15] . This has been suggested for the primate retrovirus PFV-1 , where the cellular miR-32 was found to target PFV-1 sequences . PFV-1 overcomes microRNA-mediated antiviral pressure by the expressing the RSS Tas protein . In principle , viruses could also escape from antiviral microRNA pressure by acquisition of one or a few point mutations within the target sequence [40 , 41] . It is therefore likely that there is a more general RNAi response against PFV infection that necessitates the presence of an RSS . Little is known about enhanced virus replication in cells with a defective RNAi mechanism . However , recently it has been reported that HIV-1 replication is increased in human cells in which Dicer and Drosha expression is inhibited [42] . This suggests that RNAi indeed plays a central role in anti-HIV-1 defence responses in human cells . Another report describes enhanced accumulation of the mammalian vesicular stomatitis virus in C . elegans with a defective RNAi machinery , which confirms that RNAi has an antiviral activity in nematodes [4] . The other indication for RNAi-mediated antiviral activity in mammals is the fact that a number of mammalian viruses encode potent RSSs . However , it is still unclear to what extent these suppressors contribute to virus replication , because these factors have multiple functions that are difficult to separate . Moreover , the relevance of RSS activity measured in reporter assays has been questioned because non-specific binding of siRNAs by overexpression by dsRNA-binding proteins might also result in RNAi suppression [13] . Therefore , it is essential to test the importance of RSS activity in a viral context instead of in reporter assays . Using the HIV-rtTA virus as a tool to investigate RNAi suppression , we were able to separate the transactivation function of HIV-1 Tat from its RSS function . We showed that Tat-mediated RSS activity is essential for HIV-1 production without possible side effects of Tat-mediated transcriptional transactivation . These data are in agreement with data published by Bennasser et al . in 2005 [11] . The HIV-rtTA virus was used as a tool to identify EBOV VP35 protein as an RSS that can complement this Tat function . Both HIV-1 Tat and EBOV VP35 were found to complement HIV-rtTA-Tatfs production efficiently at low concentrations , indicating specific RSS activity . Moreover , RSSs encoded by other mammalian viruses are also able to complement the Tat RSS activity . The results presented here support the importance of RNAi in innate antiviral defence responses in mammalian cells and the essential function of RSSs in mammalian virus replication . In plants , insects , and nematodes , antiviral RNAi responses are triggered by virus-specific dsRNAs . In mammalian cells , virus-specific dsRNAs induce the IFN pathway via members of the Toll-like receptor family , or via a replication-dependent pathway involving the cytoplasmic dsRNA sensors RIG-I/MDA5 ( retinoic-acid-inducible protein I/melanoma-differentiation-associated gene 5 ) [43 , 44] . Other antiviral proteins that are induced by dsRNA include the 2′-5′ oligoadenylate cyclase ( 2′-5′ OAS ) /RNAseL and PKR [45 , 46] . Since RNAi , IFN responses , and 2′-5′ OAS/RNAseL/PKR are triggered by dsRNA , it is likely that these pathways cooperate in the innate antiviral defence response . The helicases RIG-I/MDA5 are candidate proteins that could link antiviral RNAi and IFN responses because they can be activated by siRNAs [44 , 47] . In agreement with this model is the recent observation that EBOV VP35 inhibits RIG-I induced activation of type I IFN responses [31] . In part , this inhibition is dependent on dsRNA-binding capacity of VP35 [31] . The K309A mutant was shown to have a partial defect in IFN antagonistic activity , whereas the other mutants , R312A , K309A/R312A , and R300T , were severely impaired in blocking IFN responses . Similarly , our assays show that mutation of the dsRNA-binding domain results in a loss of RNAi suppression activity . Only mutant K309A showed partial RSS activity and was able to complement HIV-rtTA-Tatfs production . These data suggest that VP35-mediated RNAi suppression and VP35-dsRNA-dependent IFN antagonistic properties are linked . We therefore propose that VP35 is able to sequester virus-specific siRNAs or dsRNA precursors of siRNA , resulting in suppression of an antiviral RNAi response that acts upstream of the 2′-5′ OAS/RNAseL/PKR and IFN pathways . In this scenario , the amount of cytoplasmic virus–specific siRNAs needs to reach a certain threshold level before RIG-I/MDA5 and the IFN pathway are activated ( Figure 8 ) . In this way , virus-specific siRNAs would function as signal molecules for activation of the IFN response . Virus-mediated RNAi suppression therefore has two functions . First , it suppresses the RNAi response that acts as the first line antiviral defence . Second , the inhibition of siRNA accumulation prevents RIG-I/MDA5-mediated activation of the IFN pathway . Important in this respect are the immature plasmacytoid dendritic cells ( PDCs ) , which play a predominant role in antiviral immunity . If the accumulation of virus-specific siRNAs in these cells is blocked by viral RSS activity , there will be no trigger to induce the secretion of class I IFNs that in turn induce a range of antiviral genes , resulting in the antiviral state of the neighbouring cells , dendritic cell maturation , and subsequent adaptive immunity [48] . This results in a delayed onset of the immune responses , and neighbouring cells will remain permissive to the invading virus for a prolonged time [49] . Evasion of antiviral immune responses is a key process during virus replication . The results presented here suggest that RNAi plays an important role in innate antiviral defences and that HIV-1 needs to counter this mechanism in order to replicate . The fact that many RSS proteins also have IFN antagonistic properties [50–56] supports the idea that RNAi and IFN responses work together against invading viruses . It remains to be determined how the RNAi and IFN responses cooperate in mechanistic terms . In addition , future experiments should reveal the precise viral signature that activates the antiviral RNAi response in mammals . Although viral RSS factors will be able to counter RNAi during natural infections , these factors will likely not reduce the effectiveness of RNAi-based antiviral therapeutics . High concentrations of exogenous synthetic siRNAs , for example , to block EBOV replication will easily saturate the VP35 RSS activity , rendering VP35 ineffective , and subsequently still inhibit virus replication [57] . Similarly , plant viruses that encode RSS factors can also be silenced by RNAi , as can viruses such as HIV-1 , influenza , hepatitis C virus , and Flock house virus that encode RSS factors . Besides revealing novel aspects of the virus–host interaction , the in trans complementation of viruses lacking their RSS by heterologous RSSs creates opportunities for improving production of virus particles in mammalian cells . Moreover , this phenomenon could be used to develop a new generation of live attenuated viral vaccines that improves upon the current antiviral measures .
The NS1 ( from influenza A virus strain PR8 [20] ) , VP35 ( from Ebola virus strain Zaire ) , E3L ( from vaccinia virus strain Ankara ) , and EGFP open reading frames were cloned into the mammalian expression vector pEF5-V5-DEST containing human EF1α promoter using GATEWAY technology ( Invitrogen , http://www . invitrogen . com ) . The constructs for VP35wt and the various VP35 mutants contain the CMV promoter ( pcDNA3 . 1-VP35wt , K309A , R312A , K309A/R312A , and R300T ) and were a kind gift from S . T . Nichol [30] . The HIV-1 Tat expression plasmids , pcDNA3-wtTat/pKV-wtTat , and the mutants , pcDNA3-Y26A and pKV-F32A , were described previously [38] . HIV-rtTA-Tatfs was created by deletion of A58 in the Tat open reading frame of HIV-rtTA-TatY26A ( variant KYK in [37] ) , thus creating a frame shift at codon 20 . Human embryonic kidney ( HEK293T ) cells and African green monkey kidney Vero cells were grown as a monolayer in DMEM ( Gibco BRL , http://www . invitrogen . com ) supplemented with 10% fetal calf serum ( FCS ) ( Hyclone , http://www . hyclone . com ) , minimal essential medium with non-essential amino acids , penicillin ( 100 U/ml ) , and streptomycin ( 100 μg/ml ) at 37 °C and 5% CO2 . One day before transfection , cells were trypsinized , resuspended in DMEM , and seeded in 24-well plates at a density of 1 . 5 × 105 cells per well . At the time of transfection , the cells were 60%–70% confluent . The transfection was performed in duplicates using Lipofectamine 2000 ( Invitrogen ) according to the instructions of the manufacturer . For the luciferase RNAi assay , cells were transfected with 100 ng of luciferase-expressing plasmid pGL3 ( Promega , http://www . promega . com ) and 10 ng of expression plasmid shLuc ( pShh1-Ff1 ) , from which an shRNA against luciferase is expressed under control of the U6 promoter [27] . Cells were lysed 2–3 d post transfection in 150 μl of 1x Passive Lysis Buffer ( Promega ) by shaking for 30 min at room temperature . The cell lysate was centrifuged for 5 min at 1 , 500 rpm , and luciferase expression was measured in 10 μl of supernatant with the luciferase reporter assay system ( Promega ) . For the complementation studies , 100 ng of HIV-rtTA-Tatfs was transfected with the indicated amounts of RSS plasmid . The total amount of DNA was brought to 1 μg using pBluescript ( Stratagene , http://www . stratagene . com ) . Two to three days after transfection , virus production was determined by measuring CA-p24 levels by enzyme-linked immunosorbent assay ( ELISA ) . For the testing of the effect of Tat and the various RSS proteins on the 8tetO promoter , we transfected HEK293T cells with 20 ng of 8tetO-luc expression plasmid containing a luciferase reporter under control of the 8tetO promoter ( the same promoter as in HIV-rtTA-Tatfs ) , 0 . 4 ng of rtTA expression plasmid , 0 . 5 ng of pRL-CMV , and the different RSS vectors . Two to three days after transfection , luciferase expression was measured . Transcriptional transactivation capacity of Tat , Tat mutants , and the various RSS proteins was measured using TZM-bl cells . These cells were obtained from the NIH AIDS Research and Reference Reagent Program ( also termed JC53-BL cells; catalog number 8129; https://www . aidsreagent . org ) . TZM-bl cells are genetically modified Hela cells that express CD4 , CXCR4 , and CCR5 and contain the firefly reporter gene under control of the HIV-1 LTR , which is Tat responsive . The cells were seeded similarly to the HEK293T cells and transfected with 0 . 2 μg of Tat or RSS expression plasmid , and luciferase expression was measured 2–3 d after transfection . HEK293T cells ( T25 flask ) were transfected with 6 . 6 μg of HIV-rtTA-Tatwt , HIV-rtTA-Tatfs , and HIV-rtTA-Tatfs in combination with 0 . 132 μg of Tatwt or 0 . 132/0 . 66/1 . 32/6 . 6 μg of VP35 expression plasmid . Dox was added to the medium to activate HIV-rtTA virus production . Two to three days post transfection , virus production was measured via CA-p24 ELISA , and total RNA was isolated using the mirVana RNA isolation kit ( Ambion , http://www . ambion . com ) . For detection of genomic HIV-1 RNAs , gel electrophoresis of 10 μg of total RNA was performed on a denaturing formaldehyde , 1% agarose gel . RNA was transferred to a positively charged nylon membrane ( Boehringer , http://www . boehringer-ingelheim . com ) via capillary blotting and crosslinked to the membrane with a UV crosslinker ( Stratagene ) . A 19-nt LNA-modified oligonucleotide complementary to the HIV-1 Nef gene was used as a probe , which was 5′ end labeled using the kinaseMax kit ( Ambion ) in the presence of 1 μl of [γ-32P]ATP ( 0 . 37 MBq/μl; Amersham Biosciences , http://www . gelifesciences . com ) and purified over a MicroSpin G-25 column ( Amersham Biosciences ) . Prehybridization and hybridization was performed in Ultrahyb buffer ( Ambion ) at 60 °C for 30 min and 18 h , respectively . The membrane was washed twice for 15 min at 60 °C with high-stringency buffer ( 0 . 2x SSC , 0 . 2% SDS ) . Images were obtained using the Typhoon Trio phosphor imager ( Amersham Biosciences ) .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/index . html ) accession numbers for the proteins discussed in this article are E3L ( from vaccinia virus ) ( U94848 ) , NS1 ( from influenza A virus ) ( EF467817 ) , and VP35 ( from Ebola virus ) ( AF086833 ) . | Cells have evolved mechanisms to protect themselves from virus infection . A well-known antiviral mechanism in mammals is the interferon ( IFN ) response of the innate immune system . In plants , insects , and worms , RNA silencing or RNA interference ( RNAi ) is a strong antiviral defence mechanism . It is still debated whether RNAi is also used as an antiviral mechanism in mammals . Many mammalian viruses encode essential factors that suppress the innate antiviral responses of the host . Such innate immunity suppressor proteins , or IFN antagonists , have recently been reported to also suppress RNAi in mammalian cells . We now demonstrate that the Ebola virus VP35 protein , a known IFN antagonist , suppresses RNAi in human cells . In addition , VP35 restores the production of an HIV-1 variant with a defective RNAi suppressor Tat protein . These results indicate that RNAi is part of the innate antiviral defence response in mammals and that viruses need to counteract this response in order to replicate . Whereas RNAi and INF act in concert to prevent the infection of mammalian cells , the invading viruses encode a protein that counteracts both defence mechanisms . | [
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| 2007 | The Ebola Virus VP35 Protein Is a Suppressor of RNA Silencing |
The Dobzhansky-Muller model of incompatibilities explains reproductive isolation between species by incorrect epistatic interactions . Although the mechanisms of speciation are of great interest , no incompatibility has been characterized at the gene level in mammals . The Hybrid sterility 1 gene ( Hst1 ) participates in the arrest of meiosis in F1 males of certain strains from two Mus musculus subspecies , e . g . , PWD from M . m . musculus and C57BL/6J ( henceforth B6 ) from M . m . domesticus . Hst1 has been identified as a meiotic PR-domain gene ( Prdm9 ) encoding histone 3 methyltransferase in the male offspring of PWD females and B6 males , ( PWD×B6 ) F1 . To characterize the incompatibilities underlying hybrid sterility , we phenotyped reproductive and meiotic markers in males with altered copy numbers of Prdm9 . A partial rescue of fertility was observed upon removal of the B6 allele of Prdm9 from the azoospermic ( PWD×B6 ) F1 hybrids , whereas removing one of the two Prdm9 copies in PWD or B6 background had no effect on male reproduction . Incompatibility ( ies ) not involving Prdm9B6 also acts in the ( PWD×B6 ) F1 hybrids , since the correction of hybrid sterility by Prdm9B6 deletion was not complete . Additions and subtractions of Prdm9 copies , as well as allelic replacements , improved meiotic progression and fecundity also in the progeny-producing reciprocal ( B6×PWD ) F1 males . Moreover , an increased dosage of Prdm9 and reciprocal cross enhanced fertility of other sperm-carrying male hybrids , ( PWD×B6-C3H . Prdm9 ) F1 , harboring another Prdm9 allele of M . m . domesticus origin . The levels of Prdm9 mRNA isoforms were similar in the prepubertal testes of all types of F1 hybrids of PWD with B6 and B6-C3H . Prdm9 despite their different prospective fertility , but decreased to 53% after removal of Prdm9B6 . Therefore , the Prdm9B6 allele probably takes part in posttranscriptional dominant-negative hybrid interaction ( s ) absent in the parental strains .
Hybrid sterility is a condition in which two fertile parental forms produce progeny with disturbed gametogenesis . In mammals and Drosophila , it affects spermatogenesis more often than oogenesis [1] . Hybrid sterility acts as a reproductive barrier between species [2] . Although its molecular mechanism is of great interest , only five animal genes involved in hybrid sterility have been cloned and characterized , four of them from Drosophila [3]–[9] . The Dobzhansky-Muller model of incompatibilities of genes [10] explains the reproductive isolation between species by their incorrect epistatic interactions . These interactions ( or lack of the correct ones ) result in hybrid fitness reduction , probably because the combination of the diverged alleles of the interactors did not pass through natural selection . The mouse Hybrid sterility 1 gene ( Hst1 ) is one of the major genes causing meiotic arrest in F1 male hybrids between Mus m . musculus ( Mmm ) mice harboring the Hstws allele ( e . g . , the PWD strain ) and laboratory strains bearing the Hst1s allele [11] . Strains with the Hst1s allele include Mus m . domesticus ( Mmd ) -derived C57BL/6J ( henceforth B6 ) and various substrains of the 129 strain . While the male offspring from the crosses of Hst1s strains with PWD females , e . g . , ( PWD×B6 ) F1 , are azoospermic , the males from the reciprocal crosses using PWD males [ ( B6×PWD ) F1] can sire offspring ( Figure 1 , [6] , [12] ) . Other laboratory strains ( C3H , the congenic B6 . C3H-Hst1f , etc . ) harbor the Hst1f allele and produce sperm-carrying F1 males in crosses with PWD females [13] . The male offspring of B6 with C3H , as well as F1 females in all PWD , B6 , and C3H crosses are fertile . The Hst1 gene is the first mammalian candidate for a speciation gene . Hst1 was identified by consecutive mapping of the Hst1 alleles [14]–[16] , expression profiling [17] , allelic sequencing [18] , [19] , and transgenic rescue [6] . The best candidate was confirmed by comparing the phenotypes of its null allele with the phenotypes of the sterile hybrids [6] . Hst1 is also called Prdm9 ( PR-domain containing 9 ) or Meisetz ( Meiotic gene with SET/PR domain and Zinc fingers ) . The product of this gene trimethylates histone 3 on lysine 4 ( H3K4m3; [20] ) . Spermatocytes of Prdm9−/− animals and sterile hybrids arrest at the pachytene stage of meiotic prophase I , displaying defects in chromosomal pairing and sex body formation , as well as downregulation of meiotic genes . The downregulation correlates with a lower level of H3K4m3 at promoters , at least in the case of the Morc2b gene ( 4932411A10Rik; [6] , [20] ) . The Morc2b mRNA is elevated in ( PWD×B6-C3H . Hst1f ) F1 compared to ( PWD×B6 ) F1 prepubertal testis , while the levels of all known Prdm9 transcripts are similar [6] . The chromatin of mouse meiotic recombination hotspots is marked by H3K4m3 at the start of meiosis [21] . Genetic mapping of a gene acting in trans to influence the activity of recombination hotspots also led to the identification of Prdm9 [22] , [23] . PRDM9 binds DNA at recombination hotspots via its zinc-fingers ( ZnFs ) in vitro , and genetic manipulation of ZnFs changes the localization of the hotspots [22] , [24] . The Prdm9 genes from C3H and B6 strains ( alleles Hst1f and Hst1s , respectively ) , display many polymorphisms [6] . The difference that may underlie hybrid sterility is the number of C-terminal ZnFs [6] . The number of ZnFs corresponds to Hst1 alleles in other classical Mmd laboratory strains [19] , [25] . The ZnF-encoding region of the PWD allele differs from both C3H and B6 [6] , but whether Prdm9PWD is identical to Hstws and whether it contributes to hybrid sterility is not known . Although accelerated evolution of the minisatellite-like ZnF-encoding region of PRDM9 was manifested in human and animals [26] , it remains to be shown whether PRDM9 has a more general role in speciation . The Prdm9B6 allele is necessary but not sufficient for hybrid sterility . The Hstw gene from Mmm is also located on chr17 [12] . In the sterile ( PWD×B6 ) F1 males , chr17 carries the combination Hstws/Prdm9B6 , but the same genotype in the B6 background of ( B6 . PWD-Chr17×B6 ) F1 yields fertile males ( Figure 1; [27] ) . It was shown recently that Hstws/Prdm9B6 is the only combination resulting in a complete meiotic arrest in ( PWD×B6 ) F1 hybrid males , because both Prdm9B6/Prdm9B6 and Hstws/Hstws homozygotes on the same background were fertile [12] . However , even on the F1 background , the Hstws/Prdm9B6 combination does not always lead to azoospermic males , as the reciprocal ( B6×PWD ) F1 males carry sperm . Thus , mouse hybrid sterility reflects incompatibilities among multiple hybrid sterility loci , one of them being the Prdm9 gene [12] . Here we manipulated the dosage and allelic combinations of Prdm9 in an attempt to characterize the role of this mouse hybrid sterility gene in the incompatibilities . If Prdm9B6 has a dominant-negative effect ( s ) , its deletion should alleviate the meiotic arrest and rescue the fertility of hybrids . Moreover , a Prdm9-overexpressing transgene might dilute the Prdm9B6 incompatibility ( ies ) , which should rescue fertility regardless of the Prdm9 allelic origin . We show that the fertility of all F1 intersubspecific hybrids tested is proportional to the dosage of Prdm9 regardless of its allele; the only exception is the combination of one Prdm9B6 allele with one Prdm9PWD allele in either type of reciprocal hybrid that results into a more sterile phenotype than the corresponding hybrid harboring only one Prdm9PWD allele . This exception indicates an F1 hybrid-specific dominant-negative interaction ( s ) of Prdm9B6 .
The fertility of azoospermic intersubspecific ( PWD×B6 ) F1 hybrid males is rescued by Prdm9C3H-carrying BACs [6] . Moreover , six copies of the Prdm9C3H allele in a transgene ( BAC24 ) increased reproductive fitness compared to two copies of the same allele ( BAC5 transgene ) in Prdm9PWD/B6 ( PWD×B6 ) F1 intersubspecific hybrid males ( [6] and Table 1 ) , although no effect of increased Prdm9 dosage appeared on the intrasubspecific background ( a mix of 129 and B6 genomes , henceforth B6 * 129 [6] ) . There are three possible explanations for the fertility rescue by the Prdm9C3H BACs: first , by allelic replacement; second , by increased dosage regardless of allelic origin; third , both . To distinguish among these hypotheses , a transgene harboring the Prdm9B6 allele was utilized . The C57BL/6J-Tg ( RP23-159N6 ) 75Bdm strain carries two Prdm9-expressing copies of a B6 BAC transgene on B6 background [24] . In this background , the transgene has no effect on fertility ( Table S1 ) . After outcrossing the heterozygous transgenic males to PWD , non-transgenic F1 hybrid males were azoospermic ( as expected ) , while their transgenic littermates had an increased testicular weight ( TW ) and carried sperm ( Table 1 ) . The increased copy number of the Prdm9B6 allele also rescued fertility of azoospermic hybrids produced by another Mmm strain , STUS ( Table S1 ) . Thus the improvement of fertility by increased Prdm9 dosage is not restricted to the C3H allele . To further investigate the effect of Prdm9 dosage on hybrid sterility , we used two null alleles of Prdm9 , a large deletion of proximal chr17 ( Sod2df14J [28] ) and a knock-out of Prdm9 that removes the first five coding exons ( Prdm9tm1Ymat [20] ) . Males hemizygous for these alleles on ( 129 * B6 ) as well as on B6 background display similar fertility parameters as their littermates ( [20] , [29] , and Table S2 ) . To determine the effect of the null alleles on intersubspecific F1 hybrids , the hemizygous males were outcrossed to PWD . Unlike their azoospermic Prdm9PWD/B6 F1 littermates , most of the Prdm9PWD/− intersubspecific hybrids were semisterile with TW , sperm count ( SC ) , and offspring production ( offspring per female per month , OFM ) significantly higher than in the ( PWD×B6 ) F1 littermates ( Table 1 and Table S3 ) . All Prdm9PWD/− hybrid males resulting from the cross of PWD females with Prdm9B6/− on B6 background carried a low but detectable amount of sperm and most of them produced offspring ( 0 . 3±0 . 2 OFM ) . As an additional control , we introduced the Prdm9tm1Ymat null allele into the PWD background . Here , one copy of Prdm9PWD was sufficient to maintain the fertility parameters of the Prdm9PWD/PWD littermates ( Table S2 ) . Therefore , the ( PWD×B6 ) F1 hybrid genetic background appears to be more sensitive to low Prdm9 dosage than that of either parent . Both addition and removal of Prdm9B6 improves the phenotype of ( PWD×B6 ) F1 males . The fertility rescue of azoospermic hybrid males by the Prdm9 null alleles , albeit partial , suggests that an aberrant interaction ( s ) of Prdm9B6 occurs in ( PWD×B6 ) F1 hybrids that is not present in the parental strains . The Prdm9C3H allele rescues the fertility phenotype of the ( PWD×B6 ) F1 hybrid in a dosage-dependent manner . To compare the effect of a single C3H allele and a null allele on hybrid males resulting from a cross segregating these alleles , we utilized the congenic strain B6-Prdm9C3H . After outcrossing animals hemizygous for Prdm9 to this congenic and then crossing the preselected Prdm9C3H/− males to PWD females , the resulting Prdm9PWD/− hybrids had a significantly lower TW and SC than their Prdm9PWD/C3H littermates ( Table 1 ) . The Prdm9PWD/− hybrids produced markedly less progeny ( 0 . 3±0 . 2 OFM ) in comparison with the Prdm9PWD/C3H hybrids ( 3 . 6±0 . 5 OFM , Table S4 ) . Thus the intersubspecific F1 hybrid males carrying Prdm9PWD/− were not just more fertile than Prdm9PWD/B6 , but they were also less fertile than Prdm9PWD/C3H . To analyze the sensitivity of Prdm9PWD/− F1 background , the dosage of Prdm9C3H was increased by utilizing the BAC5 ( two copies of Prdm9C3H ) or the BAC24 ( six copies of Prdm9C3H ) transgenes . Again , the fertility parameters of the F1 transgenic hybrids improved with Prdm9C3H dosage ( Table 1; pSC = 0 . 006 , pTW = 0 . 008 , but no significant difference in relative testes weight: 6 . 6 versus 7 . 3 , p = 0 . 33 ) . The Prdm9 dosage effect is therefore observed in both Prdm9PWD/B6 and Prdm9PWD/− F1 hybrids . In conclusion , the B6 allele displays different properties than the C3H allele when present in one copy in the Prdm9PWD/B6 F1 hybrid male , because it decreases the fertility compared to Prdm9PWD/− F1 hybrid . This could be the result of a dominant-negative interaction of Prdm9B6 with specific loci in ( PWD×B6 ) F1 and/or with the Prdm9PWD allele . Hybrid sterility is most often sex-specific [1] and dependent on the origin of parents . The ( PWD×B6 ) F1 hybrid displays a complete male-specific arrest of gametogenesis that is at least partially alleviated in reciprocal ( B6×PWD ) F1 and in 94% of males from the backcross ( ( PWD×B6 ) ×B6 ) [12] . To determine whether the semisterility of the Prdm9PWD/− intersubspecific males displays the same features , additional phenotyping was performed . All four tested Prdm9PWD/− females produced offspring , in a number ( 4 . 8±0 . 8 OFM ) similar to their Prdm9PWD/B6 littermates ( 4 . 5±0 . 2 ) and ( PWD×B6-Prdm9C3H ) F1 females ( 5 . 5±0 . 6 ) . The semisterility of Prdm9PWD/− F1 hybrids is thus male-specific . When the Prdm9PWD/− F1 females were backcrossed to PWD , the resulting BC1 Prdm9PWD/− hemizygous males displayed a four-fold increase of average SC compared to Prdm9PWD/− F1 hybrid males ( p = 0 . 013 ) , indicating that the compromised fertility is most pronounced in the F1 generation . The TW and SC of the reciprocal ( B6-Prdm9−/wt×PWD ) F1 Prdm9−/PWD males ( Table 2 ) was markedly higher than that of the ( PWD×B6-Prdm9−/wt ) F1 Prdm9PWD/− hybrids ( Table 1 ) , displaying an asymmetry similar to the F1 cross of B6 and PWD . Thus , the fertility of Prdm9PWD/− hybrid likely suffers from an incompatibility ( ies ) , but to a lesser degree than in the presence of Prdm9B6 . Previously , the fertility of the azoospermic ( PWD×B6 ) F1 males resulting from the cross of PWD females with B6 males was rescued by Prdm9C3H overexpression [6] , but Prdm9 dosage has not been studied in the reciprocal , sperm-carrying ( B6×PWD ) F1 hybrids , although these males do not reach the reproductive fitness of fully fertile males ( Table 2 and Table 3 ) . To analyze the Prdm9 dosage effect in the reciprocal hybrids , we crossed PWD males with females carrying a variable number of four different Prdm9 alleles on B6 background . The fertility parameters of the Prdm9−/PWD hybrid males were superior to those of their ( B6×PWD ) F1 Prdm9B6/PWD littermates ( Table 2 ) . The parameters of the Prdm9B6/PWD transgenics carrying BAC5 were also better than those of the Prdm9B6/PWD control ( Table 2 ) . In contrast , BAC21 overlapping most of BAC5 but carrying truncated Prdm9 [6] did not improve the fertility of Prdm9B6/PWD hybrids ( Table S5 ) . To discern whether a single copy of Prdm9C3H can improve the fertility of reciprocal hybrids , males from the cross ( ( B6×B6-Prdm9C3H ) ×PWD ) were inspected . The increased fecundity of a subset of these males could be ascribed to the presence of Prdm9C3H ( Table 2 , pTW<0 . 001 , pSC = 0 . 003 ) . The reciprocal Prdm9C3H/PWD F1 males displayed superior fertility parameters than the Prdm9PWD/C3H hybrids ( Table S4; pTW = 0 . 004 , pSC = 0 . 03 , pOFM = 0 . 04 ) . To determine whether the fertility rescue of the reciprocal hybrids is limited to the Prdm9C3H allele and the PWD Mmm strain , we again used the C57BL/6J-Tg ( RP23-159N6 ) 75Bdm ( carrying Prdm9B6 ) and STUS strains . The Mmm-derived strain STUS produces sterile ( azoospermic or oligospermic ) male offspring with B6 females [30] , [31] . In agreement with this finding , the non-transgenic F1 male progeny of STUS males with B6 females heterozygous for the Prdm9B6 BAC were sterile; however , the transgenic littermates were fertile ( Table S1 ) . Thus , a single copy of the Prdm9B6 allele caused a decrease in fertility of F1 hybrids compared to the males having this allele removed , while the increased dosage of the same allele improved fertility in all F1 intersubspecific hybrid backgrounds tested . The substitution of chr17B6 with chr17PWD using the B6 . PWD-Chr17 strain rescued fertility of ( PWD×B6 ) F1 azoospermic hybrids , because the ( PWD×B6 . PWD-Chr17 ) F1 males are fertile [12] . To assess the role of chr17 heterozygosity and Prdm9PWD dosage in the semifertile reciprocal Prdm9B6/PWD hybrids , ( B6 . PWD-Chr17×PWD ) F1 males were phenotyped . The replacement of chr17B6 with chr17PWD in ( B6×PWD ) F1 hybrids led to full fertility ( Table 2 , pTW<0 . 001 , pSC<0 . 001 ) . Thus , the reciprocal hybrids are sensitive to the same combinations of Prdm9 alleles and dosage as the ( PWD×B6 ) F1 hybrids , but with a shift towards higher fertility ( Table 4 and Table S4 ) . Because one copy of Prdm9B6 reduced fecundity when added to Prdm9−/PWD F1 hybrids , the semifertility of the ( B6×PWD ) F1 hybrids appears to be affected by a dominant-negative interaction ( s ) of Prdm9B6 . The Prdm9PWD/B6 , Prdm9PWD/− , and Prdm9PWD/C3H F1 hybrids show a progressive increase in overall fertility , yet even the Prdm9PWD/C3H F1 hybrid does not reach the parameters of other fertile males ( Table 1 ) . The fecundity defects in these hybrids could either represent different degrees of the same arrest or multiple breakdowns affecting different stages of spermatogenesis . To compare the progress of spermatogenesis in these hybrids , indirect immunofluorescence microscopy was performed on surface-spread nuclei of adult testicular cells ( chromosome spreads , Table 3 and Table S4 ) . In agreement with the SC data but in contrast to sterile hybrids carrying Prdm9PWD/B6 , the chromosome spreads revealed the presence of spermatids in ( PWD×B6-Prdm9wt/− ) F1 Prdm9PWD/− males . The relative number of round spermatids in these Prdm9PWD/− testes did not reach the number observed in Prdm9PWD/C3H hybrids ( Table 3 and Table S4 , p<0 . 001 ) . Due to an arrest at pachynema , the relative number of the four stages of primary spermatocytes in Prdm9PWD/B6 was different from Prdm9PWD/− and Prdm9PWD/C3H F1 intersubspecific hybrids ( Table 3 and Table S4 , Figure S1 ) . A sex body was formed in 67% of pachytene spermatocytes of Prdm9PWD/− hybrids ( Figure 2 ) , a higher proportion in comparison to Prdm9PWD/B6 ( p<0 . 001 ) but lower compared to Prdm9PWD/C3H hybrids ( p = 0 . 001 ) . The Prdm9PWD/C3H hybrid carried a lower ratio of pachytene spermatocytes displaying a sex body than the B6 ( p = 0 . 004 ) and PWD ( p = 0 . 03 ) fertile controls . The staining of spermatocyte chromosome spreads with MLH1 and SYCP1 revealed that the proportion of nuclei with fully synapsed pachytene chromosomes carrying over 20 recombination nodules is higher in the Prdm9PWD/− than in the Prdm9PWD/B6 hybrids ( p<0 . 001 ) . The Prdm9PWD/− hybrids were similar in this respect to Prdm9PWD/C3H , but both carried less pachytene spermatocytes with completed recombination than B6 and PWD ( Table S4 ) . The meiotic phenotypes thus correlate with the TW-SC-OFM data; the Prdm9PWD/B6 , Prdm9PWD/− , and Prdm9PWD/C3H F1 hybrids display a gradual increase in meiotic progress , yet even the Prdm9PWD/C3H F1 hybrid does not reach the parameters of B6 or PWD . To determine whether the partial arrest of spermatogenesis of the semifertile reciprocal ( B6×PWD ) F1 hybrids involves meiosis I , spermatocyte chromosome spreads were analyzed . The relative number of pachytene cells carrying a sex body in the Prdm9B6/PWD hybrid was lower than in B6 and PWD ( Table 3 and Table S4 ) and it was elevated by removing the Prdm9B6 allele from the Prdm9B6/PWD hybrid ( p = 0 . 03 , Table 3 and Table S4 ) . The number was higher in the Prdm9C3H/PWD intersubspecific male in comparison to the Prdm9B6/PWD F1 hybrid ( p = 0 . 03 , Table S4 ) , and increased in the BAC5-carrying reciprocal hybrid compared to the same hybrid harboring BAC21 ( p = 0 . 003 , Table S5 ) . Analysis of MLH1 recombination nodules indicated that Prdm9B6/PWD hybrid testes carried less pachytene spermatocytes with completed recombination than B6 or PWD ( Table S4 ) . The relative number of the four stages of primary spermatocytes in Prdm9B6/PWD differed from that in fertile males ( Table S4 , Figure S1 ) . The number of offspring ( OFM ) correlated with meiotic phenotypes in all investigated hybrids ( Table S4 ) , thus postmeiotic incompatibilities may play a minor role in our model of F1 mouse hybrid sterility . The fertility of the azoospermic Prdm9PWD/B6 F1 hybrids can be rescued by Prdm9 overexpression [6]; however , the amounts of the Prdm9 mRNAs are similar in prepubertal Prdm9PWD/B6 and Prdm9PWD/C3H hybrids that differ in prospective fertility but not in Prdm9 dosage [6] . To understand the mechanism of the partial fertility rescue inflicted by the Prdm9 null alleles in PWD hybrids , the transcript levels of Prdm9 were investigated in prepubertal hybrid testes . The expression of Prdm9 was analyzed using five qRT-PCR amplicons along the gene to account for all alternative transcripts [6] . The mRNA levels of Prdm9 were similar in four investigated types of 14-day-old F1 hybrid testes carrying Prdm9PWD/B6 , Prdm9PWD/C3H , Prdm9B6/PWD , and Prdm9C3H/PWD , but were significantly decreased to 52 . 9±2 . 3% in the prospectively sperm-carrying Prdm9PWD/− F1 hybrids compared to the prospectively azoospermic Prdm9PWD/B6 littermate controls ( Figure 3 ) . In other words , the transcription from the PWD , C3H , and B6 alleles seems to be similar and dosage-dependent . Therefore , the dominant-negative interaction ( s ) of the Prdm9B6 allele contributing to sterility in the ( PWD×B6 ) F1 hybrid is most likely not a consequence of a change in the Prdm9 transcript level .
Our “digital genetics“ approach ( additions and subtractions of Prdm9 copies ) brings a new insight into the interactions of Prdm9 and other hybrid sterility genes participating in the genetic Dobzhansky-Muller incompatibilities ( DMIs ) and controlling the reproductive fitness of intersubspecific mouse hybrids . The phenotype of intersubspecific hybrid males is affected by Prdm9 allelic combination and dosage ( summarized in Table 4 ) . One copy of Prdm9B6 on multiple F1 intersubspecific hybrid backgrounds is one of the causes of reduced fertility , but a rescue can be achieved with a transgene carrying multiple copies of Prdm9B6 or Prdm9C3H . The replacement of Prdm9B6 with Prdm9C3H in ( PWD×B6 ) F1 males significantly improves fecundity , but it nevertheless leads to semifertility that can be improved by an increased Prdm9 dosage . The fertility rescue of hybrids by Prdm9 transgenes is also dependent on the Prdm9 copy number . The F1 background is sensitive to Prdm9 dosage , indicating DMIs between PWD and B6 genomes . These DMIs could involve genetic interactions between Prdm9 and other loci , but they might also be explained by interactions between Prdm9-independent loci appearing as the consequence of sensitization by the Prdm9 dosage . Although the overexpression of both Prdm9B6 and Prdm9C3H alleles improved the fertility of F1 hybrids , the variation of effects among the Prdm9 alleles when in one or two copies suggests either variation in the strength of the same interaction ( s ) or specific DMIs for each Prdm9 allele . Prdm9B6 was the only allele that resulted in a worse phenotype when one copy was added to either type of F1 reciprocal hybrids carrying one Prdm9PWD allele , indicating a dominant-negative effect of Prdm9B6 specific for ( PWD×B6 ) F1 and ( B6×PWD ) F1 . The beneficial effect of the increased copy number of Prdm9 transgenes irrespective of the transgenic allele suggests that the “toxic“ effect of the Prdm9B6 incompatibility can be diluted by the overabundance of Prdm9B6 . The fertility of F1 hybrid males harboring chrXB6 was always better than that of the comparable reciprocal chrXPWD-carrying males , suggesting a chrXPWD DMI ( s ) occur ( s ) in intersubspecific hybrids . Although theoretical options also include the interactions of chrY , mitochondrial genome or genomic imprinting , the interaction of chrXPWD and chr17PWD/B6 was revealed by mapping hybrid sterility loci in ( ( PWD×B6 ) ×B6 ) backcross , as well as in F1 using chrX subconsomics [12] . The decreased fertility of the reciprocal hybrid males Prdm9B6/PWD compared to Prdm9−/PWD and Prdm9C3H/PWD could be explained by the incompatibility of Prdm9B6-Hstws with chrXB6 or autosomal loci . Another DMI ( s ) not involving Prdm9B6 probably also acts in F1 hybrids , since the null Prdm9 alleles do not restore complete fertility . As the fertility of the reciprocal Prdm9−/PWD hybrids was superior to that of the Prdm9PWD/− F1 males , this DMI ( or one of these DMIs ) independent of Prdm9B6 could involve chrXPWD . Both the elimination of Prdm9B6 and Prdm9 overexpression rescued fecundity in the reciprocal ( B6×PWD ) F1 hybrids , suggesting that Prdm9B6 also participate ( s ) in a DMI ( s ) not involving chrXPWD . Supposing the same DMIs also work in the ( PWD×B6 ) F1 male , a hypothesis supported by its complete sterility , there seems to be at least three sets of incompatibilities affecting the meiotic arrest in this male: Prdm9B6 with chrXPWD , Prdm9B6 with an unknown autosomal locus or loci , and chrXPWD with an unknown autosomal locus or loci . Alternatively , the sets of incompatibilities could be: interautosomal B6 versus PWD sensitive to the dosage of any Prdm9 allele; chrXPWD with B6 autosomes; Prdm9B6 with Prdm9PWD and/or with PWD autosomal loci . Although backcrosses using the Prdm9 null alleles could reveal the number and map positions of the unknown autosomal loci , we already have a good candidate for one of these loci , Hstws on chr17PWD . The Hst1s-Hstws ( Prdm9B6-chr17PWD ) incompatibility in F1 hybrids is alleviated by deletion of Prdm9B6 and substitution of chr17B6 with chr17PWD leading to increased fertility . An epistatic interaction of chr17PWD/B6 with chrXPWD is necessary , albeit not sufficient for sterility of ( ( PWD×B6 ) ×B6 ) BC1 males [12] . However , at the moment we cannot distinguish between the effects of intergenic and interallelic interactions , also because the impact of Prdm9PWD on hybrid sterility has not been directly investigated . While there is strong evidence that Prdm9 is identical with Hst1 in Mmd [6] , we are unable to exclude that the Hstw locus in Mmm is linked to but different from Prdm9 . On the other hand , Prdm9 carries the fastest evolving ZnF domain in metazoans [26] and Prdm9PWD/− hybrids display a reduced number of pachytene spermatocytes harboring sex bodies , as well as other features of partial meiotic arrest . Therefore , the incompleteness of the rescue of hybrid fertility by Prdm9B6 deletions in the ( PWD×B6 ) F1 hybrid can be interpreted as the consequence of Prdm9PWD haploinsufficiency in the context of the F1 hybrid background , because Prdm9PWD/− F1 males were more affected than Prdm9PWD/− backcross males . The hybrid sterility phenotype shows the features of spermatogenesis seen in the Prdm9−/− male and it can be alleviated by Prdm9 transgenes [6] . Prdm9B6 thus appears to participate in a loss-of-function DMI [32] . However , hybrid sterility can also be partially corrected by Prdm9 null alleles , suggesting a gain-of-function DMI . Thus Prdm9B6 might take part in multiple DMIs , both gain- and loss-of-function . Alternatively , the increased dosage of Prdm9 could leave its part not participating in a gain-of-function DMI ( s ) for the normal function . The viability of certain Drosophila hybrids is affected by the gene dosage of Hmr ( Hybrid male rescue ) [33] and by the DNA-regulatory divergence of the Lhr ( Lethal hybrid rescue ) gene [34] . Although the fertility of mouse hybrids can be rescued by the increased dosage of Prdm9 , we excluded that the key difference between the Prdm9C3H and Prdm9B6 alleles lies in increased transcription , because the expression of Prdm9 mRNAs in Prdm9PWD/B6 , Prdm9PWD/C3H , Prdm9B6/PWD , and Prdm9C3H/PWD prepubertal hybrid testes of the same age were similar despite the different prospective fertility ( Figure 3 ) . Increased translational efficiency remains a possibility for the key allelic difference , as Prdm9C3H and Prdm9B6 differ in the 5′-untranslated region [6] . However , the polymorphism in the ZnF region of the protein products could also provide an explanation for the functional allelic difference . As hybrid sterility can be overcome by the increased dosage of any Prdm9 allele , one must control the number of copies in experiments designed to discern the functional sequence differences in the Hst1 alleles . Grey et al [24] successfully used transgenesis to learn that the distribution of meiotic recombination hotspots is affected by the ZnF domain allele of Prdm9; no difference in the distribution was seen in the control Prdm9B6 BAC transgenics . It might seem that the correction of hybrid sterility caused by the same Prdm9B6 BAC could be caused by a different mechanism than redistribution of recombination hotspots . However , the increased dosage of Prdm9 in F1 hybrids may overcome the DMIs and change the localization of hotspots . Nevertheless , it is unknown how a changed distribution of hotspots could lead to sterility , especially when considering that Prdm9 function is dispensable for fertility in the dog [35] , [36] . Thus ( an ) other function ( s ) of Prdm9 may be involved in hybrid sterility , e . g . , transactivation of meiotic genes . While azoospermia was rare or absent , fertility reduced below the range found in pure species was found in one third of males in the Bavarian part of the natural house mouse hybrid zone [37] . The lack of azoospermic males can be explained by the absence of F1-like animals in the zone [37] , [38] . F1 male sterility may thus be more important for establishing than for maintaining the hybrid zone . Although most of the fertility differences detected in our study were robust enough to affect the number of offspring , they are likely to have even greater impact in nature considering the sperm competition during multiple mating [39] . Prdm9B6 plays a role in the complete meiotic arrest of ( PWD×B6 ) F1 hybrids [6] . The importance of this finding for mouse speciation could be somewhat limited considering that only males carrying a certain allele resulting from one direction of a cross between two subspecies are affected . In this report , we demonstrated that Prdm9 also participates in the sterility of the reciprocal ( B6×STUS ) F1 and in the partial meiotic failure of the ( B6×PWD ) F1 males . The meiosis of ( PWD×B6-Prdm9C3H ) F1 hybrids harboring another Mmd Prdm9 allele is adversely affected by a DMI that can be alleviated by an increased Prdm9 dosage or using the reciprocal cross , ( B6-Prdm9C3H×PWD ) F1 . The reciprocal crosses of PWD and of the wild-Mmd-derived strain WSB/Ei also display differences in hybrid sterility [40] . Although many quantitative trait loci were detected in ( WSB×PWD ) F2 intercross males , heterozygosity in a region overlapping the genomic position of Prdm9 decreases SC and relative TW; regions associated with fertility were also found on chrXPWD [40] . The Prdm9 allele of WSB is similar to C3H , being the same in the ZnF domain [23] , yet WSB differs from C3H in other parts of Prdm9 [19] , [41] . Therefore , the semisterility of ( PWD×WSB ) F1 males seems to involve Prdm9 . Admittedly , the degree of importance of Prdm9 for mouse speciation also depends on the frequency of alleles causing reduced fertility near the hybrid zone that is currently unknown; however , the relevance of Prdm9 for hybrid sterility now appears to be greater than shown previously .
The mice were kept at the Specific Pathogen-Free Facility of the Institute of Molecular Genetics , Prague , and in a conventional breeding facility of the Institute of Vertebrate Biology in Studenec . Principles of laboratory animal care obeyed the Czech Republic Act for Experimental Work with Animals ( Decree No . 207/2004 Sb , and the Acts Nos . 246/92 Sb , and 77/2004 Sb ) fully compatible with the corresponding EU regulations and standards , namely Council Directive 806/609/EEC and Appendix A of the Council of Europe Convention ETS123 . The STUS and PWD/Ph strains are derived from wild mice of Mmm subspecies [30] , [42] . Mice carrying a deletion of chr17 , Sod2df14J , were generated through embryonic stem cells [28] harboring Prdm9B6 on ( 129×B6 ) F1 background and were transfected with BAC5 . The deletion causes lethality when homozygous , it is several Mbp in length , and it includes the Hst1 region [6] . The BAC5 , BAC21 , and BAC24 C3H/HeJ transgenes have no effect on fertility in non-intersubspecific hybrid males , but BAC5 and BAC24 rescue fertility of sterile hybrids [6] . The results of quantitative PCR [6] indicate that BAC24 line contains six and BAC5 two copies of Prdm9C3H; BAC21 carries two copies of truncated Prdm9 ( only the last , ZnF-encoding exon ) . BAC5 and BAC24 transgenes rescue fertility in Prdm9−/− ( data not shown ) . All three transgenic lines were transferred to B6 background through 10 generations of backcrossing . The B6-Prdm9C3H ( B6*B10 . C3H-Hst1f ) congenic carries the C3H polymorphisms at Prdm9 and the differential segment is 3 . 3 Mbp ( position in the mm9 genome assembly 12 . 5 to 15 . 9 Mbp ) to 6 . 4 Mbp ( 10 . 2 to 16 . 5 Mbp ) in length . The knock-out line Prdm9tm1Ymat was generated in 129P2/OlaHsd ES cells by replacement of the first five coding exons with LacZ [20] and maintained on mixed 129P2/OlaHsd * C57BL/6 background . The C57BL/6J-Tg ( RP23-159N6 ) 75Bdm strain ( transgene Accession ID: MGI:5311012 ) was generated by injection of a circular BAC DNA into zygotic male pronuclei; it carries four to five copies of RP23-159N6 BAC harboring Prdm9B6 ( Hst1s ) on B6 background , and the Prdm9 steady-state mRNA level in primary spermatocytes is about 1 . 3- to 2 . 5-times increased compared to non-transgenic animals [24] , suggesting that two Prdm9 copies are expressed from the transgene . See Text S1 for the PCR primers and conditions used for genotyping . Body weight ( BW ) and testicular weight ( TW , from paired testicles ) were determined in adult males ( 9 to 12- week-old ) . Sperm count ( SC ) was obtained from paired caput epididymides at room temperature [6] , except for the experiments using the Prdm9B6 transgene , where the entire left epididymis was extracted at 37°C [43] . Multiple biological replicates of each genotype were also analyzed for cellular phenotypes and RNA expression . Slides with surface-spread nuclei ( chromosome spreads ) were obtained from adult testicular cells using isotonic [44] or hypotonic [45] treatment; see Text S1 for the antibodies . Semiquantitative real-time RT-PCR was performed using total testicular RNAs of 14-day-old F1 intersubspecific hybrids exactly as described previously [6] . The significance of BW , TW , and qRT-PCR values was analyzed using Welsch's t-test , SC and OFM with Wilcoxon rank sum test , and cellular phenotypes with χ2 test . Unless stated otherwise , the comparison significant for TW was also significant for relative TW ( TW/BW ) . | Disturbed gametogenesis in the progeny of two fertile parental forms is called hybrid sterility; it is an important part of reproductive barriers between species . The Dobzhansky-Muller model of incompatibilities explains reproductive isolation between species by incorrect interactions between genes . Hybrid sterility 1 ( Hst1 ) is one of the genes causing meiotic arrest in F1 male hybrids between certain Mus musculus musculus ( e . g . , the PWD strain ) and M . m . domesticus ( C57BL/6J etc . ) mice . Hst1 , the first mammalian candidate for a speciation gene , was identified as a meiotic PR/SET-domain gene , Prdm9 , but the mechanism causing sterility has remained unknown . While the F1 male offspring of C57BL/6J males and PWD females produce no sperm , the males from the reciprocal cross using PWD males and C57BL/6J females yield progeny . Here we show that the meiotic progress and fertility of hybrid males from both F1 crosses improved by removal as well as overexpression of the C57BL/6J allele of Prdm9 , suggesting that Prdm9 interactions not present in the parental species ( incompatibilities ) play a role in hybrid sterility . Furthermore , the Prdm9 dosage also controlled fecundity in other F1 hybrids , indicating that this gene is an important regulator of mouse hybrid fertility . | [
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"traits"
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| 2012 | Interallelic and Intergenic Incompatibilities of the Prdm9 (Hst1) Gene in Mouse Hybrid Sterility |
Polyomaviruses are a family of small non-enveloped DNA viruses that encode oncogenes and have been associated , to greater or lesser extent , with human disease and cancer . Currently , twelve polyomaviruses are known to circulate within the human population . To further examine the diversity of human polyomaviruses , we have utilized a combinatorial approach comprised of initial degenerate primer-based PCR identification and phylogenetic analysis of nonhuman primate ( NHP ) polyomavirus species , followed by polyomavirus-specific serological analysis of human sera . Using this approach we identified twenty novel NHP polyomaviruses: nine in great apes ( six in chimpanzees , two in gorillas and one in orangutan ) , five in Old World monkeys and six in New World monkeys . Phylogenetic analysis indicated that only four of the nine chimpanzee polyomaviruses ( six novel and three previously identified ) had known close human counterparts . To determine whether the remaining chimpanzee polyomaviruses had potential human counterparts , the major viral capsid proteins ( VP1 ) of four chimpanzee polyomaviruses were expressed in E . coli for use as antigens in enzyme-linked immunoassay ( ELISA ) . Human serum/plasma samples from both Côte d'Ivoire and Germany showed frequent seropositivity for the four viruses . Antibody pre-adsorption-based ELISA excluded the possibility that reactivities resulted from binding to known human polyomaviruses . Together , these results support the existence of additional polyomaviruses circulating within the human population that are genetically and serologically related to existing chimpanzee polyomaviruses .
Over recent years the rate of identification of new viruses within human and animal populations has increased exponentially . Since 2007 , more than 20 novel animal polyomaviruses have been discovered , and 12 genetically distinct human polyomaviruses are currently known . Polyomaviruses are non-enveloped viruses with a circular double-stranded DNA genome of approximately 5 , 000 base-pairs . All polyomaviruses encode proteins ( large and small T antigens; LTag and STag ) that have potential oncogenic capacity . However , transformation by these viruses is influenced by the individual virus type , as well as by the animal species undergoing infection [1]–[4] . With the exception of Merkel cell polyomavirus ( MCPyV ) , the contribution of infection by polyomaviruses to human cancer remains unclear [5]–[7] . Infection with human polyomaviruses usually occurs in childhood or during adolescence without severe acute symptoms and results in lifelong persistence with no apparent disease . However , polyomavirus reactivation can cause serious disease in immunocompromised patients [8] . BK virus ( BKPyV ) was initially identified associated with nephropathy in renal transplant patients and with hemorrhagic cystitis in bone marrow transplant patients [9] , [10] . Similarly , JCPyV was recognized as the causative agent of progressive multifocal leukoencephalopathy in iatrogenically immunosuppressed or HIV-infected individuals [11] . MCPyV was first identified in 2008 , and has since been shown to be the etiological agent responsible for Merkel cell carcinoma [12] . Recently , a new human polyomavirus was detected in a patient suffering from Trichodysplasia spinulosa , and has been designated Trichodysplasia spinulosa-associated polyomavirus ( TSPyV ) [13] . Seven additional human polyomaviruses have been identified , but these viruses have not been linked to any disease [14]–[20] . Serological evidence indicates that most human adults have been exposed to many , if not all , of the known human polyomaviruses [20]–[25] . Human sera have been observed to serologically cross-react with polyomaviruses of nonhuman primates ( NHPs ) that have closely related human counterparts . For example , human sera reactive against BKPyV and JCPyV are cross-reactive with the closely related Old World monkey ( OWM ) polyomavirus simian virus 40 ( SV40 ) , and sera reactive against human polyomavirus 9 ( HPyV9 ) have been shown to cross-react with the closely related OWM lymphotropic polyomavirus ( LPyV ) [21] , [23] , [25]–[27] . We propose that this cross-reactivity between human and closely related NHP polyomavirus counterparts may be used as an indicator for presence of unknown human polyomaviruses circulating within the human population . In the present study , we have performed a comprehensive search for unknown NHP polyomaviruses by using degenerate primer-based PCR . Identified novel polyomaviruses were then sequenced to determine phylogenetic position within the polyomavirus family , followed by the use of serological assays of human sera to assess for the presence of reactivity against these newly identified NHP polyomaviruses . Our main focus was placed on chimpanzees , since they are our closest phylogenetic relatives and might therefore harbor polyomaviruses closely related to those found in humans [28] . We report on the discovery of 20 new NHP polyomaviruses ( 6 in chimpanzees ) , and the sequencing of 10 viruses at the complete genome level . Serological assays identify reactivity in human sera for a number of evolutionary distinct chimpanzee polyomaviruses , supporting the existence of currently unknown human polyomaviruses circulating within the human population .
Degenerate primer PCR-based analysis was performed to ascertain the diversity of polyomaviruses in wild NHPs . For this analysis , blood , tissue and fecal samples ( n = 792 ) collected from live or deceased great apes , OWMs , NWMs and prosimians ( 44 different species; Table S1 ) were analysed by using two generic polyomavirus PCRs ( PCR1 and PCR2; Table S2 ) . Both PCRs target highly conserved regions of the gene encoding for the major structural protein VP1 , and had previously been successful in identifying multiple chimpanzee polyomaviruses [29] , [30] . Testing of 359 samples with PCR1 , and 433 samples with PCR2 identified 61/792 ( 8% ) positive samples . Among the organs for which more than 20 samples were available , spleen , lymph node , intestine , and lung revealed the highest detection rates ( 20% , 16% , 15% and 7% , respectively ) . In addition , 3/7 skin samples ( 43% ) were PCR-positive . In contrast , less than 4% of feces , blood , urine and kidney samples were PCR-positive ( Table 1 ) . The amplified VP1 sequences were shown by BLAST analysis to originate from 24 distinct polyomaviruses , all exhibiting less than 90% nucleic acid identity to each other , or to the corresponding region of known polyomaviruses . Novel polyomaviruses were detected in eight catarrhine ( OWM and great apes ) and four platyrrhine ( NWM ) species , and were provisionally named according to their host species as described in the Methods section . We discovered 9 polyomaviruses in great ape species ( six in chimpanzees; two in the gorilla; one in orangutan ) . These viruses were further characterized in the present study and are listed in Table 2 . Four additional great ape polyomaviruses ( three of chimpanzee; one of gorilla ) showed a high similarity to the human MCPyV . Their full-genome sequence was previously published [30] ( Table S3 ) . Six chimpanzees were co-infected with combinations of multiple chimpanzee polyomaviruses . In five OWM and four NWM species we detected five and six novel polyomaviruses , respectively ( Table 2 ) . In one NWM ( white-fronted capuchin; Cebus albifrons ) co-infection was observed . Prosimian ( strepsirrhine ) polyomaviruses were not detected , which may be a reflection of the small ( n = 20 ) sample size . To characterize the complete genomes of the nine great ape and 11 monkey polyomaviruses , specific nested primers in an inverse orientation were used to target the partial VP1 sequences obtained from the initial degenerate primer PCR for long-distance PCR amplification of the remaining virus genome . Complete genome sequences of ten polyomaviruses were amplified and sequenced: four from chimpanzees [Western chimpanzee ( Pan troglodytes verus ) and Eastern chimpanzee ( Pan troglodytes schweinfurthii ) ] , three from OWMs [Eastern red colobus ( Piliocolobus rufomitratus ) , red-eared guenon ( Cercopithecus erythrotis ) and crab-eating macaque ( Macaca fascicularis ) ] and three from NWMs [black spider monkey ( Ateles paniscus ) , white-fronted capuchin ( Cebus albifrons ) and common squirrel monkey ( Saimiri sciureus ) ] . Repeated amplification attempts from the remaining ten polyomaviruses were unsuccessful , most likely due to low genome copy numbers . The sequence information of these ten complete genomes and ten partial VP1 sequences has been deposited in the GenBank database . The accession numbers are listed in Table S3 . The full-length genomes have a length of 4970 bp to 5349 bp and exhibit the typical set of polyomavirus open reading frames ( ORFs ) . The early regions are comprised of two ORFs encoding the non-structural proteins LTag and STag . The late regions code for the structural proteins VP1 , VP2 and VP3 , separated by a non-coding control region ( NCCR ) . Only CeryPyV1 ( from red-eared guenon ) also harbors sequence information for a putative agnoprotein ORF . An ORF encoding a middle T-antigen was not identified in any of the viruses . The ORF locations and their lengths are listed in Table S4 . We examined the NCCRs for the presence of LTag binding sites ( GAGGC ) and inverted repeats ( see Text S1; Figures S1 and S2 ) . We also performed a detailed analysis of LTag for sequences corresponding to known functional motifs described in the SV40-derived form of the protein ( see Text S2; Figures S3 , S4 , and S5; Table S5 ) . All NCCRs possess one or several LTag binding sites and AT-rich stretches . Only the MfasPyV1 NCCR contains an inverted repeat ( Figure S1 ) . The LTag of all 10 novel nonhuman primate polyomaviruses contain a K/R-rich nuclear localization signal and CR1 , DnaJ , Zn-finger and ATPase consensus motifs . Remarkably , only 6 out of the 10 possess the conserved LXCXE pRb1 pocket , suggesting some of the LTag of these novel polyomaviruses may not bind the retinoblastoma protein . Putative interaction domains with Bub-1 and CUL-7 are present in some of the LTag ( Table S5; Figures S3 and S4 ) . Ancient recombination events among polyomavirus lineages has recently been evidenced [31] . We therefore conducted independent phylogenetic analyses on amino acid alignments of three coding regions , VP1 , VP2 and LTag ( respective alignment lengths: 244 , 90 and 443 amino acids ) . All alignments were comprised from the novel polyomaviruses and those currently available in GenBank , including all known human polyomaviruses ( as of February 2013; Table S3 ) . Maximum likelihood and Bayesian analyses of these alignments were performed . This confirmed the likely recombinant nature of some polyomaviruses and notably of those belonging to the Wukipolyomavirus genus ( Figure 1; Figures S6 and S7 ) . In addition , it also revealed that primate polyomaviruses were scattered over the entire polyomavirus tree , whether considering VP1 , VP2 or large T phylogenetic trees ( Figure 1; Figures S6 and S7 ) . We identified 7 well-supported clades relevant to the novel polyomaviruses described in this study ( Figure 1; Supplemental Figures S6 and S7; Table 3 ) : To study the reactivity of human sera against the NHP polyomaviruses , VP1 proteins from four completely sequenced chimpanzee polyomaviruses ( ChPyV , PtrovPyV3 , PtrovPyV4 , PtrosPyV2 ) with no close counterparts in humans were selected for use in indirect ELISA [clades ( b ) , ( e ) and ( f ) in Figure 1] . For these studies , VP1 from JCPyV and HPyV9 were selected as positive control proteins , and an avian polyomavirus [APyV , also known as Budgerigar fledgling disease virus ( BFDV ) ] [32] was chosen as negative control . VP1 proteins expressed in E . coli are known to form pentameric capsomer structures [33] , and have proved effective for analysis of polyomavirus serology [21] , [25] , [34] , [35] . To serologically assess the level of ChPyV , PtrovPyV3 , PtrovPyV4 and PtrosPyV2 circulating in chimpanzees , ELISA was performed on plasma samples of 40 chimpanzees . A high seroprevalence was shown for each virus ( ChPyV , 100%; PtroPyV3; 73%; PtrosPyV4 , 90%; PtrosPyV2 , 88% ) . These results indicate that all 4 polyomaviruses are hosted by chimpanzees , with ChPyV being the most prevalent ( Figure 2 ) . A serum panel from German individuals and a plasma panel from individuals from Côte d'Ivoire were then evaluated for their reactivity to the 4 chimpanzee polyomaviruses and to JCPyV and HPyV9 . For the German sera ( n = 111 ) , the following seroreactivities were determined: ChPyV , 84%; PtrovPyV3 , 24%; PtrovPyV4 , 50%; PtrosPyV2 , 33%; HPyV9 , 21%; JCPyV , 42% ( Table 4 ) . Fourteen German sera ( 13% ) exhibited seroreactivity against all four chimpanzee polyomaviruses , and 14 samples ( 13% ) were completely negative ( Figure S8A ) . The Côte d'Ivoire plasma samples ( n = 115 ) showed more frequent reactivity: ChPyV , 97%; PtrovPyV3 , 60%; PtrovPyV4 , 96%; PtrosPyV2 , 77%; HPyV9 , 76%; JCPyV , 65% ( Table 4 ) . Each plasma reacted with at least one chimpanzee polyomavirus , with fifty-three ( 46% ) samples being reactive against all four tested chimpanzee polyomaviruses ( Figure S8B ) . Comparison of German and Côte d'Ivoire samples revealed that the seroprevalences were lower in the German samples ( P<0 . 001 for all viruses; Table 4; Figure 3 ) . This difference was also observed when analysis was age-restricted to individuals between 20 and 60 years ( P = 0 . 004 for ChPyV , P<0 . 001 for all other viruses ) . Mean absorbance values were significantly lower in German samples for all viruses ( P<0 . 001 for all viruses ) ( Figure 3 ) . Age had no significant effect on sera/plasma absorbance values against any virus in either Germans or individuals from Côte d'Ivoire ( P>0 . 05 for all viruses; Figure S9 ) . To visualize possible correlations of seroreactivity against the tested VP1 antigens , OD450 values were plotted against each other , and correlation analysis was performed to check for statistical evidence of cross-reaction . Rank correlation showed only slight to moderate correlations ( 0 . 178 to 0 . 62 ) , and for none of the antigen pairs was a correlation >0 . 5 measured for any serum/plasma panel ( Table S6 ) , indicating the absence of marked cross-reactions . To assess possible antigenic cross-reactivity between the four chimpanzee polyomaviruses and known human polyomaviruses , competitive inhibition of seroreactivity was tested . Serum and plasma samples ( n = 5–7 ) reactive against VP1 of a particular chimpanzee polyomavirus ( ChPyV , PtrovPyV3 , PtrovPyV4 or PtrosPyV2 ) were tested by ELISA using the respective chimpanzee polyomavirus VP1 as the antigen . Prior to use in the assay , all sera were pre-adsorbed with soluble VP1 antigen from BKPyV , HPyV9 , JCPyV , MCPyV or TSPyV . Incubation with the soluble homologous chimpanzee polyomavirus VP1 , and soluble APyV VP1 served as positive and negative controls , respectively . Pre-adsorption with the homologous chimpanzee polyomavirus antigens reduced the ELISA reactivity by approximately 80% or more in all cases ( Table 5 ) , and pre-incubation with APyV VP1 had no effect on reactivity ( data not shown ) . This showed efficacy and specificity of the pre-adsorption procedure . With one exception , pre-incubation with VP1 from human polyomaviruses did not reduce reactivity of sera for VP1 of ChPyV , PtrovPyV3 , PtrovPyV4 or PtrosPyV2 . In the one exception , pre-incubation of PtrosPyV2-reactive human sera with soluble HPyV9-VP1 reduced the PtrosPyV2-specific ELISA reactivity by 31% , indicating a potential weak cross-reactivity ( Table 5 ) . This cross-reactivity was consistent with presence of these two viruses in sister phylogenetic clades ( Figure 1 ) , with their VP1 proteins showing 75% identity . For this relatively high level of identity cross-reactive antibodies have been detected [21] , [25] . However , the non-adsorbable reactivity between PtrosPyV2 and HPyV9 ( Table 5 ) implied – beside HPyV9 – the involvement of PtrosPyV2 and/or another unknown polyomavirus in the reactivity of human sera against PtrosPyV2 . In summary , the presence of reactivity in human sera against VP1 from multiple NHP polyomaviruses with no currently known human homologue supports the presence of one or more unidentified human polyomaviruses phylogenetically related to each of these novel NHP viruses .
In the present study , multiple , hitherto unknown , highly diverse polyomaviruses were detected in great apes and monkeys . These viruses were localized mainly to lymphoid organs , lungs and intestinal tissue ( Tables 1 and 2; Table S1 ) . In phylogenetic analysis using VP1 , VP2 and LTag antigen protein sequences , four chimpanzee polyomaviruses ( ChPyV , PtrovPyV3 , PtrovPyV4 , PtrosPyV2 ) showed no close relationship to any of the known human polyomaviruses , including the most recently discovered human polyomaviruses HPyV10 , MWPyV , MXPyV , STLPyV and HPyV12 ( Figure 1 and Figures S6 and S7 , respectively ) . Positive ELISA reactivities against the VP1 structural proteins of these four chimpanzee polyomaviruses were observed in panels of human sera/plasma samples . Experiments involving competitive inhibition of seroreactivities with a panel of VP1 proteins from five human polyomaviruses ruled out the presence of cross-reactivity between the chimpanzee polyomaviruses and human polyomaviruses ( except for a weak cross-reactivity between HPyV9 and PtrosPyV2 ) ( Table 5 ) . This was confirmed by the lack of any significant correlation of seroreactivity against the different polyomavirus VP1 proteins for any of the sera/plasma samples tested . Therefore , the reactivity of human sera against the four chimpanzee polyomaviruses suggests that the majority of human subjects tested have been exposed to as yet unknown polyomaviruses . The use of serology for the detection of unknown polyomaviruses circulating within the human population is not without precedent . Several research groups had observed that up to 30% of human sera react against the monkey polyomavirus LPyV [21] , [36] , [37] . About 30 years after the first observation , it was discovered that human seroreactivity against LPyV was due to infection by HPyV9 [25] , a human polyomavirus closely related to LPyV . Ivorian plasma samples consistently showed higher levels of VP1 reactivity compared to samples from German individuals ( Figure 3 ) . One possible interpretation of this stronger reactivity is that it reflects increased ‘spillover’ of NHP polyomaviruses into humans , perhaps due to the possibility for closer interaction between humans and NHP species . However , the Ivorian samples reacted more strongly with all polyomaviruses investigated , including VP1 from the two human viruses , JCPyV and HPyV9 . This observation indicates a generally higher sero-reactivity , and is most likely not a result of zoonotic transmission events . Instead , it may reflect African-European differences in humoral immunity , similar to the differences in cellular immunity observed previously between patients from Gabon and Austria [38] , [39] , and Cameroonese children compared to other African and Caucasian populations [39] . Such immunological differences , together with differences in the level of transmissibility of local viral strains as well as social factors influencing person-to-person transmission , may result in pronounced geographic differences in seroprevalence rates . Seroprevalences of the human polyomaviruses BKPyV and MCPyV have for example been shown to range from 25% to 100% , depending on the geographic origin of the samples [16] , [21]–[23] , [26] , [27] , [36] , [40] , [41] . Using degenerate PyV PCR in NHPs , we found a high prevalence of polyomaviruses in spleen , lymph node and intestine samples . This observation led us to test comparable human tissue samples for the presence of human counterparts of ChPyV , PtrovPyV3 , PtrovPyV4 and PtrosPyV2 . Surprisingly , human spleen and lymph node samples were largely PCR negative . However , we did identify a novel human polyomavirus in liver and intestine samples that showed no close genetic relationship to any of the known polyomaviruses ( designated human polyomavirus 12; HPyV12 ) [20] , and only exhibited 55%–62% amino acid identity with VP1 sequences of ChPyV , PtroPyV3 , PtroPyV4 and PtrosPyV2 . Cross-reactivity of VP1 proteins in serological assays have thus far only been observed for proteins of more than 75% identity [17] , [21] , [23] , [24] , [42] , with polyomaviruses with lower VP1 identity values showing no cross-reactivity [42]–[44] . Therefore , we have substantial confidence that HPyV12 is not one of the putative unknown human polyomaviruses that were predicted in the present study . Identifying the human polyomaviruses predicted in this study will likely be no easy task . Their lack of detection in the face of the massive screening effort performed by the scientific community over recent years already testifies that these viruses are not easy targets . The underlying reason could be technical . For example , although efficient generic PCR methods are available , there is no guarantee that the systems in use can amplify these elusive human polyomaviruses . Another explanation may also lie in the biology of these polyomaviruses . For example , their tissue tropism may hamper detection if the corresponding tissue type is not commonly used for polyomavirus detection and/or is difficult to obtain . Therefore , the gain of molecular information about these human polyomaviruses may require the use of alternative detection methods , e . g . PCR systems specifically designed to target meaningful subsets of polyomaviruses , and/or targeting of body compartments that have not commonly been analyzed . Importantly , the results from the present study can be used to develop targeted nucleic-acid based detection methods for their identification in the future . Clearly , the limitation of the serological approach is the inability to discern single from multiple polyomaviruses within a phylogenetically related group . However , this strategy does indicate the presence of at least one , if not multiple , human polyomaviruses closely related to ChPyV , PtrovPyV3 and PtrovPyV4 ( and possibly PtrosPyV2 ) , circulating at substantial levels within the human population . The specific identity of the human correlate polyomaviruses and the disease implications associated with infection by these viruses remain to be determined .
General permission for sample collection from deceased wild primates was obtained from the authorities of national parks of each country . Deceased animals were found during the course of a long term project focused on the behavior and infectious disease in wild-living nonhuman primates , mainly in Côte d'Ivoire . Most animals included in this study had died due to anthrax and respiratory diseases [45] , [46] . No animal was anaesthetised or handled for the sole purpose of sample collection . All samples from sanctuary-living wild-born great apes were collected during routine health checks by the sanctuary on-site veterinarian . No animal was sampled specifically for this study , and diagnostics were performed at RKI at request by the respective sanctuary . Therefore , no approval from our institutional committee was needed . All samples were collected according to the guidelines: PASA 2004 . Pan African Sanctuary Alliance Veterinary Manual . Available at http://www . panafricanprimates . org/ . For animals living in zoological gardens and primate facilities , samples were obtained during routine health checks by the zoo and facility veterinarians . No animal was sampled specifically for the present study . Therefore , no approval from our institutional committee was needed . All samples were collected according to the guidelines laid down by Fowler and Miller [47] and according to the rules of the respective zoological gardens and primate facilities . Samples collected during necropsies on primates which died from various causes in zoological gardens and primate facilities were also included in these studies . For all samples , importations occurred according to German veterinary regulations for import of organic materials . Tissue and blood samples were exported with the appropriate CITES permissions from the respective country and Germany . Plasma samples of human volunteers in Côte d'Ivoire were sampled under the permission of the ministry of health of Côte d'Ivoire and the Institute Pasteur Côte d'Ivoire . Written informed consent was obtained from all participants of the study . The study was performed in cooperation with local health professionals . The aim of the study ( specifically , to study broadly zoonotic diseases in the region ) was explained to the local population during various educational campaigns . German serum samples were anonymously collected ‘residual materials’ , and the collection was approved by the ethics committee of the Charité - Universitätsmedizin Berlin . All samples were collected according to the declaration of Helsinki . Plasma samples ( n = 115 ) from Côte d'Ivoire were collected from 57 women and 58 men , ( age range: 9–79 years; mean: 42 years; six samples without age information ) participating in a broad study to investigate zoonotic diseases at the human – wildlife interface in Côte d'Ivoire . Serum samples ( n = 111 ) were collected from healthy German adults ( 55 female/56 male , age range: 20–60 years; mean: 32 years ) at the Charité University Hospital , Berlin , Germany . A total of 792 blood , fecal and tissue samples were collected from live or deceased individuals of 44 primate species ( apes , OWMs , NWMs and prosimians ) [30] , [48] . 316 samples originated from wild primates in Africa ( n = 313 ) and South America ( n = 3 ) , 54 samples from wild-born great apes housed in wildlife sanctuaries in West and East Africa ( n = 49 ) and Asia ( n = 5 ) . 422 samples derived from captive primates held in several zoological gardens and primate facilities in Europe . Protection measures for the collection of fecal samples and autopsies and extraction of DNA of blood and tissue samples as well as fecal samples were carried out as described previously [30] . Blood of 40 chimpanzees was collected in EDTA tubes living on Ngamba Island Chimpanzee Sanctuary , Uganda , between 2001 and 2008 during the annual routine health checks under anaesthesia . Plasma was separated by centrifugation at 3000 rpm for 10 minutes at room temperature . Two generic PCRs for polyomavirus identification [29] , [30] and long-distance PCR for genome amplification , as well as PCR product purification and sequencing were carried out as described previously [30] . For each novel polyomavirus nested specific primers for long-distance PCR were derived from the sequences amplified with the generic PCR . The primer pairs are listed with their annealing temperatures in Table S2 . Complete/partial VP1 , VP2 and LTag protein coding sequences generated for this study were translated into amino acids using SeaView [49] before being assembled with representative sequences of all polyomaviruses currently recognized as species by the International Committee on Taxonomy of Viruses ( ICTV [32] or possibly qualifying as new species according to recent publications . The three sets of sequences were aligned with SeaView using Muscle [50] and on the T-Coffee webserver using T-Coffee [51] , [52] . CORE indices were computed for all alignments using the T-Coffee webserver and the following command line: t_coffee -infile = filename -output = html -score . Average scores were comparable for the three protein alignments; Muscle alignments were used in the following . Well-aligned blocks were selected using Gblocks v0 . 91b [53] as implemented in SeaView , which resulted in retaining 90 , 244 and 443 positions from the initial VP2 , VP1 and LTag alignments . Best-fit models of amino acid evolution were determined using ProtTest v3 [54] . The seven empirical matrices of substitution rates implemented in BEAST v1 . 7 . 4 [55] were assessed in combination with empirical or dataset-borne ( +F ) amino acid frequencies and various hypotheses of rate variation along sequences ( rate heterogeneity , +G and/or proportion of invariant sites , +I ) . Likelihoods were computed for all resulting 56 models using the slow optimization option of ProtTest ( parameter values , branch lengths and topology were optimized ) . Best-fit models were determined using a combination of statistics: Akaike information criterion ( AIC ) , corrected AIC and three Bayesian information criteria ( BIC ) . CpREV+ G was selected for VP2 , WAG+I+G for VP1 , WAG+I+G+F for LTag . Phylogenetic analyses were then performed under the given models of amino acid evolution in ML and Bayesian frameworks . ML analyses were performed with PhyML v3 . 0 [56] as implemented on the PhyML webserver [57] . All analyses were performed using the BEST RANDOM option , meaning that one nearest-neighbor interchange ( NNI ) and one subtree pruning and re-grafting ( SPR ) search were started using a BIONJ tree while five additional SPR searches used random starting trees , the best of the seven resulting trees being chosen as the output . Where applicable , site-specific rate heterogeneity was modeled using a four-category gamma law ( +G4 ) . Branch lengths and topologies were optimized . Branch support was estimated by performing non-parametrical bootstrapping ( Bp; 500 pseudo-replicates ) . Bayesian analyses were performed using BEAST v1 . 7 . 4 and the associated suite of softwares [55] . For all analyses , a relaxed clock model was implemented so as to account for among lineage rate variation and a speciation model ( birth-death model ) was chosen as depicting the shape of the trees . Two Markov chain Monte Carlo ( MCMC ) runs of 10 , 000 , 000 generations were run under these conditions for each alignment , sampling trees and numerical values of model parameters every 1000 generations . Convergence of the runs was checked with Tracer v1 . 5 ( available at http://tree . bio . ed . ac . uk/software/tracer/ ) . Visual confirmation that the stationary distribution had effectively been reached was obtained for both runs ( a plateau was observed ) . In addition , model parameters apparently converged to undistinguishable distributions for both runs . Finally , combined effective sample sizes ( ESS ) were above 200 for all parameters . Trees sampled after a visually conservative burn-in of 1 , 000 , 000 generations were assembled into a single file using LogCombiner v1 . 7 . 1 before the information that this tree sample ( in total 20000 trees ) contained was summarized onto the maximum clade credibility ( MCC ) tree with TreeAnnotator v1 . 7 . 4 . Posterior probabilities ( pp ) were taken as branch support values . All trees presented in this article were made up with FigTree v1 . 3 . 1 ( available at http://tree . bio . ed . ac . uk/software/figtree/ ) . The sequences of the major capsid proteins VP1 of HPyV9 , BKPyV , JCPyV , MCPyV , TSPyV , APyV , ChPyV , PtroPyV3 , PtroPyV4 and PtrosPyV2 were codon-optimized , commercially synthesized ( MrGene GmbH , Regensburg , Germany ) and expressed in E . coli K12 as pentameric structures as described previously [25] . IgG ELISAs , including use of APyV VP1 as a negative control to exclude non-specific seroreactivity ( due to binding of antibodies to conserved VP1 epitopes or due to unspecific binding ) , estimation of cut-off values , calculation of the correlation of antibody reactivity using the Spearman rank correlation test , and adsorption assays with soluble VP1 capsomers were performed essentially as described [25] . The only exceptions from the earlier cited protocol were dilution of serum and plasma samples 1∶100; and , in adsorption assays , serum and plasma samples were preincubated with 2 µg/ml of antigen . The database was established in Excel for Windows before being transferred into Stata ( Stata/SE 10 . 0 for Windows , Stata Corp , College Station , TX ) for statistical analyses . Absorbance values and prevalence of the individual viruses and the effect of age and gender on absorbance values were analyzed using regression models and Fischer exact test . For the purpose of this paper , tentative names and abbreviations for the novel NHP polyomaviruses were derived from species and subspecies name of the host in which the virus was detected ( for example Pan troglodytes verus polyomavirus , PtrovPyV ) and listed in Table 2 . Using this naming rationale , the MCPyV-related polyomaviruses of Pan troglodytes verus , Pan troglodytes schweinfurthii and Gorilla gorilla gorilla , published in our earlier study [30] , were renamed for consistency . Old names: GggPyV , PtvPyV , PtsPyV; new names: GgorgPyV , PtrovPyV , PtrosPyV . Nucleotide sequence accession numbers of the novel NHP polyomaviruses are listed in Table S3 . | Polyomaviruses are able to cause severe disease in immunocompromised individuals . The discovery of Merkel cell polyomavirus and its association with Merkel cell carcinoma has increased interest in these viruses , resulting in the identification of several novel human polyomaviruses in recent years . The existence of one of these recently identified viruses , human polyomavirus 9 ( HPyV9 ) , had been predicted nearly 30 years prior due to the ability of human sera to neutralize infection of an African green monkey polyomavirus ( Lymphotropic polyomavirus; LPyV ) . HPyV9 and LPyV are now known to be antigenically and phylogenetically closely related . We hypothesized that nonhuman primate ( NHP ) polyomaviruses , in particular those of the closely related chimpanzee , may serve as genetic and immunological predictors for the existence of yet unknown human polyomaviruses . In the present study , we discovered 20 novel NHP polyomaviruses , six of which were isolated from chimpanzees . Of the 9 chimpanzee polyomaviruses now known , 5 do not presently have a closely related human counterpart . Serologic reactivity against these novel chimpanzee viruses was observed in humans from European and African populations . From these data we predict that additional human polyomaviruses exist which are genetically and serologically related to the novel chimpanzee polyomaviruses . | [
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]
| 2013 | Novel Polyomaviruses of Nonhuman Primates: Genetic and Serological Predictors for the Existence of Multiple Unknown Polyomaviruses within the Human Population |
Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps . In contrast to a hard selective sweep , in which a single adaptive haplotype rises to high population frequency , in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously , producing distinct patterns of genetic variation in the vicinity of the adaptive site . Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps . This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster , where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus . Here , we develop a statistical test based on a measure of haplotype homozygosity ( H12 ) that is capable of detecting both hard and soft sweeps with similar power . We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel ( DGRP ) . Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies , consistent with signatures of soft sweeps . We further develop a second haplotype homozygosity statistic ( H2/H1 ) that , in combination with H12 , is capable of differentiating hard from soft sweeps . Surprisingly , we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps . We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes .
The ability to identify genomic loci subject to recent positive selection is essential for our efforts to uncover the genetic basis of phenotypic evolution and to understand the overall role of adaptation in molecular evolution . The fruit fly Drosophila melanogaster is one of the classic model organisms for studying the molecular bases and signatures of adaptation . Recent studies have provided evidence for pervasive molecular adaptation in this species , suggesting that approximately 50% of the amino acid changing substitutions , and similarly large proportions of non-coding substitutions , were adaptive [1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9] . There is also evidence that at least some of these adaptive events were driven by strong positive selection ( ~1% or larger ) , depleting levels of genetic variation on scales of tens of thousands of base pairs in length [10 , 11] . If adaptation in D . melanogaster is indeed common and often driven by strong selection , it should be possible to detect genomic signatures of recent and strong adaptation [12 , 13 , 14] . Three cases of recent and strong adaptation in D . melanogaster are well documented and can inform our intuitions about the expected genomic signatures of such adaptive events . First , resistance to the most commonly used pesticides , carbamates and organophosphates , is known to be largely due to three point mutations at highly conserved sites in the gene Ace , which encodes the neuronal enzyme Acetylcholinesterase [15 , 16 , 17] . Second , resistance to DDT evolved via a series of adaptive events that included insertion of an Accord transposon in the 5’ regulatory region of the gene Cyp6g1 , duplication of the locus , and additional transposable element insertions into the locus [18 , 19] . Finally , increased resistance to infection by the sigma virus , as well as resistance to certain organophosphates , has been associated with a transposable element insertion in the protein-coding region of the gene CHKov1 [20 , 21] . In-depth population genetic studies [17 , 19 , 21] of adaptation at these loci revealed that in all three cases adaptation failed to produce classic hard selective sweeps , but instead generated patterns compatible with soft sweeps . In a hard selective sweep , a single adaptive haplotype rises in frequency and removes genetic diversity in the vicinity of the adaptive locus [22 , 23 , 24] . In contrast , in a soft sweep multiple adaptive alleles present in the population as standing genetic variation ( SGV ) or entering as multiple de novo adaptive mutations increase in frequency virtually simultaneously bringing multiple haplotypes to high frequency [25 , 26 , 27 , 28 , 29] . In the cases of Ace and Cyp6g1 , soft sweeps involved multiple de novo mutations [17 , 19 , 21] that arose after the introduction of pesticides , whereas in the case of CHKov1 , a soft sweep arose in out-of-African populations from standing genetic variation ( SGV ) [17 , 19 , 21] present at low frequencies in the ancestral African population [20 , 21] . Unfortunately , most scans for selective sweeps in population genomic data have been designed to detect hard selective sweeps ( although see [30] ) and focus on such signatures as a dip in neutral diversity around the selected site [22 , 24 , 31] , an excess of low or high-frequency alleles in the frequency spectrum of polymorphisms surrounding the selected site ( i . e . Tajima’s D , Fay and Wu’s H , and Sweepfinder ) [32 , 33 , 34 , 35 , 36] , the presence of a single common haplotype [37] , or the observation of a long and unusually frequent haplotype ( iHS ) [36 , 38 , 39 , 40] . In a soft sweep , however , multiple haplotypes linked to the selected locus can rise to high frequency and levels of diversity and allele frequency spectra should therefore be perturbed to a lesser extent than in a hard sweep . As a result , methods based on the levels and frequency distributions of neutral diversity have low power to detect soft sweeps [13 , 28 , 41 , 42] . Some genomic signatures do have power to detect both hard and soft sweeps . In particular , linkage disequilibrium ( LD ) measured between pairs of sites or as haplotype homozygosity should be elevated in both hard and soft sweeps . This expectation holds for hard sweeps and for soft sweeps that are not too soft , that is soft sweeps that have such a large number of independent haplotypes bearing adaptive alleles that linkage disequilibrium is no longer elevated beyond neutral expectations [41 , 43] . Given that none of the described cases of adaptation at Ace , Cyp6g1 , and CHKov1 produced hard sweeps , it is possible that additional cases of recent selective sweeps in D . melanogaster remain to be discovered . Here we develop a statistical test based on modified haplotype homozygosity for detecting both hard and soft selective sweeps in population genomic data . We apply this test in a genome-wide scan in a North American population of D . melanogaster using the Drosophila Genetic Reference Panel ( DGRP ) data set [44] , consisting of 162 fully sequenced isogenic strains from a North Carolina population . Our scan recovers the three known soft sweeps at Ace , Cyp6g1 , and CHKov1 , and identifies a large number of additional recent and strong selective sweeps . We develop an additional haplotype homozygosity statistic that can distinguish hard from soft sweeps and argue that the haplotype frequency spectra at the top 50 candidate sweeps are best explained by soft selective sweeps .
In this paper , we develop a set of new statistics for the detection and characterization of positive selection based on measurements of haplotype homozygosity in a predefined window . Our reasoning in developing these statistics is that haplotype homozygosity , defined as a sum of squares of the frequencies of identical haplotypes in a window , should be a sensitive statistic for the detection of both hard and soft sweeps , as long as the window is large enough that neutral demographic processes are unlikely to elevate haplotype homozygosity by chance [41 , 43] . At the same time , the window must not be so large that even strong sweeps can no longer generate frequent haplotypes spanning the whole window . In order to determine an appropriate window length for the measurement of haplotype homozygosity in the DGRP data set , we first assessed the length scale of linkage disequilibrium decay expected in the DGRP data under a range of neutral demographic models for North American D . melanogaster . This length scale should roughly correspond to the window size over which we are unlikely to observe substantial haplotype structure by chance . We considered six demographic models ( Fig . 1 ) . The first demographic model is an admixture model of the North American D . melanogaster population proposed by Duchen et al . [45] . In this model , the North American population was co-founded by flies from Africa and Europe 3 . 05×10–4 Ne generations ago ( where Ne ≈ 5x106 ) . The second model is a modified admixture model , also proposed by Duchen et al . [45] , in which the founding European population underwent a bottleneck before the admixture event ( see S1 Table for complete parameterizations of both admixture models ) . The third model has a constant effective population size of Ne = 106 [46] , which we considered for its simplicity , computational feasibility and , as we will argue below , its conservativeness for the purposes of detecting selective sweeps using our approach in the DGRP data . The fourth model is a constant Ne = 2 . 7x106 demographic model fit to Watterson’s θW estimated from short intron autosomal polymorphism data from the DGRP dataset ( Methods ) . Finally , we fit a family of out-of-Africa bottleneck models to short intron regions in the DGRP data set using DaDi [47] ( S2 Table ) ( Methods ) . The two bottleneck models we ultimately used are a severe but short bottleneck model ( NB = 0 . 002 , TB = 0 . 0002 ) and a shallow but long bottleneck model ( NB = 0 . 4 , TB = 0 . 0560 ) , both of which fit the data equally well among a range of other inferred bottleneck models ( see S1 Fig . for parameterization ) . All models except for the constant Ne = 106 model fit the DGRP short intron data in terms of the number of segregating sites ( S ) and pairwise nucleotide diversity ( π ) ( S3 Table ) . We compared the decay in pair-wise LD in the DGRP data at distances from a few base pairs to 10 kb with the expectations under each of the six demographic models using parameters relevant for our subsequent analysis of the DGRP data ( Fig . 2 ) . Specifically , we matched the sample depth of the DGRP data set ( 145 strains after quality control ) and assumed a mutation rate ( μ ) of 10–9 events/bp per generation [48] and a recombination rate ( ρ ) of 5×10–7 centimorgans/bp ( cM/bp ) [49] . In the DGRP data analysis below , we exclude regions with a low recombination rate ( ρ < 5x10–7 cM/bp ) . The use of ρ = 5x10–7 cM/bp should therefore generate higher LD in simulations than in the DGRP data and thus should be conservative for the purposes of defining the expected length scale of LD decay . Fig . 2 shows that LD in the DGRP data is elevated beyond neutral expectations at all length scales ( consistent with the observations in [50] ) , and dramatically so at the 10 kb length scale . The elevation in LD observed in the data is indicative of either linked positive selection driving haplotypes to high frequency , a lack of fit of current demographic models to the data , or both . Simulations under the most realistic demographic model , admixture [45] , have the fastest decay in LD ( S2 Fig . ) . This is likely because admixture models with two bottlenecks that are fit to diversity statistics generate more haplotypes compared to single bottleneck models , since the same haplotype is unlikely to be sampled independently in both bottlenecked ancestral populations . In contrast , LD under the constant Ne = 106 demographic scenario decays slower than in any other demographic scenario , as expected given that this model has the smallest effective population size . Fig . 2 suggests that windows of 10 kb are large enough that neutral demography is unlikely to generate high values of LD and elevate haplotype homozygosity by chance , and should thus prevent a high rate of false positives . At the same time , the use of 10 kb windows for the measurement of haplotype homozygosity should still allow us to detect many reasonably strong sweeps , including the known cases of recent adaptation . The footprint of a hard selective sweep extends over approximately s/[log ( Nes ) ρ] basepairs , where s is the selection strength , Ne the population size , and ρ the recombination rate [22 , 23 , 51] . Sweeps with a selection coefficient of s = 0 . 05% or greater are thus likely to generate sweeps that span 10 kb windows in areas with recombination rate of 5×10–7 cM/bp . As the recombination rate increases , only selective sweeps with s > 0 . 05% should be observed in the 10 kb windows . Genomic analyses have suggested that adaptation in Drosophila is likely associated with a range of selection strengths , including values of ~1% [7 , 8 , 10] or greater as observed at Ace , Cyp6g1 , and CHKov1 . Our use of 10 kb windows in the rest of the analysis should thus bias the analysis toward detecting the cases of strongest adaptation in Drosophila . We investigated haplotype spectra in simulations of neutral demography and both hard and soft selective sweeps arising from de novo mutations as well as SGV . For all haplotype spectra and homozygosity analyses in this paper we use windows of 400 SNPs , corresponding roughly to 10 kb in the DGRP data ( Fig . 2 ) . Haplotypes within a 400 SNP window are grouped together if they are identical at all SNPs in the window . We fixed the number of SNPs in a window to eliminate variability in the haplotype spectra due to varying numbers of SNPs . The lower SNP density of the constant Ne = 106 model ( S3 Table ) effectively increases the size of the analysis window in terms of the number of base pairs when defining the windows in terms of the number of SNPs . Thus , the constant Ne = 106 model should reduce the rate of false positives because the recombination rate under this model is artificially increased . We therefore use the constant Ne = 106 model for the subsequent simulations of neutrality and selective sweeps . To visualize sample haplotype frequency spectra , we simulated incomplete and complete sweeps with frequencies of the adaptive mutation ( PF ) at 0 . 5 or 1 at the time when selection ceased . ( Note that below we will investigate a large number of scenarios , focusing on the effects of varying selection strength and the decay of sweep signatures with time ) . The number of independent haplotypes that rise in frequency simultaneously in soft sweeps—we call this “softness” of a sweep—should increase either ( i ) when the rate of mutation to de novo adaptive alleles at a locus becomes higher and multiple alleles arise and establish after the onset of selection at a higher rate , or ( ii ) when adaptation uses SGV with previously neutral or deleterious alleles that are present at higher frequency at the onset of selection [27 , 29] . More specifically , for sweeps arising from multiple de novo mutations , Pennings and Hermisson [29] showed that the key population genetic parameter that determines the softness of the sweep is θA = 4NeμA , proportional to the product of Ne , the variance effective population size estimated over the period relevant for adaptation [14 , 52] , and μA , the mutation rate toward adaptive alleles at a locus per individual per generation [14] . The mutation-limited regime with hard sweeps corresponds to θA << 1 , whereas θA > 1 specifies the non-mutation-limited regime with primarily soft sweeps . As θA becomes larger , the sweeps become softer as more haplotypes increase in frequency simultaneously [29] . In the case of sweeps arising from SGV , the softness of a sweep is governed by the starting partial frequency of the adaptive allele in the population prior to the onset of selection . For any given rate of recombination , adaptive alleles starting at a higher frequency at the onset of selection should be older and should thus be present on more distinct haplotypes and give rise to softer sweeps [27] . As can be seen in Fig . 3 , most haplotypes in neutral demographic scenarios are unique in our 400 SNP windows , whereas selective sweeps can generate multiple haplotypes at substantial frequencies . Our plot of the haplotype frequency spectra and the expected numbers of adaptive haplotypes show that sweeps arising from de novo mutations become soft with multiple frequent haplotypes in the sample when θA ≥ 1 . Sweeps from SGV become soft when the starting partial frequency of the adaptive allele prior to the onset of selection is ≥ 10–4 ( 100 alleles in the population ) . In both cases , sweeps become monotonically softer as θA increases or , respectively , the starting partial frequency of the adaptive allele becomes higher . These results conform to the expectations derived in [29] . The increase of haplotype population frequencies in both hard and soft sweeps can be captured using haplotype homozygosity [30 , 39 , 41] . If pi is the frequency of the ith most common haplotype in a sample , and n is the number of observed haplotypes , then haplotype homozygosity is defined as H1 = Σi = 1 , …n pi2 . We can expect H1 to be particularly high for hard sweeps , with only one adaptive haplotype at high frequency in the sample ( Fig . 4A ) . Thus , H1 is an intuitive candidate for a test of neutrality versus hard sweeps , where the test rejects neutrality for high values of H1 . A test based on H1 may also have acceptable power to detect soft sweeps in which only a few haplotypes in the population are present at high frequency . However , as sweeps become softer and the number of sweeping haplotypes increases , the relative contribution of individual haplotypes towards the overall H1 value decreases , and the power of a test based on H1 is expected to decrease . To have a better ability to detect hard and soft sweeps using homozygosity statistics , we developed a modified homozygosity statistic , H12 = ( p1 + p2 ) 2 + Σi>2 pi2 = H1 + 2p1p2 , in which the frequencies of the first and the second most common haplotype are combined into a single frequency ( Fig . 4B ) . A statistical test based on H12 is expected to be more powerful in detecting soft sweeps than H1 because it combines frequencies of two similarly abundant haplotypes into a single frequency , whereas for hard sweeps the combination of the frequencies of the first and second most abundant haplotypes should not change haplotype homozygosity substantially [53] . We also considered a third test statistic , H123 , which combines frequencies of the three most prevalent haplotypes in a sample into a single haplotype and then computes homozygosity . We will primarily employ H12 in subsequent analyses but will consider the effects of using H1 and H123 briefly as well . To assess the ability of H12 to detect sweeps of varying softness and to distinguish positive selection from neutrality , we measured H12 in simulated sweeps arising from both de novo mutations and SGV while varying s , PF , and the time since the end of the sweep , TE , measured in units of 4Ne generations in order to model the decay of a sweep through recombination and mutation events over time . We first investigate the behavior of H12 under different selective regimes and then investigate its power in comparison with the popular haplotype statistic iHS . Fig . 5A shows that for complete and incomplete sweeps with s = 0 . 01 and TE = 0 , H12 monotonically decreases as a function of θA over the interval from 10–2 to 102 . When θA ≤ 0 . 5 , many sweeps are hard and H12 values are high . When θA ≈ 1 , and practically all sweeps are soft , but not yet extremely soft , H12 retains much of its power . However , for θA > 10 , where sweeps are extremely soft , H12 decreases substantially . Similarly , H12 is maximized when the starting frequency of the allele is 10–6 ( one copy of the allele in the population generating hard sweeps from SGV ) and becomes very small as the frequency of the adaptive allele increases beyond >10-3 ( >1000 copies of the allele in the population ) ( Fig . 5B ) . Therefore , H12 has reasonable power to detect soft sweeps in samples of hundreds of haplotypes , as long as they are not extremely soft , but remains somewhat biased in favor of detecting hard sweeps . H12 also increases as the ending partial frequency of the adaptive allele after selection ceased ( PF ) increases from 0 . 5 to 1 ( Fig . 5A and 5B ) and as the selection strength increases from 0 . 001 to 0 . 1 ( Fig . 5C and 5D ) . We observe that sweeps arising from SGV with low selection coefficients have lower H12 values ( Fig . 5D ) . This is most likely because such weak sweeps are effectively harder: as more of the haplotypes fail to establish , fewer haplotypes end up sweeping in the population leading to higher values of haplotype homozygosity . Fig . 5E and 5F further show that incomplete and complete sweeps decay with time due to recombination and mutation events , resulting in monotonically decreasing values of H12 with time . Overall this analysis demonstrates that H12 has most power to detect recent sweeps driven by strong selection . We also assessed the ability of H12 to detect selective sweeps as compared to H1 and H123 by calculating the values of H1 , H12 , and H123 for sweeps generated under the parameters s = 0 . 01 , TE = 0 and PF = 0 . 5 . H12 consistently , albeit modestly , increases the homozygosity for younger soft sweeps as compared to H1 ( S3 Fig . ) . The increase in homozygosity using H123 is marginal relative to homozygosity levels achieved by H12 , so we chose not to use this statistic in our study . Finally , we compared the abilities of H12 and iHS ( integrated haplotype score ) , a haplotype-based statistic designed to detect incomplete hard sweeps [39 , 40] , to detect both hard and soft sweeps . We created receiving operator characteristic ( ROC ) curves [54] , which plot the true positive rate ( TPR ) of correctly rejecting neutrality in favor of a sweep ( hard or soft ) given that a sweep has occurred versus the false positive rate ( FPR ) of inferring a selective sweep , when in fact a sweep has not occurred . In our simulations of selective sweeps we used θA = 0 . 01 as a proxy for scenarios generating almost exclusively hard sweeps , and θA = 10 as a proxy for scenarios generating almost exclusively soft sweeps . We chose θA = 10 for soft sweeps because this is the highest θA value with which H12 can still detect sweeps before substantially losing power given our window size of 400 SNPs and sample size of 145 . Note that for soft sweeps with a lower value of θA the power of H12 should be higher . We modeled incomplete sweeps with PF = 0 . 1 , 0 . 5 , and 0 . 9 , with varying times since selection had ceased of TE = 0 , 0 . 001 , and 0 . 01 in units of 4Ne generations . We simulated sweeps under three selection coefficients , s = 0 . 001 , 0 . 01 , and 0 . 1 . Fig . 6 and S4 Fig . show that the tests based on H12 and iHS have similar power for the detection of hard sweeps , although in the case of old and strong hard sweeps ( TE = 0 . 01 , s ≥ 0 . 01 ) iHS performs slightly better than H12 . On the other hand , H12 substantially outperforms iHS in detecting soft sweeps and has high power when selection is sufficiently strong and the sweeps are sufficiently young . As sweeps become very old , neither statistic can detect them well , as expected . We applied the H12 statistic to DGRP data in sliding windows of 400 SNPs with the centers of each window iterated by 50 SNPs . To classify haplotypes within each analysis window , we assigned the 400 SNP haplotypes into groups according to exact sequence identity . If a haplotype with missing data matched multiple haplotypes at all genotyped sites in the analysis window , then the haplotype was randomly assigned to one of these groups ( Methods ) . To assess whether the observed H12 values in the DGRP data along the four autosomal arms are unusually high as compared to neutral expectations , we estimated the expected distribution of H12 values under each of the six neutral demographic models . Fig . 7 shows that genome-wide H12 values in DGRP data are substantially elevated as compared to expectations under any of the six neutral demographic models . In addition , there is a long tail of outlier H12 values in the DGRP data suggestive of recent strong selective sweeps . To identify regions of the genome with H12 values significantly higher than expected under neutrality , we calculated critical values ( H12o ) under each of the six neutral models based on a 1-per-genome false discovery rate ( FDR ) criterion . Our test rejects neutrality in favor of a selective sweep when H12 > H12o ( Methods and S1 Text ) . The critical H12o values under all neutral demographic models are similar to the median H12 value observed in the DGRP data ( Table 1 ) , consistent with the observations of elevated genome-wide haplotype homozygosity and much slower decay in LD at the scale of 10 kb in the DGRP data compared to all neutral expectations ( Fig . 2 ) . We focused on the constant Ne = 106 model because it yields a relatively conservative H12o value ( Table 1 ) and preserves the most long-range , pair-wise LD in simulations ( Fig . 2 ) . For our genomic scan we chose to use the 1-per-genome FDR value calculated under the constant Ne = 106 model with a recombination rate of 5×10–7 cM/bp . Note that most H12o values are similar to the genome-wide median H12 value of 0 . 0155 . In order to call individual sweeps , we first identified all windows with H12 > H12o in the DGRP data set under the constant Ne = 106 model . We then grouped together consecutive windows as belonging to the same ‘peak’ if the H12 values in all of the grouped windows were above H12o for a given model and recombination rate ( Methods ) . We then chose the window with the highest H12 value among all windows in a peak and used this H12 value to represent the entire peak . We focused on the top 50 peaks with empirically most extreme H12 values , hypothesized to correspond to the strongest and/or most recent selective events ( Fig . 8A ) . The windows with the highest H12 values for each of the top 50 peaks are highlighted in Fig . 8A . The highest H12 values for the top 50 peaks are in the tail of the distribution of H12 values in the DGRP data ( Fig . 7 ) and thus are outliers both compared to the neutral expectations under all six demographic models and the empirical genomic distribution of H12 values . We observed peaks that have H12 values higher than H12o on all chromosomes , but found that there are significantly fewer peaks on 3L ( 2 peaks ) than the approximately 13 out of 50 top peaks expected when assuming a uniform distribution of the top 50 peaks genome-wide ( p = 0 . 00016 , two-sided binomial test , Bonferroni corrected ) . The three peaks with the highest observed H12 values correspond to the three known cases of positive selection in D . melanogaster at the genes Ace , Cyp6g1 , and CHKov1 [17 , 19 , 21] , confirming that the H12 scan is capable of identifying previously known cases of adaptation . In S4 Table , we list all genes that overlap with any of the top 50 peaks . Fig . 9A and S5 Fig . show the haplotype frequency spectra observed at the top 50 peaks . In contrast , Fig . 9B shows the frequency spectra observed under the six demographic models with the corresponding critical H12o values . We performed several tests to ensure the robustness of the H12 peaks to potential artifacts ( S1 Text ) . We first tested for associations of H12 peaks with inversions in the sample , but did not find any ( S1 Text , S5 Table ) . In addition , we reran the scan in three different data sets of the same population and confirmed that unaccounted population substructure and variability in sequencing quality do not confound our results ( S1 Text , S7 Fig . ) . We also sub-sampled the DGRP data set to 40 strains ten times and plotted the resulting distributions of H12 values . We found that in all subsamples there is an elevation in haplotype homozygosity relative to neutral demographic scenarios , suggesting that the elevation in haplotype homozygosity values is driven by the whole sample and not a particular subset of individuals ( S8 Fig . ) . Finally , to ensure that haplotype homozygosity is not elevated by family structure , we excluded all related individuals and reran the scan , again recovering the majority of our top peaks ( S1 Text , S7 Fig . ) . We scanned chromosome 3R using H1 and H123 as our test statistics in order to determine the impact of our choice of grouping the two most frequent haplotypes together in our H12 test statistic on the location of the identified peaks ( S9 Fig . ) . We found that the locations of the identified peaks are similar with all three statistics , but that some smaller peaks that cannot be easily identified with H1 are clearly identified with H12 and H123 , as expected . We applied the iHS statistic as described in Voight et al . 2006 [40] to all SNPs in the DGRP data to determine the concordance in the sweep candidates identified by iHS and H12 ( Methods ) . Briefly , we searched for 100 kb windows that have an unusually large number of SNPs with standardized iHS values ( |iHS| ) > 2 . The positive controls Ace , Cyp6g1 , and CHKov1 are located within the 95 top 10% iHS 100 kb windows ( Fig . 8B ) , validating this approach . To determine how often a candidate region identified in the H12 scan is identified in the iHS scan and vice versa , we overlapped the top 50 H12 peaks with the 95 top 10% iHS 100Kb windows . We defined an overlap as the non-empty intersection of the two genomic regions defining the boundaries of a peak in the H12 scan and the non-overlapping 100Kb windows used to calculate enrichment of |iHS| values . We found that 18 H12 peaks overlap 28 |iHS| 100Kb enrichment windows . In contrast , fewer than 5 H12 peaks are expected to overlap approximately 7 iHS 100Kb windows by chance ( Methods ) . The concordance between the two scans confirms that many of the peaks identified in the two scans are likely true selective sweeps and also suggests that the two approaches are not entirely redundant . Our analysis of H12 haplotype homozygosity and the decay in long range LD in DGRP data suggests that extreme outliers in the H12 DGRP scan are in locations of the genome that may have experienced recent and strong selective sweeps . The visual inspection of the haplotype spectra of the top 10 peaks in Fig . 9A and the remaining 40 peaks in S5 Fig . reveals that they contain many haplotypes at substantial frequency . These spectra do not appear similar to those generated by hard sweeps in Fig . 3 or extreme outliers under neutrality in Fig . 9B , but instead visually resemble incomplete soft sweeps with s = 0 . 01 and PF = 0 . 5 either from de novo mutations with θA between 1 and 20 or from SGV starting at partial frequencies of 5x10–5 to 5x10–4 prior to the onset of selection ( Fig . 3 ) . The sweeps also appear to become softer as H12 decreases , consistent with our expectation that H12 should lose power for softer sweeps . In order to gain intuition about whether the haplotype spectra for the top 50 peaks can be more easily generated either by hard or soft sweeps under various evolutionary scenarios , we developed a new haplotype homozygosity statistic , H2/H1 , where H2 = Σi>1 pi2 = H1—p12 is haplotype homozygosity calculated using all but the most frequent haplotype ( Fig . 4C ) . We expect H2 to be lower for hard sweeps than for soft sweeps because in a hard sweep only one adaptive haplotype is expected to be at very high frequency [53] . The exclusion of the most common haplotype should therefore reduce haplotype homozygosity precipitously . As sweeps get softer , however , multiple haplotypes start appearing at high frequency in the population and the exclusion of the most frequent haplotype should not decrease the haplotype homozygosity to the same extent . Conversely H1 , the homozygosity calculated using all haplotypes , is expected to be higher for a hard sweep than for a soft sweep as we described above . The ratio H2/H1 between the two measures should thus increase monotonically as a sweep becomes softer , thereby offering a summary statistic that , in combination with H12 , can be used to test whether the observed haplotype patterns are more likely to be generated by hard or soft sweeps . Note that we intend H2/H1 to be measured near the center of the sweep where H12 is the highest . Otherwise , when H2/H1 is estimated further away from the sweep center , mutation and recombination events will decay the haplotype signature and hard and soft sweep signatures can become indistinguishable . To assess the behavior of H2/H1 as a function of the softness of a sweep , we measured H2/H1 in simulated sweeps of varying softness arising from de novo mutations and SGV with various s , PF , and TE values . Fig . 10 shows that H2/H1 has low values for sweeps with θA ≤ 0 . 5 or when the starting partial frequency of the adaptive allele prior to the onset of selection is <10–5 , i . e . , when sweeps are mainly hard . As a sweep becomes softer , H2/H1 values approach one because no single haplotype dominates the haplotype spectrum . In the case of sweeps arising from de novo mutations , H2/H1 values are similar for partial ( PF = 0 . 5 ) and complete sweeps ( PF = 1 ) and for sweeps of varying strengths ( s = 0 . 001 , 0 . 01 , 0 . 1 ) . However , in the case of sweeps arising from SGV , sweeps with higher selection strengths do have higher H2/H1 values , reflecting the hardening of sweeps for smaller s values as we discussed previously ( Fig . 5D ) . Both sweeps from de novo mutations and SGV have higher H2/H1 values for older sweeps , reflecting the decay of the haplotype frequency spectrum over time . While hard sweeps and neutrality cannot easily generate both high H12 and H2/H1 values , soft sweeps can do both . In Fig . 11 we assess the range of H12 and H2/H1 values expected under hard and soft sweeps . To compare the likelihood of a hard versus soft sweep generating a particular pair of H12 and H2/H1 values , we calculated Bayes factors: BF = P ( H12obs , H2obs /H1obs |Soft Sweep ) /P ( H12obs , H2obs /H1obs |Hard Sweep ) . We approximated BFs using an approximate Bayesian computation ( ABC ) approach under which the nuisance parameters—selection coefficient ( s ) , partial frequency of the adaptive allele after selection has ceased ( PF ) , and age ( TE ) —are integrated out by drawing them from uniform prior distributions: s ~ U[0 , 1] , PF ~ U[0 , 1] , and TE ~ U[0 , 0 . 001]×4Ne . We stated the hard and soft sweep scenarios as point hypotheses in terms of the θA value generating the data . Specifically , we assumed that hard sweeps are generated under θA = 0 . 01 . For soft sweeps , we generated sweeps of varying softness by using θA values of 5 , 10 , and 50 . Note that hard and soft sweeps can also be simulated from SGV with various starting frequencies of the beneficial allele , but for the purposes of generating hard sweeps with a single sweeping haplotype versus soft sweeps with multiple sweeping haplotypes , simulations from SGV or de novo mutations are mostly equivalent . The panels in Fig . 11 show BFs calculated under several evolutionary scenarios for a grid of H12 and H2/H1 values . All panels in Fig . 11 show that hard sweeps are common when H2/H1 values are low for most H12 values tested . For very low H12 ( <0 . 05 ) values , when sweeps display low haplotype homozygosity to begin with and are difficult to detect with H12 , both hard and soft sweeps are likely for a wide range of H2/H1 values . Soft sweeps are common for any high H2/H1 values conditional on H12 being sufficiently high when simulating soft sweeps with θA = 10 and 5 ( Fig . 11A and 11B ) . However , soft sweeps generated with θA = 50 are too soft to produce high H12 values , confirming our results in Fig . 5 . As a consequence hard sweeps are common for high H12 values regardless of H2/H1 values under this scenario ( Fig . 11C ) . In Fig . 11A , 11D and 11E , the recombination rate is varied , and a comparison of these panels show that the recombination rate has little impact on the space where hard sweeps can be expected to be more likely . Fig . 11F shows that simulations under admixture increase support for soft sweeps in regions of the space already in support of soft sweeps generated under the constant Ne = 106 demographic scenario ( Fig . 11A–E ) . Fig . 10 shows that there is clearly a dependency between H12 and H2/H1 and that both values need to be taken into account when determining the softness of a peak . In particular , H2/H1 is most informative when applied to regions of the genome with the highest H12 values . Overlaid on all panels in Fig . 11 are the H12 and H2/H1 values at the top 50 peaks . Note that in almost all cases , the top 50 peaks have H12 and H2/H1 values that are easiest explained by soft sweeps . In order to more explicitly test each candidate sweep for its compatibility with a hard and soft sweep model , we generated hard sweeps with θA = 0 . 01 and soft sweeps with a maximum a posteriori θA value ( θAMAP ) , i . e . , our best estimate of the softness for a particular peak . We used an ABC method to infer the θAMAP for each peak by sampling the posterior distribution of θA conditional on the observed values H12obs and H2obs /H1obs from a candidate sweep ( S1 Text ) . All θAMAP values inferred for the top 50 peaks were significantly greater than 1 with the smallest being 6 . 8 ( S10 Fig . ) , suggesting that soft sweeps would be commonly generated under any of the θAMAP values estimated ( Fig . 3 ) . We used recombination rates estimated for each peak [49] and simulated the data under the constant population size model with Ne = 106 for computational feasibility . Among our top 50 peaks , we found strong evidence in support of soft sweeps in all 50 cases ( BF > 10 ) , very strong evidence in 47 cases ( BF > 30 ) , and almost decisive evidence ( BF > 98 ) in 44 cases ( S3 Table ) . Taken together , these results provide evidence that soft sweeps most easily explain the signatures of multiple haplotypes at high frequency observed at the top 50 H12 peaks .
In this study , we found compelling evidence for a substantial number of recent and strong selective sweeps in the North Carolina population of D . melanogaster and further found that practically all these events appear to display signatures of soft rather than hard sweeps . To detect sweeps , we used our new haplotype statistic , H12 , which measures haplotype homozygosity after combining the frequencies of the two most abundant haplotypes into a single frequency in windows of 400 SNPs ( ~10 kb in the DGRP data ) . We chose to use windows defined by a constant number of SNPs rather than windows of constant physical or genetic length in order to simplify the statistical analysis . This is because windows of constant physical or genetic length tend to have varying SNP density , and therefore also varying distributions of haplotypes even under neutrality . Our choice of a fixed number of SNPs avoids this source of noise , but it raises the question of whether the H12 peaks simply define regions that have particularly low recombination rates or high SNP densities , and thus short windows in terms of the number base pairs or genetic map length . We made sure to avoid the first pitfall by analyzing only windows with reasonably high recombination rates ( ρ ≥ 5x10–7 cM/bp , 82% of the genome ) and by using conservative thresholds for the significance cutoffs . We also confirmed that the analysis windows with the highest H12 values in our top 50 peaks do not have shorter windows in terms of base pairs than on average ( S11 Fig . ) . We were further concerned that our choice of using windows with a fixed number of SNPs would bias us against detecting complete hard sweeps . However , our simulations showed that this was not the case ( Fig . 5 ) . We fully acknowledge that the result of applying the haplotype statistics developed in this manuscript to the North Carolina population may be idiosyncratic to the particular demographic structure of this one population . However , H12 in the DGRP data is substantially elevated compared to the expectation under any of the tested neutral demographic models , including both published admixture models [45] and the bottleneck models we fit to the DGRP short intron SNP data . In fact , the median value of H12 in the genome lies in the tails of distributions of H12 values generated from > 105 simulations for each neutral demographic scenario . Similarly , pairwise LD in DGRP data decays much more slowly than expected under neutrality ( Fig . 2 ) . These patterns can be due either to ( i ) pervasive and strong positive selection that drives long haplotypes to high frequency in the population , ( ii ) misspecification of the demographic model , or ( iii ) both . Although background selection ( BGS ) is pervasive in D . melanogaster [55] and strongly impacts levels of polymorphism , it is unlikely to be responsible for high levels of haplotype homozygosity [56 , 57] . Both selective and neutral demographic explanations of the elevated LD need to be investigated further . It will be important to determine whether current estimates of the rate and strength of adaptation in D . melanogaster are consistent with the elevated levels of haplotype homozygosity and LD in general , even under simple demographic models . Alternatively , an unusually high rate of adaptation in the recent past might be required to explain the signatures we observe in the data . Likewise , it is possible that some demographic model of the North Carolina population , which is yet to be specified , can account for the observed LD patterns . Both extensive forward simulations and additional studies of LD and haplotype homozygosity patterns in other populations will be important to resolve these issues . Importantly , however , the top fifty H12 peaks we focused on in this study are outliers not only under all tested demographic models , but also relative to the empirical genome wide H12 distribution . The top three peaks correspond to the well-known cases of soft selective sweeps arising from de novo mutations and SGV at the loci Ace , Cyp6g1 , and CHKov1 [17 , 19 , 21] as described in the Introduction . The recovery of these positive controls further validates that our method can identify sweeps arising from both de novo mutations and SGV and is robust to misspecifications of demographic models . In order to confirm the robustness of the H12 peaks , we ran iHS [40] on the DGRP data and recovered 18 of the top 50 peaks , including the three positive controls , demonstrating the validity of both methods and that the two methods are not entirely redundant ( Fig . 8B ) . We also failed to detect any correlation between H12 peaks and inversions in the genome . We tested for any unaccounted substructure in the data confounding our results by rerunning the scan in several data sets , including one where all related individuals were excluded . In all cases , we found that our top peaks remained unchanged and that haplotype homozygosity was consistently elevated in the data relative to neutral demographic simulations ( S1 Text ) . We are thus confident that the top H12 peaks are true outliers and likely indicate recent and strong selective events in the North Carolina population of D . melanogaster . To assess whether the top 50 peaks can be more easily generated by hard versus soft sweeps , we developed a second statistic , H2/H1 , which is a ratio of haplotype homozygosities calculated without ( H2 ) and with ( H1 ) the most frequent haplotype in a sample . We demonstrate that this statistic has a monotonically increasing relationship with the softness of a sweep ( Fig . 10 ) , in contrast to H12 , which has a monotonically decreasing relationship with the softness of a sweep . H2/H1 and H12 together are informative in determining the softness of a sweep . Specifically , hard sweeps can generate high values of H12 in a window centered on the adaptive site but cannot simultaneously generate high H2/H1 values in the same window . However , soft sweeps can generate both high H12 and H2/H1 values in such a window . Note that in order to differentiate hard and soft sweeps with reasonable power , H2/H1 can only be applied in cases where H12 values are already high and there is strong evidence for a sweep . Indeed , as can be seen in all evolutionary scenarios presented in Fig . 11 , when H12 is high and H2/H1 is low , hard sweeps are common , and when both H12 and H2/H1 are high , soft sweeps are common . However , when H12 is low , i . e . when there is little evidence for a sweep to begin with , either because the sweep was driven by weak selection or happened a long time ago , a wider range of H2/H1 values are compatible with hard sweeps . This demonstrates that H2/H1 can be used only in windows with very high H12 values . In most cases this should not unduly restrict the analysis as all robustly identified sweeps must have high H12 values given the difficulties of correctly specifying demographic models for any population . The visual inspection ( Fig . 9 and S5 Fig . ) and the Bayesian analysis of the H12 and H2/H1 values suggest that all top 50 H12 peaks were driven by soft sweeps . Note that we simulated hard and soft sweeps for the Bayesian analysis under the constant Ne = 106 demographic model for computational feasibility and to make our analysis conservative for the purposes of rejecting the hard sweep scenario . This is because the lower SNP density in the Ne = 106 model ( S3 Table ) , as compared to DGRP data , effectively increases the analysis window size in terms of base pairs , and by extension , also increases the number of recombination events each window experiences . Thus , hard sweeps should look “softer” under this choice of demographic model [53] . Even still , soft sweeps and not hard sweeps seem to more easily explain the signatures at our top 50 peaks . If soft sweeps are indeed common in D . melanogaster , then adaptation must commonly act on SGV at low enough frequencies to generate high H12 values or involve multiple de novo adaptive mutations entering the population simultaneously . The SGV scenario is clearly plausible , particularly if much adaptation in out-of-Africa populations of D . melanogaster utilized variants that are rare in Africa . We do , however , expect that many adaptive events will involve SGV at higher frequencies and such adaptive events will generate sweeps that are too soft to be detectable using the H12 statistic . Similarly , θA values much larger than 10 will also generate sweeps too soft to be detected by H12 . Curiously , this upper bound of θA is consistent with the median θA inferred from our top 50 peaks , ~12 . 8 ( S10 Fig . ) . This coincidence suggests that we might still be missing many sweeps that are too soft for detection using H12 . Is it plausible that some of the sweeps were generated by de novo mutation ? The answer must be clearly yes given that two of three known cases of recent adaptation , at Ace and Cyp6g1 , were generated by de novo mutation . In order for this to be possible , the total population scaled adaptive mutation rate ( θA ) must be on the order of one or even larger [27 , 29] . The commonly assumed value of Ne = 106 for the effective population size in D . melanogaster and mutation rate per base pair ( ~10–9 bp/generation [48] ) implies θA values of approximately 1% , assuming that adaptation at a given locus relies on mutation at a single nucleotide . One reason why θA can be commonly greater than 0 . 01 is that many mutations at a locus can be adaptive , for instance if adaptation relies on gene loss and any stop codon or indel is equally adaptive . In this case , all such adaptive mutations at a locus will combine to generate a soft sweep . In addition , the population size relevant for recent adaptation might be much closer to the census population size at the time of adaptation and thus can be much larger than the commonly assumed value of Ne = 106 for the effective population size in D . melanogaster . We favor this explanation of a much larger effective population size of D . melanogaster relevant for recent and strong adaptation for two reasons . First , it is unlikely that every single case of recent and strong adaptation was driven by a situation where the adaptive mutation rate at a locus was a hundred times higher than a mutation rate at a single site . Second , in the case of adaptation at Ace , adaptation was driven by three point mutations , and the soft sweeps at Ace are incompatible with the relevant population size being on the order of 106 [17] . The relevant population size for recent and strong adaptation in D . melanogaster should be thus more than 100-fold than 106 . Note that the relevant population size here is that of the D . melanogaster population as a whole and not just the North Carolina DGRP population . A likely possibility is that we observe signatures of multiple local hard sweeps arising within sub-demes of the North American Drosophila population or in the ancestral European and African populations prior to admixture , that combine to generate signatures of soft sweeps [58] . Nevertheless , it is quite puzzling that we were unable to detect any hard sweeps . One possibility is that hard sweeps do exist but are driven by weaker selection than we can detect in our scan . Indeed , Wilson et al . [52] argued that sweeps driven by weak selection could become hard even when they occur in populations of large size . This is because such sweeps take a long enough time to increase in frequency allowing rare but sharp bottlenecks to eliminate all but the highest frequency adaptive allele . It is also possible that hard sweeps were common in the past and degraded over time , while recent adaptation from de novo or rare variants produced primarily soft sweeps . While it is possible that hard sweeps correspond to the weaker and older selection events that we lack the power to identify , it is reassuring that our method is biased toward discovering the strongest , most recent , and thus most consequential adaptive events in the genome . The abundance of signatures of soft sweeps in D . melanogaster has important implications for the design of methods used to quantify adaptation . Some methods may work equally well whether adaptation proceeds via hard or soft sweeps . For instance , estimates of the rate of adaptive fixation derived from McDonald-Kreitman tests [59] are not expected to be affected strongly because these estimates depend on the rate of fixation of adaptive mutations and not on the haplotype patterns of diversity that these adaptive fixations generate in their wake . Tests based on the prediction that regions of higher functional divergence should harbor less neutral diversity [10 , 11 , 60] are generally consistent with recurrent hard and soft sweeps , as both scenarios are expected to increase levels of genetic draft , and thus reduce neutral diversity in regions of frequent and recurrent adaptation . Note that soft sweeps generate less of a reduction in neutral diversity . As a consequence , such methods might underestimate the rate of adaptation . However , methods that quantify adaptation based on a specific functional form of the dependence between the level of functional divergence and neutral diversity may lead to different conclusions under hard and soft sweeps [10] . Finally , methods that rely on the specific signatures of hard sweeps , such as the presence of a single frequent haplotype [39 , 40] , sharp local dips in diversity [22] , or specific allele frequency spectra expected during the recovery after the sweep might often fail to identify soft sweeps [35] . Hence , such methods might give us an incomplete picture of adaptation . Moreover , such methods might erroneously conclude that certain genomic regions lacked recent selective sweeps , which can be problematic for demographic studies that rely on neutral polymorphism data unaffected by linked selection . Our statistical test based on H12 to identify both hard and soft sweeps and our test based on H12 and H2/H1 to distinguish signatures of hard versus soft sweeps can be applied in all species in which genome-scale polymorphism data are available . The current implementation requires phased data but the method can easily be extended to unphased data as well by focusing on the frequencies of homozygous genotypes . Our method requires a sufficiently deep population sample for the precise measurement of haplotype frequencies , which is essential for determining whether a haplotype is unusually frequent in the sample . For example , in our DGRP scan , the majority of the 50 highest H12 peaks had a combined frequency of the two most common haplotypes below 30% , while only the top three peaks had a combined frequency of approximately 45% . Determination of whether a sweep is hard or soft should be particularly sensitive to the depth of the population sample . Finally , in order to determine whether an observed H12 value is sufficiently high enough to suggest that a sweep has occurred in the first place , reliable estimates of recombination rates are needed . We encourage the use of an empirical outlier approach to identify sweep candidates , especially because it is often difficult to accurately infer appropriate demographic models . Our results provide evidence that signatures of soft selective sweeps were abundant in recent evolution of D . melanogaster . Soft sweep signatures may be common in many additional organisms with high census population sizes , including plants , marine invertebrates , insects , microorganisms , and even modern humans when considering very recent evolution in the population as a whole . Indeed , the list of known soft sweeps is large , phylogenetically diverse , and is constantly growing [14] . A comprehensive understanding of adaptation therefore must account for the possibility that soft selective sweeps are a frequent and possibly dominant mode of adaptation in nature .
Population samples under selection and neutrality were simulated with the coalescent simulator MSMS [61] . We simulated samples of size 145 to match the sample depth of the DGRP data and always assumed a neutral mutation rate of 10–9 events/bp/gen [48] . MSMS can simulate selective sweeps both from de novo mutations and SGV . We simulated sweeps of varying softness arising from de novo mutations by specifying the population parameter θA = 4NeμA at the adaptive site . We simulated sweeps arising from SGV by specifying the initial frequency of the adaptive allele in the population at the onset of positive selection . The adaptive site was always placed in the center of the locus . We assumed co-dominance , whereby a homozygous individual bearing two copies of the advantageous allele has twice the fitness advantage of a heterozygote . To simulate incomplete sweeps we specified the ending partial frequency of the adaptive allele after selection has ceased . To simulate sweeps of different ages , we conditioned on the ending time of selection ( TE ) prior to sampling . When simulating selection with the admixture demographic model , it was unfortunately not possible in MSMS to condition on TE . For this demographic scenario , we instead conditioned on the start time of selection in the past and the starting partial frequency of the adaptive allele prior to the onset of selection , with selection continued until the time of sampling . In doing so , we assumed a uniform prior distribution of the start time of selection , U[0 to 3 . 05×10–4Ne] generations , with the upper bound specifying the time of the admixture event . We simulated loci of length 105 bp for sweep simulations with s < 0 . 1 and 106 bp for sweep simulations with s = 0 . 1 . For neutral simulations , we simulated loci of length 105 bp . We assumed a constant effective population size of Ne = 106 and a recombination rate of 5×10–7 cM/bp , reflecting the cutoff used in the DGRP analysis . Our statistics H12 and H2/H1 were estimated over windows of size 400 SNPs centered on the adaptive site . Simulated samples that yielded fewer than 400 SNPs were discarded . For the comparison with iHS , we calculated iHS values for the SNP immediately to the right of the selected allele , and determined the size of the region by cut-off points at which iHS levels decayed to values observed under neutrality . In some simulation runs under the extreme scenario with s = 0 . 1 and TE = 0 , iHS had not yet decayed to neutral levels at the edges of the simulated sweep . However , this should have only minor impact on the ROC curves . The DGRP data set generated by Mackay et al . ( 2012 ) [44] consists of the fully sequenced genomes of 192 inbred D . melanogaster lines collected from Raleigh , North Carolina . Reference genomes are available only for 162 lines . Of these 162 lines , we filtered out a further 10% of the lines with the highest number of heterozygous sites in their genomes , possibly reflecting incomplete inbreeding . The IDs of these strains are: 49 , 85 , 101 , 109 , 136 , 153 , 237 , 309 , 317 , 325 , 338 , 352 , 377 , 386 , 426 , 563 , and 802 . Any remaining residual heterozygosity in the data was treated as missing data . Our final data set consisted of 145 strains . We measured linkage disequilibrium ( LD ) in DGRP data and in simulations of neutral demographic scenarios in samples of size 145 . Simulations were performed assuming a neutral mutation rate of 10–9 events/bp/gen and a recombination rate of 5x10–9 cM/bp . LD was measured using the R2 statistic in sliding windows of 10 kb iterated by 50 bps . LD was measured between the first SNP in the window with an allele frequency between 0 . 05 and 0 . 95 and the rest of the SNPs in the window with allele frequencies between 0 . 05 and 0 . 95 . If any SNP had missing data , the individuals with the missing data were excluded from the LD calculation . At least 4 individuals without missing data at both SNPs were required to compute LD , otherwise the SNP pair was discarded . LD plots were smoothed by averaging LD values binned in non-overlapping 20 bp windows until a distance of 300 bps . After that , LD values were averaged in bins of 150 bp non-overlapping windows . We scanned the genome using sliding windows of 400 SNPs with intervals of 50 SNPs between window centers and calculated H12 in each window . If two haplotypes differed only at sites with missing data , we clustered these haplotypes together . If multiple haplotypes matched a haplotype with missing data , we clustered the haplotype with missing data at random with equal probability with one of the other matching haplotypes . We treated heterozygous sites in the data as sites with missing data ( “N” ) . To identify regions with unexpectedly high values of H12 under neutrality , we calculated the expected distribution of H12 values under the admixture , admixture and bottleneck , constant Ne = 106 , constant Ne = 2 . 7x106 , severe short bottleneck , and shallow long bottleneck demographic scenarios specified in Fig . 1 . For each scenario , we simulated ten times the number of independent analysis windows ( approximately 1 . 3x105 simulations ) observed on chromosomes 2L , 2R , 3L , and 3R using three different recombination rates: 10–7 cM/bp , 5×10–7 cM/bp , and 10–6 cM/bp . All simulations were conducted with locus lengths of 105 basepairs . We assigned a 1-per-genome FDR level to be the 10th highest H12 value in each scenario . Consecutive windows with H12 values that are above the 1-per-genome-FDR level were assigned to the same peak by the following algorithm: first , we identified the analysis window with the highest H12 value along a chromosome above the 1-per-genome-FDR with a recombination rate greater than 5×10–7 cM/bp . We then grouped together all consecutive windows with H12 values that lie above the cutoff and assigned all these windows to the same peak . After identifying a peak , we chose the highest H12 value among all windows in the peak to represent the H12 value of the entire peak . We repeated this procedure for the remaining windows until all analysis windows were accounted for . We scanned the DGRP data using a custom implementation of the iHS statistic written by Sandeep Venkataram and Yuan Zhu . iHS was calculated for every SNP with a minor allele frequency ( MAF ) of at least 0 . 05 without polarization . Any strain with missing data in the region of extended haplotype homozygosity for a particular SNP was discarded in the computation of iHS . All iHS values were standardized by the mean and variance of iHS values calculated at all SNPs sharing a similar MAF ( within ± 0 . 05 ) . As described in Voight et al . [40] , we calculated the enrichment of SNPs with standardized iHS values > 2 in non-overlapping 100 Kb windows . To determine the number of top H12 peaks that should overlap the top |iHS| enrichment regions by chance , we calculated the expected fraction of the genome that should overlap the top candidates in both scans . The top 50 H12 peaks cover a total of 7 , 166 , 386 bps of the genome , or , 7 . 42% of the genome . Similarly , the top 95 |iHS| enrichment windows with |iHS| > 2 cover 9 , 500 , 000 bps of the genome , or 9 . 83% of the genome . Thus , only 0 . 73% of the genome should overlap both the top H12 peaks and top |iHS| enrichment windows by chance . Multiplying this percentage with the total number of bps in the DGRP data set ( 96 , 595 , 864 ) and normalizing by the total area of the genome covered by the top 50 H12 peaks and top 95 |iHS| enrichment regions , only ~10% of the fraction of the genome covered by H12 peaks should overlap ~7 . 4% of the fraction of the genome covered by |iHS| enrichment regions . Assuming a uniform distribution of H12 peaks in the region of the genome covered by H12 peaks , approximately 5 H12 peaks should overlap approximately 7 |iHS| enrichment regions by chance . We fit six simple bottleneck models to DGRP data using a diffusion approximation approach as implemented by the program DaDi [47] . DaDi calculates a log-likelihood of the fit of a model based on an observed site frequency spectrum ( SFS ) . We estimated the SFS for presumably neutral SNPs in the DGRP using segregating sites in short introns [62] . Specifically , we used every site in a short intron of length less than 86 bps , with 16 bps removed from the intron start and 6 bps removed from the intron end [63] . We projected the SFS for our data set down to 130 chromosomes ( after excluding the top 10% of strains with missing data ) , resulting in 42 , 679 SNPs out of a total of 738 , 024 bps . We specified a constant population size model as well as six bottleneck models with the sizes of the bottlenecks ranging from 0 . 2% to 40% of the ancestral population size . Using DaDi [47] , we inferred three free parameters: the bottleneck time ( TB ) , final population size ( NF ) , and the final population time ( TF ) ( S1 Fig . and S2 Table ) . All six bottleneck models produced approximately the same log likelihood values and estimates of NF and TF . Further , the estimates of S and π obtained from simulated data matched the estimates obtained from the observed short intron data ( S3 Table ) . Note that the estimate of TB is proportional to NB , reflecting the difficulty in distinguishing short and deep bottlenecks from long and shallow bottlenecks . We inferred Ne = 2 , 657 , 111 ( ≈2 . 7x106 ) for the constant population size model , assuming a mutation rate of 10–9/bp/generation . To infer θAMAP values for the top 50 peaks ( S1 Text ) , we assumed uniform distributions for all model parameters in our ABC procedure: The adaptive mutation rate ( θA ) took values on [0 , 100] , the selection coefficient s on [0 , 1] , the ending partial frequency of the adaptive allele after selection has ceased ( PF ) on [0 , 1] , and the age of the sweep ( TE ) on [0 , 0 . 001]×4Ne . We assigned a recombination rate to each peak according to the estimates from Comeron et al . ( 2012 ) [49] for the specific locus . For the ABC procedure , we binned recombination rates into 5 equally spaced bins . Then , for each peak , we simulated the recombination rate from a uniform distribution over the particular bin its recombination rate fell in . The recombination rate intervals defining the 5 bins were: [5 . 42*10–7 , 1 . 61*10–6 ) , [1 . 61*10–6 , 2 . 68*10–6 ) , [2 . 68*10–6 , 3 . 74*10–6 ) , [3 . 74*10–6 , 4 . 81*10–6 ) , [4 . 81*10–6 , 5 . 88*10–6 ) in units of cM/bp . We assumed a demographic model with constant Ne = 106 and a non-adaptive mutation rate of 10–9 bp/gen in our simulations . For each peak , we sampled an approximate posterior distribution of θA by finding 1000 parameter values that generated sweeps with H12 and H2/H1 values within 10% of the observed values H12obs and H2obs /H1obs for the particular peak . We calculated the lower and upper 95% credible interval bounds for θA using the 2 . 5th and 97 . 5th percentiles of the posterior sample . On each posterior sample , we applied a Gaussian smoothing kernel density estimation and obtained the maximum a posteriori estimate θAMAP for each peak . We used the same procedure for obtaining approximate posterior distributions of θA and θAMAP estimates under the admixture model . In this case , instead of sampling the time when selection ceased , we sampled the time of the onset of selection with uniform prior distribution: U[0 , 3 . 05×10–4]×Ne , where 3 . 05×10–4Ne generations is the time of the admixture event . The prior distributions for all other parameters were the same as for the constant Ne = 106 model . We used an ABC approach to calculate Bayes factors for a range of H12 and H2/H1 values . We simulated hard sweeps with θA = 0 . 01 and soft sweeps with θA = 5 , 10 , 50 , or the θAMAP inferred for a particular peak , depending on the scenario being tested . In the constant Ne = 106 models shown in Fig . 11A–E , selection coefficients , partial frequencies of the adaptive allele after selection has ceased , and sweep ages were drawn from uniform distributions as follows: s ~ U[0 , 1] , TE ~ U[0 , 104]×4Ne , PF ~ U[0 , 1] . For the admixture model in Fig . 11F , the age of the onset of selection was sampled from a uniform distribution: U[0 , 3 . 05×10–4]Ne generations , where 3 . 05×10–4Ne generations corresponds to the time of the admixture event . We calculated Bayes factors by taking the ratio of the number of data sets simulated with H12 and H2/H1 values with a Euclidean distance < 0 . 1 from the observed values H12obs and H2obs /H1obs for each set of 106 simulated data sets under soft versus hard sweeps ( 105 data sets were generated for explicitly testing each peak with θAMAP ) . We calculated the Euclidean distance as follows: di = [ ( H12obs—H12i ) 2 /Var ( H12 ) + ( H2obs/H1obs—H2i/H1i ) 2 /Var ( H2/H1 ) ]1/2 , where Var ( H12 ) and Var ( H2/H1 ) are the estimated variances of the statistics H12 and H2/H1 calculated using all simulated data sets . | Evolutionary adaptation is a process in which beneficial mutations increase in frequency in response to selective pressures . If these mutations were previously rare or absent from the population , adaptation should generate a characteristic signature in the genetic diversity around the adaptive locus , known as a selective sweep . Such selective sweeps can be distinguished into hard selective sweeps , where only a single adaptive mutation rises in frequency , or soft selective sweeps , where multiple adaptive mutations at the same locus sweep through the population simultaneously . Here we design a new statistical method that can identify both hard and soft sweeps in population genomic data and apply this method to a Drosophila melanogaster population genomic dataset consisting of 145 sequenced strains collected in North Carolina . We find that selective sweeps were abundant in the recent history of this population . Interestingly , we also find that practically all of the strongest and most recent sweeps show patterns that are more consistent with soft rather than hard sweeps . We discuss the implications of these findings for the discovery and quantification of adaptation from population genomic data in Drosophila and other species with large population sizes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2015 | Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps |
We present a new approach to modeling languages for computational biology , which we call the layer-oriented approach . The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms ( e . g . differential equations ) , while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain . Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms . Additional functionality can be transparently added to the language by adding more layers . This approach is specifically concerned with declarative languages , and throughout the paper we note some of the limitations inherent to declarative approaches . The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation , while abstracting away details of particular programming languages and simulation environments . To illustrate this process , we define an example language for describing models of ionic currents , and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments . We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development . We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible , flexible modeling language design . We conclude by describing in detail the semantic transformations defined for our language .
Scientists who construct computational models of biological processes often find it necessary to use several different software tools in order to carry out various forms of data analysis and model simulation . However , each tool may employ its own model description format , consisting of diverse syntactic structures , and often can make implicit assumptions that are not reflected in the corresponding technical documentation [1] , [2] . As a result , constructing an exact implementation of a published model is a complex and time-consuming task . As an example , in computational neuroscience , both the GENESIS [3] and NEURON [4] simulators provide a parameterized form of the Hodgkin-Huxley model [5] as a basic object for model construction , but with some important differences between their description languages . The Hodgkin-Huxley object that exists in the Genesis language allows the rate equations to be specified in functional form and thus it can express not only the standard formulation of the model , but a whole family of conductance-based models of ionic currents . The NEURON HOC language also provides a Hodgkin-Huxley object , but its rate equations are fixed and it only allows different values for the parameters and initial states . NEURON includes a separate language , NMODL , which is intended for detailed descriptions of ionic current mechanisms that are distinct from the Hodgkin-Huxley equations . Hence , the two simulators have very different assumptions about what is meant by a “Hodgkin-Huxley model” . In These efforts are now facing their own information exchange challenges [12] . For instance , the Simulation Experiment Description Markup Language ( SED-ML ) [13] , which is an emerging standard for encoding numerical simulation protocols on top of SBML and CellML , has faced problems such as different sets of mathematical expressions allowed in different modeling languages and representing a diverse range of simulation time courses in the simulator software [14] . Other limitations of existing markup languages for biological modeling are pointed out in Section Discussion . These issues suggest that a more comprehensive approach may be necessary to build an interoperable ‘stack’ of extensible declarative languages for model description , simulation protocols , data analysis and so on . The layer-oriented approach described in this paper is a methodology to specify the syntax and semantics of several interlinked declarative languages ( or layers ) , each targeted at a particular problem domain , and formally describe how they relate to one another . We refer to syntax as the grammar according to which the sentences of a language are constructed; semantics is the system of rules that gives meaning to those sentences . The layers are not standalone languages , such as in the case of SED-ML , SBML and CellML , but share common properties in order to ensure their compatibility . The work presented here was developed prior to the authors' involvement in the NineML effort , which is a model description language developed as part of the Large-Scale Network Modeling initiative of the International Neuroinformatics Coordinating Facility ( http://www . incf . org/ ) [15] . The design of NineML is also divided in semantic layers , however its focus is on describing large-scale networks of integrate-and-fire neurons , and its design significantly diverges from the language presented here , which is oriented towards conductance-based models of ionic currents . The rest of this paper is structured as follows . Section Results gives an informal introduction to an example language for describing ionic currents , presents a high-level overview of the layer-oriented design of the language and highlights several language features necessary to express a complex model of currents in the Purkinje neuron . Section Discussion relates the layer-oriented approach to other model description language efforts and discusses its advantages and limitations . Section Methods presents a detailed syntactic and semantic specification of all layers in the example language and includes a brief summary of pertinent computer science literature .
We first informally illustrate the layer-oriented approach with an example language for describing conductance-based ionic current models . Some technical details are omitted here , but complete formal grammar and semantic rules for the language are given in Section Methods . In the following sections we show how to use this language to describe a complex model of ionic currents in the Purkinje neuron . The example language provides convenient idioms for common neuroscience modeling concepts . The layer-oriented approach ensures that each language idiom has a consistent mathematical representation that can be understood by each simulation or analysis software we desire to use . Furthermore , we will be able to extend the language by defining new concepts in terms of differential equations and other mathematical abstractions . We begin with a representation of a Hodgkin-Huxley-style model , which implicitly relies on several physiological modeling concepts such as Ohmic currents and gating variables . For the reader interested in technical details , the syntax presented below uses SXML , an alternative XML Infoset implementation based on Lisp s-expressions [17] . This syntax has an exact equivalent in conventional XML , but the use of s-expressions eliminates the necessity of closing tags and considerably reduces syntactic clutter . ( Membrane-potential ( Membrane-capacitance 1 . 0 uF/cm*cm ) ( Ohmic-current Na ( E = 115 mV ) ( g_max = 120 mS/cm*cm ) ( gating m ( power 3 ) ( forward-rate ( 2 . 5 - 0 . 1*V ) / ( ( exp ( 2 . 5 - 0 . 1*V ) ) - 1 ) ) ( reverse-rate ( 0 . 125 * exp ( -V/80 ) ) ) ) ( gating h ( power 1 ) ( forward-rate … ) ( reverse-rate … ) ) ) ;; end of Ohmic-current Na ( Ohmic-current K ( E = … ) ( g_max = … ) ( gating n ( power 4 ) ( forward-rate … ) ( reverse-rate … ) ) ) ;; end of Ohmic-current K ( Ohmic-current Leak … ) ) ;; end of Membrane-potential Although the sentences above are a fairly idiomatic representation of the Hodgkin-Huxley model , we must ensure that the underlying mathematics are consistently represented when this model is loaded in different software environments . To meet this requirement we need a language mechanism to automatically transform the above model code into the corresponding equations: Capacitance = 1 . 0 uF/cm*cm g_Na = g_max_Na * m_Na * m_Na * m_Na * h_Na I_Na = g_NA * ( V - E_Na ) dm_Na/dt = alpha_m_Na ( V ) * ( 1 - m_Na ) - beta_m_Na * m_Na alpha_m ( V ) = ( 2 . 5 - 0 . 1*V ) / ( ( exp ( 2 . 5 - 0 . 1*V ) ) - 1 ) … V = - ( I_Na+I_K+… ) /Capacitance Figure 1 is a conceptual overview of the steps performed by such a transformation mechanism in order to construct ionic current and membrane potential equations . In step A . 1 the gating variable declarations are used to construct the gating dynamics equation , and in step A . 2 the maximal conductance and reversal potential declarations are combined together to form the complete ionic current equation . In step B . 1 all Ohmic current declarations are assembled together and used to construct the membrane potential equation in step B . 2 . This kind of transformation mechanism is key to ensuring consistency of the mathematical representations of our model . Furthermore , extending the set of available model description concepts then becomes a matter of defining appropriate transformation rules . For example , to accommodate Goldman-Hodgkin-Katz ( GHK ) currents we use the transformation rules illustrated in Figure 2 . This example already demonstrates the extensibility of the layer-oriented approach . Figure 2 clearly shows that incorporating this important feature requires only minimal extensions to the structures presented in Figure 1 . Note that the gating mechanisms are identical for Figures 1A and 2A and that Figure 2B just adds GHK currents at the appropriate structure without disturbing the overall model structure . Analogously with the Ohmic current transformation , step A . 1 constructs the gating dynamics and step A . 2 constructs the GHK current equation . Thus , the layer-oriented approach is primarily concerned with definitions of biological modeling concepts and their equivalent equational form . The transformation from one to the other is explained in detail in the following sections . The layer-oriented approach is a structured methodology to define notations for declarative computational models . It involves: The question of which biological modeling and mathematical concepts are chosen and grouped in layers is one that must be properly answered by the scientific community . The layer-oriented approach provides the technical means to formalize the relationships between the domains of biological modeling and mathematical concepts . The process of formalizing these relationships is a way to identify and eliminate potential flaws in the language and to communicate the language semantics in a concise manner . More on this topic can be found in Section When and how to use the layer-oriented approach . As a concrete example , Figure 3 illustrates the structure and the relationships of several language layers that together can describe the structure of computational neuroscience models of ionic currents as well as voltage clamp protocols , explained below . The ionic current layer consists of elements that correspond to neuroscience modeling concepts , such as channel gates and ionic conductances . The mathematical layer consists of elements that correspond to general mathematical concepts , such as rate equations and functions . Figure 3 is not meant to be an exhaustive representation of the biological modeling ‘universe’ . It can be easily conceived that e . g . adding stochastic differential equations to the mathematical layer will allow a range of stochastic models to be included in the higher layers . The point is that the layer-oriented approach enables such additions to be explicitly and clearly specified while preserving full compatibility with existing definitions , as illustrated with the GHK example in Figure 2 . In our approach , a semantic transformation function is a collection of rules that specify how concepts from one layer are represented as a combination of concepts from another . An important practical aim is to represent the layers and the transformations between them by means of a mathematical notation that does not have the clutter of programming details inherent to a concrete implementation yet can be easily expressed in any reasonable programming language . Thus , the semantic transformation functions in this paper are written in a metalanguage that contains the essence of some typical patterns of programming languages . With this approach , the semantics of layer-oriented language can be described independently of the implementation language by a sequence of various layer transformation functions , e . g . :The sequence of transformation functions comprising describes a set of common operations necessary to express a model of ionic currents as an environment of equations conforming to the syntax of the equation-oriented mathematical layer ( detailed definitions are given in Section Methods ) . The specification of a layer-oriented language then takes the form of semantic transformation functions for all layers , which can be straightforwardly mapped to an implementation . We note here that the metalanguage is not concerned with issues such as error handling for invalid input as these are details unique to each implementation . A further practical benefit of this manner of specification is that semantic transformation functions provide a convenient blueprint for code generation , or the process of transforming computational biology models to computer-executable form [18] , in this specific instance generation of Matlab or NMODL language . The two sequences of transformation functions below describe code generation for two very different software platforms ( Matlab and the NEURON simulator ) using largely identical sequences of steps ( details are given in Section Methods ) . From a practical standpoint , and its constituent parts need only be implemented once and reside in a standard software library , which can then be shared between multiple simulators and other software that aims to read this particular model description language . Additional information for code generation , such as provided by ( needed for NEURON ) can also be specified with semantic transformation functions and implemented either as part of the standard library or for specific platforms . The transformations specific to neuroscience modeling software are briefly described in the following sections . All transformation functions mentioned in this section are defined in Section Methods Our model examples thus far have included the use of two layers , one for ionic current descriptions and one for equations and functions . The equation layer omitted any of the structure associated with biological interpretation of the equations , such as the gating elements . But the language must have the capability not merely to represent a set of equations , but to group related definitions and equations across layers . We therefore introduce the notion of model components , which encapsulate related equations and functions that are part of a model . They are generic entities that are not concerned with how these equations are grouped together and permit arbitrary nesting of sub-components . We further characterize a component by its type and output quantities . From the point of view of biological modeling , only particular combinations of nesting are valid . Wimalaratne et al . observed that allowing arbitrary structuring of hierarchical biological models leads to difficulties in model exchange , and therefore we need to define rules that require models of ionic currents to be structured according to the accepted principles of computational biology [19] . The syntax and semantics of the component layer and structuring rules are given in Section Methods . These rules stipulate that the following structure must be followed: ( Membrane-potential Modelname ( Membrane-capacitance ( out C ) … definition of capacitance … ) ( Ohmic-current ( name ion ) ( gating ( out m ) … equations for channel gate dynamics … ) ( pore ( out gbar ) … equations and parameters of maximal conductance … ) ( permeating-ion ( name ion ) ( out e ) … definition of reversal potential … ) ) ) Compared with the previous example , the model structure above explicitly labels the sub-components of the ohmic current component ( gating , pore and permeating-ion ) . While slightly more verbose , this notation allows easier formulation of transformation rules , as we explain in Section Methods . These are not intended to be authoritative rules , but an illustration of the capabilities of the layer-oriented approach . A different set of rules can be easily formulated and formalized as determined by discourse in the scientific community . Further details can be found in Section Discussion and Section Methods . We have used the prototype language to implement a previously published model of the Purkinje neuron . The component abstraction gives us the ability to construct models as aggregations of components containing definitions of ionic gates , conductances and so on . Figure 4 illustrates the component structure of our description of the Khaliq-Raman model of cerebellar Purkinje neurons ( ModelDB accession number 48332 ) [20] . The complete prototype listing is given in Supporting Text S1 . Our layer-oriented description of this model consists of the Ohmic and GHK current components already mentioned as well as a calcium concentration dynamics components that will be explained in the following subsections . The layer-oriented approach can be easily applied to describing simulation experiments . The simulation results in Figures 5 and 6 were produced from the same model description with code generated by our prototype implementation of a translator for layer-oriented neuroscience models , as applied to the Khaliq-Raman model . The simulation software used was NEURON 7 . 1 and GNU Octave 3 . 2 , in both cases running under Debian Linux 5 . 0 on a Dell Precision T5400 workstation . The code generation algorithm is based on the transformation rules defined in Section Methods . Additional simulation results addressing runtime efficiency in the NEURON simulation environment are described in Section Ionic current mechanism mapping problem in NEURON and presented in Supporting Figure S1 . The transformation functions for simulation experiments are given in Section Methods . We define two new types of components , simulation and voltage-clamp , and use them to specify simulation and voltage clamp parameters for the different currents of the model , e . g . : ( simulation ( out duration stepsize ) ( const duration = 2000 ) ( const stepsize = 1e-4 ) ) ( voltage-clamp ( name CaBK ) ( out hold base stepsize nsteps holding-duration base-duration ) ( const hold = −90 ) ( const base = −40 ) ( const stepsize = 10 ) ( const nsteps = 5 ) ( const holding-duration = 5 ) ( const base-duration = 20 ) ) The semantics associated with each component of type voltage-clamp require that there must be a corresponding ionic current component of the same name . This then allows the generation of voltage clamp scripts that are consistent with the currents of the model .
The different domains and subdomains of computational biology each require that models are structured according to the accepted terminology and classification of that domain . Therefore , successful development of future biological modeling languages will depend on appropriately formalised representation of domain knowledge . One common approach to developing such formalizations are the multiple ontological efforts to represent various biological entities for multiple levels of granularity [24] . Our layer-oriented approach complements ontologies with the systematic development of domain-specific language rules so that the conventions and categories of the domain are distinctly and clearly represented to the user , while generality is preserved by the underlying layers that provide access to general mathematical and algorithmic concepts . By ‘extensibility’ we mean functionality to describe new modeling techniques in addition to those provided by standard model databases . For example , suppose that a scientist wishes to use conditional expressions in the mathematical layer of a layer-based language , which is necessary for e . g . threshold detection in the integrate-and-fire formalism [25] . In such a case , the mathematical layer can be extended with conditional primitives to express transitions between dynamical systems and the neuroscience modeling layer can be extended with a regime concept , which encapsulates the subthreshold equations and specifies the firing condition and reset equation . The mapping between the high-level regime concept and the condition primitives can be defined by a semantic transformation function . As another example , suppose that a scientist wishes to integrate morphological descriptions in a layer-based language . While the examples in this paper do not address geometric descriptions and partial differential equations the same transformation approaches can be applied to define complex surfaces and dynamics based on core abstractions for spatial PDEs . Once the precise hierarchy of concepts and mathematical mechanisms are defined by the community , a layer-oriented language can allow scientists using the language to formally describe new approaches and make them shareable without having to alter the core language specification , as must be done for NeuroML . As already observed by Wimalaratne , et al . , explicitly defined hierarchical structuring rules are a necessity for many kinds of biological modeling . As we discuss in Section Existing biological modeling languages , some of the key evolutionary improvements in existing and emerging modeling languages are related to modularity , hierarchical structuring and expressing relationships between different components of a biological model . These properties are well-addressed in our approach by means of compositionality . We refer as compositionality to the ability to compose a model from pre-existing parts . For example , given a set of standard ion channel objects from a model library and a set of parameters provided by the user , the equations for the model could be automatically constructed depending on the chosen channel objects . A sophisticated component model is required to support descriptions such as a dendritic maximal conductance that is dependent on distance from the soma [26] . In order to support such functionality , the language must have formal semantics for composition and extension . Layer-based components in a biological modeling language can express different functional and structural relationships and allow scientists to invent and share their own components , as well as build on the existing mechanisms . One of the most important problems facing biological modeling languages is formulating the extent and requirements of the target domain . The layer-oriented approach provides the technical means to formalize the relationships between the domains of biological modeling and mathematical formalisms , but the researchers who wish to design and use such a language must already have some informal understanding of these relationships . Once the domain is well-defined in terms of mathematical formalisms , as is the case with deterministic models of ionic currents in computational neuroscience , our layer-oriented approach can be applied by constructing a formal grammar for the language and corresponding transformation rules that explicitly link the biological modeling concepts to mathematical formalisms . As we show in Section Methods , the transformation rules can be written in a metalanguage that generalizes the typical patterns of programming languages without the operational details of a real implementation . The process of writing and understanding such rules assists researchers in clarifying and refining the semantics of the language , as Scott and Strachey showed in their influential work on programming language specification [27] , [28] . Constructing a set of transformation rules for a given biological modeling concept may be a whole scientific endeavor , such as , for example , approximating the voltage dynamics of 3D cell membranes with the 1D cable formalism commonly used in computational neuroscience [29] . Furthermore , our approach relies on a mathematical language that is sufficiently rich to formulate all concepts and problems of the scientific field of interest . The development of computational science suggests that mathematical languages based on ODEs and PDEs are well-suited to express many theories and concepts of physics and chemistry . However , additional formalisms , such as stochastic equations , may be necessary to model problems in computational biology . The layer-oriented approach as a method for interoperability assumes that such additional formalisms would be consistently supported by several software platforms . It is possible that for some biological concepts there exist semantic ambiguities , i . e . several alternative mathematical formulations . The layer-oriented approach is modular and can accommodate different sets of transformation rules for the same concepts in the form of namespaces or modules [30] , but ultimately it is the responsibility of the language designers to use such technical tools to resolve the differences between the mathematical approaches . The layer-oriented approach would not be applicable in a case where a biological modeling concept has only an empirical algorithmic representation and no consistent underlying mathematical theory . This is a consequence of the declarative nature of the approach . For example , the exact stochastic simulation algorithm ( SSA ) is widely used in computational biology [31] . However , the necessity to simulate every reaction event causes the algorithm to be too slow for some applications . An approximation strategy known as tau-leaping sacrifices exactness for reduced computational cost [32] . At present there is no widely adopted declarative generalization of SSA and tau-leaping , although proposals have been made [33] . Applying the layer-oriented approach to modeling problems based on tau-leaping , or other approximations of SSA , would require that the various decision procedures involved are represented in a declarative form that reflects the underlying mathematical model . In this sense our approach is limited by the scientific understanding of the concepts in the particular modeling domain . Another important aspect of designing biological modeling language is the process of community validation . For instance , the community validation process of SBML Level 3 involves having at least two independent software implementations of a proposed feature before that feature can be considered for inclusion in the standard . From our personal observations on the development process of the emerging NineML language , the NineML committee has also converged on peer-reviewing implementation code as means to ensure that prototype implementations of the language not only have the same grammar , but also have consistent and community-approved semantics . However , the informal processes of SBML and NineML are limited by the fact that code in different programming languages cannot in general be directly compared . The layer-oriented approach is a way to lift this restriction . It enables the community first to agree on the semantic transformation rules , then to relate them to particular software implementation . It does not mandate a particular implementation , or a particular programming language , and thus can be used by a diverse community of developers . As a further step in language specification , the layer-oriented approach opens the possibility for using mathematical reasoning methodologies [34] to formally prove that a particular software implementation is faithful to a particular set of semantic transformation rules . Variations of the layer-oriented approach are not new to computational neuroscience . The NEURON simulator has pioneered the use of an introspective interpreter ( HOC or Python ) and a declarative model description language ( NMODL ) for extending the available modeling mechanisms . However , the work presented here is specifically concerned with layers of purely declarative languages . In our approach , the interfaces between layers are explicitly specified in an implementation-neutral mathematical notation and additional layers can be introduced in a consistent and conceptually clear manner . In contrast , simulators such as NEURON typically employ an algorithmic language for experiment control and a declarative language for model equations , and the details of interfacing the two languages are unique to the particular software implementation . Declarative languages describe problems in a particular domain , and possibly some properties of the desired solutions , rather than explicit mechanisms for computing solutions [35] , [36] . Algorithmic languages take the form of stepwise machine instructions for performing computation . Algorithmic languages have much greater expressive power than declarative ones , however they introduce operational details that might be entirely irrelevant to the higher-level concepts that they express . Because of the expressiveness of algorithmic languages , it could be argued that all tasks in neuroscience simulation and modeling could be accomplished with a combination of NMODL and HOC or Python , or similar combination of declarative and algorithmic languages . However , the many engineering details of interfacing such languages – variable scoping , data representation and propagation , control flow – would make any such combination of languages unique and difficult to comprehend and to replicate in different software implementations . Because in our approach each layer is declarative and constrained to a specific purpose , a complete set of rules can be given for how the different layers relate to one another and how executable code can be generated from a layer-based description . Such rules then provide a convenient blueprint for consistent and interoperable software implementations . Designing modeling languages involves the translation of the concepts of the domain into semantic concepts appropriate for computer representation . Often the transformation from domain-specific concepts into computer code cannot be done in a single step but requires several intermediate steps . The layer-oriented approach is an attempt to discern these intermediate semantic steps . The layer-oriented approach relies heavily on model structuring . Structuring is a common modeling technique of dividing an object into a number of parts and indicating relationships between these parts . In this way quite naturally a layered model arises . Our prototype language defines a form of structuring based on components that allows models of arbitrary complexity to be constructed . In this way , the language provides extensibility and flexibility in describing new models that involve detailed biophysical modeling .
The following syntactic constructs are used in the metalanguage:Expressions in the metalanguage are typically constructors for the various data structures that correspond to the domain-specific syntaxes discussed in this paper . For example , the definitionmeans that metafunction matches the sequence consisting of the symbol const , followed by the pattern variable ( which must be of a defined type ) , the symbol = and the pattern variable . The result of the function is an entry constructed using the pattern variables and the Parameter constructor defined previously . The language we have developed for describing models of ionic currents has a hierarchical structure that is meant to reflect the logical relationships between the different parts of ionic current descriptions . For example , an Ohmic current consists of ionic current name , gating dynamics description and maximal conductance definition . The syntax of this language in Backus-Naur form [47] is given below . We note that the definition of the Equation domain is not part of this language but refers to the equation-oriented language in the next section . We base our layer-oriented approach on a domain-specific language that is capable of expressing differential and algebraic equations and later use it to construct complex models of ionic currents . The language has a simple syntax for expressing relations and first-order differential equations , and we define a transformation function on this syntax that transforms every declaration in the language to an intermediate form suitable for further processing , such as code generation , or some type of model transformation , such as parameter perturbation . The syntax of this language in Backus-Naur form is: [Constant during integration] [Algebraic equation] [Relation] [ODE of the form dx/dt = e] [Forward kinetic scheme] [Forward and reverse kinetic scheme] The equation-oriented language is transformed to an intermediate semantic form suitable for further processing . We use an intermediate language of the following form:where The transformation function describes the process of creating new entities: Entities are characterized by name , type and expression . However , we must use these entities together in order to solve the corresponding system of equations . We use an environment structure in which entities are indexed by name and type , and which can be queried to extract information for further model processing . We represent environments by a functionwhich we call the current environment of entities . We use the metafunction notation to express the extension of the current environment with a new entity . The syntax of the component language in Backus-Naur form is: We use the set of types defined above to identify structures specific to ionic current models , although the schema of supported types can be naturally extended to support a broader range of modeling concepts . The transformation function transforms the component syntax into nested environments: The metafunction returns the set of names defined in the given environment . The transformation function is as defined before . We do not define the case when the list of output entities is not a subset of the entities defined in the given component , but a real implementation must signal an error in such case . Furthermore , although our definitions allow the nesting of environments , our target numerical platforms , such as Matlab , do not necessarily support namespace control . In order to generate code for such environments , we must conduct flattening of the nested environments , so that all identifiers can occupy the same namespace without collision . The transformation function flattens nested environments by replacing all identifiers with explicit paths based on their enclosing environments: The metafunction substitutes identifiers in an expression , given a substitution environment that maps identifiers to expressions . The metafunction builds nested substitution environments: during the substitution process , if an identifier is not found in the immediate environment , it is looked up in the enclosing environment , and so on . Let denote the set of components of type contained in the environment . Let indicate the outputs declared for component . can then be defined as follows: and take an additional argument , , which indicates the current scope , or environment nesting path . The path specified by is used to disambiguate the variable names that are used in the current and voltage equations that are constructed by the transformation functions . The transformation functions defined above require that ionic current models consist of one component of type membrane-capacitance , and one or more components of type Ohmic-current . Components of type Ohmic-current must in turn contain one or more components of type gating ( gate dynamics ) , one component of type pore ( maximal conductance ) and one component of type permeating-ion ( reversal potential ) . The procedure takes input in the form of nested environments . We rely on the metafunctions and to perform operations on a list of components . applies a given function to every member of a list of components and returns a list of the results . ( also known as in Python or in C++ ) iterates a given function over a list of components and builds up a cumulative result . The transformation function for HH-gating-dynamics has the following definition: To support the GHK formalism , we first extend the grammar of the ionic current description language from the beginning of this section with the requisite clauses:Then must be extended with matching clauses: Having defined the structures for describing ionic currents and component-based systems of equations , we can now define a transformation function that takes in an environment of entities as input and produces code for a given solver API . We abstract away the details of implementation by using idealized mathematical structures that mimic the structure of the target API . Nevertheless , we indicate what procedures are necessary to turn our abstract notation into concrete programming language syntax . We first define a code generation function for a Matlab-like language , following the API required by the Matlab ODE solver: where receives the independent variable and a state vector and must return the vector of derivatives that corresponds to the given input . In order to proceed with code generation , we must have the following representation of the system of equations: We first define transformation function , , which computes the free variables of every expression and orders the entries in the environment according the dependencies in their associated expression:where the metafunction computes the free variables in an expression , and inserts a new entry in an ordered collection according to a partial order predicate: Given an ordered environment , we can now define a transformation function to construct a structure suitable for input to code generation procedures . In this particular case , our target structure is a 5-tuple of the form:The last element in the tuple is a mapping between state vector indices and state variable names . The transformation function can then be defined as follows:We use the characters to indicate tuple construction , the metafunction to indicate list concatenation and the metafunction returns the largest integer plus one from the given map . The concrete code generation procedures can be defined as follows:Each of the metafunctions are relatively simple procedures that map the abstract representation to the concrete syntax of the target language . foreach applies the given procedure to each element of the given list . We have conducted an implementation study of a prototype layer-oriented language for describing models of ionic currents . The implemented prototype is closely related to the semantics presented in this paper , but is not identical . The software is available for download at http://wiki . call-cc . org/nemo . It is developed in the Scheme programming language using the Chicken Scheme compiler ( http://www . call-cc . org/ ) . The Scheme and Lisp family of languages have a long tradition of domain-specific language development [48] and are intrinsically suitable for XML processing [17] . | The pursuit for understanding of neural function by computational modeling has produced a variety of software tools , with each tool targeting specific audiences and often requiring input in its own distinct language . Consequently , comprehending and communicating neuroscience models is a difficult and time-consuming task . In this paper we suggest a new approach towards designing biological modeling languages , which we call the layer-oriented approach . The approach stems from the observation that diverse biological phenomena are described using a small set of mathematical formalisms ( e . g . differential equations ) , which are structured according to some biological principles . Our proposal is illustrated by means of a computer language for describing computational models of ionic currents . The language consists of rules for expressing mathematical equations as well as rules to organize these equations according to the specific terminology used by neuroscientists . The layer-oriented approach offers two chief advantages . First , it allows the flexible use of mathematical equations to represent many different kinds of biological models . Second , it restricts the language within a framework of biological concepts so that existing modeling software can be reused . The goal of the layer-oriented approach is to help define appropriate notations for computational biology while enabling interoperability of software for biological modeling . | [
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| 2012 | The Layer-Oriented Approach to Declarative Languages for Biological Modeling |
The halophilic archaeon Haloferax volcanii has a multireplicon genome , consisting of a main chromosome , three secondary chromosomes , and a plasmid . Genes for the initiator protein Cdc6/Orc1 , which are commonly located adjacent to archaeal origins of DNA replication , are found on all replicons except plasmid pHV2 . However , prediction of DNA replication origins in H . volcanii is complicated by the fact that this species has no less than 14 cdc6/orc1 genes . We have used a combination of genetic , biochemical , and bioinformatic approaches to map DNA replication origins in H . volcanii . Five autonomously replicating sequences were found adjacent to cdc6/orc1 genes and replication initiation point mapping was used to confirm that these sequences function as bidirectional DNA replication origins in vivo . Pulsed field gel analyses revealed that cdc6/orc1-associated replication origins are distributed not only on the main chromosome ( 2 . 9 Mb ) but also on pHV1 ( 86 kb ) , pHV3 ( 442 kb ) , and pHV4 ( 690 kb ) replicons . Gene inactivation studies indicate that linkage of the initiator gene to the origin is not required for replication initiation , and genetic tests with autonomously replicating plasmids suggest that the origin located on pHV1 and pHV4 may be dominant to the principal chromosomal origin . The replication origins we have identified appear to show a functional hierarchy or differential usage , which might reflect the different replication requirements of their respective chromosomes . We propose that duplication of H . volcanii replication origins was a prerequisite for the multireplicon structure of this genome , and that this might provide a means for chromosome-specific replication control under certain growth conditions . Our observations also suggest that H . volcanii is an ideal organism for studying how replication of four replicons is regulated in the context of the archaeal cell cycle .
In all prokaryotic organisms , and in certain unicellular eukaryotes , DNA replication is thought to initiate at well-defined chromosomal sites . These origins of replication serve as assembly sites for the protein machinery that unwinds the DNA duplex and initiates bidirectional DNA synthesis [1 , 2] . Bacterial chromosomes typically carry a single replication origin ( oriC ) and cognate initiator protein ( e . g . , DnaA ) , whereas eukaryotic chromosomes contain large numbers of replication origins , which are bound by a multiprotein origin recognition complex ( ORC ) . Archaea use DNA replication proteins similar to those of eukaryotes but have circular chromosomes like bacteria [3] . Relatively little is known about origin utilization in archaea , and the available data suggest major differences in how chromosomes are replicated in the key archaeal groups . In particular , the chromosome of Pyrococcus abyssi ( Euryarchaeota ) is replicated from a single oriC [4] , whereas three different oriCs are used to replicate the single chromosome of Sulfolobus species ( Crenarchaeota ) [5 , 6] . Archaeal replication origins consist of a long intergenic sequence containing an A/T-rich duplex unwinding element ( DUE ) , which facilitates the local duplex opening required for replication fork assembly . The intergenic region is typically located upstream of a cdc6/orc1 gene , which encodes a putative initiator protein that is homologous to both a subunit ( Orc1 ) of eukaryotic ORC and the helicase loader Cdc6 . Protein complexes formed by archaeal initiator proteins could therefore have a dual function in origin recognition and loading of minichromosome maintenance ( MCM ) helicase at the origin . However , available biochemical data are consistent with a role in origin recognition only . The intergenic region of the replication origin also carries multiple conserved sequence elements ( origin recognition boxes , ORB ) that are bound by Cdc6/Orc1 initiator proteins [6 , 7] . Binding of initiator proteins at the archaeal origin has been shown to proceed in a cooperative manner [8] , suggesting that in archaea a defined multimeric initiator protein complex forms at the origin . Direct support for this idea has come from recent experiments indicating that higher-order assembly of Aeropyrum pernix Orc proteins results in structural and topological changes in the origin DNA [9] . Current knowledge of archaeal DNA replication is based on biochemical observations , and genetic studies are needed to test the extant models . In this respect , haloarchaea are ideal model organisms since they are easily cultured and amenable to genetic manipulation [10 , 11] . The genome sequences of Halobacterium sp . NRC-1 and Haloarcula marismortui have revealed that haloarchaea contain multiple replicons , each with essential genes [12 , 13] . However , the mechanisms that coordinate the replication of these multiple replicons remain unknown . The genome of Haloferax volcanii has a multireplicon structure consisting of a main chromosome of 2 . 9 Mb and four smaller replicons ( pHV1 [86 kb] , pHV2 [6 . 4 kb] , pHV3 [442 kb] , and pHV4 [690 kb] ) [14] . Like other haloarchaea , the H . volcanii genome encodes numerous putative cdc6/orc1 genes that are distributed amongst the different replicons ( except pHV2 ) ( Table 1 ) . This suggests that lineage-specific duplication of replication origins and/or initiator proteins has driven dynamic genome evolution in haloarchaea . However , previous experimental studies have identified only one likely replication origin on the main chromosome of the related haloarchaeon Halobacterium sp . NRC-1 [15] . Analysis of the genome sequence of H . volcanii ( Hartman et al . , unpublished data ) suggested the presence of numerous replication origins , prompting us to search for autonomously replicating sequence ( ARS ) elements corresponding to each replicon . Replication initiation point ( RIP ) mapping was used to confirm that these ARS elements are functional in their chromosomal context . Our genetic data suggest a genome-wide hierarchy of some ARS elements , raising the possibility of chromosome-specific origin regulation in halophilic archaea . This study also provides a framework for investigating how the coordinated replication of four replicons is achieved in halophilic archaea .
To avoid any a priori bias regarding the location of replication origins , we carried out a genetic screen for ARS elements from H . volcanii . A partial HpaII digest was performed with genomic DNA from H . volcanii strain WR340 [18] , which like all laboratory strains of H . volcanii lacks the smallest replicon pHV2 [19 , 20] . DNA fragments of between 4 and 8 kb were cloned in the nonreplicating pyrE2-marked plasmid pTA131 [21] and used to transform the ΔpyrE2 ΔradA strain H49 to prototrophy for uracil ( pyrE2+ ) . H . volcanii radA mutants such as H49 are defective in homologous recombination [22]; this precaution was taken to prevent integration of the plasmid into the genome by homologous recombination . A total of 35 transformants were obtained . Plasmid DNA from seven transformants was sequenced , and in all cases , the insert corresponded to the same sequence on contig number 454 ( Figure 1A ) . Since H . volcanii has more than one replicon , it was striking that only one ARS element was recovered . To ensure this was not a technical artifact of the screen , we repeated the library construction using AciI to generate the partial digest of H . volcanii genomic DNA and transformed H49 as before . Plasmid DNA from six AciI library transformants was sequenced , and all contained the same region of the H . volcanii genome isolated in the initial screen . This region comprises two divergently transcribed genes including orc10 , separated by a 910-bp intergenic region featuring a 104-bp A/T-rich putative DUE ( 68% A/T versus 33% for overall genome ) and several direct repeats ( Figure 1A ) . Similar features are encountered at almost all characterized archaeal origins of DNA replication ( e . g . , [4] ) . However , it is notable that the nucleotide sequence of the direct repeats ( Figure 1B ) bears only little similarity to the ORB identified by Robinson et al . [6] . To determine the minimal sequence needed for DNA replication activity , we carried out a partial AciI digest of a representative 5-kb ARS insert that was cloned in plasmid pTA194 ( Figure 1A ) . Fragments of 1–1 . 3 kb , 1 . 3–1 . 6 kb , 1 . 6–2 kb , 2–3 kb , and 3–4 kb were excised separately from an agarose gel and ligated with pTA131 to construct an ARS subclone library . Transformants of H . volcanii H49 were obtained with DNA from each size range . Plasmid DNA from six transformants in the 1–1 . 3-kb range was sequenced , and in all cases , the insert included the 910-bp intergenic region ( Figure 1A ) . We were able to delimit the minimal origin further by amplifying a 633-bp fragment of the intergenic region using PCR ( Figure 1A ) . This fragment was subcloned in pTA131 to generate pCN12 and used to transform the ΔpyrE2 ΔradA strain H112 . Thus , the minimal origin is located in this region , and in contrast to what had been observed for Halobacterium sp . NRC-1 [15] , the cdc6/orc1 gene is not required in cis for ARS activity in H . volcanii . We used the ARS insert in pTA194 to probe a Southern blot of intact H . volcanii DNA displayed on a pulsed field gel ( PFG ) . Two bands were observed ( Figure 1C ) , which correspond in size to replicons pHV4 ( 690 kb ) and pHV1 ( 86 kb ) [14] . The intensity of the two bands was similar , suggesting that this replication origin is present on both pHV1 and pHV4 and that both replicons are present in all cells . We employed the minimal ARS element from the AciI subclone library ( in pTA250 ) to generate low copy–number shuttle vectors with pyrE2 , trpA , hdrB , and leuB selectable markers [21] ( Figure S1 ) . The principal advantage of these shuttle vectors over existing plasmids based on the origin of replication from pHV2 is their smaller size ( ∼4 . 5 kb for pHV1/4-based vectors versus ≥7 . 5 kb for pHV2-based vectors ) . The fact that only one ARS element was recovered in the initial genetic screen suggests that this sequence might be “dominant” and thereby prevent the isolation of other origins . We hypothesized that deleting this sequence from pHV1 and pHV4 by a gene knockout system [18 , 21] would allow the isolation of ARS elements corresponding to origins of DNA replication on the main chromosome and pHV3 . The pHV1/4 replication origin and adjacent genes were subcloned to generate pTA252 , and a 1-kb fragment containing the intergenic region necessary for ARS activity was replaced by a trpA selectable marker for tryptophan biosynthesis [21] . This plasmid ( pTA266 , Figure 1D ) was used to transform the ΔpyrE2 ΔtrpA strain H53 , and integration at the orc10 locus was verified by Southern blot ( Figure 1E , strain H220 ) . Counter-selection with 5-fluoroorotic acid ( 5-FOA ) was used to ensure loss of integrated pTA266 by intramolecular recombination and to yield a trpA-marked deletion of the intergenic region ( Figure 1D ) . While origin deletion events ( trpA+ 5-FOA-resistant cells ) were obtained , they were outnumbered >1 , 000-fold by events leading to restoration of the wild-type ( trpA– 5-FOA-resistant cells ) , indicating a strong bias for maintenance of the replication origin . The deletion was verified by Southern blots of a genomic DNA digest ( Figure 1E , strain H230 ) and intact DNA displayed on a PFG ( Figure 1C ) . The pHV1 and pHV4 replicons are still present ( unpublished data ) and must therefore be using alternative replication origins . Since the deletion strain H230 did not show any obvious growth defects , these alternative origins presumably act as sites of efficient DNA replication initiation . This observation prompted us to search for other ARS elements . We repeated the screen outlined above using the deletion strain H230 as a source of genomic DNA . AciI was used for a partial digest of H230 genomic DNA , fragments of 3–5 kb were cloned in pTA131 , and H49 was transformed as before . Plasmid DNA from nine transformants was sequenced , and in all cases the insert localized to a single region of the H . volcanii genome ( contig number 455 , which corresponds to the main chromosome ) . This region ( Figure 2A ) comprises two divergently transcribed genes including orc1 , separated by a 1 , 360-bp intergenic region featuring two A/T-rich DUEs and several direct repeats . The nucleotide sequence of the direct repeats ( Figure 2B ) is 89% identical to the ORB sequence ( euryarchaeal consensus 5′-GTTCCAGTGGAAAC-AAA‐‐‐‐GGGGG-3′ ) [6] . In addition to orc1 , a number of other genes related to DNA replication and repair is found in the vicinity of the ARS ( Figure 2A ) , encoding the DP1 exonuclease subunit of the archaeal D family DNA polymerase [23] , the Hef helicase/endonuclease [24] , a homologue of the bacterial UvrC nucleotide excision repair protein , an NAD-dependent DNA ligase [25] , and the Hel308 helicase [26] . The ARS insert in one plasmid ( pTA313 ) was used to probe a Southern blot of intact H . volcanii DN5A on a PFG . As expected , one band was observed ( Figure 2C ) that corresponds in size to the main chromosome ( 2 . 9 Mb ) [14] . A 1 . 1-kb Sau3AI-HindIII fragment of pTA313 , comprising the intergenic region only ( Figure 2A ) , was subcloned in pTA131 to generate pTA441 and used to transform H49 . Transformants were obtained with high efficiency indicating that , as with the pHV1/4 origin , the cdc6/orc1 gene is not required in cis for ARS activity . To delimit the minimal origin further , a 692-bp fragment of the intergenic region was amplified by PCR ( Figure 2A ) , subcloned in pTA131 to generate pCN11 , and used to transform the ΔpyrE2 ΔradA strain H112 . The sequence was able to maintain the plasmid . To explain why only one ARS element was recovered in the initial genetic screen , we determined whether the replication origin located on pHV1 and pHV4 might be “dominant” to other origins . The ΔpyrE2 ΔradA strain H112 was transformed with an equimolar mixture of pTA194 and pTA313 ( 0 . 5 μg each ) . These are ARS plasmids from the initial and secondary genetic screens , which carry the pHV1/4 and chromosomal origins , respectively . A total of 23 transformants were analyzed by Southern blotting for the presence of each ARS plasmid ( Figure 3 ) . The vast majority ( 21/23 ) contained the ori-pHV1/4 plasmid pTA194 and in most cases ( 17/23 ) this was the sole plasmid detectable . Only six transformants contained the oriC plasmid pTA313 , and in most cases ( 4/6 ) pTA194 was also present . To determine the fate of ARS plasmids in transformants containing both pTA194 and pTA313 , cells were propagated by restreaking , both while maintaining selection for the pyrE2 marker and without selection . In all three cases analyzed ( Figure 3B , transformants 3 , 5 , 10 ) , only pTA194 remained after further propagation . This was not due to greater stability of pTA194 relative to pTA313 , since transformants propagated without selection showed a marked loss of pTA194 , whereas pTA313 was well maintained in the transformant where it was present exclusively ( Figure 3C , transformant 8 ) . These results recapitulate the outcome of the genetic screens and indicate that the ori-pHV1/4 plasmid pTA194 is dominant to the oriC plasmid pTA313 . The stability of ARS plasmids in the absence of selection was investigated in a quantitative manner . Cultures of H112 transformed with pTA250 or pTA441 , which carry the intergenic region of ori-pHV1/4 and oriC , respectively , were propagated for ∼25 generations in nonselective Hv-YPC broth . At regular intervals , the fraction of uracil+ cells ( indicative of ARS plasmids ) was determined by plating on selective and nonselective media . As shown in Figure 3D , the ori-pHV1/4 plasmid pTA250 is significantly less stable than the oriC plasmid pTA441; plasmid loss was calculated to be 16% per generation for pTA250 and 9% per generation for pTA441 . The leading and lagging strands of bacterial and archaeal genomes differ in their base composition [27 , 28] , and a surplus of G over C is usually found on the leading strands of replication . Thus , abrupt changes in the strand-specific nucleotide compositions indicate the presence of a replication origin or terminus . We analyzed the G/C disparity of the largest contig ( number 455 ) of the H . volcanii genome sequence , which corresponds in size to the main chromosome ( 2 . 9 Mbp ) . Two different algorithms , ORIGINX [29] ( unpublished data ) and Z-CURVE ( Figure 4A ) [30] gave similar results . The “GC-skew” of H . volcanii suggests the presence of multiple origins on the main chromosome , similar to what has been proposed for Halobacterium sp . NRC-1 [30 , 31] . The amplitude of the calculated H . volcanii GC-skew was similar to that of H . marismortui , but substantially smaller than was observed for the normalized GC-skews of the main chromosomes of three other halophilic archaea and a non-halophilic archaeon ( Figure 4B ) . Whereas the oriC of the halophilic bacterium Salinibacter ruber has a well-defined GC-skew minimum at the origin , the four halophilic archaea show a GC-skew with inverted polarity , when compared to bacteria and thermophilic archaea . Therefore , the leading strand of replication contains an excess of C in haloarchaea , similar to what has been suggested for Mycoplasma species [32] . While the mechanisms that establish these skews at a molecular level are still poorly understood , it is of note that their formation seems to be independent of GC% of genomes [32] . For example , the chromosome of the haloarchaeon Haloquadratum walsbyi , which is not GC-rich ( 48% GC ) , also shows an inverted polarity . The H . volcanii G/C disparity curve has three peaks for the main chromosome ( Figure 4A ) : ( 1 ) A peak near the ARS element identified in genomic libraries from H230 , which is associated with the orc1 gene ( oriC1 ) ; ( 2 ) A peak associated with the orc11 and orc14 genes . This chromosomal region has a base composition different than the rest of the genome and contains putative viral genes such as HNHc endonuclease , VirB4 , and tragVirD4 helicases or bacteriophage T4-like integrase ( unpublished data ) . Moreover , it does not coincide with any ARS elements isolated in this work; and ( 3 ) A peak associated with the orc5 gene , suggesting the presence of a second replication origin on the main chromosome ( oriC2 ) . GC , AT , and G + C content disparities were also carried out for contigs corresponding to the smaller replicons and predicted the location of DNA replication origins for pHV1 ( confirming the origin identified in the first ARS screen ) , pHV3 , and a second origin on pHV4 ( Figure 4A ) ; all of these peaks are located near cdc6/orc1 genes . Strikingly , ori-pHV1/4 sequence could only be found on contig number 454 ( pHV1 ) and not on contig number 452 ( which should correspond in size to pHV4 ) . To test the possibility of a second chromosomal origin and to identify origins on the smaller replicons , we directly cloned candidate ARS elements . A region carrying the putative second chromosomal replication origin ( oriC2 ) was isolated as a 9 . 3-kb NotI fragment from a genomic DNA library and was cloned in pTA416 ( Figure 5A ) . This fragment contains two divergently transcribed genes coding for Orc5 and a putative Rad25/Xpb-related helicase , separated by a 2 , 094-bp intergenic region featuring an A/T-rich DUE and several direct repeats with similarity to the ORB consensus ( Figure 5B ) . A 2 . 4-kb fragment comprising this intergenic region was amplified by PCR , cloned in pTA131 to generate pTA612 , and used to transform the ΔpyrE2 ΔradA strain H112 . Transformants were obtained with high frequency , indicating that this region supports ARS activity ( Figure S2 ) . However , after 15 d of incubation the colonies were significantly smaller than those seen with H112 transformed with pTA250 ( pHV1/4 origin ) or pTA441 ( oriC1 ) . This suggests that the efficiency of this putative second chromosomal origin ( or the stability of the plasmid ) is significantly lower than that of the pHV1/4 and orc1-associated origins . Moreover , the intergenic sequence upstream of orc5 , which contains oriC2 , can be deleted efficiently by using the same gene knockout procedure as was used to delete the pHV1/4 origin [21] . The resulting strain CN28 shows no growth defect ( unpublished data ) . The 793-bp intergenic sequence on contig number 453 ( corresponding to pHV3 ) is located between genes coding for a putative TATA box-binding protein and Orc6 , and features two DUEs and several direct repeats with similarity to ORB consensus sequence ( Figure 6A and 6B ) . To test if this intergenic region confers ARS activity , a 693-bp PCR fragment including the DUEs and repeats was inserted in pTA131 to generate pCN26 and used to transform H112 . Transformants were obtained with high frequency ( Figure S2 ) . When probed with the ori-pHV3 sequence , a PFG confirmed that this origin is located on the pHV3 replicon ( Figure 6C ) . The 688-bp intergenic region on contig number 452 ( which should correspond to pHV4 ) is located between a putative translation initiation factor and orc3 genes and features a DUE and multiple direct repeats that are different from the ORB consensus sequence ( Figure 6D and 6E ) . This putative origin was tested for ARS activity by cloning a 592-bp PCR fragment of the intergenic sequence in pTA131 to generate pCN27 and used to transform H112 . Significantly fewer transformants were obtained with pCN27 ( ori-pHV4–2 ) than with pCN26 ( ori-pHV3 ) or pTA250 ( ori-pHV1/4 ) , indicating that the second origin of pHV4 is not used efficiently ( Figure S2 ) . Moreover , a PFG probed with ori-pHV4–2 sequence showed that this origin is actually located on the 2 . 9-Mb main chromosome ( Figure 6F ) , suggesting that the strains used in our experiments and/or sequenced by TIGR may have undergone genome rearrangements . The stability of ARS plasmids pTA612 ( oriC2 ) , pCN26 ( ori-pHV3 ) , and pCN27 ( ori-pHV4–2 ) was determined in a qualitative manner . Six transformants of H112 containing each ARS plasmid were grown on nonselective agar and then restreaked on selective medium . After restreaking , none of the transformants showed growth on selective agar , indicating complete loss of the plasmid ( Table 2 ) . In comparison , transformants with pTA441 ( oriC1 ) showed normal growth on selective agar , indicating efficient maintenance of the plasmid . Transformants with pTA250 ( ori-pHV1/4 ) showed growth on selective agar , but the number of colonies was significantly reduced relative to growth on nonselective agar , indicating some loss of the plasmid . As a control , we tested whether the intergenic region adjacent to orc4 could function as an ARS element; this region does not coincide with a peak of G/C disparity ( Figure 4A ) and does not feature a DUE . A 1 . 7-kb ApaI-BglII genomic DNA fragment of this intergenic region was cloned in pTA131 to generate pTA611 and used to transform H112 . No transformants were observed , indicating that not every cdc6/orc1 gene is associated with a potential DNA replication origin . RIP mapping [33] was used to determine the exact positions of DNA replication initiation at the origins we had identified . A preparation of enriched nascent DNA strands was used in primer extension reactions using 32P-labeled primers hybridizing to the leading strand either side of the DUEs of each ARS element ( Table S1 ) . The sizes of the amplification products were determined using electrophoresis under denaturing conditions ( Figure 7A ) , allowing identification of the shortest amplification products that mark the start site of the leading strand . Clear transition points between leading and lagging strand replication within or near the DUEs were revealed for both strands of each origin ( Figure 7B ) . It is of note that the detected transition points colocalize with chromosomal regions where DNA is predicted to be bent . Amplification products obtained in negative control experiments , which used linearized plasmids containing the origin sequences that were isolated from Escherichia coli , are indicated in Figure 7B using dots . As expected , they do not coincide with the extension products obtained using H . volcanii replication intermediates . These results indicate that all the ARS elements isolated during this work are functional in their chromosomal context .
Initiation of DNA replication at an origin depends on cis-acting sequence elements . In bacteria and unicellular eukaryotes , these have been well-characterized using genetics . In contrast , archaeal replication origins were identified relatively recently [2] , and only limited genetic methods have been used to study DNA replication in archaea [15] . In this work , we isolated several ARS elements that use chromosomally encoded factors for their replication . RIP assays were used to confirm that these ARS elements correspond to functional replication origins in their chromosomal context . The origins are distributed on the different replicons of H . volcanii , including two on the main chromosome . Previous work in Sulfolobus solfataricus and recent studies in A . pernix have shown that Crenarchaeota can use more than one origin to replicate a circular chromosome [5 , 6] . Bioinformatics has suggested that Halobacterium sp . NRC-1 , a euryarchaeon like H . volcanii , might have two chromosomal replication origins [34] , but attempts to identify the second origin experimentally were not successful [15] . Our work provides the first proven example from the Euryarchaeota of multiple origins per replicon . The general characteristics of the H . volcanii replication origins are similar . All contain AT-rich sequences required for unwinding and/or bending of the origin DNA . These are surrounded by repeated sequence motifs that correspond to the classic ORB elements found at other archaeal origins . However , some of these repeats show significant differences to the archaeal mini-ORB consensus . Many archaeal ORBs also feature a characteristic “G-string” element ( Figure 2 ) that contributes to Cdc6/Orc1 binding at the origin ( S . Bell , personal communication ) . While “G-string” elements are found at all origins of H . volcanii , the two ORBs surrounding the primary DUE of oriC1 also contain an extended “G-string” that appears to be specific to halophiles ( Figure 2 ) . Furthermore , long GT-stretches are found after some repeats , such as the two ORBs surrounding the DUE of ori-pHV1/4 ( Figure 1B ) . The subtle variations found in halophilic ORB sequences might contribute to origin-specific binding by different Cdc6/Orc1 proteins . Alternatively , these halophile-specific features could promote DNA bending at high intracellular salt concentrations , thus favoring the formation of a higher order complex between the origin and initiator proteins [9] . The number of Cdc6/Orc1 proteins is almost certainly higher than the number of origins in H . volcanii; for example , the intergenic region adjacent to orc4 does not show ARS activity . Furthermore , we were able to delete several cdc6/orc1 genes ( orc1 , orc5 , and orc10 , unpublished data ) , suggesting that their functions at least partially overlap . It is possible that some Cdc6/Orc1 proteins might promote the initiation of “routine” DNA replication , while other initiators are adapted to function under specific physiological conditions . For example , our observation that the second chromosomal origin ( oriC2 ) appears to function less efficiently in laboratory conditions suggests that it is only used under certain circumstances . This could be due to location of the origins on ARS plasmids , as opposed to their native chromosomal loci . On the other hand , it is noteworthy that the overall pI values of the different Cdc6/Orc1 proteins vary between 3 . 98 and 5 . 59 ( Table 1 ) , with the oriC1-associated Orc1 being the most acidic , raising the possibility that they might function optimally under different salt concentrations . A surprising result that emerged from our initial genetic screen for ARS elements was that only one origin ( ori-pHV1/4 ) was isolated . This is unlikely to be due to technical limitations of the method , since extensive libraries were constructed using different restriction enzymes . Moreover , we were able to recapitulate the outcome of the screen by cotransforming H . volcanii with a mixture of ori-pHV1/4 and oriC1 ARS plasmids ( Figure 3 ) . The majority ( 74% ) of transformants contained only the ori-pHV1/4 plasmid , and less than 9% contained only the oriC1 plasmid ( Figure 3A ) . This disparity is unlikely to be due to differences in plasmid establishment , since the two ARS constructs show the same transformation efficiency ( Table 2 ) and confer a similar phenotypic load on their host ( Figure S2 ) . Instead , it would appear that the ori-pHV1/4 ARS plasmid is dominant to the oriC1 ARS plasmid . Evidence in favor of this suggestion emerged from the examination of transformants containing both plasmids ( Figure 3B ) . Upon further propagation , only the ori-pHV1/4 ARS plasmid remained while the oriC1 ARS plasmid was lost . This result is all the more intriguing given that ori-pHV1/4 plasmids are significantly less stable than oriC1 plasmids ( Figure 3C and 3D ) . Furthermore , the copy number of ori-pHV1/4 plasmids is lower than that of oriC1 plasmids ( unpublished data ) . Thus , our results cannot be explained by mere incompatibility of ARS plasmids , since this would favor the retention of the oriC1 plasmid . Instead , we suggest that in H . volcanii there is a functional hierarchy of replication origins , which arises from competition for common replication factors . “Dominant” origins such as ori-pHV1/4 might be bound more efficiently by Cdc6/Orc1 or other proteins such as MCM helicase and primase that participate in replication initiation . Other factors might also contribute to differential usage of origins . ARS elements that initiate efficiently can be selectively enriched from a genomic library of Saccharomyces cerevisiae [35] , and reduced initiation efficiency was found to correlate with transcriptional activity directed towards the ARS element . This suggests that transcriptional interference with the prereplication complex is a determinant of origin hierarchy in eukaryotes . It is noteworthy that four of the five origins identified in this work ( and almost all other archaeal origins ) feature transcription units that are orientated away from replication initiation sites ( Figures 1 , 2 , 5 , and 6 ) . Therefore , transcriptional interference might also regulate origin firing in archaea . Our genetic observations indicate that while the functions of initiator proteins at least partially overlap in H . volcanii , the replication origins oriC1 and ori-pHV1/4 play a key role in the replication of their corresponding chromosomes . This suggests that duplication of replication origins , and not of the initiator genes , could have allowed the H . volcanii genome to develop toward a multireplicon structure . This process might still be continuing , since there are important differences between our data and the assembled genome sequence . For instance , both pHV1 and pHV4 carry a similar ( if not identical ) replication origin . A comparable situation is found with Halobacterium sp . NRC-1 plasmids pNRC100 and pNRC200 , which share a large region of around 150 kb including long ( 33 and 39 kb ) inverted repeats [13] . An additional example is ori-pHV4–2 , which in the current version of the assembled genome sequence is located on the contig corresponding to pHV4 , while in our laboratory strains is carried on the main chromosome . Detailed manual examination of the genome sequencing data ( including shotgun data and closure results ) indicate that the assembly is an accurate representation of the strain used for sequencing . Though it is possible the assembly is inaccurate , we believe it is more likely that there are genomic differences between the strain sequenced and strains used in our experiments . For instance , the H . volcanii DS2 strain was used to isolate DNA for sequencing ( Table 3 ) , whereas genetic and biochemical experiments used derivatives of H . volcanii WFD11 or DS70 [19 , 20] . These discrepancies suggest that H . volcanii , whose genome is considered relatively stable compared to other halo-archaea , undergoes periodic genome arrangements that are possibly mediated by recombination at the replication origins . In this respect , it is noteworthy that the two rRNA operons are located close to the two chromosomal origins described in this work . The rrnA operon is 200 kb away from oriC1 , and rrnB is only 6 kb away from oriC2 . Homologous recombination between identical sequences at these rrn operons could provide a mechanism for origin movement throughout the genome . Intriguingly , the rrn operons are both oriented away from the origins , presumably to avoid collisions between replication fork and rRNA transcription machinery . It is notable that the H . volcanii GC-skew plots do not delineate origins as clearly as the plots of the other sequenced haloarchaea ( Figure 4 ) . For example , the peak in contig 454 does not match the ARS isolated in the initial screen . Conversely , there is a peak in contig 455 that has higher amplitude than the two origins detected but is not associated with an experimentally determined origin ( Figure 4A ) ; this peak is most likely due to a recent integration by an AT-rich prophage . These quirks could indicate either that the H . volcanii “origin signal” ( i . e . , the skew caused by origin usage ) is weak , or that the origin is mobile and has left behind a residual signal at its previous location . Finally , the fact that haloarchaeal genomes present “inverted” GC-skews indicates that the haloarchaea may not follow the same rules for genome structure patterning as in other species . Given the apparent plasticity of the H . volcanii genome , we believe that pHV4 , pHV3 , and pHV1 should be considered as secondary chromosomes and not plasmids , in the sense that a plasmid is an extrachromosomal element and is often characterized by “selfish” behavior . Prokaryotic chromosome biology has been dominated by the archetype of E . coli , but there are many examples of bacteria with more than one chromosome [36]; like H . volcanii , they often show evidence of dynamic rearrangements between the replicons ( e . g . , [37] ) . The distinction between mini-chromosomes and mega-plasmids is usually made on the basis of replicon size and the presence of essential ( housekeeping ) genes . In this regard , both pHV4 and pHV3 are large ( 690 and 442 kb , respectively ) , and while genes predicted to be essential are only found on the former , H . volcanii strains that have lost pHV3 grow very slowly and filament [20] . More relevant to this work , pHV4 , pHV3 , and pHV1 all use Cdc6/Orc1-dependent origins for replication , as does the main chromosome . By contrast , pHV2 is very small ( 6 . 4 kb ) , lacks homology with chromosomal sequences , is easily cured , and uses a distinct ( presumably Rep-dependent ) replication origin [19] . Thus it is a plasmid . In conclusion , we have shown that the four chromosomes of H . volcanii are replicated in an analogous manner using Cdc6/Orc1-dependent origins . Since efficient tools now exist to characterize the cis-acting requirements for replication initiation in archaea , H . volcanii will serve as an interesting model system for studies on the regulated replication of complex archaeal genomes .
H . volcanii strains are shown in Table 3 , plasmids in Table 4 , and oligonucleotides in Table S1 . H . volcanii strains were grown at 45 °C on either complete ( Hv-YPC ) or casamino acids ( Hv-Ca ) agar , or in Hv-YPC or casamino acids broth , as described previously [21 , 39] . Transformation of H . volcanii and isolation of total genomic DNA were carried out as described previously [21] . To isolate plasmid DNA , 2 ml of a saturated culture ( grown in Hv-Ca broth ) was centrifuged at 3 , 300 ×g for 8 min and the cells were resuspended in 50 μl of 1 M NaCl , 20 mM Tris HCl ( pH 7 . 5 ) ; thereafter , a QIAprep miniprep kit ( Qiagen , http://www . qiagen . com ) was used according to the manufacturer's instructions . To isolate crude DNA , cells were resuspended in 400 μl of water and incubated at 70 °C for 10 min . H . volcanii DNA was digested with HpaII , AciI , or TaqI for 30 min at the recommended temperature , using ∼0 . 2 units of enzyme/μg DNA in suboptimal buffer ( e . g . , New England Biolabs buffer 1 for AciI ) . DNA fragments of the desired size were excised from agarose gels and ligated with plasmid pTA131 [21] , which had previously been cut to completion with ClaI and the DNA ends dephosphorylated . The plasmid library was used to transform an E . coli dam– strain , and DNA was prepared directly from colonies to avoid differential amplification . Coverage of the libraries was >99 . 5% ( >5 , 000 E . coli colonies were used in DNA preparation and H . volcanii has a genome size of ∼4 Mb ) . DNA was used to transform H . volcanii pyrE2 radA mutants H49 or H112 , and transformants were selected on Hv-Ca plates lacking uracil . A 3-kb BamHI-StuI fragment of pTA194 with the pHV1/4 replication origin was subcloned to generate pTA252 , and a 1-kb BsmI-XbaI fragment of the intergenic region was replaced by a trpA selectable marker [21] to generate pTA266 ( Figure 2D ) . pTA266 was used to transform ΔpyrE2 ΔtrpA strain H53 , and transformants were selected on Hv-Ca plates lacking uracil and tryptophan . One transformant ( H220 ) was grown without selection for ∼30 generations and plated on Hv-Ca + 5-FOA ( 5-fluoroorotic acid ) to select for loss of the integrated plasmid . Around 0 . 4% of cells in the culture were 5-FOA-resistant but only 0 . 13% of these were Trp+ and therefore had deleted the pHV1/4 replication origin . H112 containing pTA250 or pTA441 ( maintained by selection on Hv-Ca agar ) was used to inoculate a culture in Hv-YPC broth and grown at 45 °C . At regular intervals aliquots were plated Hv-YPC agar , and colonies patched on Hv-Ca to determine the fraction of uracil+ cells . % plasmid loss per generation ( l ) was calculated using the formula where n is the number of generations and ura+ is the fraction of uracil+ cells . Intact H . volcanii DNA was prepared in agarose plugs . 2 ml of culture ( OD650nm of 1 . 0 ) was pelleted at 3 , 300 ×g for 10 min , 4 °C , resuspended in 1 ml of cold spheroplasting solution ( 15% sucrose , 1 M NaCl , 27 mM KCl , 50 mM Tris-HCl [pH 8 . 5] ) + 0 . 1% NaN3 , and pelleted again . Cells were gently resuspended in 80 μl of spheroplasting solution , transferred to 42 °C , mixed with 100 μl of 1 . 5% low-melt agarose ( in 0 . 5× spheroplasting solution , 100 mM EDTA ) and pipetted into plug moulds ( Bio-Rad , http://www . bio-rad . com ) . Plugs were incubated in 5 ml of lysis solution ( 1% sarkosyl , 500 mM EDTA , 20 mM Tris-HCl [pH 8 . 8] ) + proteinase K ( 0 . 5 mg/ml ) for 3 h at 52 °C , then transferred to fresh lysis buffer + proteinase K + RNaseA ( 30 mg/ml ) and incubated overnight at 52 °C . Plugs were washed three times in 10 ml of 100 mM EDTA , 25 mM Tris-HCl ( pH 7 . 5 ) at 37 °C , equilibrated in 0 . 5× Tris-borate-EDTA ( TBE ) for 90 min at 20 °C and exposed to 50 Gy of γ radiation ( 137Cs , 375 Gy/s ) to linearize circular DNA molecules . Plugs were loaded onto a 1% agarose 0 . 5× TBE gel and electrophoresis was performed at 14 °C in a CHEF mapper ( Bio-Rad ) using 0 . 5× TBE buffer , voltage gradient of 6 V/cm , switch angle of 120° , and switch times of 0 . 47 s ( initial ) to 1 min 33 . 83 s ( final ) . Total run time was 20 h 18 min . H . volcanii DS2 was diluted to an OD650nm of 0 . 15 in 100 ml of Hv-YPC media , grown to OD650 nm of 0 . 3 , and pelleted . Cells were resuspended in 4 ml of lysis buffer ( 25 mM Tris-HCl [pH 7 . 5] , 20 mM EDTA , 100 mM NaCl , 200 μg ml−1 proteinase K , 1% SDS ) . After 1 h incubation at 50 °C , 4 g of CsCl and 100 μl of Hoechst-33342 ( 5 mg ml−1 ) were added and the refractive index adjusted to 1 . 410 with CsCl ( 1 g ml−1 ) . DNA was purified by CsCl gradient ultracentrifugation . To enrich for replicating intermediates , total DNA was passed down a BND-cellulose column pre-equilibrated with NET buffer ( 10 mM Tris-HCl [pH 8 . 0] , 1 mM EDTA , and 1 M NaCl ) to selectively bind single-stranded DNA . After washing with NET buffer , bound DNA was eluted with NET buffer + 1 . 8% caffeine at 50 °C . DNA was isopropanol-precipitated and resuspended in TE buffer ( 10 mM Tris-HCl [pH 7 . 5] , 1 mM EDTA ) at 1 μg μl−1 . After phosphorylation of 5′-OH ends with T4 polynucleotide kinase ( Promega , http://www . promega . com ) , DNA was treated with λ-exonuclease to digest 5′ nicked DNA ends; replication intermediates protected by RNA primers are unaffected by this treatment . Primer extension reactions used ∼500 ng of enriched replicating intermediates , 25 ng of radiolabeled primer ( labeled using [γ32P]-ATP , and T4 polynucleotide kinase ) and 2 units of Deep Vent ( exo- ) DNA polymerase . After 30 cycles of reaction ( 60 s at 94 °C , 60 s at 70 °C , and 90 s at 72 °C ) , amplification products were separated on a 6% polyacrylamide gel under denaturing conditions . Radioactive material was detected using a Phosphorimager system ( Amersham , http://www . gehealthcare . com ) . Control experiments using linearized plasmid DNA isolated from E . coli were performed under similar conditions . Nucleotide representation disparities were calculated using either ORIGINX ( http://www . cbs . dtu . dk/services/GenomeAtlas/suppl/origin ) or ZPLOTTER ( http://tubic . tju . edu . cn/zcurve ) programs . Publicly available sequences released in June 2006 were used to calculate local minima and maxima in nucleotide disparities . DNA curvature was calculated using a BEND . IT server ( http://hydra . icgeb . trieste . it/∼kristian/dna/bend_it . html ) , which predicts in qualitative terms a curvature propensity of a given DNA sequence using DNase I based bendability parameters [41] and the consensus bendability scale [42] . The resulting data of each program were plotted using Origin Pro 7 . 5 software ( OriginLab Corporation , http://www . originlab . com ) . | Haloferax volcanii is a member of the archaea , which are renowned for thriving in extreme environments . Archaea have circular chromosomes like bacteria but use enzymes similar to those found in eukaryotes to replicate their DNA . Few archaeal species have systems for genetics , and this has limited our understanding of DNA replication . We used genetics to map the chromosomal sites ( origins ) at which DNA replication initiates in H . volcanii . This species has a multipart genome comprising one main chromosome , three secondary chromosomes , and a plasmid . Five DNA replication origins were found and confirmed to function in vivo . All are adjacent to genes for the initiator protein Cdc6/Orc1 , a common feature of archaeal replication origins . Two of the sequences are located on the main chromosome , confirming that multiple origins are often used to replicate circular chromosomes in archaea . Intriguingly , one of the origins from a secondary chromosome appears “dominant” to the principal chromosomal origin , suggesting either a hierarchy or differential usage of origins . This might reflect the different replication requirements of their respective chromosomes . Given the ease of genetic manipulation , H . volcanii holds great promise for studying how replication of four chromosomes is regulated in the context of the archaeal cell cycle . | [
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| 2007 | Genetic and Physical Mapping of DNA Replication Origins in Haloferax volcanii |
Antisense ( as ) lncRNAs can regulate gene expression but the underlying mechanisms and the different cofactors involved remain unclear . Using Native Elongating Transcript sequencing , here we show that stabilization of antisense Exo2-sensitivite lncRNAs ( XUTs ) results in the attenuation , at the nascent transcription level , of a subset of highly expressed genes displaying prominent promoter-proximal nucleosome depletion and histone acetylation . Mechanistic investigations on the catalase gene ctt1 revealed that its induction following oxidative stress is impaired in Exo2-deficient cells , correlating with the accumulation of an asXUT . Interestingly , expression of this asXUT was also activated in wild-type cells upon oxidative stress , concomitant to ctt1 induction , indicating a potential attenuation feedback . This attenuation correlates with asXUT abundance , it is transcriptional , characterized by low RNAPII-ser5 phosphorylation , and it requires an histone deacetylase activity and the conserved Set2 histone methyltransferase . Finally , we identified Dicer as another RNA processing factor acting on ctt1 induction , but independently of Exo2 . We propose that asXUTs could modulate the expression of their paired-sense genes when it exceeds a critical threshold , using a conserved mechanism independent of RNAi .
Eukaryotic genomes are pervasively transcribed [1] , generating plenty of non-coding ( nc ) transcripts , distinct from the housekeeping rRNAs , tRNAs and sn ( o ) RNAs , and that are arbitrarily classified into small ( < 200 nt ) and long ( ≥ 200 nt ) ncRNAs [2 , 3] . Long ( l ) ncRNAs are produced by RNA polymerase II ( RNAPII ) , capped and polyadenylated , yet lack protein-coding potential [4 , 5] , although this last point is subject to exceptions [6] . Several lines of evidence suggest that they are functionally important . First , lncRNAs show tissue-specific expression [7] and respond to diverse stimuli , such as oxidative stress [8] , suggesting that their expression is precisely controlled . Second , several lncRNAs are misregulated in diseases including cancer and neurological disorders [9 , 10 , 11] . Furthermore , there is a growing repertoire of cellular processes in which lncRNAs play important roles , including X-chromosome inactivation , imprinting , maintenance of pluripotency and transcriptional regulation [12 , 13] . Several classes of lncRNAs have been described [2] . Among them , large intervening non-coding ( linc ) RNAs , which result from transcription of intergenic regions , have attracted a lot of attention as being involved in cis- and trans-regulation , mostly at the chromatin level , of genes important for development and cancer [13] . Another class of lncRNAs consists of antisense transcripts , that are produced from the DNA strand antisense to genes [14] . Several examples of regulatory antisense ( as ) lncRNAs acting on sense gene expression in cis or in trans have been described in the budding yeast Saccharomyces cerevisiae [15 , 16 , 17 , 18 , 19 , 20 , 21] , in the fission yeast Schizosaccharomyces pombe [22 , 23 , 24] , in plant [25] and in mammalian cells [26 , 27] . Our previous studies in budding and fission yeasts revealed that aslncRNAs are globally unstable and are mainly targeted by the cytoplasmic 5’-3’ RNA decay pathway dependent on the Xrn1 and Exo2 exoribonucleases in S . cerevisiae [28 , 29] and S . pombe [30] , respectively . Inactivation of Xrn1/Exo2 leads to the stabilization of a family of lncRNAs , referred to as Xrn1-sensititve Unstable Transcripts ( XUTs ) , the majority of which are antisense to protein-coding genes [28 , 29 , 30] . Interestingly , in S . cerevisiae , we defined among these antisense ( as ) XUTs a subgroup for which the sense-paired genes ( referred to as class 1 ) undergo antisense-mediated transcriptional silencing [28] . However , the molecular mechanism by which asXUTs could regulate sense gene expression remains largely unknown to date , still interrogating whether sense transcription is impaired at the initiation and/or elongation and/or termination stages , whether any post-transcriptional event is in play , and whether the epigenetic landscape contributes in the regulatory determinants . In addition , such a transcriptional aslncRNA-mediated regulation has not been documented yet in S . pombe . Here we used Native Elongating Transcript sequencing ( NET-Seq ) to identify genome-wide in fission yeast the genes attenuated at the nascent transcription level upon stabilization of their paired-asXUTs . This so-called class 1 corresponds to highly transcribed genes , displaying marks of active transcription at the chromatin level . Mechanistic investigation on a model class 1 representative , the inducible catalase-coding gene ctt1 , confirmed that it is transcriptionally attenuated upon oxidative stress when its paired-asXUT is stabilized , and the level of the attenuation correlates with the abundance of the asXUT . The attenuation is characterized by low RNAPII Ser5-phosphorylation ( Ser5-P ) and requires histone deacetylase ( HDAC ) activity and the conserved Set2 histone methyltransferase ( HMT ) . Finally , we identified Dicer as an additional regulator of ctt1 induction , acting independently of Exo2 and the asXUT . Together , our data support a model where asXUTs could modulate the expression of the paired-sense genes when it exceeds a critical threshold , using a conserved mechanism independent of RNAi .
In budding yeast , stabilization of asXUTs results into the attenuation , at the transcriptional level , of a subset of paired-sense genes , which are referred to as class 1 [28] . We recently annotated XUTs in fission yeast [30] . We observed that asXUTs accumulation correlates with down-regulation of the paired-sense mRNAs , at the RNA level [30] , suggesting that the regulatory potential of asXUTs has been conserved across the yeast clade . However , whether this regulation occurs at the level of transcription in fission yeast remains unclear . To define class 1 in S . pombe , we performed NET-Seq in WT and exo2Δ cells . Although global mRNA synthesis was found to be unchanged upon exo2 inactivation ( S1A Fig ) , differential expression analysis discriminated genes for which transcription in exo2Δ was significantly reduced ( classes 1 & 2 , n = 723 ) or not ( classes 3 & 4 , n = 4405 ) . Within each category , we distinguished genes with ( classes 1 & 3 ) or without ( classes 2 & 4 ) asXUTs ( Fig 1A and 1B; lists in S1–S4 Tables ) . Among the 723 genes transcriptionally attenuated in exo2Δ , 175 have asXUTs ( class 1 ) . Despite the proportion of class 1 genes among the attenuated genes is limited ( 24 . 2% ) , it is significantly higher than expected if presence of asXUT and sense gene attenuation were independent ( Chi-square test , P = 0 . 03 ) , suggesting that the attenuation could depend on the stabilized asXUTs , at least in some cases . On the other hand , the transcriptional down-regulation of class 2 ( no asXUT ) is likely to be an indirect effect reflecting the slow growth phenotype of the exo2Δ mutant [31] . Consistently , class 2 is significantly enriched for GO terms “ribosome biogenesis” ( P = 1 . 36e-08 ) and “cellular component biogenesis ( P = 1 . 04e-02 ) , and it is known that the expression of genes involved in these biological processes directly depends on the growth rate [32] . Altogether , these observations suggest that for a subgroup of genes , stabilization of the asXUT might contribute to attenuate transcription of the paired-sense gene . Both classes 1 and 3 have asXUTs , but only class 1 is transcriptionally attenuated upon asXUTs stabilization . This suggests the existence of specificities discriminating the two classes . Indeed , in WT cells , class 1 is transcribed to higher levels than class 3 ( Fig 1C ) , the latter actually showing the lowest transcription levels among the four classes ( S1B Fig ) . In exo2Δ , transcription of class 1 falls to the low , basal level of class 3 ( Fig 1D ) . Notably , transcription of XUTs antisense to class 1 and 3 genes is globally unaffected in the exo2Δ mutant ( S1C Fig ) , indicating that XUTs accumulation in this context is due to the inactivation of their decay and not to a global increase of their synthesis ( Fig 1E ) . We also noted that in WT cells , the nascent antisense transcription signal surrounding the TSS of class 1 genes is higher than for class 3 ( S1D Fig ) , suggesting that sense TSS overlap could constitute a factor for the potential regulatory activity of the XUTs antisense to class 1 genes . At the chromatin level , class 1 shows a more pronounced nucleosome depletion in the TSS-proximal region than class 3 ( S1E Fig ) , higher H3K14 ( S1F Fig ) and H4K5/8/12/16 acetylation ( Fig 1F ) . When compared to the four classes , the levels of histone acetylation at the TSS-proximal region are similar for classes 1 and 2 ( Fig 1F , see also S1F Fig ) . Together , these results show that transcriptional attenuation correlates with asXUT stabilization in fission yeast , suggesting that asXUTs might be involved in the modulation of sense genes expression . To further investigate the possibility that asXUTs can regulate expression of their paired-sense gene and to get insights into the underlying molecular mechanism , we characterized a class 1 gene , ctt1 , and its paired-antisense XUT0794 ( Fig 2A , see also S2A Fig ) . ctt1 encodes a catalase , an enzyme required for survival to oxidative stress upon exposure to H2O2 [33] , and it is strongly induced in this condition [34] . Interestingly , we observed that exo2Δ cells displayed a slight sensitivity to H2O2 in addition to the slow growth and temperature sensitivity ( S2B and S2C Fig ) . This suggests that ctt1 expression in exo2Δ cells might also be impaired upon oxidative stress . We therefore analyzed ctt1 mRNA induction in WT and exo2Δ cells upon oxidative stress . Northern-blot ( Fig 2B ) and RT-qPCR kinetics analyses ( Fig 2C ) showed that exo2Δ exhibits a 3-fold reduction in induction rate , with a peak of induction reached 15 minutes after H2O2 addition vs 10 for the WT ( Fig 2C ) . Strikingly , we observed that XUT0794 is also activated upon oxidative stress in a WT context ( Fig 2D ) . Furthermore , its peak of induction is reached very rapidly ( 5 min ) , before the ctt1 mRNA peak ( 10 min ) , suggesting that it might be part of a natural attenuation mechanism ( feedback loop ) for ctt1 expression . In summary , our data suggest that ctt1 induction requires Exo2 activity for maintaining a low level of XUT0794 , antisense to ctt1 . Upon induction , the XUT is activated and could modulate expression of ctt1 , in a similar way as shown for asXUT-associated genes in S . cerevisiae , such as GAL1-10 [19 , 20] . We designed several experiments in order to test whether ctt1 attenuation directly depends on antisense XUT0794 . Firstly , we overexpressed it in cis , in WT cells , using a regulatable P41nmt1 promoter ( S3A Fig ) . When the promoter is active , XUT0794 accumulates and ctt1 is not induced in response to H2O2 addition ( S3A Fig ) . This demonstrates a causal role of antisense XUT0794 expression in attenuating ctt1 . However , in this particular context where XUT0794 expression is driven by the strong P41nmt1 promoter , it is difficult to draw up any conclusion about a possible role of the lncRNA itself , as the ctt1 silencing observed here probably mainly results from transcriptional interference . Note that P41nmt1-driven expression of XUT0794 in trans , from a plasmid , failed to attenuate ctt1 ( S3B Fig ) . Secondly , we disrupted the XUT0794 promoter in WT cells using the ura4 gene ( Fig 3A ) , which is controlled by a promoter much weaker than P41nmt1 . Surprisingly , ura4 insertion did not abolish XUT0794 expression ( Fig 3B ) . However , it resulted in XUT0794 levels similar to those of the exo2Δ mutant ( Fig 3B ) . Notably , ctt1 was significantly attenuated in the ura4-XUT0794 strain , and ctt1 mRNA levels were similar to those of exo2Δ cells ( Fig 3C ) . Hence , there is a positive correlation between ctt1 attenuation and antisense XUT0794 levels . In a third experiment , we inserted a self-cleaving hammerhead ribozyme ( RZ ) at position 254/815 of XUT0794 ( Fig 3D ) and integrated the construct at the ctt1 locus in WT and exo2Δ strains , without any manipulation of XUT0794 promoter . In the WT + RZ context , neither the 5’ nor the 3’ fragment of XUT0794 accumulated ( Fig 3E ) , and ctt1 induction was similar to WT cells ( Fig 3F ) . In the exo2Δ + RZ context , the 5’ fragment was not detected , but the 3’ fragment accumulated 5x more than in exo2Δ without RZ ( Fig 3E ) . This imbalance between the two RNA parts indicates that RZ was efficiently cleaved , the 5’ fragment being presumably degraded [35] while the 3’ fragment accumulated . Importantly , the higher abundance of the 255–815 fragment of XUT0794 in exo2Δ + RZ cells compared to exo2Δ without RZ correlated with a significantly stronger attenuation of ctt1 ( Fig 3F ) . We conclude that the 255–815 fragment of XUT0794 is sufficient to attenuate ctt1 and that the level of the attenuation depends on the abundance of the asXUT , which is consistent with the hypothesis that the regulation is mediated by the asXUT but is not an indirect effect of Exo2 inactivation . To determine whether the attenuation of ctt1 induction occurs at the transcriptional level , we performed RNAPII ChIP experiments in WT and exo2Δ cells . Upon oxidative stress , RNAPII occupancy in the mutant showed a significant 2- to 4-fold decrease along the ctt1 locus ( Fig 4A and 4B ) , indicating that the attenuation is transcriptional . Analysis of the distribution of differentially phosphorylated forms of the C-terminal domain ( CTD ) of Rpb1 , the largest subunit of RNAPII , provided further insights into the mechanism of transcriptional attenuation . Ser5-P RNAPII is associated to the early stages of the transcription cycle and predominates in the promoter-proximal region of the gene , while Ser2-P RNAPII is associated to transcription elongation and increases along the gene core [36] . Upon oxidative stress , we observed a 30% decrease of Ser5-P RNAPII in the 5’ and core regions of ctt1 , in the exo2Δ mutant ( Fig 4C ) . In contrast , Ser2-P RNAPII occupancy was unaffected ( Fig 4D ) . Interestingly , we noted that Ser5-P RNAPII levels remain high across the ctt1 gene body , especially in the XUT0794 overlapping region ( Fig 4C , probe C ) . This could possibly reflect re-initiation events following collision between convergent RNAPII , in keeping with that XUT0794 expression is also activated upon oxidative stress ( Fig 2D ) . In summary , stabilization of XUT0794 impairs ctt1 transcription , with less RNAPII loaded on the gene in response to oxidative stress and an additional reduction of Ser5P . Several studies in budding yeast have pointed out the role of HDAC , including the class II HDAC Hda1 , in aslncRNA-mediated gene silencing [16 , 18 , 19] . To test whether the attenuation of ctt1 induction involves an HDAC activity , WT and exo2Δ cells were treated with trichostatin A ( TSA ) , an inhibitor of class I-II HDAC . When exposed to oxidative stress , TSA-treated exo2Δ cells accumulated ctt1 mRNA to the same level as the control ( DMSO-treated ) WT strain ( Fig 5A ) . We also noted that the basal levels of ctt1 mRNA were increased in the TSA-treated WT and exo2Δ cells , indicating that ctt1 repression requires an HDAC activity . Furthermore , both ctt1 mRNA and XUT0794 levels in TSA-treated WT cells showed a 2-fold increase compared to the DMSO-treated control after H2O2 addition ( Fig 5A and 5B ) . This indicates that the XUT0794-associated feedback loop that could modulate ctt1 expression in WT cells upon exposure to H2O2 is impaired when HDAC activity is inhibited . On the basis of this observation , we predicted histone acetylation along ctt1 to decrease upon XUT0794 stabilization . ChIP experiments in ctt1 induction conditions revealed a significant 50% and 30% reduction of histone H4K5/8/12/16 acetylation and H3K14 acetylation , respectively , in the exo2Δ mutant , in the region where ctt1 gene and XUT0794 overlap ( Fig 5C and S5A Fig; probe C ) . These data support the idea that asXUT-mediated gene attenuation depends on HDAC , resulting in reduced levels of histone acetylation . Importantly , significant histone deacetylation in exo2Δ was also detected across SPAPB24D3 . 07c , another class 1 gene ( Fig 5D ) , but not across the class 2 genes ptb1 and cuf1 ( S5B and S5C Fig ) . This indicates that histone deacetylation is not a general feature of all the genes that are transcriptionally down-regulated in exo2Δ cells ( classes 1–2 ) but is specific to those with asXUT ( class 1 ) . In an attempt to identify the HDAC involved , we tested the effect of Clr3 ( the ortholog of Hda1 ) , Hos2 ( a class I HDAC ) and Clr6 ( the ortholog of class I HDAC Rpd3 ) . As Clr6 exists in at least two distinct complexes ( Clr6-CI and -CII ) , we also tested a specific subunit for each them , namely the ING family protein Png2 ( Clr6-CI ) and the Sin3 family protein Pst2 ( Clr6-CII ) , respectively [37] . We used null mutants for Clr3 , Hos2 , Png2 and Pst2 , which are non-essential . For Clr6 , which is essential , we used the thermo-sensitive clr6-1 point mutation [38] . Except for pst2Δ , we could successfully combine these mutations with exo2Δ . Attenuation of ctt1 was not suppressed in the exo2Δ clr3Δ , exo2Δ hos2Δ , exo2Δ png2Δ and exo2Δ clr6-1 mutants ( S6A–S6D Fig ) , indicating that none of the four tested factor is involved in the attenuation mechanism . In contrast , the png2Δ , pst2Δ and clr6-1 single mutants exhibited a strong defect of ctt1 induction ( S6C–S6E Fig ) . In addition , the png2Δ and clr6-1 mutations were synergic with exo2Δ ( S6C and S6D Fig ) . This indicates that Exo2 and the Clr6 HDAC complexes are required for efficient ctt1 induction , but act independently . In conclusion , XUT-mediated attenuation of ctt1 requires an HDAC activity , suggesting that mechanisms of regulation of gene expression by lncRNAs have been conserved across the yeast clade . During transcription , elongating RNAPII recruits the histone methyltransferase ( HMT ) Set2 , which methylates H3K36 across the gene body [39 , 40] . Set2-mediated H3K36 trimethylation ( me3 ) then promotes HDAC recruitment and histone deacetylation [41 , 42 , 43] , in order to suppress spurious intragenic transcription initiation [44 , 45] . The observation of decreased histone acetylation levels across the ctt1 gene body in exo2Δ cells ( Fig 5C ) prompted us to test the role of Set2 and H3K36 methylation in the regulation . In WT cells , upon oxidative stress , we observed a peak of Set2 occupancy and H3K36me3 in the region overlapping XUT0794 ( probe C; Fig 6A and 6B ) , confirming that Set2 is recruited when ctt1 expression is induced . In the exo2Δ mutant , H3K36me3 levels were significantly increased , in some positions of ctt1 , including the 5’ region ( probe B ) and the 3’ extremity ( probe E ) . Surprisingly , in the region overlapping XUT0794 ( probe C ) , where Set2 occupancy and histone deacetylation are the most pronounced ( Figs 5C and 6A ) , the difference with the WT control was not statistically significant ( Fig 6B ) . Perhaps a local change of H3K36me3 does not result into histone deacetylation at that position but to the nearby region . In parallel , we analyzed the effect of Set2 inactivation on XUT0794 expression and ctt1 mRNA induction . We could only characterize single mutants , as despite our efforts , we failed to combine set2Δ and exo2Δ , suggesting that the double mutant is lethal . Strikingly , we found that set2Δ cells accumulate XUT0794 into levels similar to the exo2Δ mutant ( Fig 6C ) . However , ctt1 induction was found to be normal in the set2Δ context ( Fig 6D ) . These data indicate that the XUT0794-associated regulation of ctt1 is impaired when Set2 is inactivated . In summary , Set2 is recruited to ctt1 upon oxidative stress . In absence of Set2 , XUT0794 accumulation and ctt1 attenuation are decoupled , indicating that Set2 is required for the XUT0794-associated regulation of ctt1 . The data presented above show that the Exo2-dependent RNA decay controls ctt1 induction and restricts the level of the antisense XUT0794 . We asked whether other RNA processing factors could be involved in the regulation . We tested the role of Dicer ( Dcr1 ) , involved in RNAi . As shown in Fig 7A , ctt1 attenuation is not suppressed in the exo2Δ dcr1Δ double mutant . Rather , ctt1 was found to be attenuated in the dcr1Δ single mutant , and we observed a synergic effect in the exo2Δ dcr1Δ double mutant . On the other hand , Dicer overexpression in exo2Δ cells had no impact on ctt1 attenuation ( S7A–S7C Fig ) . These data indicate that Exo2 and Dcr1 control ctt1 induction through distinct mechanisms . This is further supported by the observation that XUT0794 levels are unchanged in exo2Δ dcr1Δ cells compared to exo2Δ cells ( Fig 7B ) and by ChIP experiments showing that in oxidative stress conditions , RNAPII ( Fig 7C ) and H3K36me3 ( S7D Fig ) levels are normal in dcr1Δ cells while histone H4K5/8/12/16 acetylation is strongly reduced across the whole ctt1 locus , including the promoter region ( Fig 7D ) . Thus , Exo2 and Dcr1 regulate ctt1 induction through independent mechanisms , which is consistent with the observation that asXUTs are globally not targeted by RNAi in S . pombe [30] .
Previous studies in different eukaryotic models have shown that aslncRNAs can regulate sense gene expression [14] . However , the molecular bases for aslncRNAs-mediated regulation remain largely unknown . In budding and fission yeasts , aslncRNAs are actively degraded by the Xrn1/Exo2-dependent cytoplasmic 5’-3’ RNA decay pathway [28 , 29 , 30] . These Xrn1/Exo2-sensitive aslncRNAs are named XUTs [28] . In budding yeast , asXUTs stabilization was shown to result into transcriptional attenuation of a subset of genes , referred to as class 1 [28] . Whether such an asXUT-associated regulation is conserved in other organisms was unknown . Here , we used NET-Seq to identify genes showing transcriptional attenuation upon stabilization of their paired-asXUT ( class 1 ) in S . pombe . Importantly , asXUT presence and sense gene attenuation in exo2Δ are not independent , supporting the idea that the regulation is mediated by the stabilized asXUTs and is not a side effect of Exo2 inactivation . However , additional mechanistic analyses are required to confirm this hypothesis . In a previous study , we reported that the asXUT-associated genes are globally less transcribed than the ‘solo’ ones ( without asXUT ) , displaying an hypoacetylated promoter and hyperacetylation across the gene body [30] . Here we show that the asXUT-associated genes can be separated in two distinct subgroups , namely class 1 ( attenuated upon asXUT stabilization in exo2Δ ) and class 3 ( unchanged in exo2Δ ) . Class 1 corresponds to highly transcribed genes showing prominent nucleosome depletion and high histone acetylation levels at the promoter . In addition , class 1 displays high TSS-proximal antisense transcription , suggesting that the TSS region could be a possible determinant for aslncRNA-mediated regulation . In contrast , class 3 is weakly transcribed , and displays poor promoter-proximal nucleosome depletion and low histone acetylation . Upon stabilization of asXUTs , transcription of class 1 drops down to the basal levels of class 3 . This suggests the existence of a regulatory threshold , ie asXUTs would modulate expression of their associated sense genes , only if expression is above this threshold . This hypothesis contrasts with a previous model based on the analysis of sense-antisense RNA levels in budding yeast , which proposed that antisense-mediated repression would be restricted to low sense expression [46] . Our data suggests that a subset of asXUTs could regulate gene expression at the transcriptional level , reducing sense transcription , as previously shown in S . cerevisiae [18 , 28] . XUTs could also act at other steps of the gene expression process , especially at the post-transcriptional level . In this regard , aslncRNAs have been shown to modulate protein production in response to osmotic stress in S . pombe [23] . In S . cerevisiae , disruption of several aslncRNAs results into increased protein synthesis from their paired-sense mRNAs , indicating a role of these aslncRNAs in the control of protein abundance [47] . Future investigations will be required to explore the regulatory potential of asXUTs and to determine the step ( s ) of the gene expression process they act on . To get insights into the mechanism by which asXUTs could attenuate gene expression , we selected a class 1 representative , the catalase-coding gene ctt1 , for further characterization . Induction of ctt1 in response to oxidative stress was attenuated in exo2Δ cells , correlating with antisense XUT0794 accumulation . Our data indicate that ctt1 attenuation in Exo2-deficient cells occurs at the transcriptional level and is mediated by HDAC activity . Our attempts to identify the HDAC involved in the attenuation mechanism were unsuccessful , most likely due to redundancy of HDAC activities , considering the results obtained upon TSA treatment . On the other hand , we show that the Clr6CI-II complexes ( the homolog of Rpd3L and Rpd3S , respectively ) are required for ctt1 induction ( S6C–S6E Fig ) . The mechanism of HDAC recruitment also remains to be determined . Although we cannot formally exclude a direct recruitment by the asXUT itself , the HDAC is probably recruited through the Set2-dependent H3K36me3 marks . Consistent with this hypothesis , the most hypoacetylated region of ctt1 corresponds to the peak of Set2 occupancy and H3K36me3 . Furthermore , at the RNA level , the loss of HDAC activity and the inactivation of Set2 have a similar effect , decoupling XUT0794 accumulation and ctt1 regulation . Whether ctt1 attenuation in exo2Δ cells depends on the stabilized asXUT per se and/or on the act of antisense transcription remains unsolved to date . On one hand , the ribozyme experiment shows that ctt1 attenuation level positively correlates with XUT0794 abundance ( Fig 3E and 3F ) , in a context where the promoter of the XUT ( and presumably the level of antisense nascent transcription ) remains unchanged , which is consistent with a regulation mediated by the RNA . On the other hand , the local increase of H3K36me3 across the ctt1 gene body in exo2Δ cells ( Fig 6B ) suggests that ctt1 regulation could also depend on transcriptional interference . In fission yeast , gene repression by transcriptional interference requires Set2 and the Clr6CII complex [48] . Here , we show that Set2 and Clr6CII have different effects on ctt1 induction: it is normal in Set2-deficient cells ( Fig 6D ) but attenuated upon inactivation of Clr6CII components ( S6E Fig ) . This suggests that the roles of Set2 and Clr6CII might differ from a gene to another . The model of a RNA-mediated regulation raises a key mechanistic question: how could an asXUT , which in all likelihood accumulates in the cytoplasm in exo2Δ cells , act in the nucleus to regulate the transcription of their paired-sense genes ? This remains unknown to date . One possibility is that the stabilized XUTs could shuttle between the cytoplasm to the nucleus , as shown for tRNAs in budding yeast [49 , 50] and also in mammalian cells [51] . Most of the effects we described have been observed in mutant cells . But does antisense XUT0794 play any role in WT cells ? Interestingly , we observed that XUT0794 is also rapidly induced in WT cells after H2O2 addition , suggesting that it could participate in the modulation of ctt1 induction . In this respect , a recent study in human fibroblasts identified a class of stress-induced aslncRNAs , which are activated upon oxidative stress [8] , suggesting that aslncRNAs induction might be part of a conserved response to oxidative stress in Eukaryotes . Demonstrating that XUT0794 plays a direct role in the modulation of ctt1 during oxidative stress relies , among others , on the ability to block its expression . Unfortunately , none of the strategies we used in this work succeeded into blocking XUT0794 . Additional work will be required to implement in fission yeast other techniques developed in S . cerevisiae to strand-specifically block aslncRNA synthesis [47 , 52] , which remains technically challenging yet . For instance , at some loci , the CRISPR interference approach is not strand-specific and results in the production of novel isoforms of the targeted aslncRNA [53] . Efficient induction of ctt1 upon oxidative stress depends on multiple factors [54] . In addition to Exo2 , we showed that Dcr1 also contributes to ctt1 induction . Our data indicate that Dcr1 and Exo2 regulate ctt1 through distinct mechanisms , which is consistent with the observations that asXUTs are not targeted by Dicer in fission yeast [30] . Interestingly , Dcr1 was recently shown to promote efficient termination of a set of highly transcribed genes , corresponding to sites of replication stress and DNA damage [55] , and ctt1 belongs to this set of Dcr1-terminated genes . Thus , one possibility could be that Dcr1 regulates ctt1 induction at the level of transcription termination . However , our ChIP data did not reveal any significant change of RNAPII occupancy at the 3’ extremity of ctt1 in dcr1Δ cells , upon oxidative stress ( Fig 7C ) . Additional analyses are therefore required to decipher the mechanism by which Dcr1 regulates ctt1 induction . In conclusion , our work in budding and fission yeasts shows that the cytoplasmic 5’-end RNA decay plays a key role in controlling aslncRNAs endowed with regulatory potential . Given the high conservation of Xrn1 in Eukaryotes , it is tempting to speculate that asXUTs and their regulatory activity are conserved in higher eukaryotes , contributing in buffering genome expression , and adding another layer to the complexity of gene regulation .
All the strains used in this study are listed in S5 Table . Mutant strains were constructed by meiotic cross or transformation , and verified by PCR on genomic DNA and/or RT-qPCR . Plasmid pAM353 for expression of XUT0794 in trans was constructed by cloning XUT0794 in the SalI site of pREP41 ( ars1 LEU2 P41nmt1 ) . Sanger sequencing confirmed the correct orientation of the insert and the absence of mutation . Hammerhead ribozyme [56] was inserted in XUT0794 by two-step PCR , giving a 3 . 2 Kb final product corresponding to ctt1 mRNA coordinates +/- 500 bp that was cloned in pREP41 . After verification of absence of additional mutations by Sanger sequencing , the ribozyme-containing construct was excised and transformed in the YAM2534 strain ( ctt1::ura4 ) . Transformants were selected on 5-FOA plates and analyzed by PCR on genomic DNA . Deletion of exo2 was performed subsequently . Strains were grown at 32°C to mid-log phase ( OD595 0 , 5 ) in YES or EMM-L medium . For ctt1 induction , 1 mM H2O2 was added for 15 minutes [34] , or different time points for analysis of kinetics of induction . Expression from P41nmt1 was repressed by growing cells in EMM-L + 15 μM thiamine for 24 hours . NET-Seq libraries were constructed from biological duplicates of YAM2507 ( exo2Δ rpb3-flag ) cells and sequenced as previously described [30] . Libraries for the WT strain YAM2492 ( rpb3-flag ) were described in the same previous report [30] . After removal of the 5’-adapter sequence , reads were uniquely mapped to the reference genome ( ASM294v2 . 30 ) using version 0 . 12 . 8 of Bowtie [57] , with a tolerance of 2 mismatches . Differential analysis was performed between the IP samples from WT and exo2Δ using DESeq [58] . Genes showing significant decrease ( P-value <0 . 05 , adjusted for multiple testing with the Benjamini-Hochberg procedure ) in the mutant were defined as class 1 & 2 . Raw sequences have been deposited to the NCBI Gene Expression Omnibus ( accession number GEO: GSE106649 ) . A genome browser for visualization of NET-Seq processed data is accessible at http://vm-gb . curie . fr/mw3 . Total RNA was extracted from exponentially growing cells using standard hot phenol procedure , resuspended in nuclease-free H2O ( Ambion ) and quantified using a NanoDrop 2000c spectrophotometer . 10 μg of total RNA were loaded on denaturing 1 . 2% agarose gel and transferred to Hybond-XL nylon membrane ( GE Healthcare ) . ctt1 mRNA and U3B were detected using AMO2063 and AMO2081 oligonucleotides , respectively ( see S6 Table ) . 32P-labelled probes were hybridized overnight at 42°C in ULTRAhyb-Oligo hybridization buffer ( Ambion ) . Quantitation used a Typhoon Trio PhosphorImager and the ImageQuant TL v5 . 2 sofware ( GE Healthcare ) . Strand-specific reverse transcription ( RT ) reactions were performed from at least three biological replicates , using 1 μg of total RNA and the SuperScript II Reverse Transcriptase kit ( Invitrogen ) , in the presence of 6 , 25 μg/ml actinomycin D . For each sample , a control without RT was included . Subsequent quantitative real-time PCR were performed on technical duplicates , using a LightCycler 480 instrument ( Roche ) . Oligonucleotides used are listed in S6 Table . ChIP analysis was performed from three biological replicates , for each strain . Exponentially growing ( OD595 0 , 5 ) cells were fixed for 10 minutes at room temperature using formaldehyde ( 1% final concentration ) , then glycine was added ( 0 , 4 M final concentration ) for 5 minutes . Chromatin was sonicated using a Bioruptor sonication device ( Diagenode ) . Antibodies used were 8WG16 ( Covance ) for RNAPII , H14 ( Covance ) for RNAPII S5-Pho , 3E10 ( Millipore ) for RNAPII S2-Pho , ab1791 ( Abcam ) for histone H3 , 05–1355 ( Millipore ) for acetyl-H4 ( Lys5/8/12/16 ) , 07–353 ( Millipore ) for acetyl-H3 ( Lys14 ) , ab9050 ( Abcam ) for H3K36me3 and 9E10 ( Protein Expression and Purification Core Facility , Institut Curie ) for Myc . Quantitative real-time PCR were performed in technical duplicates on a StepOnePlus machine ( Applied Biosystems ) or on a LightCycler 480 instrument ( Roche ) . Oligonucleotides used are listed in S6 Table . | Examples of regulatory antisense ( as ) lncRNAs acting on gene expression have been reported in multiple model organisms . However , despite their regulatory importance , aslncRNAs have been poorly studied , and the molecular bases for aslncRNAs-mediated regulation remain incomplete . One reason for the lack of global information on aslncRNAs appears to be their low cellular abundance . Indeed , our previous studies in budding and fission yeasts revealed that aslncRNAs are actively degraded by the Xrn1/Exo2-dependent cytoplasmic 5’-3’ RNA decay pathway . Using a combination of single-gene and genome-wide analyses in fission yeast , here we report that the stabilization of a set of Exo2-sensitive aslncRNAs correlates with attenuation of paired-sense genes transcription . Our work provides fundamental insights into the mechanism by which aslncRNAs could regulate gene expression . It also highlights for the first time that the level of sense gene transcription and the presence of specific chromatin features could define the potential of aslncRNA-mediated attenuation , raising the idea that aslncRNAs only attenuate those genes with expression levels above a “regulatory threshold” . This opens novel perspectives regarding the potential determinants of aslncRNA-dependent regulation , as previous models in budding yeast rather proposed that aslncRNA-mediated repression is restricted to lowly expressed genes . | [
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| 2018 | Bases of antisense lncRNA-associated regulation of gene expression in fission yeast |
Recent evidence suggests that the timing of DNA replication is coordinated across megabase-scale domains in metazoan genomes , yet the importance of this aspect of genome organization is unclear . Here we show that replication timing is remarkably conserved between human and mouse , uncovering large regions that may have been governed by similar replication dynamics since these species have diverged . This conservation is both tissue-specific and independent of the genomic G+C content conservation . Moreover , we show that time of replication is globally conserved despite numerous large-scale genome rearrangements . We systematically identify rearrangement fusion points and demonstrate that replication time can be locally diverged at these loci . Conversely , rearrangements are shown to be correlated with early replication and physical chromosomal proximity . These results suggest that large chromosomal domains of coordinated replication are shuffled by evolution while conserving the large-scale nuclear architecture of the genome .
Mammalian genomes are complex and heterogeneous entities , consisting of many thousands of functional elements that are packed into chromosomes and organized in nuclear space . Our understanding of the global implications of genome organization , its effect on gene regulation and its evolutionary consequences is still quite limited . Recent advances in epigenomic profiling have begun to uncover large-scale genomic domains that are marked with specific histone modifications [1]–[5] , interact with important nuclear landmarks [6] or replicate as units at specific times during S phase [7]–[14] . Data on inter chromosomal interactions hint as to how such large scale domains may be organized in the three-dimensional nucleus structure [15] . Yet the origin of large-scale genome organization is unclear: How does the genome self-organize into domains ? How are these domains exploited for regulation and how can the cell propagate them to daughter cells ? From an evolutionary perspective , the extent to which the genome's domain organization is conserved is unclear , as are the evolutionary mechanisms that contribute to such conservation [16] , [17] . Even if domains are conserved , the origin of such conservation may have several explanations . If domains are functionally important , for example as scaffolds for gene clusters [18] , we may expect genome rearrangements that break them to be selected against . On the other hand , if genome rearrangements are enriched at particular hotspots [19] , [20] , or are affected by various epigenetic factors , the genome may conserve domains with low rates of rearrangements without selection . Genomic replication domains were shown to exhibit a particularly robust large-scale behavior . Domains of tens of kilobases to megabases collectively replicate at particular timings during S-phase in mice [7] , [8] , [21] , human [9] , [12]–[14] , [22] , [23] and flies [10] , [11] . Such modular behavior was suggested to be driven by the coordinated firing of a large number of spatially clustered origins of replication . Recently , studies in mouse and human cells reveals that approximately one third of the genome changes its ToR between tissues [8] , [21] , [23] . DNA replication timing was shown to be highly correlated with other genomic features , most notably the regional G+C content but also gene density , gene expression , open chromatin and mutability ( reviewed in [24] ) . Genomic replication domains therefore naturally describe an important type of large-scale genomic organization and are ideal markers for studying such organization from an evolutionary perspective . In this work , we measure and compare the time of replication of the human and mouse genomes . We use the data to test the correlation between the divergence of large-scale chromosome structure and the divergence of replication timing . We find that while chromosome structure is constantly being challenged by evolution , the genome's time of replication is remarkably conserved . Our analysis of the correlation between genome rearrangements , time of replication and chromosomal conformation suggests that the evolution of chromosome architecture may be confined by the static and dynamic organization of the genome in the nucleus . These results put some of the open questions on chromosome structure and function in a new evolutionary perspective and suggest that additional comparative analysis may be important for their investigation
The human and mouse genome sequences differ locally on about 30% of the nucleotides [29] . Furthermore , the two species are separated by hundreds of large-scale genome rearrangement events ( such as fusions , translocations and inversions ) . Despite these differences the correlation between human and mouse ToR is striking . As shown in Figure 1 ( see also Figure S4 and Figure S5 ) , the global replication landscape of the human genome matches that of the mapped mouse regions ( overall Spearman = 0 . 74 for fibroblasts , and = 0 . 78 for lymphoblasts , P<10−100 ) . The levels of human-mouse ToR correlations are similar to those derived by comparing the two cell types within each species ( = 0 . 7 for human , and = 0 . 83 for mouse ) . This correlation confirms previous observation of ToR conservation which were based on analysis of ToR of genes [7] and expands it to the entire genome . Our estimations of the extent of ToR conservation are higher than those proposed recently [30] , probably due to the more careful genome alignment procedure we used here . Our data show that ToR conservation is higher in gene deserts than in gene rich domains ( Figure 1C and Figure S6 ) , suggesting that ToR conservation is not a simple consequence of gene expression conservation . Furthermore , the observed conservation is not likely to be a consequence of global sequence conservation , since sequence divergence and ToR divergence are uncorrelated ( Spearman = 0 . 02 ) . The ToR landscape consists of large-scale domains , as shown before for the mouse genome [7] , and we will focus below on the ToR evolution at these scales . We reconfirmed that our 50Kb tiling resolution is capturing most of the large scale ToR structure in the genome by analysis of ToR in one human and one mouse chromosomes that were densely tiled on our arrays ( Figure S7 ) . To systematically characterize genomic domains with evolutionary conserved , diverged or tissue specific time of replication , we used the spatial clustering algorithm [31] . The algorithm works in an unsupervised fashion to identify and characterize spatial clusters . A spatial cluster is a collection of contiguous genomic regions that display a similar multivariate ToR trend ( see Materials and Methods ) . Data that do not fit any of the clusters is attributed to a default background cluster . The algorithm thus identifies frequently recurring patterns in the data , while taking into account the strong spatial coupling between adjacent genomic loci . The algorithm can in theory discover clusters that display ToR conservation or any mixture of diverged ToR trends , allowing the above conclusion on global ToR conservation to be revaluated from a regional perspective . Analysis of the human and mouse aligned maps revealed that 92% of the mapped regions fell into four spatial clusters , all of which display ToR conservation ( Figure S8 ) . Interestingly , two of the inferred clusters ( representing 25% and 15% of the probes respectively ) exhibit distinct tissue-specific replication patterns ( clusters 3 and 4 in Figure S8 ) . The ToR difference between the two cell types is conserved between mouse and human , suggesting that tissue-specific ToR is evolutionary conserved . This was directly confirmed by computing profiles of the difference in ToR between fibroblasts and lymphoblasts ( for both human and mouse ) , and measuring the correlation between these profiles across the two species ( = 0 . 22 , p<10−292 ) . Analysis of the four inferred clusters in light of other genomic features confirmed previous observations that late replication regions are significantly poor in genes and transcription and showed additional correlations with genomic features , including increased frequency of interaction with the nuclear lamina and biased distance from the telomere ( Figure S9 ) . Replication time was previously shown to be correlated with the genomic regional G+C content [7] , [12]–[14] . Indeed , we observe a strong correlation between ToR and G+C content in both human and mouse ( Figure 2A , Figure S10 ) . The regional G+C content is known to be conserved between mouse and human ( Figure 2B ) . On the other hand , ToR structure is known to affect mutability and may therefore contribute to G+C content heterogeneity [32] , [33] . To test if conservation of ToR and G+C content are two aspects of the same phenomenon we subtracted from the ToR of each probe the mean of ToR in probes with similar G+C contents , forming a residual ToR profile that was uncorrelated with the G+C content by design ( see Materials and Methods , Figure 2C ) . We found that the residual ToR profiles are still highly correlated between mouse and human , which demonstrates that the conservation of ToR between species is not a mere consequence of slow G+C content divergence . Furthermore , the independence of ToR conservation from G+C conservation is supported by the conservation of tissue specific differences in ToR ( discussed above ) , since such tissue specific differences cannot possibly be a direct consequence of G+C content . We did not find a significant correlation between ToR conservation and sequence conservation ( see Materials and Methods ) , which suggests that ToR conservation is not reflecting global sequence conservation , but rather conservation of short subsequences at specific regulatory elements . The conserved large-scale genomic ToR domains we have characterized , with their correlation to different genomic features , are likely to represent physical chromosomal domains with specific nuclear preferences . We analyzed published Hi-C chromosomal interaction data [15] , which was measured on a human lymphoblastoid cell line ( GM06990 ) , and tested the interaction preferences of 4 equal-sized groups , each replicating in one of the quarters of the S phase . We measured the amount of interactions ( paired-end reads ) within groups and between groups , and studied it separately for intra- and inter-chromosomal interactions ( Materials and Methods ) . We first found that late replicating domains are generally less represented in the Hi-C dataset , either due to their relative isolation or due to technical issues with chromatin extraction and shearing ( Figure 3A ) . After normalizing this effect , we found that domains with similar ToR tend to trans-interact with each other more often than with domains with different ToRs ( Figure 3B ) . When examining intra-chromosomal interactions , we found that early replicating domains have more Hi-C interactions than late replicating domains ( Figure 3C ) . Interestingly , the additional chromosomal interactions of early replicating domains are primarily short-ranged ( <500kb ) . This result is in agreement with the previously noted distributions of interaction distances for open ( early replicating ) and closed ( late replicating ) chromosomal domains [15] . The interactions of late replicating domains are relatively more biased toward long distances , while more of the interactions of early domains are representing local interactions . Since the ToR domain structure of the human and mouse genome is highly conserved in alignable regions , we next focused on the conservation and divergence patterns near breakpoints . Rearrangements continuously reorganize the large-scale layout of the genome through translocations , inversions and duplications . Such events may shuttle a genomic region from one genomic context into an entirely different one . The dynamics of ToR divergence that follow rearrangements can hint at the mechanisms that regulate ToR . If replication initiation is mostly determined by local elements we can expect low ToR divergence even around rearrangements , but if the chromosomal neighborhood is a major factor in ToR regulation we would expect significant ToR divergence there . We used the inferCARs algorithm [34] to extract a collection of 1382 syntenic blocks ( >50Kb ) shared by human , rhesus , mouse , rat and dog , which cover over 92% of the human genome . Using dog as an outgroup , we identified 880 simple fusion events ( Materials and Methods ) , which are events that can be associated to a unique branch in the phylogenetic tree . In Figure 4A we show the phylo-tree and the number of events on each branch . Most of these events are between domains of similar ToR ( Figure S11 ) , yet we are interested in events that fused domains of different ToR . An example of a simple event that is assigned to the common mouse-rat lineage branch is shown in Figure 4B . We considered two alternative scenarios for ToR divergence following fusion of an early replicating domain and a late replicating domain ( Figure 4C ) . The first scenario involves an early-to-late invasion , where the late side accommodates and advances its replication . The opposite scenario involves late-to-early invasion , where the replication of the originally early domain is delayed following fusion . Examples of both types of invasions are given in Figure 4D . Analysis of all simple fusion events ( Figure 4E ) indicated that near breakpoints , ToR is more diverged than expected by chance ( with more cases than expected representing significant divergence near fusion points , hyper geometric P<0 . 00015 ) . This analysis provided us with a detailed list of genomic regions that went through ToR divergence following a change in genomic context ( Table S1 ) , opening new evolutionary avenues for further refining our understanding of ToR regulation . For example , we note that early-to-late invasion is more common than late-to-early invasion ( 15 versus 7 events for fibroblasts , 23 versus 14 events for lymphoblasts , see Figure S12 for more examples on both cell types ) . The mechanisms underlying ToR divergence can only be hypothesized given the current data ( ToR measurements of a third outgroup species is needed to reconstruct evolutionary histories with higher certainty ) . For a subset of the early-to-late invasions ( e . g . , Figure S12C ) the most simple mechanism , in which a single replication fork crosses the fusion point , is a valid explanation . In other cases , ( e . g . , Figure 4D ) , divergence encompass a territory that is much larger than the scope of a single fork . Importantly , despite these specific cases , genome rearrangements are not causing massive divergence of replication timing , and the overall replication structure is largely conserved between human and mouse , suggesting evolution typically shuffles ToR domains rather than breaking and fusing them . We focused next on fusion events in the mouse lineage that involve segments that are distal ( located more than 5Mb apart or on a different chromosome ) in both human , dog and rhesus . We observed that these events preferentially involve early replicating domains ( Figure 5A ) . Furthermore , rearrangements preferentially bring together genomic fragments of similar ToR ( Figure 5B ) . This may be a consequence of a preference for rearrangements that involve early replicating regions , or a general mechanistic tendency to fuse breakpoints occurring at the same time in S phase . Another possibility is that the fusion of segments with very different ToR is more frequently deleterious since it violates the overall organization of the genome . We limited the analysis further , focusing on 55 fusion events on the mouse lineage for which the two human domains reside on different chromosomes ( while their mouse orthologs are adjacent ) , and examined the level of interaction between the segments' ends , as reflected in the Hi-C dataset . We found that the interaction probability between these specific set of pairs is above the background ( Figure 5C ) , which suggests that rearrangements occur between parts of the genome that are more often occupying the same nuclear compartment . Taken together , replication timing and Hi-C data suggest that genome rearrangements are correlated with the replication and nuclear architecture at an evolutionary scale , and that breakpoints generally shuffle genomic segments with similar ToR and prior chromosomal proximity . We anticipate that future data on chromosomal interactions at higher resolution and for additional species will allow a more quantitative estimation of the effect of replication timing and physical interactions on rearrangement rates .
We have shown that the mammalian genome is subject to a conserved replication timing structure that divides the genome into megabase-scale segments of coordinately replicating sequences . As suggested before [7] , [8] , this structure is highly correlated with other genomic traits ( G+C content , gene density ) but we show that its conservation is independent of these features . The human and mouse genomes differ significantly in their chromosomal landscape , which is a result of dramatic events like chromosomal fusion , switch to acrocentric layout and numerous large-scale rearrangements [29] . If ToR regulation was influenced by the location in the one-dimensional chromosomal space , such changes should have resulted in major ToR divergence between mouse and human , and in fragmentation of the ToR domain structure . As such divergence is not observed there must be some mechanism preventing it from occurring . The genomic features that regulate ToR locally are currently uncharacterized . In theory , such elements could actively regulate the ToR of their surrounding genomic domains , and their conservation may be a consequence of strong selection working to conserve a functionally important ToR landscape . Under these assumptions , ToR would serve as a scaffold for the emergence of domains of active genes , thereby explaining the correlation between gene activity and early replication . Alternatively , local ToR regulation may be a consequence of gene activity rather than an enabler of it . According to this scenario , ToR conservation may be an indirect result of the conservation of gene activity and not a participant in driving it . However , the high conservation of ToR in gene deserts regions suggests that this latter possibility cannot fully account for the data . The more common early-to-late invasions of ToR that we observed ( Figure 4E ) support the notion of a simple in cis mechanism in which early replicating domains are positively regulated to retain their ToR regardless of their genomic context , while late replicating domains are passively regulated by lack of predisposition to early replication . The conserved ToR landscape and the global map of chromosome interactions of the human genome reveal how genome rearrangements interact with the chromosomal architecture of the genome in four dimensions ( the nucleus space and time of replication ) . We showed that rearrangements tend to bring together domains with similar ToR , and that pairs of loci that were fused in the mouse lineage , tend to trans-interact in the human genome . One explanation for these observations is that the mechanisms of breakpoint and repair increase the likelihood of rearrangements that involve loci with similar replication timing , or prior chromosomal proximity . Indeed , chromosomal proximity was recently suggested to underlie the cancer-prone translocation TMPRSS2-ERG [35] . An alternative explanation for the association between ToR , chromosomal interactions and breakpoints , may be a selective constraint on rearrangements that would significantly change the nuclear architecture by moving a domain to a foreign genomic context . A large-scale and deleterious change in ToR due to translocation was observed in human lymphocytes in cytogenetic resolution [36] . Data from additional species would allow for true phylogenetic reconstruction of the ToR history in different parts of the genome , and could help to resolve and refine the above hypotheses . We expect such data to provide unique insights into the regulation of DNA replication and to expand significantly our understanding of this key aspect of genome organization .
Mouse L1210 lymphocytic leukemia cells ( ATCC CCL219 ) were grown in CO2-independent L-15 medium supplemented with 2 gr/L dextrose . Human Molt-4 acute lymphoblastic leukemia cells ( ATCC CRL-1582 ) were grown in RPMI . Mouse Embryonic cells ( MEF ) were grown in DMEM supplemented with 0 . 2% beta-mercaptoethanol and 1% NeAA ( Non-Essentials Amino-Acids ) . Primary foreskin fibroblasts transfected with the h-TERT gene ( FFT ) were grown in RPMI . All cells were supplemented with 10% fetal bovine serum , penicillin G and streptomycin sulfate . Cells were washed twice with PBS , fixed with ethanol , stained with 5 propidium iodide , and incubated with 50 RNASE A . Then , the cells were sorted by their DNA content using the fluorescence-activated cell sorting ( FACS ) Vantage machine . DNA was extracted from S phase and G1 phase isolated cells using incubation with standard lysis buffer followed by Proteinase K treatment , phenol-chloroform extraction and ethanol precipitation . The resulting DNA was sonicated , cleaned using QIAGEN PCR purification kit and its concentration was measured using the Nano-Drop . 250ng-1 DNA isolated from G1 or S phase cells was labeled with dUTP-cy3 or dUTP-cy5 respectively , using Agilent's CGH labeling protocol ( www . embl-heidelberg . de/courses/Agilent05/CGH-Protocol . pdf ) . Pairs of samples were co-hybridized on Agilent custom design mouse or human microarrays according to Agilent's hybridization protocol . The arrays were scanned using an Agilent scanner and raw data was analyzed using Agilent's feature extraction software . We have optimized the protocol to yield high quality data using as little as 250ng of input DNA ( Figure S13 ) . The ToR of each cell type was measured using two replicates ( Figure S14 ) . Arrays were designed using Agilent's website “eArray” . The mouse array covered the entire mouse genome with an average spacing of 40Kb and chromosome 19 with an average spacing of 1Kb ( Agilent Microarray Design Identification ( AMADID ) #018925 ) . The human array covered the entire human genome with an average spacing of 38Kb and all the sequenced part of chromosome 22 with an average spacing of 1Kb ( AMADID #021214 ) . All experiments were performed using Agilent's 2×105K CGH arrays . To allow simple comparison between human and mouse the data was binned to a 50Kb resolution , which resulted in ∼47 , 000 mouse bins and ∼53 , 000 human bins . To reduce noise on the single probe level ( which is caused by dye bias [37] ) we smoothed out the raw ToR data . We define the smooth ToR value of bin to be:window size of . Throughout the paper we use the smooth ToR values . For the breakpoint analysis we define the ( smooth ) right-sided ToR to be: , with ( left-sided ToR is defined in a similar fashion ) . The confidence interval around the ToR profiles is the standard deviation of the 2W+1 values that were averaged when computing the smooth ToR profiles . For each 50Kb bin we defined the ToR conservation to be the difference between human fibroblast ToR and mouse fibroblast ToR . We defined the sequence conservation of the bin to be the percentage of conserved nucleotides between human and mouse ( using maf files ) . Sequence conservation and ToR conservation are not correlated ( Spearman ) . We used the Human March 2006 Assembly ( hg18 ) and the Mouse February 2006 Assembly ( mm8 ) for human and mouse G+C content computation . To generate the G+C to ToR trend of Figure 2A we divided the G+C content spectrum ( computed on windows with a width of 50Kb ) between 0 . 2 to 0 . 7 into 50 equal sized segments [0 . 2 , 0 . 21] , [0 . 21 , 0 . 22] , … , [0 . 69 , 0 . 7] , and computed the average ToR of each segment , which we call the GC-predicted ToR ( ) . We defined the residual ToR to be: . The residual ToR is “G+C normalized” in the sense that it has no significant correlation with the G+C content . We used the “liftOver” tool of UCSC [25]–[28] to project the ToR , the residual ToR and the regional G+C content of mouse onto the human genome . For each 50Kb mouse bin we defined a window of 20Kb centered on the middle of the bin , and attempted to project that window onto the human genome ( with liftOver ) , requiring at least a 30% match , in order to obtain a high quality mapping . If a window succeeded projection we associated the original bin value to middle of the projected window ( on human coordinates ) . In this manner we succeeded to project ∼30 , 000 bins ( 62% ) onto the human genome , where we aligned them to the human 50Kb bins to allow easy comparison . We discarded all bins that had missing human data , leaving us with ∼27 , 000 bins . The result is a comparative ToR map that contains four aligned ToR profiles ( FFT , MOLT4 , MEF , L1210 ) . The spatial clustering algorithm [31] works in an unsupervised fashion to identify contiguous genomic regions with similar trends of ToR in the two species and two tissues . We used as input for the algorithm the 4 ToR profiles of the ToR map . We normalized the profiles such that all had the same mean and standard deviation . We learned the most likely parameters of a star shaped hidden Markov model , with one central hidden non-emitting state , and N emitting states ( the petals of the star ) , for N = 4 , 5 , … , 10 . We noticed 4 states that appear in a robust manner for all N values , and therefore focused on the N = 4 model ( data not shown ) . We measured several genomic features for each of the ToR spatial clusters ( Figure S9 ) . Gene expression of FFT and Molt-4 were downloaded from UCSC [25] . Expression decreases with ToR , as previously shown . The Spearman correlation between the ToR difference and the gene expression difference ( between FFT and Molt-4 ) is 0 . 11 ( with a very small P value ) . ToR is correlated with distance to telomeres ( Figure S9 ) . We validated the known correlations between ToR and transcription density ( amount of transcribed sequence , according to RefSeq genes ) , exon density , number of transcription start sites ( in bins of 50Kb ) , amount of lamina interaction , and number of gene . For the Hi-C analysis we represent ToR by the Molt-4 ToR track ( Human lymphoblasts ) . We downloaded the Hi-C dataset [15] which is composed of ∼60M interacting pairs . We discarded interactions between loci without ToR data , which left us with ∼46M interactions; about half of them were inter-chromosomal and half were intra- chromosomal . We split the genome into 4 equal parts according to the FFT ToR quartiles ( E:early , EM:early-medium , LM:late-medium , L:late ) . To generate Figure 3B , we counted for each possible pair of ToR groups the number of inter-chromosomal interactions . We then randomly paired all the loci that participated in an interaction to generate a background simulated interaction map , and computed for each pair a random count with its standard deviation . The ratio value for each pair is defined to be the number of counted interactions divided by the random count , and in the figure we display the log10 ratio . To generate Figure 3C we counted for each ToR group the number of intra-chromosomal close interactions ( <500K ) and far interactions ( >500K ) . It should be noted that although the original Hi-C paper limits its analysis to 1Mb resolution , we were able to infer insights with higher resolution ( <500Kb ) since we were interested in four ToR categories , instead of in individual loci . Using the algorithm of Ma et al . we identified 1382 homologous segments that are free of chromosome fissions or fusions as well as inversions or translocations larger than 50Kb . These long segments cover 92% of the human genome . In the phylogenetic tree of Figure 4A , we used only simple events , which are events that we could assign with high confidence to a unique branch of the phylotree ( Figure S15 , example in Figure 4B ) . Each event has a set of posterior species ( leaves of the tree rooted below branch ) and a set of prior species ( leaves of tree rooted above branch ) . The prior distance of an event is the minimal distance between the two fused points among all prior species . In Table S1 we specify all events identified in this manner . We tagged all fusions with a prior distance greater than 5Mb as distal events , and tagged all other fusions as close-range events . We focused on distal murine lineage events ( events in the branches leading from the human-mouse ancestor to mouse ) . We computed ToR divergence on both sides of each fusion site . For each fusion , we defined the late-side domain to be the domain with the later human ToR ( associated with ) , and the early-side domain to be the domain with the earlier human ToR ( associated with ) . We defined the late divergence and early divergence to be and , respectively . These two values reflect how much the mouse ToR has diverged from the ancestral ToR over evolution . For example , a positive late divergence ( ) implies that after fusion the ToR of the murine domain advanced in time . In Figure 4E we show in a scatter plot late divergence versus early divergence . To generate Figure 5C we counted the number of Hi-C interactions in a window of 1MB between each pair distal simple fusion events in the mouse lineage . As control we shuffled all pairs , using only random pairs that reside on different chromosomes . Using the 75% percentile as a threshold , we tagged all pairs that were in the top quartile as interacting pairs and tagged all other pairs as non-interacting pairs . The original breakpoint pairs were enriched in the interacting group ( 1 . 6 enrichment hyper geometric P<0 . 01 ) when compared to the shuffled control pseudo breakpoints . We show the tagging threshold as a dashed line in Figure 5C . ToR data was deposited in the GEO database , accession GSE17236 . | During S-phase of the cell cycle , chromosomal DNA is replicated in a complex process involving the coordinated activity of thousands of replication forks , each of which duplicates a long stretch of DNA . Recent experiments revealed that the genome is replicating as a mosaic of large-scale early and late chromosomal domains and that this high-level domain organization is correlated with genomic properties like gene density and nucleotide composition . We compared genome-wide replication time maps of compatible human and mouse cells and revealed that their organization into replication domains is highly conserved despite the numerous large-scale genome rearrangements separating the two species . Analysis of recent chromosomal interaction data shows that regions with similar time of replication are more frequently interacting with each other than expected . The data also show that evolutionary rearrangements have predominantly occurred between regions that have similar time of replication and higher-than-expected chromosomal proximity . Our data suggests that the genome , while being continuously rearranged by evolution , maintains a conserved domain organization . Whether this conservation is driven by selection , or is a consequence of the rearrangement process itself , can be resolved by enhancing the comparative approach proposed here . | [
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| 2010 | Comparative Analysis of DNA Replication Timing Reveals Conserved Large-Scale Chromosomal Architecture |
RNA interference-related silencing mechanisms concern very diverse and distinct biological processes , from gene regulation ( via the microRNA pathway ) to defense against molecular parasites ( through the small interfering RNA and the Piwi-interacting RNA pathways ) . Small non-coding RNAs serve as specificity factors that guide effector proteins to ribonucleic acid targets via base-pairing interactions , to achieve transcriptional or post-transcriptional regulation . Because of the small sequence complementarity required for microRNA-dependent post-transcriptional regulation , thousands of microRNA ( miRNA ) putative targets have been annotated in Drosophila . In Drosophila somatic ovarian cells , genomic parasites , such as transposable elements ( TEs ) , are transcriptionally repressed by chromatin changes induced by Piwi-interacting RNAs ( piRNAs ) that prevent them from invading the germinal genome . Here we show , for the first time , that a functional miRNA pathway is required for the piRNA-mediated transcriptional silencing of TEs in this tissue . Global miRNA depletion , caused by tissue- and stage-specific knock down of drosha ( involved in miRNA biogenesis ) , AGO1 or gawky ( both responsible for miRNA activity ) , resulted in loss of TE-derived piRNAs and chromatin-mediated transcriptional de-silencing of TEs . This specific TE de-repression was also observed upon individual titration ( by expression of the complementary miRNA sponge ) of two miRNAs ( miR-14 and miR-34 ) as well as in a miR-14 loss-of-function mutant background . Interestingly , the miRNA defects differentially affected TE- and 3' UTR-derived piRNAs . To our knowledge , this is the first indication of possible differences in the biogenesis or stability of TE- and 3' UTR-derived piRNAs . This work is one of the examples of detectable phenotypes caused by loss of individual miRNAs in Drosophila and the first genetic evidence that miRNAs have a role in the maintenance of genome stability via piRNA-mediated TE repression .
In many , if not most , eukaryotes , RNA silencing is responsible for the regulation of gene expression via the association of small , 20–30 nucleotide ( nt ) -long , non-coding RNAs with Argonaute proteins ( reviews: [1–4] ) . Partial or perfect base pairing between the small RNAs and their RNA targets provides the specificity for the repressive activities of the Argonaute-containing effector complexes , called RNA-induced silencing complexes ( RISCs ) . In Drosophila melanogaster , the Argonaute protein family includes two clades: the AGO proteins ( AGO1 and AGO2 ) and the PIWI proteins ( Piwi , Aubergine ( Aub ) and Argonaute 3 ( AGO3 ) ) . Each RISC contains one of three types of small regulatory RNAs that have different roles and mechanisms of action . Specifically , more than 230 AGO1-associated microRNAs ( miRNAs; 21- to 23 nt in length ) regulate gene expression , during development ( reviews: [5 , 6] . On the other hand , AGO2-associated small-interfering RNAs ( siRNAs; 21 nt-long ) , and PIWI-interacting RNAs ( piRNAs; 23 to 30 nt in length ) are more dedicated to the defence against exogenous and endogenous parasites , such as viruses and transposable elements ( TEs ) [7–11] . Argonaute-mediated silencing can occur at the transcriptional or post-transcriptional level . Most Argonaute proteins , such as siRNA-loaded AGO2 [12] or piRNA-loaded Aub [13] , are endowed with endo-nucleolytic slicer activity that is required for their post-transcriptional gene silencing ( PTGS ) function through direct cleavage of RNA targets . In contrast , slicer-independent miRNA-mediated PTGS usually occurs through the association of AGO1 with GW182 ( also called Gawky ) , leading to mRNA translation inhibition and destabilization [14 , 15] . The slicer activity is conserved in Piwi , but does not seem to be required for its silencing function [16 , 17] . Indeed , piRNA-loaded Piwi guides the deposition of repressive chromatin marks on TE sequences resulting in their transcriptional silencing [18–23] . In Drosophila adult ovarian somatic support cells ( follicle cells ) , piRNA-mediated TE transcriptional repression is exclusively achieved by the loading onto Piwi of primary piRNAs generated by unidirectional transcription of heterochromatic loci , called piRNA clusters , such as flamenco [24] . A number of coding genes also give rise to piRNAs from their 3’ untranslated regions ( 3’UTRs ) [25] . In follicle cells , the traffic jam ( tj ) , jim and GC32000 genes are the major producers of 3’UTR-derived piRNAs [26] . The role of this class of Piwi-loaded genic piRNAs is still elusive . Although TE- and 3’UTR-derived piRNAs originate from different genomic loci , they seem to use the same biogenesis pathway , because 3'UTR-derived piRNAs are affected by defects in all the proteins known to be involved in the biogenesis of TE-derived piRNAs [17 , 27 , 28] . Two recent genetic screens have highlighted the complexity of the somatic ovarian piRNA pathway that involves many proteins with different gene ontologies [29 , 30] . However , except Gawky , none of the proteins that are directly involved in the miRNA pathway were identified by these screens [29] . We show here that piRNA-mediated TE transcriptional repression is impaired in follicle cells in which the miRNA pathway is defective following Drosha , Gawky or AGO1 inactivation . Moreover , we report that individual titration of two miRNAs ( miR-14 and miR-34 ) leads to a similar TE de-repression phenotype . New germinal insertions of retroviral-like TEs can result from their somatic ovarian expression [31 , 32] . Therefore , these findings provide the first genetic evidence that loss of miRNA function could impair maintenance of genome stability via TE de-repression . Moreover , differently from what observed for TE-derived piRNAs , accumulation of 3'UTR-derived piRNAs was not affected by the same defects in the miRNA pathway , highlighting unsuspected differences between these piRNA pathways .
To test whether miRNAs are required for TE repression in Drosophila follicle cells , we impaired the miRNA pathway by expressing either double stranded RNAs ( RNAi ) or a dominant negative mutant construct under the control of the tissue-specific driver traffic jam Gal4 ( tj-GAL4 ) [27 , 33] . To preserve the essential miRNA roles during early development , we restricted miRNA depletion ( thereafter called "soma KD" ) to the adult stage ( follicle cells ) by transiently inactivating the Gal80ts thermo-sensitive Gal4 inhibitor [34] . After a shift at 25°C for five days , TE desilencing was monitored in follicle cells using the ZAM-lacZ reporter transgene [35] . We first impaired miRNA biogenesis , by targeting the Drosha protein . Indeed , Drosha functions as the catalytic subunit of the Microprocessor complex that initiates miRNA production [36] . To achieve efficient RNAi against Drosha in follicle cells , we had to co-express the Dicer-2 RNAi enhancer with two long hairpins against Drosha ( S1 and S2 Tables ) . We also constructed and expressed a trans-dominant negative Drosha mutant ( TN-Drosha ) , which contains a point mutation in each RNAseIII domain , and a wild-type Drosha construct ( WT-Drosha ) as control ( see Materials and Methods , S1 Text and S1A Fig ) . As the TN-Drosha mutant had been previously used to impair miRNA production only in cell culture [37] , we first checked whether this trans-dominant negative approach was effective also in Drosophila follicle cells ( S1 Fig ) . Expression of TN-Drosha in follicle cells resulted in the formation of an inactive Microprocessor complex that could not process pri-miRNAs ( S1B and S1C Fig ) leading to a detectable depletion of miRNAs ( S1D Fig ) . We then monitored TE repression after having impaired Drosha function in follicle cells either by RNAi ( tj-GAL4>drosha-IR ) or by using the trans-dominant negative approach ( tj-GAL4>TN-Drosha ) . We found that the ZAM-lacZ reporter activity was de-repressed in the posterior follicular epithelium compared to cells expressing the respective negative controls ( Ø>drosha-IR; tj-GAL4>WT-Drosha ) ( Fig 1A ) . To test whether the whole miRNA pathway is required for TE repression , we also knocked down Gawky and AGO1 , two proteins of the miRNA effector complex , by short hairpin ( sh ) -mediated RNAi [38] . Again , we observed tissue-specific β-Gal staining only in ovaries in which Gawky or AGO1 was knocked down specifically in follicle cells ( tj-GAL4>shgawky and tj-GAL4>shAGO1 respectively ) , indicating de-repression of the ZAM-lacZ reporter activity ( Fig 1B ) . Thus , the effector complex of the miRNA pathway seems to be essential also for somatic TE repression . To quantify the extent of reporter de-repression and to investigate whether somatic endogenous TEs were also de-repressed , we determined by quantitative RT-PCR the steady-state RNA levels of the lacZ reporter transgene and of members of two TE families ( ZAM and Tabor ) that are specifically repressed in follicle cells ( Fig 1C ) . In addition to the transcripts of the ZAM-lacZ reporter transgene , transcripts of the ZAM and Tabor TE families accumulated significantly upon follicle cell-specific impairment of Drosha and AGO1 . Conversely , the expression of the F-element , a TE specifically repressed in the germline ( negative control ) , was not affected by inactivation of the miRNA pathway in follicle cells ( Fig 1C ) . Therefore , our data indicate that the repression of the ZAM-lacZ reporter transgene and of two TE families , repressed specifically in follicle cells , is miRNA-dependent . We then checked whether the TE de-repression observed upon impairment of the miRNA pathway occurred at the transcriptional level . Indeed , in follicle cells , Piwi-dependent TE transcriptional silencing is associated with the presence of the Histone H3 lysine 9 trimethylation ( H3K9me3 ) "repressive mark" and the absence of the Histone H3 lysine 4 dimethylation ( H3K4me2 ) "active mark" on active TE copies [21 , 39] . As the chromatin immunoprecipitation-quantitative polymerase chain reaction ( ChIP-qPCR ) technique does not always discriminate between active euchromatic and defective heterochromatic copies of a TE family [40] , we focused our study on two regions of the single-copy ZAM-lacZ reporter transgene ( Fig 2A: PCR1 and PCR2 ) . We studied the chromatin changes on this reporter transgene upon tj-driven expression of TN-Drosha in follicle cells ( Fig 2B ) . The absence of Drosha activity resulted in the increase of H3K4me2 and the decrease of H3K9me3 marks on the transgene . We also observed a significant H3K9me3 decrease by using the ZAM primer pair that detects members of the ZAM TE family ( Fig 2B , left panel ) . To compare the chromatin changes observed upon tj-driven expression of TN-Drosha with the chromatin changes caused by Piwi depletion , we studied , by ChIP-qPCR , the chromatin of the ZAM-lacZ transgene upon tj-driven piwi soma KD . As AGO3 is not expressed in this tissue , we used AGO3 soma KD as negative control ( Fig 2C ) . TN-Drosha expression and the piwi somatic knockdown resulted in comparable H3K4me2 increase and H3K9me3 decrease on the ZAM-lacZ transgene . These findings suggest that the miRNA pathway is required in follicle cells for the piRNA-mediated transcriptional silencing of retrotransposons . To determine whether specific piRNA populations were affected upon miRNA depletion , we sequenced and analysed total ovarian small RNA ( 18 to 29 nt ) libraries from five genetic backgrounds shifted at 25°C for five days ( S1 and S2 Tables ) . We prepared the first library using ovaries containing the driver alone ( tj-GAL4>Ø ) . In two other libraries ( annotated with asterisks in Fig 3 ) , we combined the driver with the TN-Drosha or the WT-Drosha control transgene . The last two libraries ( Fig 3A and S3 Fig ) were replicates of the two previous ones , except that we used ovaries containing the conditional tub-Gal80ts thermo-sensitive Gal4 inhibitor ( S1 and S2 Tables ) . We normalized the libraries to 1 million of piRNAs produced by the 42AB germline-specific piRNA cluster . Fig 3A shows the number of piRNA reads mapping to each of the 85 most targeted Drosophila TEs [41] . First , we observed that expression of WT-Drosha had no effect on the piRNA populations , because the number of piRNAs targeting each TE family was comparable in the tj-GAL4>Ø and tj-GAL4>WT-Drosha libraries ( Fig 3A ) . Therefore , we used ovaries that express WT-Drosha in follicle cells as controls to compare the effect of TN-Drosha expression in the same cells . In two independent experiments , we observed a decrease of piRNA reads for the soma-dominant TEs ( i . e . , the most highly targeted TEs in this tissue ) [41] , in both tj-GAL4>TN-Drosha libraries compared to the respective tj-GAL4>WT-Drosha control libraries ( green dots in Fig 3A ) . Particularly , in both tj-GAL4>TN-Drosha libraries , the number of antisense piRNAs mapping across the ZAM and Tabor sequences ( two examples of soma-dominant TEs ) was reduced , whereas piRNAs targeting germline-dominant TEs , such as the F-element , were not affected ( Fig 3B and S3A Fig ) . In follicle cells , piRNAs are mainly produced by a soma-specific piRNA cluster called flamenco . Differently from germline-specific piRNA clusters , such as cluster 42AB ( used as a normalizer in this study ) and 80EF , flamenco piRNAs were four times less abundant in the tj-GAL4>TN-Drosha* than in the tj-GAL4>WT-Drosha* library ( Fig 3C ) . Surprisingly , accumulation of piRNAs produced by the 3’ untranslated region of the traffic jam ( tj ) gene did not seem to be affected by miRNA depletion in both TN-Drosha libraries ( Fig 4 ) . This was also true for jim and CG32000 , two other genes that produce abundant piRNAs in ovarian somatic cultured cells ( Fig 4 ) [26] . The 3’UTR piRNA profile seemed therefore to be unaffected in TN-Drosha libraries . Conversely , the previously described Yb mutant libraries showed a general reduction of all classes of ovarian somatic piRNAs ( Fig 4 ) [42] . To monitor individual piRNAs without the need of high throughput sequencing , we adapted a procedure for miRNA quantification [43] to quantify individual small RNAs by RT-qPCR ( see S1 Text and S3 Table ) . Using this method , we could demonstrate that , like in tj-GAL4>TN-Drosha ovaries , two major ZAM and Tabor antisense piRNAs were also significantly depleted in ovaries upon AGO1 soma-KD , whereas the amounts of two major 3’UTR piRNAs ( tj and jim ) were unaffected ( Fig 5 ) . Altogether , our observations suggest that , in follicle cells , accumulation of TE-targeting piRNAs is specifically dependent on the activity of the miRNA pathway . As our results indicated that TE de-repression is caused by follicle cell-specific general miRNA depletion , we then screened individual Drosophila miRNAs to identify which miRNA ( s ) is ( are ) essential for TE regulation . Around 230 miRNAs have been annotated in Drosophila . To determine which miRNAs are effectively expressed in follicle cells , we took advantage of the inability of TN-Drosha to cleave its pri-miRNA targets that , therefore , remain strongly bound to it . By immunoprecipitation of RNA bound to TN-Drosha in tj-GAL4>TN-Drosha ovarian extracts ( Materials and Methods ) , we identified a subset of 53 Drosha-dependent miRNAs expressed in this tissue ( S5 Table ) . We could then test 47 of these miRNAs using second generation miRNA-sponges ( miR-SP ) . These sponges allow the titration of a given miRNA by tissue-specific over-expression of a non-coding RNA containing 20 binding sites for that miRNA [44 , 45] . Each of the 47 miR-SP constructs was expressed by two tj-driven autosomal transgenes , in the presence of two TE repression reporters ( gypsy-lacZ and ZAM-lacZ ) . MiR-SP mediated titration of two miRNAs ( miR-14 and miR-34 ) resulted in lacZ de-repression , as indicated by β-Gal staining and RT-qPCR ( Fig 6A–6B ) . After 1h of staining , we observed only the gypsy-lacZ pattern , in agreement with the fact that the ZAM-lacZ reporter has got a much lower expression level ( see Materials and Methods ) . We also detected de-repression of endogenous copies of three other somatic TE families ( ZAM , Tabor and Stalker2 ) ( Fig 6C ) . Moreover , like upon drosha and AGO1 knock down , the level of two piRNAs ( ZAM and Tabor ) , quantified by RT-qPCR , was clearly decreased following miR-SP-induced miR-14 and miR-34 titration ( Fig 6D ) . We could partly confirm the results of this screen by using a miR-14 null mutant . The presence of a comparable piRNA loss ( Fig 6E ) and TE de-repression ( S4 Fig ) , in this mutant ruled out a possible off-target effect of the miR-14-SP approach . Two Drosha-dependent miRNAs , miR-14 and miR-34 , are therefore individually required for both TE repression and TE-derived piRNA accumulation in follicle cells . Next we wanted to identify the gene ( s ) that are regulated by miR-14 and miR-34 for piRNA-mediated TE repression in follicle cells . Using the Targetscan miRNA target predictor ( http://www . targetscan . org/fly_12/ ) , we found 153 and 98 putative targets for miR-14 and miR-34 , respectively . As depletion of these miRNAs leads to up-regulation of their targets and TE-derived piRNA collapse , these target genes should be considered as inhibitors of the piRNA pathway . This may explain why none of them corresponded to any of the many hits of two previous screens performed to identify genes required for piRNA-mediated TE repression [29 , 30] . To determine whether these putative inhibitors of the piRNA pathway are part of a single biological process that antagonizes piRNA-mediated TE repression , we performed a gene ontology ( GO ) term enrichment analysis on the miR-14 and miR-34 target genes using GOrilla ( http://cbl-gorilla . cs . technion . ac . il/ ) . We compared the 153 miR-14 and the 98 miR-34 putative targets using as background set the 3759 genes that are putatively targeted by all Drosophila miRNAs . The miR-34 target genes only overlapped modestly with the GO term “basal lamina component” . Conversely , the “plasma membrane component” GO term was significantly enriched in miR-14 target genes ( P-value 5 . 2E-4 ) ( Fig 7 and S6 Table ) . This observation might be related to the hypothesis that a transmembrane signalling pathway is involved in somatic ovarian TE repression [46] . Alternatively , a piRNA pathway modifier might be an indirect miRNA target controlled by a regulatory cascade downstream of a direct miRNA target gene . The effect of miRNAs on their direct targets is usually very small as the reduction of the mRNA level is no more than 2-fold [47 , 48] . By contrast , taking advantage of the inputs of the RIP-seq experiments to compare transcriptomes , we noted that , upon miRNA depletion in follicle cells , most changes corresponded to more than 2-fold increase or to a decrease of the total ovarian steady-state RNA level ( S7 Table ) . It is technically very difficult to determine how many of these indirect target genes are actual effectors of piRNA-dependent repression . For this reason we did not try to identify the direct or indirect miRNA target genes ( either agonists or antagonists of the piRNA pathway ) that are responsible for the observed miRNA-dependent TE repression . We then asked whether Drosha could be directly involved in nuclear processing of piRNA precursors , in addition to its indirect effect on piRNAs via miRNA biogenesis . Indeed , we hypothesized that piRNA cluster transcripts , which are likely to fold into many hairpins because of their length and repetitive content , could be putative Drosha substrates . To test this theory , we first quantified the steady state level of RNAs derived from the flamenco locus in control and tj-Gal4>TN-Drosha ovaries . By quantitative RT-PCR using primer pairs spanning five different regions of flamenco [49] ( S3 Table ) , we found no evidence that flamenco-derived long RNAs accumulated in tj-Gal4>TN-Drosha ovaries ( S5 Fig ) . Moreover , in RNAs co-immunoprecipitated with the TN-Drosha protein we did not observe any enrichment of reads mapping to flamenco as it was the case for the reads mapping to pri-miRNAs ( S4 Table ) . Considered as a whole , these data strongly suggest that Drosha endonucleolytic activity is not directly involved in the production of primary piRNAs from the flamenco locus .
Recent advances from genetic and genomic studies have highlighted the importance of miRNAs in many aspects of animal development such as cell proliferation , differentiation , morphogenesis and apoptosis [50 , 51] . For instance , in Drosophila , oogenesis requires the miRNA pathway in both follicle and germ cells [52–57] . We show here that in follicle cells with defective miRNA function ( by knocking down effectors of the miRNA pathway , such as AGO1 and Gawky ) or biogenesis ( through drosha knock down or expression of a dominant negative Drosha protein ) , TE-derived piRNA levels are strongly reduced and piRNA-mediated transcriptional TE repression is impaired . We observed similar phenotypes upon individual titration ( by expression of the corresponding miR-sponge ) of miR-14 and miR-34 , and also in a miR-14 loss of function mutant . As retroviral-like TEs need to be expressed in the somatic ovarian tissue to invade the germinal genome , our data add maintenance of genome integrity , via piRNA-mediated TE repression , to the list of miRNA-controlled biological functions . As tj-driven constitutive knockdown of the miRNA pathway affected ovarian morphology , we considered the possibility that piRNA impairment could be caused by the loss of follicle cell fate . However , the following four observations do not support this hypothesis: ( 1 ) Based on the finding that the steady state level of flamenco transcripts was unaffected ( S5 Fig ) , this hypothetical cell fate change would not result in the lack of piRNA precursors due to the overall reduction of tissue-specific transcription of piRNA clusters . ( 2 ) Even in distorted ovaries where the vitellogenic oocyte was no longer located at the posterior end of the egg chamber , the ZAM-lacZ reporter was always derepressed in the area of the follicular epithelium facing the vitellus ( S6 Fig ) . This was reminiscent of the normal tissue-specific ZAM expression pattern , suggesting that miRNA depletion did not affect cell fate to such an extent as to prevent EGF-receptor signaling-dependent ZAM expression in the posterior-like follicle cells [58] . Therefore , cells where ZAM was de-repressed did not seem to have lost their precise differentiation fate . ( 3 ) The same was true for the typical antero-posterior gradient of gypsy-lacZ de-repression ( Fig 6A ) that was originally described following specific loss of gypsy piRNAs in flamenco-permissive mutants [59] . ( 4 ) Depletion of individual miRNAs showed that distorted morphology and TE de-repression are two independent phenotypes of miRNA-defective follicle cells . Indeed , we show that no miRNA is involved in these two processes . For instance , oogenesis did not seem to be affected when miR-sponge-mediated titration of either miR-14 or miR-34 resulted in gypsy- and ZAM-lacZ de-repression . Drosha , the RNase III enzyme involved in miRNA biogenesis recognizes and cleaves not only pri-miRNAs , but also many other targets , such as cellular mRNAs [37 , 60] , TEs [61] , viral RNAs [62] and long non-coding RNAs [63] . Many Drosha cleavage sites can be folded into local or more long-range secondary structures that could provide the double-stranded substrates preferred by this enzyme . Therefore , we asked whether Drosha could also be directly involved in nuclear processing of the long structured piRNA precursors , in addition to its indirect effect on piRNAs via miRNA biogenesis . Our results do not support this possibility ( S5 Fig and S4 Table ) . In the absence of any evidence for a direct involvement of Drosha endonucleolytic activity in piRNA precursor processing , its crucial role in the biogenesis of at least two miRNAs required for the piRNA pathway integrity remains the most parsimonious way to explain the phenotypes reported here following Drosha activity impairment in follicle cells . In animals , miRNA target recognition is determined by the seed , a short sequence that includes nucleotides 2–8 of the small RNA [64] . The rest of the small RNA matches imperfectly , if at all , to its target . This implies that a single miRNA can target many mRNAs and often operates in highly complex regulatory networks in combination with other miRNAs in the same or different biological processes . This could explain why the ovarian transcriptome is much affected by the global miRNA depletion in follicle cells that express the trans-dominant negative Drosha construct ( S7 Table ) . In striking contrast with these pleiotropic effects on gene expression , we observed a very specific loss of TE-derived piRNAs with no effect on the accumulation of 3’UTR-derived piRNAs . Indeed , the amounts of piRNA ( s ) originating from the 3'UTR of genes were not reduced following expression of the trans-dominant negative Drosha mutant , shAgo1 , miR-14SP or miR-34SP . They were not affected either in the ΔmiR-14 mutant genetic background ( Figs 4 , 5 , 6D and 6E ) . Therefore the piRNA pathway was not impaired at the level of the piRNA producing center that involves Armi , Yb , Shut and the other cytoplasmic proteins known to affect both TE- and 3’UTR-derived piRNAs . Our data suggest that in the case of TEs and the 3’UTR of genes , piRNA biogenesis and/or stability require different actors . Therefore , these piRNAs could follow two somehow separated pathways , at least in follicle cells . More investigations are needed to appreciate to what extent these two somatic piRNA pathways actually differ .
BAC R26A26 ( Genbank accession number: AC007084 ) was digested with EcoRV and NdeI restriction enzymes , and the resulting 4 , 5kb fragment containing the drosha gene was cloned in the SmaI/NdeI restriction sites of the puc19 vector to obtain the pucWT-Drosha vector . A Flag-HA tag was introduced at the 3’ end ( see S1 Text ) . To impair Drosha slicer activity [65] , a point mutation in each RNAseIII domain of Drosha was introduced by PCR to produce the pucTN-Drosha construct ( see S1 Text ) . pucWT-Drosha and pucTN-Drosha were digested with NdeI and XbaI and cloned in the KpnI and XbaI sites of the pUASp vector . The resulting pUASp-WT-Drosha and pUASp-TN-Drosha plasmids were introduced in the w1118 strain to get P-element-mediated transgenes ( BestGene Inc services ) . Ovaries from 5-day-old flies were dissected in PBS , kept on ice , fixed in 0 . 2% glutaraldehyde/2% formaldehyde/PBS at room temperature for 5 min and rinsed three times with PBS . They were then incubated in staining solution ( 1x PBS pH7 . 5 , 1mM MgCl2 , 4mM potassium ferricyanide , 4mM potassium ferrocyanide , 1% Triton , 2 . 7mg/ml X-Gal ) at 37°C for either 1h ( gypsy-lacZ detection ) or 4h ( ZAM-lacZ detection ) . Total RNA was isolated from ovaries with Trizol , following the manufacturer's instructions . RNA was DNase-treated ( Turbo DNA-free AM1907 , Ambion ) . ChIP assay were performed as previously described [67] . Briefly , dissected ovaries were fixed in 1 . 8% formaldehyde at room temperature for 10 min . Chromatin was sonicated and used for immunoprecipitation with anti-trimethyl-Histone H3 Lys9 ( ab8898; Abcam ) , or anti-dimethyl-Histone H3 Lys4 ( ab7766; Abcam ) antibodies . DNA precipitates were amplified by real-time quantitative PCR . PCR product levels were normalized to input and expressed relative to a positive control gene ( the 1360-element for the immunoprecipitation with the anti-H3K9me3 antibody and Rpl15 for the immunoprecipitation with the anti-H3K4me2 antibody ) . The relative DNA levels were calculated using the following formula: E ( target ) CtIP *E ( ref ) CtInput / E ( ref ) CtIP * E ( target ) CtInput , where E is the efficiency of each primer pair and ref the positive control . Primers are listed in S3 Table . Small RNAs from tj-GAL4>Ø , tj-GAL4>WT-Drosha* or tj-GAL4>TN-Drosha* ovaries ( lacking the tub-Gal80ts thermo-sensitive Gal4 inhibitor ) were isolated on HiTrap Q HP anion exchange columns ( GE Healthcare ) , using the ÄKTA purifier FPLC system as previously described in [67] . The histogram of size distribution and the U1 presence in the small RNA populations sequenced confirmed that this small RNA extraction method efficiently eliminates degradation products ( S2 Fig ) . Small RNAs from tj-GAL4>WT-Drosha and tj-GAL4>TN-Drosha ovaries were manually isolated on HiTrap Q HP anion exchange columns ( GE Healthcare ) as described in [68] . Library construction and 50nt read sequencing were performed by Fasteris SA ( Switzerland ) on an Illumina HiSeq 2000 instrument for the tj-GAL4>Ø , tj-GAL4>WT-Drosha* and tj-GAL4>TN-Drosha* and on an Illumina HiSeq 2500 instrument for the tj-GAL4>WT-Drosha and tj-GAL4>TN-Drosha libraries . Sequencing data were annotated according to the sequences available in several reference databases . rRNA , tRNA and snRNA sequences were retrieved from modEncode ( http://www . modencode . org/ ) [69] , miRNA sequences were retrieved from miRBase ( http://www . mirbase . org/ ) [70] and mRNA transcript sequences were retrieved from Flybase ( http://flybase . org/ ) . The analysis of small RNA libraries was performed as described in [67] . Briefly , after subtracting the reads matching abundant cellular rRNAs , tRNAs and snRNAs , the remaining reads were considered as bona fide small regulatory RNAs reads ( siRNAs , miRNAs and piRNAs ) . miRNAs were separated from the other bona fide reads based on their identity with miRBase . Then , piRNAs and siRNAs were identified based on their size ( 21 nt for siRNAs and 23 to 29nt for piRNAs ) . piRNA cluster sequences were retrieved according to previously published genomic coordinates [24] . To compare small RNA counts between small RNA-seq samples , libraries were normalized to one million 42AB-derived genome-unique piRNAs ( unaffected by tj-GAL4>WT-Drosha or TN-Drosha follicle cell-specific expression ) ( see S4 Table ) . To compare RNA-IP samples , library read counts were normalized to one million genome-unique reads ( see S4 Table ) . 200 ovaries from tj-GAL4>Ø , tj-GAL4>WT-Drosha and tj-GAL4>TN-Drosha flies were dissected in PBS and homogenized in 500μl ice-cold lysis buffer ( 20mM Tris-HCl pH8 , 137mM NaCl , 10% glycerol , 1% Nonidet P40 ) with complete EDTA-free protease inhibitor ( Roche supplier ) . All further steps were performed at 4°C or on ice . Debris was pelleted at 3 000g for 1 min; supernatants were then collected and pre-cleared with 40 μl mouse IgG-Agarose ( Sigma A0919 ) for 1 h . An aliquot of pre-cleared input was stored . Pre-cleared lysates were immunoprecipitated with anti-FLAG M2 affinity gel ( Sigma A2220 ) at 4°C overnight . The anti-FLAG M2 affinity gel was washed four times with lysis buffer and the precipitated complexes were eluted with 200ng/μl 3X FLAG Peptide ( Sigma F4799 ) in lysis buffer . Eluates were then immunoprecipitated with the anti-HA antibody ( Santa-Cruz SC805 ) coupled with Dynabeads protein G ( Invitrogen 10004D ) . Immunoprecipitates were washed four times with lysis buffer . RNA from inputs and immunoprecipitates was extracted with TRIzol , rRNA-depleted using the RiboMinus Eukaryote Kit for RNA-sequencing ( Invitrogen ) and DNAse-treated ( Turbo DNA-free AM1907 , Ambion ) . Samples were then processed and sequenced by Fasteris SA ( Switzerland ) . Briefly , RNAs were fragmented ( zinc treatment , Illumina protocol ) , reverse transcribed with random hexamer primers and 260 to 280 bp fragments ( i . e . , insert of 160–240 nt ) were purified on acrylamide gels . Reads from 50nt were sequenced on HiSeq2000 ( Illumina ) . The RNA orientation was ignored in these experiments . All statistical analyses were performed using the SciPy library ( http://scipy . jp/scipylib/citing . html ) . P-values were calculated using two-tailed Student’s t-tests for samples displaying normal distribution ( tested with the Shapiro-Wilk test ) . The variance homogeneity was tested with the Levene’s test . When at least one of the two series did not have a normal distribution , P-values were calculated using the Mann-Whitney rank-sum test ( with correction for continuity ) . GO analysis was performed using the Gorilla [71][72] online tool ( http://cbl-gorilla . cs . technion . ac . il/ ) and two ranked lists of genes . The background list consisted in all the genes targeted by miRNA families in Drosophila melanogaster ( taxon id 7227 ) given by TargetScanFly ( http://www . targetscan . org/fly_12/fly_12_data_download/Conserved_Family_Conserved_Targets_Info . txt . zip ) . The adjusted P-values were corrected for multiple testing using the Benjamini-Hochberg method . Small RNA data from Yb heterozygous and mutant ovaries were previously published ( Handler et al . Embo J . 30:3977 ) and are available via the NCBI Gene Expression Omnibus ( accession no . GSM767598 and GSM767599 respectively ) . Sequencing data concerning the small RNA and RNA-IP libraries generated in this study are available via the NCBI Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo/ ) under accession no . GSE60974 ( see S4 Table for details ) . | The fine-tuning of gene expression required for the normal development of multicellular organisms involves small RNAs that are called microRNAs ( miRNAs ) . MiRNAs can reduce the stability or the activity of the many cellular messenger RNAs that contain miRNA complementary sequences . In animal gonads , the harmful expression and proliferation of genomic parasites , such as transposable elements , is prevented by a similar , sequence homology-based silencing mechanism that involves a different class of small RNAs , the Piwi-interacting RNAs ( piRNAs ) . We report here that , in Drosophila somatic ovarian tissues , two miRNAs , miR-14 and miR-34 , are required for the accumulation of piRNAs that prevent the expression of transposable elements and , probably , the subsequent invasion of the germinal genome . On the other hand , we found that other sources of piRNA production , such as the 3' end of genes , are miRNA-independent , suggesting the existence of variations in the piRNA biogenesis pathways depending on the piRNA genomic origin . Our results therefore highlight a novel miRNA function in the maintenance of genome stability through piRNA-mediated TE repression . | [
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| 2015 | MicroRNA-Dependent Transcriptional Silencing of Transposable Elements in Drosophila Follicle Cells |
Control programs for trachoma use mass antibiotic distributions to treat ocular Chlamydia trachomatis in an effort to eliminate this disease worldwide . To determine whether children infected with ocular Chlamydia are more likely to present later for examination than those who are uninfected , we compare the order of presentation for examination of children 0–5 years , and the presence of ocular Chlamydia by PCR in 4 villages in Niger where trachoma is endemic . We conducted a cluster-randomized , controlled trial where 48 randomly selected villages in Niger are divided into 4 study arms of different mass treatment strategies . In a substudy of the main trial , we randomly selected 1 village from each of the 4 study arms ( 4 total villages ) and we evaluated the odds of ocular Chlamydia versus the rank order of presentation for examination and laboratory assessment before treatment was offered . We found the odds of harboring ocular Chlamydia dropped by more than 70% from the first child examined to the last child examined ( OR 0 . 27 , 95% CI 0 . 13–0 . 59 , P = 0 . 001 ) in the 4 randomly selected villages . We found the odds of active trachoma dropped by 80% from the first child examined to the last child examined ( OR 0 . 20 , 95% CI 0 . 10–0 . 4 , P<0 . 0001 ) in the 48 villages in the main trial . This study demonstrates that even if the WHO recommended 80% treatment coverage is not reached in certain settings , children 0–5 years with the greatest probability of ocular Chlamydia have higher odds of receiving attention because they are the first to present . These results suggest there may be diminishing returns when using scarce resources to track down the last few children in a mass treatment program . ClinicalTrials . gov NCT00792922
Mass drug administration with oral azithromycin is known to be effective against trachoma , a blinding eye disease caused by ocular Chlamydia trachomatis . To achieve global elimination by 2020 , the WHO recommends district-level mass treatment with antibiotics at 80% coverage for a minimum of 3 years , if the prevalence of clinical disease exceeds 10% prevalence in 1–9 year olds [1] . The 80% coverage recommendation is based on expert opinion rather than existing data; however , achieving target treatment coverage can be difficult in some settings [2] , [3] . Children who are more difficult to locate for examination may be more likely to be infected with C . trachomatis , and using additional resources to reach these children may be even more important . In this study of 48 villages in Niger , we assess whether children aged 0–5 years who present late for examination and treatment have equal odds of active trachoma as those who present early . In a substudy of 4 villages in Niger , we assess whether children aged 0–5 years who present late for examination and treatment have equal odds of PCR-detected ocular Chlamydia as those who present early .
The WHO recommends a single dose of oral azithromycin 20 mg/kg up to 1 gram for residents over 1 year of age; infants 1 year and younger are offered topical tetracycline [1] , [4] . To determine whether children who present early for examination are more likely to be infected with ocular Chlamydia than children who present late for examination , we performed a substudy nested within a cluster-randomized clinical trial ( PRET , Partnership for the Rapid Elimination of Trachoma ) in the Matameye district of the Zinder region of Niger . The details of PRET have been previously described and are briefly summarized here [3] , [5] . For the PRET trail , a total of 48 grappes ( government health units , referred to as villages in this manuscript ) were randomized to 4 arms with different mass azithromycin treatment strategies . The 48 villages were selected from among 6 health centers ( Centre de Santé Intégrée or CSIs ) and were eligible for inclusion if they had an estimated total population of between 250 to 600 persons , generally corresponding to 50 to 100 children in the eligible age range; distance >4 kilometers from the center of any semi-urban area because communities which are close to urban population centers are believed to have a lower prevalence of trachoma; and prevalence of active trachoma ( TF using the WHO system ) ≥10% in children aged 0–5 years . Two separate assessments were performed: an analysis of active trachoma in 48 villages , and an analysis of both active trachoma and PCR-detected ocular Chlamydia in 4 substudy villages ( Figure 1 ) . If there were fewer than 100 children aged 0–5 years in one of the 48 villages , all children were included . If there were more than 100 children aged 0–5 years in one of the 48 villages , a random sample of 120 children was generated as the sentinel group for inclusion ( note the target was ≤100 children but 120 was chosen to maximize the chance of obtaining 100 for the study ) . In the 4 substudy villages , all children aged 0–5 years were included regardless of village size . Randomization of communities and individuals was done using RANDOM and SORT functions in Excel ( Version 2003 ) by BN . Only pretreatment results are presented here . A pretreatment population census was completed in all villages in April 2010 by trained local personnel . Prior to examination , parents and guardians were notified by a village mobilizer to bring children 0–5 years of age to a central village location at a prespecified time for ocular examination and conjunctival swabbing for ocular Chlamydia PCR . Eligible children had a single opportunity for examination and field workers recorded the consecutive order in which the children presented . After obtaining consent from a parent or guardian , clinical grading of the right everted superior tarsal conjunctiva was performed using the WHO grading system ( TF ) by certified graders , with a 2 . 5× magnifying loupe and torch light or adequate sunlight [5] , [6] . Following conjunctival examination , a Dacron swab was passed firmly 3 times over the right upper tarsal conjunctiva , rotating 120 degrees between each pass in all villages , as described previously [5] . Examiners changed gloves before examining each new participant . All of the samples were placed immediately at 4°C in the field and frozen at −20°C within 10 hours . Swabs were shipped at 4°C to University of California , San Francisco , CA , USA , where they were stored at −80°C until processing [7] . The Amplicor PCR assay ( Roche Diagnostics , Branchburg , NJ , USA ) was used to detect C . trachomatis DNA . For the primary analysis , individual-level data for ocular Chlamydia were available in the 4 substudy villages . In the main study of 48 villages , swabs for ocular Chlamydia PCR were pooled after collection for community-level analysis to save time and cost as previously described [8] , [9] . This pooling process for PCR precludes analysis of individual-level infection data; however , individual-level data for active trachoma were available in all 48 villages , and were included in a secondary analysis . Throughout the study , field workers were masked to ocular Chlamydia and laboratory workers were masked to field grade . The census , clinical exams , and swabbing for ocular Chlamydia PCR were all part of this research study and were performed outside of any existing mass distribution programs . We normalized presentation rank by dividing presentation rank by the total number of children 0–5 years in each of the villages , to avoid larger villages from exerting more weight in the analysis . For the primary question , we estimated the odds of positive PCR given the normalized rank order of presentation for examination using mixed effects logistic regression with village and household as random effects . As a sensitivity analysis , we performed the same logistic regression with village as a fixed effect . For a secondary question , we did similar estimates with active trachoma as the outcome variable in all 48 villages using mixed effects logistic regression with village and household as random effects . All statistical analyses were performed with STATA 11 ( College Station , TX ) . Ethical approval for this study was obtained from the Committee for Human Research of the University of California , San Francisco and le Comité Consultatif National d'Ethique du Ministère de la Santé Publique , Niger ( Ethical Committee , Niger Ministry of Health ) . Verbal consent was approved by the IRB due to the high illiteracy rates in the study area and obtained from the community leaders before examination . Verbal consent was also obtained from the child's parent or guardian at the time of examination and was documented on the registration form for each study participant prior to examination in the field . The study was carried out in accordance with the Declaration of Helsinki .
All measurements were performed in June and July 2010 . In the 4 substudy villages , there were a total of 555 children aged 0–5 years enrolled with a mean of 139 ( range 58 to 196 ) per village ( Table 1 ) . The mean age of 0–5 year old children was 2 . 7 years ( 95% CI 2 . 6 to 2 . 9 ) per village and 51 . 1% ( 95% CI 47 . 2 to 54 . 8 ) were boys . The mean village prevalence of active trachoma was 23 . 9% ( 95% CI 8 . 5 to 39 . 2 ) and the mean village prevalence of ocular Chlamydia by PCR was 18 . 7% ( 95% CI 3 . 0 to 34 . 3 ) in these 4 substudy villages ( Table 2 ) . In the larger study of 48 villages , there were a total of 4484 children aged 0–5 years enrolled with a mean of 93 . 4 children per village ( 95% CI 85 . 2 to 101 . 6 ) . The mean age of 0–5 year olds was 2 . 7 years ( 95% CI 2 . 7 to 2 . 8 ) in each village and 50 . 6% ( 95% CI 49 . 4 to 51 . 8 ) were boys . The mean prevalence of active trachoma was 24 . 8% ( 95% CI 20 . 8 to 28 . 8 ) per village . The odds of harboring ocular Chlamydia dropped in the 4 substudy villages by more than 70% from the first to the last child examined ( OR 0 . 27 , 95% CI 0 . 10 to 0 . 75 , P = 0 . 01 ) in regression analysis ( Figure 2 ) . The results were similar with village as a fixed effect ( OR 0 . 23 , 95% CI 0 . 08 to 0 . 64 , P = 0 . 005 ) . When an individual presented in the next highest rank order quartile , the odds of ocular Chlamydia dropped by more than 25% ( OR 0 . 73 , 95% CI 0 . 60 to 0 . 88 ) using clustered logistic regression with village as a random effect ( Figure 3 ) . The odds of active trachoma were 80% less ( OR 0 . 20 , 95% CI 0 . 10 to 0 . 4 , P<0 . 0001 ) for the last child compared to the first child examined . When we controlled for age and gender in the clustered logistic regression models , there was no substantial change in the results for the association of rank order and ocular Chlamydia . Finally , we found that an additional year in age ( OR 0 . 54 , 95% CI 0 . 21 to 1 . 35 ) and male gender ( OR 1 . 64 , 95% CI 0 . 92 to 2 . 93 ) were not predictive of rank order . In the analysis of all 48 villages where clinical data were available for individuals , the odds of active trachoma dropped by 55% from the first to the last child examined ( OR 0 . 45 , 95% CI 0 . 33 to 0 . 61 , P<0 . 0001 ) using clustered logistic regression with village and family as random effects . The results were similar with village as a fixed effect ( 95% CI 0 . 31–0 . 61 ) , and there were no significant differences in the analysis when controlling for age or gender .
We found that children aged 0–5 years who present earlier for examination and testing prior to treatment for trachoma have higher odds of ocular Chlamydia than those who present later , in 4 villages in trachoma-endemic Niger . The odds of ocular Chlamydia dropped with each successive quartile of presentation . Researchers and program managers have been concerned that children in more disadvantaged families are more likely to harbor ocular Chlamydia and less likely to be brought earlier for examination . Here , we found the opposite—children with ocular Chlamydia are more likely to be brought earlier . Even if the WHO 80% treatment coverage goals are not reached in certain settings , the children who form the known reservoir of infection have higher odds of receiving attention because they are the first to present . There are several potential reasons to explain why early presenters have higher odds of harboring active trachoma and ocular Chlamydia than late presenters . Although active trachoma is believed to be a relatively mild or asymptomatic condition , parents and guardians may identify factors in their children such as intermittent ocular discharge or conjunctivitis which make them more likely to be brought for attention [10] . Active trachoma is spread within families and schools and can be exacerbated by overcrowding [11] , [12] . It is reasonable to assume that children who are more social are more likely to be brought early for examination in a group setting where examinations are performed in Niger . The extra effort required to locate individuals who are absent for examination may not be as important as had previously been thought . It may be practical to forgo pursuing difficult to reach children because the critical ‘core or herd’ group is present in the earlier quartiles , and coverage of infected persons is achieved prior to achieving coverage of the overall population . Mathematical models and empirical studies suggest that disease elimination can be achieved even without treating all infected individuals in a community over time [9] , [13] , [14] . If a core group of infected individuals are repeatedly treated , then a degree of herd protection can be offered even to those who have not received treatment . Models assume that those untreated are no more likely to be infected than those treated . Here we find that those last to be examined are not more likely to be infected— they're less likely to be infected . Although this study does not include data about expenses , it suggests an alteration of the cost-benefit equation in that the benefit of pursuing the final quartile of ‘hard-to-reach’ children has diminished value because it contains fewer infected children . Delivery of mass antibiotic treatment is expensive , especially in areas that are hard to reach [15] , [16] . Even if oral azithromycin is donated , the extra effort required to locate absent individuals may be considerable . Our report supports results from an Ethiopian study that showed children examined on subsequent days after the initial monitoring day were less likely to have ocular Chlamydia than children seen on the initial day in an area hyperendemic for trachoma [17] . Antibiotic coverage is an important short-term predictor of ocular Chlamydia but may not be as important by six months after treatment , calling into question the necessity of achieving high coverage at all costs [18] . This study has some limitations . First , only 4 villages were included in the analysis of infection , and these villages may not be representative of the larger population in Niger or other trachoma-endemic countries . However , we found similar results in the 4 villages for rank order and infection as we did in the 48 villages for rank order and clinical disease . Second , only children aged 0–5 years were included , and there are children older than 5 years or adults in the communities who harbor infection . However , young children are known to be the most important reservoir of ocular Chlamydia in communities so exclusion of older children is unlikely to affect our estimates [9] . Third , this study was performed in a research setting which may not be generalizable to trachoma treatment programs that do not have the same resources . Finally , this was a study of the association of order of presentation with active trachoma and ocular Chlamydia PCR , not antibiotic treatment coverage . It is not unreasonable to assume that children who present earlier for examination are more likely to present earlier for treatment , but this was not measured in this study . In summary , we found that children 0–5 years who have ocular Chlamydia have higher odds of presenting early for ocular examination than children who do not have ocular Chlamydia in Niger where trachoma is endemic . The added effort required to reach the last infected individuals in a community may provide diminishing returns . We suggest thoughtful use of limited health-care resources in trachoma programs , perhaps to reach more villages , rather than excessive efforts to attain high coverage in individual villages . Efficient use of limited resources is critical for the WHO to achieve their trachoma elimination goals by the year 2020 . | Trachoma is the most common cause of blindness from an infection in the world . The bacterium that causes trachoma is called Chlamydia trachomatis and it can be treated with the antibiotic azithromycin . Experts recommend trying to reach at least 80% of children for treatment in a community but it is unknown if this is necessary . We began a clinical trial in Niger in 48 villages in the summer of 2010 with mass drug administration ( MDA ) of azithromycin . We found that the odds of an eye infection were the highest in the first children to come for an examination . This means the extra time and money needed to reach all of the children in a village may provide diminishing returns because the easiest children to reach have the highest odds of infection . Perhaps it would be better to try to reach more villages for MDA instead of spending a lot of time and money trying to reach every single child in every single village . | [
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| 2013 | The Easiest Children to Reach Are Most Likely to Be Infected with Ocular Chlamydia trachomatis in Trachoma Endemic Areas of Niger |
The fate of neural progenitor cells ( NPCs ) during corticogenesis is determined by a complex interplay of genetic or epigenetic components , but the underlying mechanism is incompletely understood . Here , we demonstrate that Suppressor of Mek null ( Smek ) interact with methyl-CpG–binding domain 3 ( Mbd3 ) and the complex plays a critical role in self-renewal and neuronal differentiation of NPCs . We found that Smek promotes Mbd3 polyubiquitylation and degradation , blocking recruitment of the repressive Mbd3/nucleosome remodeling and deacetylase ( NuRD ) complex at the neurogenesis-associated gene loci , and , as a consequence , increasing acetyl histone H3 activity and cortical neurogenesis . Furthermore , overexpression of Mbd3 significantly blocked neuronal differentiation of NPCs , and Mbd3 depletion rescued neurogenesis defects seen in Smek1/2 knockout mice . These results reveal a novel molecular mechanism underlying Smek/Mbd3/NuRD axis-mediated control of NPCs’ self-renewal and neuronal differentiation during mammalian corticogenesis .
Neural stem cells ( NSCs ) are self-renewing , multipotent cells that generate major neural cell types , including neurons and glia , in the developing central nervous system ( CNS ) [1 , 2] . During neurogenesis , NSCs are derived from neuroepithelial cells ( NECs ) , which first divide symmetrically to expand the population and then undergo a series of asymmetric cell divisions to produce neural progenitor cells ( NPCs ) , lineage-restricted precursor cells ( RPCs ) , and mature neural cells [3] . NSC fate determination is tightly regulated by intrinsic and extrinsic factors [4–6] . Recent findings suggest that neurodevelopmental and neurological anomalies , such as schizophrenia , autism , and depression , can emerge from abnormal specification , growth , and differentiation of NSCs [6–8] . Suppressor of Mek null ( Smek ) , an evolutionarily conserved protein family , consists of two isoforms , Smek1 ( PP4R3A ) and Smek2 ( PP4R3B ) , first reported as playing a role in the formation of a functional phosphatase group with PP4c , PP4R1 , and PP4R2 complex [9] . Smek was initially identified in Dictyostelium discoideum as a playing a role in cell polarity , chemotaxis , and gene expression [10] . Smek also has several functions in lower eukaryotes , such as Caenorhabditis elegans , including roles in longevity by modulating DAF-16/FOXO3a transcriptional activity [11] , DNA repair through dephosphorylation of phosphorylated H2AX ( g-H2AX ) during DNA replication [12] , and glucose metabolism by controlling cAMP-response element binding protein ( CREB ) -regulated , transcriptional coactivator 2 ( CRTC2 ) -dependent gene expression [13] . Notably , Smek also plays a critical role in cell-fate determination in higher eukaryotes . In Drosophila neuroblasts , PP4R3/Falafel ( Flfl ) , which is an orthologous of Smek and is conserved throughout eukaryotic evolution , regulates asymmetric cell division by controlling localization of Miranda [14–16] . In mice , which express orthologous Smek 1 and 2 , both Smek proteins suppress brachyury expression in embryonic stem cells ( ESCs ) , and Smek1 , especially , promotes NSC neuronal differentiation by negatively regulating Par3 [14–16] . Although we have shown that the Smek isoform Smek1 promotes NSC neuronal differentiation , signaling pathways required for that activity remain unclear [15] . Methyl-CpG–binding domain protein 3 ( Mbd3 ) , a core component of the repressive nucleosome remodeling and deacetylase ( NuRD ) complex , possesses a conserved methyl-CpG–binding domain ( Mbd ) [17 , 18] . Unlike other family members , which recognize 5′-methyl-cytosine ( 5′-mC ) -modified DNA , Mbd3 specifically recognizes 5′-hydroxymethyl-cytosine ( 5′-hmC ) , an epigenetic marker highly enriched in NSCs [19 , 20] . Mbd3 plays an important role in brain development . Mbd3 expression is reported to be predominant in cortical NECs of the embryonic forebrain [21] . Mice lacking Mbd3 die in utero before neurogenesis is completed [22] . Conditional knockout of Mbd3 in neural progenitor cells leads to defects of differentiation of appropriate cell types during neurogenesis [23] . Despite emerging evidence that Mbd3 has a critical function in the CNS , little is known about its regulatory mechanism in NSCs . To understand Smek protein function during mammalian CNS neurogenesis , we screened for novel Smek-binding proteins that regulate NPC neuronal differentiation and identified Mbd3 , a potent epigenetic regulator , as a Smek-interacting protein . We found that Mbd3 is highly expressed in NPC populations in the ventricular zone , and it was predominantly expressed in the nucleus . Smek interacted directly with the Mbd3’s Mbd domain , destabilizing Mbd3 protein and its interaction with NuRD components , and sequentially , preventing accumulation of the Mbd3/NuRD complex on target gene loci functioning in neurogenesis . Such dissociation of Mbd3/NuRD complex promotes NPC neuronal differentiation . Moreover , overexpression of Mbd3 significantly inhibited neuronal differentiation of wild-type NPCs , while Mbd3 depletion rescued neurogenesis defects seen in Smek knockout mice . This work identifies a novel pathway of Smek and Mbd3/NuRD complex in brain development and could encourage discovery of novel epigenetic regulators governing neuronal differentiation .
Recently , we reported that Smek1 promotes neurogenesis during mouse cortical development [15] . To further characterize Smek function in neurogenesis , we generated Smek1 and Smek2 double knockout ( dKO ) mice and set out to analyze cortical development in Smek1 knockout ( KO ) and Smek1 and Smek2 dKO embryo brains ( S1A and S1B Fig ) . To do so , we undertook immunohistochemical analysis of the embryonic cortex derived from WT and Smek1/2 dKO mice and observed a decrease in the number of cells positive for Tuj1 , an early neuronal marker ( ~15% and ~25% fewer Tuj1+ cells at E12 . 5 and E14 . 5 , respectively ) ( Fig 1A ) . We observed similar decreases in postmitotic cortical neuron marker Tbr1-positive cells ( ~15% and ~20% reductions at E12 . 5 and E14 . 5 , respectively ) and Tuj1 and Tbr1 double-positive cells ( near 20% reduction in each stage ) ( Fig 1A ) . The number of mature microtubule-associated protein 2 ( MAP2 ) -positive neurons also significantly decreased by ~12% in the E12 . 5 cortex ( Fig 1A , right and S1C Fig ) . In Smek1/2 mice , the number of Pax6-positive NPCs increased significantly ( by ~23% at both E12 . 5 and E14 . 5 ) , as did Nestin/Ki67 double-positive cells ( ~24% increase at E12 . 5 ) compared with wild-type ( WT ) mice ( Fig 1B and S1D–S1F Fig ) . As expected , neurogenesis defects in Smek1/2 dKO embryonic brains were greater than those seen in Smek1 KO mice ( S2A and S2B Fig , S1 Table ) . These results demonstrated that in the Smek1/2 dKO mice , the number of neurons is reduced while the number of neural stem cells is increased . To assess direct effects of Smek loss on NPC differentiation capacity , we cultured Smek1/2 dKO NPCs derived from E11 . 5 mouse embryo brains in the presence of basic fibroblast growth factor ( bFGF ) and then withdrew bFGF to induce neural differentiation . Consistent with in vivo results , the number of Tuj1-positive cells decreased in Smek1/2 dKO cultures while the number of Nestin positive cells increased slightly in Smek1/2 dKO cultures over the course of differentiation ( Fig 1C and S3 Fig ) . In addition , we assessed the role of Smek in maintenance of self-renewal activity using the single-cell clonal neural sphere formation assay . Smek1/2 dKO NPCs showed higher sphere-forming ability than those derived from WT NPCs in both primary and secondary sphere-forming assays ( Fig 1D ) . Further analysis using quantitative PCR ( qPCR ) showed that Smek1/2 dKO cells exhibited decreased expression of Dlx1 , Dlx2 , Tuj1 , Gad67 , NeuN , and NeuroD1 , as well as other neural differentiation genes such as Gfap and Mbp ( the latter an oligodendrocyte marker ) , and increased expression of Nestin ( Fig 1E and S3 Fig ) . Severe differentiation defects seen in cortical NPCs lacking both Smek 1 and 2 suggest that these factors compensate for each other during cortical development . All these experiments demonstrated that Smek plays a role in self-renewal and neural differentiation of NPCs in vivo and in vitro . In order to determine a detailed molecular mechanism modulating the differentiation of NPCs by Smek , we sought to identify proteins interacting with Smek protein . To identify how Smek mediates neuronal differentiation of NPCs , a yeast two-hybrid ( Y2H ) screening assay was performed and revealed that Smek interacts with full-length Mbd3 ( Fig 2A and S2 Table ) . An immunoprecipitation ( IP ) assay confirmed the interaction of Smek1 and Smek2 with Mbd3 in 293T cells ( Fig 2B , S4A and S4B Fig ) . As shown in Fig 2B and S4B Fig , Smek1 or Smek2 coimmunoprecipitate with Mbd3 , while no signals were detected in cells transfected with a negative control vector . Furthermore , colocalization of endogenous Mbd3 and Smek2 protein was also observed in the nucleus of in vitro cultured NPCs ( Fig 2C ) . Both Smek1 and Mbd3 proteins were expressed in NPCs of the ventricular zone ( VZ ) and subventricular zone ( SVZ ) in vivo ( Fig 2D , S4C and S4D Fig ) , and merged images reveal that Smek and Mbd3 staining was nuclear ( Fig 2C and 2D ) . Mbd3 is highly enriched in the nucleus of the VZ progenitor cells and its expression in the nucleus showed a gradually decreasing pattern in the direction of the intermediate zone ( IZ ) and cortical plate ( CP ) . Smek1 is expressed in the nuclear or perinuclear region of cortical progenitor cells in the VZ , but its expression and nuclear localization is significantly increased in the differentiated neurons in the IZ and CP ( Fig 2D , S4C and S4D Fig ) . The interaction of endogenous Smek and Mbd3 were further confirmed by co-IP of Smek and Mbd3 using NPC lysates . Smek/Mbd3 interaction , however , was apparently disrupted during neuronal differentiation of wild-type NPCs , suggesting that protein complexes may function in NPC differentiation ( Fig 2E and S4C Fig ) . To map Smek and Mbd3 domain ( s ) required for interaction , we generated Smek1 and Mbd3 mutants and assessed their interaction ( Fig 2F–2H ) . A Smek N-terminal deletion mutant ( ΔRanBD ) did not interact with Mbd3 protein , shown by co-IP of Smek and Mbd3 in HEK293T cells , whereas interaction of other Smek deletion mutants with Mbd3 was comparable to the full-length protein , suggesting the RanBD domain of Smek mediates Smek’s interaction with Mbd3 ( Fig 2F ) . To map to the domains on Mbd3 required for Smek interaction , we generated glutathione-S-transferase ( GST ) fusion Mbd3 mutant proteins in bacteria and incubated these proteins with HEK293T cell lysates expressing Smek2 . GST pull-down assays revealed that the Smek-Mbd3 interaction was disrupted in Mbd3 deletion mutants lacking the first 92 N-terminal amino acids ( ΔN92 ) , a region encompassing the Mbd domain ( Fig 2G , upper panel ) . This finding was confirmed by IP experiments in HEK293T cells transfected with full-length or ΔN92 forms of Mbd3 and Smek ( Fig 2H ) . Those results indicated that the Mbd domain of Mbd3 is required for Smek interaction . To characterize Smek and Mbd3 expression during neural differentiation , we examined protein and mRNA levels after withdrawal of bFGF from NPC culture media . In NPCs , Mbd3 protein levels gradually decreased by day 1 of differentiation , while Mbd3 protein levels did not decrease in Smek1 and Smek2 single KO or dKO NPCs ( Fig 3A–3C and S5A , S5B Fig ) . However , Mbd3 mRNA levels did not change during differentiation in both WT and Smek1 , Smek2 , or Smek1/2 dKO NPCs , suggesting that changes in Mbd3 protein levels that occur during differentiation require Smek ( Fig 3D and 3E and S5A Fig , lower panel ) . These observations led us to further analyze Mbd3 stability . We found that Mbd3’s half-life was ~6 h in NPCs and 293T cells in which new protein synthesis was inhibited and significantly prolonged in the presence of the proteasomal inhibitor MG132 ( Fig 3F ) . Endogenous Mbd3 protein levels in NPCs were increased by MG132 treatment ( Fig 3G ) . Overexpressed Mbd3 protein was polyubiquitylated , and polyubiquitylated proteins accumulated in cells treated with MG132 or MG-101 , respectively ( Fig 3H and 3I and S5C , S5D Fig ) . Furthermore , endogenous Mbd3 in NPCs was found to be polyubiquitylated as well ( Fig 3J ) . To determine whether Smek regulates Mbd3 protein stability , we monitored endogenous Mbd3 protein levels in wild-type NPCs , in HEK293T cell lines stably overexpressing Smek1 or Smek2 ( S5E and S5F Fig ) , or in NPCs derived from wild-type and Smek1/2 dKO embryonic mouse brains ( Fig 4A and 4B ) . Mbd3 protein turnover rate was increased by overexpression of either Smek1 or Smek2 and decreased upon Smek loss ( Fig 4A and 4B ) . Consistent with Mbd3 degradation , Smek1 or Smek2 overexpression in HEK293T cells significantly promoted Mbd3 polyubiquitylation ( Fig 4C and S6A Fig ) . Furthermore , Mbd3 was ubiquitylated in wild-type NPCs but not in Smek1/2 dKO NPCs ( Fig 4D ) . To further assess the effects of Smek expression on Mbd3 degradation , we examined Mbd3 ubiquitylation following expression of the Mbd3 ΔN92 mutant , which cannot interact with Smek . Polyubiquitylation of this mutant was not significantly changed by overexpression of either Smek1 or Smek2 ( Fig 4E and S6B Fig ) . These results suggest that interaction with Smek destabilizes Mbd3 . Smek has a nuclear localization signal ( NLS ) and is nuclear localized [15] , suggesting a potential role in regulating transcription . To determine whether the Smek protein is associated with chromatin—and if so , whether it is enriched on chromatin loci of neurogenesis-associated genes—we performed chromatin immunoprecipitation sequencing ( ChIP-seq ) in NPCs using a Smek1 antibody . Genome-wide binding profiling demonstrated that Smek proteins bind to chromatin loci of genes related to organ morphogenesis , cell-fate determination , and CNS development and differentiation ( Fig 5A and 5B and S7A , S7B Fig ) . Furthermore , Smek specifically bound proximal promoter regions and gene bodies of neuronal genes such as Dlx1 , Dlx2 , Tlx3 , NeuroD1 , Ascl1 , and Lbx1 , which were known to highly express in neuron or proneuronal cells ( Fig 5A–5C and S7C , S7D Fig ) . Unlike Smek , which lacks a known DNA-binding motif , Mbd3 exhibits the Mbd domain , which reportedly binds 5′-hydroxymethyl cytosine ( 5′-hmC ) regions [20] . We therefore asked whether Smek and Mbd3 share similar genomic regions in NPCs , initially by determining whether Mbd3 binds neuronal gene promoters that Smek binds to . To do so , we undertook ChIP-qPCR with a Mbd3 antibody in wild-type and Smek1/2 dKO NPCs cultured in undifferentiation or differentiation conditions . This analysis confirmed enrichment of Mbd3 on the Smek-bound loci of genes including Dlx1 , Dlx2 , Tlx3 , NeuroD1 , Ascl1 , and Lbx1 in undifferentiated conditions ( Fig 5D and S7C Fig ) . Moreover , Mbd3 enrichment on these gene loci significantly decreased under differentiation conditions in wild-type NPCs but was unchanged in Smek1/2 dKO NPCs under the same conditions ( Fig 5D and S7C Fig ) . Then we asked whether enrichment of the Smek1 protein on chromatin loci of neurogenesis-associated genes is dependent on Mbd3 protein , and we performed ChIP-qPCR with Smek1 and Mbd3 antibodies in shScramble or shMbd3 knockdown ( KD ) NPCs cultured in undifferentiation or differentiation conditions . These results demonstrated that occupancy of Smek1 on the promoters of Dlx1 , Dlx1as , Tlx3 , NeuroD1 , Ascl1 , and Lbx1 genes , but not Gfap genes , is dependent on Mbd3 protein ( Fig 5E and S7F Fig ) . Mbd3 has been reported to represses transcription by recruiting the NuRD complex to target gene loci [20] . Co-IP experiments showed that Mbd3 interacted with MTA1 , RbAP46 , HDAC1 , and HDAC2 , which are components of NuRD complex ( S7G Fig ) . We then undertook ChIP-qPCR with HDAC1 , HDAC2 , MTA1 , and acetyl histone H3 antibodies using wild-type and Smek1/2 dKO NPCs cultured in differentiation conditions . ChIP analysis revealed that enrichments of NuRD components HDAC1 , HDAC2 , and MTA1 to target gene loci were significantly increased in Smek1/2 dKO NPCs when compared with those of the wild type ( Fig 5F ) . Inversely , the amount of acetyl histone H3 was decreased ( Fig 5F ) . These findings suggest that Smek inhibits enrichment of Mbd3/NuRD complex to neurogenesis-associated gene loci and increases acetyl histone H3 activity for their gene transcription during neurogenesis . These data also suggest that NuRD activity is dependent on Mbd3 ability , which can bind to target DNA , and Smek , as an upstream regulator of Mbd3/NuRD complex , promotes Mbd3 degradation , potentially allowing transcription of neuronal differentiation–associated genes by disrupting association and enrichment of Mbd3/NuRD complex on target gene loci . Our findings suggest that Mbd3 regulates expression of neuronal target genes in NPCs , an activity modulated by Smek . To assess whether Mbd3 represses neurogenesis-associated target genes , we overexpressed full-length or mutant ( ΔN92 ) Mbd3 in NPCs and then induced differentiation over 2 d . Full-length Mbd3 ( but not mutant form ) overexpression attenuated Dlx1 , Tlx3 , NeuroD1 , Tuj1 , Gad67 , and NeuN gene expression , all neuronal lineage markers , but had no effect on glial cell differentiation or gene expression ( Fig 6A and S8A–S8D Fig ) . As noted above , Mbd3 bound specifically to the Dlx1 , Tlx3 , NeuroD1 , Ascl1 , and Lbx1 gene loci , and this association decreased upon induction of differentiation conditions ( Fig 5D ) . Thus , we asked whether decreased neuronal gene expression following Mbd3 overexpression paralleled increased occupancy of Mbd3 on target promoters ( Fig 6B ) . As expected , the amount of overexpressed Mbd3 bound to gene promoters in NPCs in differentiation conditions over 2 d was similar to that seen in nondifferentiation conditions , leading to attenuate Dlx1 , Tlx3 , NeuroD1 , Tuj1 , Gad67 , and NeuN gene expression , all markers of neuronal lineage ( Fig 6A and S8B Fig ) . In contrast , there was little or no accumulation of Mbd3 on the Gfap promoter under the differentiation condition over 2 d for NPCs after Mbd3 overexpression , suggesting that Mbd3 blocks neuronal rather than glial cell differentiation ( Fig 6B and S8B Fig ) . Immunocytochemistry analysis also showed that Mbd3 overexpression prevented NPC neuronal differentiation but did not affect astrocyte differentiation ( Fig 6C , 6D and S8C , S8D Fig ) . Moreover , in both control and Mbd3-overexpressing NPCs , Nestin staining was comparable in staining intensity ( S8C Fig ) . These data suggest that Mbd3 is a novel regulator for neuronal cell-fate determination of NPCs . To further investigate whether the Smek-Mbd3 axis regulates neurogenesis , we knocked down endogenous Mbd3 using an shMbd3 lentiviral vector in cultured Smek1/2 dKO NPCs and then induced differentiation for 2 d . Mbd3 KD significantly rescued effects of Smek loss on Dlx1 , Tlx3 , NeuroD1 , Tuj1 , Gad67 , and NeuN expression but had no effect on astrocyte differentiation or gene expression ( Fig 7A ) . Increased neuronal gene expression seen following Mbd3 KD was accompanied by decreased occupancy of target gene promoters by Mbd3 ( Fig 7B ) . The epistatic relationship of Mbd3 and Smek in neurogenesis was analyzed by Mbd3 knockdown in Smek1/2 dKO NPCs . Mbd3 shRNA were expressed from the same vector that coexpressed enhanced green fluorescent protein ( EGFP ) . The percentage of EGFP and Tuj1 double-positive cells among EGFP-positive cells was increased in cultures expressing Mbd3 shRNA but not in cells expressing Scramble shRNA ( Fig 7C ) . We next assessed Mbd3 function in neurogenesis using an in utero electroporation system . Electroporated embryos were readily identifiable by EGFP expression ( Fig 8A ) . About 74% of total EGFP-positive Mbd3 knockdown cells migrated toward the IZ or CP , while only ~39% of EGFP-positive control cells showed a similar migration pattern ( Fig 8B and 8C ) . Quantitative analyses showed that the number of Tuj1-positive cells significantly increased in the VZ , SVZ , and IZ regions in Mbd3 KD EGFP-positive cells relative to control EGFP-positive cells ( Fig 8D ) . These results strongly suggest that Mbd3 regulates NPC neuronal differentiation in the VZ or SVZ during cortical development .
Here , we have analyzed mouse embryos lacking functional Smek1 and Smek2 genes as well as cultured NPCs derived from those animals to understand Smek function during cortical development . We discovered that Smek1/2 dKO NPCs exhibit significantly reduced capacity for neuronal differentiation and increased self-renewal activity . Furthermore , we employed a Y2H screen to search for Smek binding partners and identified Mbd3 as a novel Smek-interacting protein ( Fig 2A and S2 Table ) . Importantly , we observed that Smek promotes Mbd3 protein degradation and reduces Mbd3 occupancy of neural differentiation–associated gene promoters , likely increasing transcription of those genes via inhibiting recruitment of the repressive NuRD complex . Interestingly , in the developing CNS , increased Mbd3 instability had an effect only on neuronal differentiation , with little or no effect on glial cell fate ( Figs 6 , 7 and S7F and S8B–S8D Figs ) . We could not determine the molecular mechanism by which the Smek-Mbd3 axis specifically regulates neuronal cell-fate determination but not glial cell fate . In our previous study , we found that protein phosphatase PP4c interacts with Smek and this complex suppressed Par3 activity for differentiation of NPCs [15] . PP4c is known to regulate neuronal cell-fate determination and organization of early cortical progenitors in the ventricular zone of the embryo brain by modulating spindle orientation during mitosis [24] , and we could confirm the role of PP4c in neuronal differentiation of NPC by a PP4c loss-of-function study ( S9A Fig ) . Interestingly , knockdown of PP4c significantly abolished neuronal cell as well as glial cell differentiation of NPCs , similar to Smek loss of function , and this finding suggests that Smek/PP4c/Par3 might have a different biological function from Smek/Mbd3 in at least regulating glial cell gene expression of NPCs ( Fig 1E and S9A Fig ) . Moreover , Par3 regulation of Smek/PP4c during neurogenesis exclusively occurs in a cytosolic fraction but not in the nucleus of NPCs [15] . However , Smek and Mbd3 expression and transcriptional repression of Mbd3/NuRD complex mainly occurs in the nucleus of NPCs ( Fig 2C and 2D ) . Our preliminary investigation of the relationship between Smek-PP4c complex and Mbd3 protein stability also reveals that loss of PP4c could not affect Smek-mediated Mbd3 polyubiquitylation ( S9B Fig ) . In addition , ChIP-seq and ChIP-qPCR data show that Smek and Mbd3 are not significantly enriched at Gfap gene loci ( S7F Fig ) . Thus , overall data suggest that the Smek-Mbd3 axis likely functions independently of the Smek-PP4c-Par3 axis , at least in regulation of Gfap gene expression , and that the Smek-Mbd3 interaction plays a crucial role in neuronal cell-fate determination in NPCs . So far , five vertebrate MBD proteins have been identified as members of the MBD protein family: Mbd1 , Mbd2 , Mbd3 , Mbd4 , and MECP2 [25 , 26] , and these members are more highly expressed in the brain than in other tissues , leading investigators to hypothesize that they may play a critical role in normal brain development and in behavior [27 , 28] . Our data indicate that Mbd3 represses neurogenesis and likely functions differently from other family members . For example , Mbd2 and Mbd3 are closely related and share a highly conserved methyl-CpG–binding domain , but mouse studies indicate that the two proteins are not functionally redundant [24] , possibly because Mbd3 specifically recognizes methylated DNA , especially , 5′-hydroxymethylcytosine ( 5′-hmC ) [19] . Deletion of Mbd3 gene in neural progenitor cells leads to generation of neurons expressing both deep- and upper-layer markers [23] , suggesting that Mbd3 is required to maintain appropriate transcription in progenitor and neurons during neural development . A recent study suggests that Mbd3 may fine-tune expression of both active and silent genes [29] . Other studies suggest that conversion of 5′-mC to 5′-hmC coincides with increased transcriptional activity by excluding Mbd proteins from target genes [30] . Consistent with these findings , we found that Mbd3 is specifically bound to neuronal gene loci , and our findings suggest that it is likely released from these loci by Smek during NPC differentiation ( Fig 5D ) . Mbd3 is a subunit of the NuRD complex , which has nucleosome remodeling and histone deacetylase activities [17 , 18] and thus regulates gene expression . The molecular function of this complex has been extensively studied in the context of tumorigenesis , stem cell pluripotency , and brain development [23 , 31–34] . Mbd3 mutation or abnormal expression may function in tumorigenesis by perturbing gene expression . Like Mbd3 , other Mbd proteins , especially Mbd2 and Mbd4 , are associated with progression of cancer such as colorectal cancer , albeit by different mechanisms [34–36] . Furthermore , Mbd3 knockdown during somatic cell reprogramming significantly increases reprogramming efficiency [31–33] . Although Mbd3 activity is likely relevant to pathologies seen in cancer , neurological disease , and developmental defects , mechanisms underlying its regulation remain unclear . We propose a novel function in which Mbd3 protein levels , depending on Smek activity , decrease during neurogenesis ( Figs 2D , 3A–3C and S4C and S4D , S5A and S5B Figs ) . Smek1/2 dKO NPCs or the embryonic cortex show aberrantly high Mbd3 levels that may repress neuronal gene expression and underlie developmental defects seen in the latter . In accordance , we report that Smek promotes ubiquitylation and degradation of Mbd3 ( Figs 3A–3C , 4A–4D and S5A–S5B , S6A Figs ) . Our data also indicate that Smek regulation of Mbd3 is not transcriptional , based on the lack of significant change in Mbd3 mRNA levels over NPC differentiation . Conversely , Mbd3 protein levels decreased during neuronal differentiation in the embryonic cortex starting at E12 . 5 in mice . In addition , decreased Mbd3 levels seen in cultured NPCs are blocked by MG132 treatment concomitant with accumulation of polyubiquitylated Mbd3 . These results overall indicate that Mbd3 activity is regulated at the level of protein stability and that Smek likely governs this process . Changes in protein stability often constitute a more rapid means of regulating protein activity than does modulation of transcription . Therefore , regulation of Mbd3 protein stability might function epigenetically to recruit the NuRD complex to 5′-hmC–modified gene promoters . To our knowledge , this is the first report of regulation of an Mbd family protein by stability changes . We also examined potential factors or complexes that might function in Smek-dependent Mbd3 degradation . To do so , we sought potential E3 ligase proteins that might catalyze Mbd3 ubiquitylation by using Biograph software and identified the E3 ligase TRIpartite Motif protein 33 ( TRIM33 ) protein , which has an N-terminal Really Interesting New Gene ( RING ) -domain ( S10 Fig ) . TRIM33 specifically targets phosphorylated nuclear proteins for degradation [37] . Interestingly , we have previously identified protein kinase C ( PKC ) lambda/iota ( λ/ι ) , a serine/threonine kinase , as a binding partner of Smek1 from a mass spectrometry analysis [15] . PKC isoforms contain an NLS and contribute diverse cellular physiology [38–40] . Smek1/2 also have NLS sequences and are localized exclusively in the nucleus in interphase [15] . To further investigate the involvement of PKCλ/ι in the molecular mechanism for Mbd3 protein stability , we performed prediction of putative kinases for phosphorylation of Mbd3 protein by GPS ( group-based prediction system ) software 3 . 0 ( http://gps . biocuckoo . org/ ) . ( S3 Table ) . Interestingly , we predicted PKCλ/ι as putative kinases for Mbd3 phosphorylation . Although it still remains unclear how Smek1/2 promotes ubiquitylation and stability of Mbd3 , accumulating data and predictions suggest that nuclear-localized Smek1/2-PKCλ/ι complex with TRIM33 may function in Mbd3 ubiquitylation and degradation . Alternatively , Aurora-A protein , a serine/threonine kinase , reportedly physically associates with Mbd3 at centrosomes in early M phase in vivo and phosphorylates Mbd3 protein in vitro [41] . These findings suggest that Aurora-A may also be involved in the regulation of Mbd3 protein stability as a different mechanism from Smek-PKCλ/ι complex . This topic will be addressed in future studies . Smek orthologues in Drosophila play a critical role in neuroblast mitosis [16] . In Smek-deficit neuroblasts , cell-fate determinants , such as Prospero and Miranda , are no longer localized to the cell cortex; instead , they are distributed in the cytoplasm of dividing neuroblasts [16] . As a result , asymmetric cell division and neurogenesis are defective . In the Drosophila system , another class of asymmetric cell division regulators are epigenetic modulators . However , it is not clear if Smek functions through epigenetic modulators . Our studies suggest that Smek and Mbd3 have the opposite function in the NPCs’ differentiation in vertebrate systems . Although these studies do not address asymmetric cell division , our research may shed light on asymmetric cell division and neurogenesis in Drosophila and mammals . Our findings also highlight the importance of Smek/Mbd3 interaction in regulating NPC differentiation . Other studies suggest that Smek and Mbd3 may have overlapping functions or activities in brain development , stem cell activity , and regulation of transcription [15 , 16 , 20 , 21 , 42] . Our studies support a functional relationship of Smek and Mbd3 in NPCs . Our mapping analysis shows that Smek/Mbd3 interaction is mediated by the Mbd domain of Mbd3 . Immunofluorescence analysis confirmed close proximity of these proteins in the NPC nucleus , and we have observed coincident expression of Smek and Mbd3 in the mouse embryonic brain [16 , 21] . Finally , we found that Smek and Mbd3 target the same neuronal gene loci for regulating transcription ( Fig 5D ) . Further analysis suggests a model in which Smek regulates target gene transcription by regulating Mbd3 protein stability , interaction with NuRD components , and recruitment of Mbd3/NuRD complex to the promoters of target genes . Smek1/2 dKO exhibits reduced neuronal differentiation and decreased expression of Dlx1 , Dlx2 , NeuroD1 , Tuj1 , Gad67 , NeuN , and stabilizing Mbd3 protein , while Mbd3 overexpression attenuated Smek-mediated neuronal differentiation ( Figs 1E , 3A–3C , 6A , 6C and 6D ) . Thus , this study is significant not only for demonstrating Smek-mediated Mbd3 protein degradation but also in providing evidence that Smek/Mbd3 interaction regulates neuronal gene expression and neuronal differentiation during cortical development . In conclusion , we report functional interaction of Smek with Mbd3 in neuronal differentiation of NPCs .
All animal procedures were approved by the Institutional Animal Care and Use Committee ( IACUC ) and the National Institutes of Health ( IACUC protocol number: 11489 ) . Mouse embryos and primary neural progenitor cells were obtained from a deceased pregnant mouse following CO2 asphyxiation . For in utero electroporation experiments , timed-mated pregnant mice had been anesthetized with Avertin ( 2 . 5% ) ( Sigma , St . Louis , MO ) following IACUC instruction . At the experimental endpoints , mice were euthanized by CO2 asphyxiation . Smek1/2 dKO mice were generated using gene trap mutant ES cells obtained from the Gene Trapping Consortium . Gene trap vectors were targeted between exons 3 and 4 of the Smek1 gene and between exons 10 and 11 of the Smek2 gene , respectively . Smek1 and Smek2 mutant ES cells ( E14 ) were injected into mouse blastocysts and chimeric mice were backcrossed with C57BL/6 mice . Smek1/2 dKO mice were generated by crossing C57BL6J-Smek1+/- with Smek2+/- mice . After six generations , mice were used for analysis . Although Smek1/2 dKO mice can die at later stages of embryonic development , we were able to obtain dKO embryos as late as E14 . 5 with a normal Mendelian distribution . Thus , we have conducted functional analysis of Smek1/2 dKO embryos at E11 . 5 , E12 . 5 , and E14 . 5 . Embryos and pups of wild-type and heterozygous KO mice were collected from timed-mated pregnant females . Antibodies used in this study were anti-Smek1 ( rabbit polyclonal 1:500 dilution ) anti-Smek2 ( rabbit polyclonal 1:500 dilution ) , anti-Flag ( mouse monoclonal 1:2 , 000 dilution ) ( Sigma ) , anti-Mbd3 ( rabbit polyclonal 1:500 dilution ) , anti-CDH3 ( rabbit polyclonal 1:500 dilution ) , anti-RbAP46 ( rabbit polyclonal 1:2 , 000 ) , anti-GFAP ( rabbit polyclonal 1:200 dilution ) , anti-MTA1 ( rabbit polyclonal 1:2 , 000 dilution ) , anti-RbAp46 ( rabbit polyclonal 1:2 , 000 dilution ) , anti-HDAC1/HDAC2 ( mouse monoclonal 1:2 , 000 dilution ) ( Cell Signaling Technology , Beverly , MA ) , anti-HA ( rabbit polyclonal 1:500 dilution ) , anti-GST ( rabbit polyclonal 1:500 dilution ) , anti–α-tubulin ( mouse monoclonal 1:5 , 000 dilution ) , anti-HA ( mouse monoclonal 1:3 , 000 dilution ) ( Santa Cruz Biotechnology , Santa Cruz , CA ) , anti-MAP2ab ( rabbit polyclonal 1:200 dilution ) ( Chemicon , Temecula , CA ) , anti-NeuN ( rabbit polyclonal 1:200 dilution ) ( EMD Millipore , Billerica MA ) , anti-Nestin ( mouse monoclonal 1:350 dilution ) ( BD Biosciences , San Jose , CA ) , anti-Tuj1 ( mouse polyclonal 1:200 dilution ) ( Covance , Princeton , NJ ) , anti-Tbr1 ( rabbit polyclonal 1:200 dilution ) , and anti-Pax6 ( rabbit polyclonal 1:200 dilution ) ( Abcam Ltd , Cambridge , MA ) . Secondary antibodies were anti-rabbit Alexa Fluor 488- , anti-mouse Alexa Fluor 488- , anti-rabbit Alexa Fluor 555- , or anti-mouse Alexa Fluor 555-conjugated IgG ( 1:200 dilution ) ( Molecular Probes , Eugene , OR ) . bFGF was purchased from PeproTech ( Rocky Hill , NJ ) . The protease inhibitor cocktail was from Roche Applied Science ( Indianapolis , IN ) . To HDAC inhibition , Trichostatin A ( TSA ) and 4- ( dimethylamino ) -N-[6- ( hydroxyamino ) -6-oxohexyl]-benzamide ( DHOB ) were purchased from Santa Cruz Biotechnology . TRIzol , Protein A/G agarose beads , and DAPI were from Sigma . The ECL Kit and KOD Hot Start DNA polymerase were from EMD Millipore . Glutathione magnetic beads , phenol:chloroform:isoamyl alcohol , and the First Strand cDNA Synthesis Kit were from Thermo Fisher Scientific ( Rockford , IL ) . Cells were gently lysed with IP buffer ( 50 mM Tris-HCl , pH 7 . 4 , 130 mM NaCl , 10mM NaF , 2 mM EGTA , 2 mM EDTA , 0 . 5% Triton X-100 , 0 . 5% NP-40 , 5% glycerol , 1 mM dithiothreitol [DTT] , and a protease inhibitor cocktail ) for 1 h on ice and then centrifuged at 14 , 000 rpm at 4°C for 15 min . The supernatant was collected and precleared with 30 μl of Protein A/G beads ( Santa Cruz Biotechnology ) for 2 h , and then precleared lysates were incubated with 4 μg of each specific antibody overnight at 4°C . Lysates were then incubated with 30 μl of Protein A/G beads for 4 h at 4°C . After immune complexes were washed six times with IP buffer , they were eluted by boiling for 3 min at 95°C in SDS sample buffer and separated on 10% SDS-PAGE . After blocking , membranes were incubated with primary antibody and then with a peroxidase-conjugated secondary antibody . Bound secondary antibody ( anti-mouse or anti-rabbit 1:10 , 000 ) ( Santa Cruz Biotechnology ) was detected using the enhanced chemiluminescence ( ECL ) reagent ( Santa Cruz Biotechnology ) . For immunohistochemistry , embryonic brains were dissected and fixed in 4% parafomaldehyde ( PFA ) at 4°C , cryoprotected in 30% sucrose , embedded and frozen in Tissue Tek OCT compound , and sectioned at 30 μm on a cryostat . Sections were incubated with primary antibody at 4°C for 18 h . For immunocytochemistry , cells cultured on coverslips were fixed with 4% PFA/PBS for 30 min and immunostained after permeabilizing with 0 . 2% Triton X-100 . Tissues and cells were incubated with secondary antibodies at room temperature for 1 h and counterstained in 4'-6-diamidino-2-phenylindole ( DAPI ) ( Boehringer Mannheim , Mannheim , Germany ) , and images were visualized using confocal microscopy ( LSM5 PASCAL; Zeiss , Jena , Germany ) . Values obtained from at least three independent experiments were averaged and reported as means ± SD . The two-tailed Student’s t test was used to compare two experimental groups . DH5 bacteria were transformed with GST-tagged plasmids ( Mbd3 , ΔN36 , ΔN92 , ΔC249 , ΔC221 , ΔC174 , and ΔC93 ) and protein expression was induced by addition of 0 . 5 mM isopropyl 1-thio-β-D-galactopyranoside ( IPTG ) at 25°C at mid-log phase . Cells were lysed with B-PER Bacterial Protein Extraction Reagent ( Thermo Fisher Scientific ) , lysates were purified , and proteins were captured using with Glutathione magnetic beads . HEK293T cells were transfected with Flag-tagged Smek2 plasmid and lysed with lysis buffer for 1 h on ice . Cell lysates were centrifuged at 14 , 000 rpm at 4°C for 15 min , and collected supernatants were incubated with Glutathione magnetic beads bound to GST or GST proteins . Bound proteins were eluted by boiling for 3 min at 95°C in SDS sample buffer , followed by immunoblotting . NPCs were prepared from E11 . 5 cortex of Wild-type , Smek1-/- , Smek2-/- , and Smek1/2 dKO mice in Hank’s balanced salt solution ( HBSS ) ( Invitrogen ) and cultured as described [43] . To maintain stem cell characteristics , NPCs were cultured in N2 medium containing bFGF for 4 d . Stemness of cultured NPCs was confirmed by Nestin and Sox2 expression . To induce NPC differentiation , cells were seeded and further cultured in the absence of bFGF2 . NPCs derived from Wild-type or Smek1/2 dKO E11 . 5 forebrain were transfected with 4 μg pUltra-hot-Mbd3-flag or a Myc-tag vector for Mbd3 gain-of-function experiments and with pLKO3G-shMbd3 for Mbd3 loss-of-function experiments using Lipofectamine LTX and Plus Reagent ( Invitrogen ) or electroporation with an AMAXA nucleofector ( Ronza AG , Basel , Switzerland ) . pLKO3G-shcontrol vector was used for negative control of pLKO3G-shMbd3 vector . After 12 h , transfection efficiency was confirmed to be >90% by monitoring mcherry or EGFP signaling . After two more days in differentiation conditions , cells were analyzed by qPCR or ChIP . Mbd3 expression vectors were constructed by subcloning full-length mouse Mbd3 from a lentiviral FUIGW-Mbd3-Flag vector we previously created into XbaI/EcoRI sites of pUltra-hot ( Addgene plasmid # 24130 ) . For Mbd3-myc , the reverse primer included the full Myc sequence . PCR was carried out using the KOD Hot Start Polymerase Kit ( EMD Millipore ) with corresponding primer pairs . PCR products were ligated into double-digested pUltra-Hot vector and inserted ligations were confirmed by PCR and DNA sequencing ( Genewiz , Inc ) . For the shRNA vector , the Mbd3 target sequence was designed based on the RNAi Consortium library top hits for mouse Mbd3 . Details for pLKO3G shMbd3 and shcontrol construction are listed in S4 Table . Cells were harvested and total RNA was isolated using TRIzol reagent ( Invitrogen ) . The SuperScript III qRT-PCR kit ( Invitrogen ) was used to synthesize cDNA from total RNA . Quantitative PCR was carried out using the ABI PRISM 7900 Sequence Detection System with SYBR Green Master Mix ( iTaq ) with conditions of 95°C for 10 min followed by 50 cycles at 95°C for 15 sec and 60°C for 3 sec . Samples were run in triplicate and Dlx1 , Dlx2 , Tlx3 , NeuroD1 , Tuj1 , Gad67 , NeuN , Mbp , Gfap , Ascl1 , and Id1 transcript quantitation was undertaken by comparing Cycle Threshold ( Ct ) values for each reaction with the Gapdh reference . Primer sets for quantitative PCR are listed in S5 Table . For the ChIP assay , NPCs derived from wild-type or Smek1/2 dKO E11 . 5 forebrain or transfected with 4 μg pUltra-hot-Mbd3-flag or a pUltra-hot-empty vector for Mbd3 gain-of-function experiments and with pLKO3G-shMbd3 or pLKO3G-shScramble for Mbd3 loss-of-function were treated with 1% formaldehyde for 10 min at room temperature and quenched with 0 . 125 M glycine for ten more minutes at room temperature . Cross-linked chromatin was sonicated to fragment DNA to 200–1 , 000 base pairs , and then immunoprecipitation was performed with rabbit anti-IgG , anti-Smek1 ( Sigma ) , anti-Mbd3 ( Cell Signaling ) , anti-HDAC1 , anti-HDAC2 , anti-MTA1 , and acetyl histone H3 ( Santa Cruz Biotechnology ) antibodies overnight at 4°C , followed by incubation with 50 μl of magnetic Protein A/G Dynabeads ( EMD Millipore ) . Abundance of sequences in immunoprecipitates was determined by PCR and normalized as a fold-value relative to input chromatin . Smek ChIP-seq data were analyzed with the MACS online tool , and cis-regulatory sequences were analyzed using the Genomic Regions Enrichment of Annotations Tool ( GREAT ) interface ( http://bejerano . stanford . edu/great/public/html/ ) . We also utilized the Intergrative Genomics Viewer ( IGV v2 . 3 ) to visualize distribution of ChIP-seq–identified peaks in different genomic regions . Primer sets for ChIP-qPCR are listed in S6 Table . All procedures followed guidelines of the Institutional Animal Care and Use Committee ( IACUC ) and the National Institutes of Health . A total of 2 . 5% ( w/v ) avertin ( 1 g/ml solution of 2 , 2 , 2-Tribromoethanol , 97% in tert-amylalcohol [99%]; Aldrich , catalog numbers T4 , 840–2 and 24 , 048–6 , respectively ) in 0 . 9% saline was injected i . p . ( 15 μl/g of body weight ) to anesthetize pregnant mice ( E13 . 5 ) . A laparotomy was performed , and the uterus with embryos was exposed . A total of ~2–5 μl of plasmid DNA ( approximately 2 μg/ μl , dissolved in water ) was injected into the lateral ventricle using a fine-glass microcapillary and a PV830 pneumatic PicoPump . Electroporation was performed using a Nepagene CUY21SC electroporator ( amplitude , 50 V [E13 . 5]; duration , 50 ms; intervals , 150 ms ) . To deliver electrical pulses , tweezer-type circular electrodes ( 7-mm diameter ) were used with the positive side directed to the medial wall of the ventricle into which DNA was injected . Uterine horns were repositioned in the abdominal cavity , and the abdominal wall and skin were sewed with surgical sutures . Mice were kept on a warm plate ( 37°C ) for recovery . Two to three days later , embryos were taken from mothers and fixed with 4% ( w/v ) PFA ( Sigma ) in PBS ( pH 7 . 4 ) . After a 24 h fixation at 4°C , embryo brains were transferred to a 30% ( w/v ) sucrose solution in 4% PFA . Tissues were sectioned at 30 μm using a cryotome ( Leica ) and analyzed by immunohistochemistry . Smek2 DNA was fused in frame to the LexA bait vectors pBTM116 for use as bait in the yeast two-hybrid screen . Preys were expressed as fusions to the activation domain of GAL4 in pACT2 ( BD Biosciences Clontech , Palo Alto , CA , US ) . Transformed bait strains with or without transformed prey strains were mated and analyzed by using β-galactosidase activity . The following preys were used: mbd3 . The Saccharomyces cerevisiae strain L40 ( MATa trp1 leu2 his3 LYS2:: lexA-HIS3 URA3::lexA-lacZ ) was cotransformed with bait and prey plasmids using the PEI method and selected for histidine prototrophy on minimal medium , containing 2% glucose; 6 . 7% yeast nitrogen base ( BD Diagnostic Systems , Sparks , MD , US ) ; complete amino acid mixture lacking histidine , leucine , and tryptophan ( Qbiogene , Carlsbad , CA , US ) ; and 2% bacto agar ( BD Biosciences , Franklin Lakes , NJ , US ) . Yeast transformants were grown for 3 d at 30°C . Statistical differences among groups were analyzed using Student’s t test and are indicated in each Fig as follows: *p < . 05 , **p < . 005 , and ***p < . 0005 . *p < . 05 was considered statistically significant . | Neural progenitor cells are self-renewing , multipotent cells that generate major neural cell types , including neurons and glia . Their fate during development of the cerebral cortex is determined by a complex interplay of genetic and epigenetic components . It has been shown that Suppressor of Mek null ( Smek ) proteins—which are evolutionarily conserved—play a role during the asymmetric cell division of neuroblasts in invertebrates . Methyl-CpG–binding domain 3 ( Mbd3 ) protein , a core component of the repressive nucleosome remodeling and deacetylase ( NuRD ) complex , is an important epigenetic regulator that plays an essential role in mammalian development . In this study , we discovered that Smek interacts with Mbd3 and promotes its degradation via a posttranslational modification called polyubiquitylation . Degradation of Mb3 , in turn , blocks recruitment of Mbd3/NuRD complex on target gene promoters , leading to an increase in neuronal differentiation during cortical development . This study not only elucidates a distinct mechanism for Smek-mediated neuronal differentiation but also identifies Smek as a negative regulator of the Mbd3 protein during cortical brain development . | [
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| 2017 | Smek promotes corticogenesis through regulating Mbd3’s stability and Mbd3/NuRD complex recruitment to genes associated with neurogenesis |
Helminthiases are a group of disabling neglected tropical diseases that affect billions of people worldwide . Current control methods use preventative chemotherapy but reinfection is common and an inter-sectoral approach is required if elimination is to be achieved . Household and community scale water treatment can be used to provide a safe alternative water supply for contact activities , reducing exposure to WASH ( water , sanitation , and hygiene ) -related helminths . With the introduction of ultraviolet light emitting diodes ( UV-C LEDs ) , ultraviolet ( UV ) disinfection could be a realistic option for water treatment in low-income regions in the near future , to provide safe alternative water supplies for drinking and contact activities such as handwashing , bathing , and laundry , but currently there is no guidance for the use of UV or solar disinfection against helminths . A qualitative systematic review of existing literature was carried out to establish which WASH-related helminths are more susceptible to UV disinfection and identify gaps in research to inform future studies . The search included all species that can infect humans and can be transmitted through water or wastewater . Five online databases were searched and results were categorized based on the UV source: sunlight and solar simulators , UV-A and UV-B ( long wavelength ) sources , and UV-C ( germicidal ) sources . There has been very little research into the UV sensitivity of helminths; only 47 studies were included in this review and the majority were carried out before the standard protocol for UV disinfection experiments was published . Only 18 species were studied; however all species could be inactivated by UV light . Fluences required to achieve a 1-log inactivation ranged from 5 mJ/cm2 to over 800 mJ/cm2 . Larval forms were generally more sensitive to UV light than species which remain as an egg in the environment . This review confirms that further research is required to produce detailed recommendations for household or community scale UV-C LED or solar disinfection ( SODIS ) of water for preventing helminthiases .
In 2016 , WASH-related helminth infections ( e . g . schistosomiasis , soil-transmitted helminthiases , taeniasis ) were responsible for over 9 . 5 million years lost due to ill-health , disability or early death [1] . They are transmitted through contact with ( or consumption of ) water , food , and soil that contain the human infective stages of the parasite . Current control methods for combating these neglected tropical diseases ( NTDs ) are primarily focused on preventative chemotherapy with anthelmintic drugs , which has been effective at reducing the global health burden [2 , 3] . However , reinfection is common and it is now widely recognized that an inter-sectoral approach is required for combatting many of these diseases [4–7] . In 2015 the World Health Organization ( WHO ) published their global strategy for WASH for NTDs , confirming that whilst WASH was one of the five key interventions in the global NTD roadmap published in 2012 , little progress has been made in linking WASH and NTD programs [8] . More recently the WHO published “WASH and Health working together” , a toolkit for WASH and NTD programs based on the BEST ( Behavior , Environment , Social inclusion , Treatment and care ) framework [9] . Access to sanitation and clean water , and promotion of safe water practices are key interventions under the behavior and environmental components of the framework for many of the NTDs , including six helminthiases . Yet 29% of the global population do not have access to managed water supplies and 61% lack access to sanitation services [10] . Whilst piped water requires significant developments in regional infrastructure , household and community scale water treatment processes can be used to treat water collected from contaminated water bodies . This reduces exposure to helminth eggs and larvae by providing safe alternative water supplies for contact activities such as hand washing , bathing , and laundry . UV disinfection is widely used for water and wastewater treatment in many parts of North America , Asia , and Europe . It has the benefits of forming no trihalomethanes or haloacetic acids , regulated by-products of chlorination , and can be successfully used against chlorine resistant pathogens such as Cryptosporidium parvum and Giardia lamblia [11 , 12] . UV radiation is the part of the electromagnetic spectrum between 100 and 400 nm , which can be categorized into four types: UV-A ( 400–315 nm ) , UV-B ( 315–280 nm ) , UV-C or the germicidal range ( 280–200 nm ) , and Vacuum UV ( 200–100 nm ) . Unlike chlorination , UV disinfection does not necessarily kill pathogens . When a microorganism is exposed to UV light , most of the photons pass through it but some are absorbed by various cellular components . In the germicidal range , proteins and the nucleotide bases that make up DNA and RNA account for most of the absorption . Absorption by proteins is highest below 230 nm , but in this range water also strongly absorbs UV light , and high fluences are generally required for protein damage to occur . Lower fluences are required for absorption by DNA or RNA , which peaks at about 260 nm . All nucleotide bases absorb UV light , but absorption by the pyrimidine base thymine is the most critical for UV inactivation of microorganisms . When two thymine bases are adjacent to each other on a DNA chain , the absorption of a photon by one of the bases leads to a new chemical bond with the neighbouring thymine base , known as a dimer . In viruses that only contain RNA a similar reaction occurs between neighbouring uracil bases . The dimer changes the structure of the DNA or RNA and prevents the formation of new chains during replication , thereby inactivating the pathogen [13] . Conventional UV technologies use low pressure mercury filled arc lamps which produce near-monochromatic light at 253 . 7 nm , very close to the absorption maximum of DNA [13] . However , these lamps are made of fragile quartz and contain toxic mercury , which requires specialist handling and disposal . They also require relatively high input power and a reliable AC electricity supply . As a result , UV disinfection is often seen as incompatible with small scale water treatment in low income regions . The recent rapid development of UV-C LEDs offers an alternative source of UV light that may be more suitable for this context . UV-C LEDs are mercury free , durable , have a lower drive voltage than conventional mercury lamps , and can be powered by battery or photovoltaic supplies , so they can be used in rural or remote settings . UV-C LEDs are also much smaller than mercury lamps , which allows for novel design of water treatment systems , particularly point-of-use applications [14 , 15] . The optical power of UV-C LEDs is currently relatively low , meaning devices need to be run for long periods of time to achieve sufficient inactivation of pathogens . The best wall plug efficiency ( WPE , ratio of optical output power to electric input power ) for a commercially available UV-C LED device is currently 4 . 1% , compared to 30 to 40% for low pressure mercury lamps [16 , 17] . However , efficiency is improving and the WPE of commercial UV-C LED devices is expected to exceed 10% by 2021 [16] . Furthermore , in the last 15 years the cost of commercially available UV-C LEDs has decreased from over 1000 USD/mW to less than 1 USD/mW [18] . If these trends continue , and UV-C LED technology follows the path of visible LEDs , household and community scale UV disinfection of water may become a realistic option for low-income regions . Sunlight is an alternative source of UV light and SODIS is now widely recognized as a sustainable form of small scale , e . g . household level , drinking water treatment . SODIS typically involves filling 2-liter polyethylene-terephthalate ( PET ) drinks bottles with water and placing them on a reflective surface for a minimum of six hours in direct sunlight ( 24 hours on overcast days ) . Pathogens are inactivated through a combination of heating and UV-A and UV-B disinfection [19] . Conventional UV disinfection and SODIS have been shown to be effective against a wide range of waterborne pathogens , but there is no guidance for their use against helminths , even though many can be spread through water . The aim of this research is to review existing literature on the UV sensitivity of WASH-related helminths , determine which helminths are more susceptible to this form of water treatment , and identify gaps in research which will inform future studies regarding the proper use of UV and SODIS for minimizing the spread of these diseases via water in low-income regions .
The studies were reviewed and classified according to the process flow diagram shown in Fig 1 . First duplicates were removed and assigned code 1 , then the papers were classified by title . Titles which suggested the studies were not about a relevant species or UV disinfection were removed and assigned codes 2–4 ( animal species of the listed genera were included ) . The remaining abstracts were read and those that were about a non-waterborne life stage or primarily about the host response to a UV-attenuated vaccine were assigned codes 5 and 6 , and removed . Papers for the remaining studies were obtained and read in full . Studies that provided limited information about the effect of UV light on the helminth or that contained significant errors ( such as using a wavelength outside the UV range ) were assigned codes 7 and 8 and were excluded; the remainder ( code 9 ) were included in the review . Additional studies that were referenced in the papers in a way that suggested they were relevant to this review were also obtained , read in full , and assigned the relevant code . Papers were obtained from Imperial College London Library , The British Library , and The Wellcome Trust and were read independently by the first and second authors . Papers that were not written in English were either translated using online translation software ( Google Docs translation tool ) or by Imperial College London students who were native speakers of the language . Notes were made on the studies and relevant information was extracted and included in a table ( S1 Table ) . Any discrepancies between the first and second authors about which studies should be included were discussed and resolved . Where possible , the log reduction was calculated using the inactivation data presented in the studies and the equation LogReduction=−log10 ( NN0 ) , where N = proportion of viable organisms in the experimental sample and N0 = proportion of viable organisms in the control sample . If the survival percentage of the control sample was not stated in a paper , it was assumed that 100% survived ( except for studies assessing the worm burden ) . If 100% of the experimental sample was inactivated , it was assumed that one organism survived in order to calculate the minimum log reduction; if the study only reported the percentage of organisms , then it was assumed that 1% survived . The log reduction values were then interpolated to calculate the UV fluence required to achieve a 1-log and 2-log reduction . If a 1-log reduction was not achieved in a study , then the data were extrapolated .
Whilst 52 species of 23 genera were included in the search , results were returned for only 18 species of 10 genera: Ancylostoma spp , Angiostrongylus spp , Ascaris spp , Echinococcus spp , Fasciola spp , Hymenolepis spp , Opisthorchis spp , Schistosoma spp , Taenia spp , and Trichuris spp ( Table 2 ) . Most studies used low pressure mercury arc lamps but other sources include: sunlight , solar simulators , fluorescent lamps emitting in the UV-A and UV-B range , medium pressure mercury lamps emitting over a broad spectrum in the UV-C range , and monochromatic excimer lamps emitting in the UV-C range . It is difficult to directly compare studies that used sunlight or simulated sunlight with mercury lamps , as sunlight contains almost no radiation in the UV-C ( germicidal ) range . Studies using sunlight and long wavelength sources ( UV-A and UV-B ) have therefore been reviewed separately to studies using UV-C sources . Where the source or wavelength was not stated in a paper it has been reviewed alongside the UV-C studies , as these are the most common . The amount of UV light applied to a water sample is known as the fluence ( mJ/cm2 ) , which is a product of the exposure time ( s ) and fluence rate ( mW/cm2 ) . The protocol for calculating the fluence from low pressure mercury arc lamps in laboratory experiments was standardized only in 2003 , using a bench top collimated beam apparatus . The method involves applying a series of corrections to the irradiance measured by a radiometer at the center of the beam , to account for reflection of light from the water surface , variation in irradiance over the surface area of the liquid , absorption of UV by the water column , and divergence of the “quasi-collimated” beam . Application of these factors to the measured irradiance will give the average germicidal fluence rate in the water sample . The method also requires that mercury lamps are allowed to warm up for at least 10 minutes to allow the output to stabilize and samples must be stirred during exposures to ensure all microorganisms receive the same fluence [22] . Only one study used this method to calculate the fluence , therefore the fluences stated for all the other studies should be considered approximate . Some studies only recorded the exposure time and the fluence could not be calculated . Exposures were carried out in a number of different containers and the sample depth also varied between studies , from droplets on a glass cover slip to 25 mm deep samples in a culture dish [23 , 24] . Different water matrices were also used for the exposures , for example deionized water , salt solutions , and filtered wastewater treatment plant effluent [25–32] . The sample depth and water matrix can affect the amount of UV light that is absorbed by the water; if this is not accounted for in the fluence calculation it may result in an overestimation of the average fluence in the sample . In the case of samples being exposed in very small volumes of water ( e . g . droplets on coverslips ) , this may cause the samples to dry out , and it is difficult to separate the effect of drying from the effect of UV light on the inactivation of the target organism . Similarly , some UV sources are known to produce a considerable amount of heat , and not all experiments controlled the temperature of the samples , which may have also contributed to inactivation of the target organism . Many microorganisms ( e . g . bacteria ) have the ability to reverse the damage caused by UV light through photoreactivation , dark repair , or excision repair [13] . However , the repair potential of helminths was explicitly examined in only one study and not all studies kept samples in the dark after UV exposure . A variety of methods were used to determine the viability of helminths following exposure to UV light , the most common was to assess the ability of eggs or larvae to reach the next stage of development inside an animal host ( also known as in vivo methods ) . In vitro methods such as assessing the motility or morphology of larvae , and the ability of eggs to embryonate in culture dishes , were also used . The most appropriate method may vary between genera . The in vivo method is often seen as the most definitive way to establish viability although it may not always produce the most reliable fluence-response curves . This is because helminths have complex lifecycles and often the number of organisms collected from a host is not directly proportional to the number of organisms in the inoculant . For example , in one study there was a considerable difference in the number of mice that developed infections depending on whether they were inoculated with 500 or 2 , 000 eggs ( 0% and 75% , respectively ) and in another the number of control organisms recovered from the host varied notably between experiments ( 4–30% ) , possibly as a result of incomplete recovery or because of an unknown underlying issue which caused a reduction or increase in the intensity of infection in some of the host animals [33 , 34] . Using the in vivo method is also likely to result in a higher level of inactivation than if an in vitro method is used to assess viability . This is because UV light inactivates pathogens by altering nucleic acids , and not all damage is immediately evident but can show up later in the development of the organism , which has been demonstrated in a number of studies in this review [24 , 35–40] . Furthermore , as migration of eggs and larvae through the body can still pose a health risk , some authors suggest it is preferable to prevent entry to the bloodstream of the host and that it is necessary to demonstrate the organisms have been inactivated in vitro [41] . It is difficult to compare results between studies that use in vivo and in vitro methods . As the infection mechanism varies between genera , in vitro viability assessments may allow for better comparison , however further research is required to establish standardized methods for in vitro viability assessments of helminths . Selective dyes which stain only alive or dead cells may be suitable for this purpose and have previously been used to determine the viability of schistosome schistosomula [42] . One study in this review used methylene blue to identify dead schistosome cercariae which were stained violet blue , whilst live cercariae were left colorless [43] . Twelve studies investigated the effect of sunlight and long wavelength UV light ( 313 and 390 nm ) on seven species of helminth: Ancylostoma caninum , Ancylostoma ceylanicum , Angiostrongylus cantonensis , Ascaris suum , Ascaris lumbricoides , Schistosoma mansoni , and Schistosoma haematobium . In sunlight experiments the eggs or larvae were exposed for a minimum of 15 minutes to over six hours of continuous sunlight . Some studies also investigated the effect of exposure to intermittent sunlight over a period of days . Much shorter time periods were required when using artificial UV-A and UV-B sources . S . mansoni cercariae were the most sensitive to natural sunlight , requiring 60 minutes for all cercariae to be rendered motionless in one study , even on cloudy days [44] . Prah and James found S . mansoni and S . haematobium miracidia were equally sensitive to sunlight , however longer exposures were required than in studies using cercariae [44 , 45] . This suggests sensitivity to UV light may vary between different life-stages of the same species . Similarly , Spindler found single cell A . suum were more sensitive to sunlight than embryonated eggs [46] ( Table 3 ) . A . suum was the most resistant to sunlight; in one study single cell eggs were exposed to simulated sunlight between 290 and 800 nm at a fluence rate of 55 mW/cm2 for over six hours and only a 1 . 42-log reduction was achieved . However , it must be noted that a fluence rate over such a broad spectrum cannot be directly compared to a fluence rate in the UV range , as not all wavelengths have the same germicidal effectiveness . Furthermore , a high concentration of approximately 1 million eggs/mL was used and the study did not consider the effect of shielding , where organisms higher in the water column may protect lower ones from UV exposure [27] . The results of this study should therefore be considered conservative ( i . e . under-estimate the true sensitivity of the eggs to UV light ) . Jones and Hollaender investigated the effect of simulated sunlight on A . lumbricoides , using a mercury source lamp which emitted light between 350 and 490 nm at a fluence rate of 0 . 1 to 30 mW/cm2 . In this experiment the highest inactivation achieved was a 0 . 98-log reduction , but the authors noted that they would expect natural sunlight to be more damaging due to the presence of infrared radiation and higher temperatures . The samples were not mixed during the exposures; an effort was made to expose the eggs in single layers but there was an issue of “clumping” in some of the experiments [47] . Two studies investigated the effect of intermittent sunlight on A . lumbricoides; both found that eggs were able to survive for much longer periods ( up to 60 hours ) than in other studies that used continuous sunlight [27 , 46 , 48–50] . However , it should be noted that these experiments were carried out in Russia ( at a high latitude ) whereas other studies either used solar simulators or tropical sunlight containing higher levels of UV radiation ( high fluence rates ) , which increases with proximity to the equator . Nenow confirmed that the germicidal effect of sunlight varies with altitude , suggesting that SODIS may be a more effective form of disinfection in communities located at higher altitudes , as shorter exposure times are required [50] . Of the hookworm species , no A . caninum larvae were able to survive 180 minutes exposure to sunlight [51] , and only 60 seconds exposure to UV-A ( 390 nm ) radiation was required for larvae of A . ceylanicum to become visibly sluggish . After 30 minutes exposure to UV-A light , larvae began to lose motility completely and when hamsters were orally infected with a dose of 100 larvae , no worms were able to develop [28] ( Table 4 ) . Similarly , larvae of the nematode A . cantonensis exposed for 15 minutes to UV-A light were unable to develop inside an animal host [52] . Only one study used UV-B light , which was shown to be relatively effective against S . mansoni miracidia . There were limited details on how the fluence was measured , but 86 . 1 mJ/cm2 ( approximately 2 minutes 30 seconds ) was sufficient to achieve a 2-log reduction in the number of daughter sporocysts in snails , even though the miracidia did not appear harmed . When exposed to fluorescent white light , immediately after irradiation , the miracidia were able to photoreactivate , with significantly higher numbers of sporocysts than in snails that were kept in the dark [23] . The eggs and larvae in these experiments were exposed to sunlight or long wavelength UV in small amounts of water , with no more than 3 mL used in any of the experiments . However , SODIS is generally carried out in 2 L bottles , which are laid on their side and left in direct sunlight . The depth of the water column will therefore be much higher than in the studies included in this review , increasing the amount of UV light that is absorbed by the water . It is therefore recommended that SODIS experiments are also carried out in the containers that will be used by households and local communities . SODIS is currently mainly used for drinking water treatment , and 2 L bottles are therefore appropriate reactors as the treated water can be drunk straight from the bottle . However , some helminthiases can be transmitted through poor personal hygiene and through contact activities such as bathing and laundry which require larger amounts of water and alternative reactors maybe required to effectively treat these volumes . Previous studies have used transparent plastic bags of various sizes as effective SODIS reactors although they have not been tested against helminths [53] . These have the advantage of a higher surface area to depth ratio whilst also allowing a larger volume of water to be treated . The underside of the bag can be coated with a reflective surface to increase the reflection of UV light into the water and they are cheap and easy to transport [54] . As the bags can be made to any size they may be more suitable for treating water for contact activities , however more research is required in this area . Germicidal mercury lamps ( 253 . 7 nm unless stated otherwise ) were used in 24 studies and an additional 12 studies used other UV-C light sources or did not specify the wavelength . There was a very large range in the inactivation data for A . suum and A . lumbricoides , with fluences from 11 to 3 , 367 mJ/cm2 required to achieve a 1-log reduction ( Table 5 ) . Only one study by Brownell and Nelson used the industry standard protocol to evaluate the fluence though Lucio-Forster et al . applied some factors , correcting for reflection , absorption , and divergence of the UV beam . The results of these two studies are reasonably similar with fluences of 100 and 84 mJ/cm2 required to achieve 1-log inactivation of intact single cell eggs , respectively , although the difference is greater for 2-log inactivation [41 , 55] . A study by Tromba suggested A . suum is more sensitive to UV light , achieving a 2 . 21-log reduction at 24 mJ/cm2 [24] , even though the eggs used were in a later stage of development and other studies reported that resistance to UV light increases with development stage [46 , 50 , 56] . However , Tromba determined viability of the eggs by assessing the worm burden in animal hosts , which may have resulted in a higher level of inactivation than if an in vitro method was used , as not all damage is immediately evident [24 , 41 , 57 , 58] . Peng et al . found that deformities began to show two weeks after single cell eggs were exposed to UV light ( unknown wavelength ) for 10–20 minutes , even though during the first week of incubation development of irradiated eggs matched that of the controls . Eggs that were exposed for less than 10 minutes appeared to develop normally for longer periods , with deformities showing only after three weeks [35] . 0 . 77-log reduction of A . lumbricoides was achieved at 20 . 3 mJ/cm2 using a prototype flow-through reactor , but in this study eggs were dissected from worm uteruses rather than collected from faeces or isolated from host intestines [31] . It is possible that these eggs were more sensitive to UV light because the eggshells may not have fully developed [41] . Furthermore , there was no information on how the fluence was calculated for the flow-through reactor . One study compared the use of UV light and microwave radiation for disinfection of soil containing A . lumbricoides eggs , although experiments were also carried out in water . The authors found fluences over 3 , 000 mJ/cm2 were required to achieve any significant inactivation in water . Whilst some factors were applied during the fluence calculation , the collimating tube used in the experiments was 6 cm in diameter but only 10 cm long [59] . In this region the beam from mercury lamps is divergent , and radiometers can produce errors if they are used to measure the irradiance very close to the source . It is generally recommended that a collimating tube four times as long as the diameter is used with mercury sources [22] . It is therefore possible that the fluence was actually less than was stated in the paper . Another study suggested that exposure to UV light actively increased the larval development of A . lumbricoides , even when exposed to fluences greater than 15 , 000 mJ/cm2 [30] . This contradicts the results of all the other studies included in this review and goes against the general understanding of the effect of UV light on microorganisms and UV disinfection . As with the previous study the irradiance was measured only 5 cm from the source and it is unclear if a collimating tube was used at all . The number of eggs in each sample was not specified and it is unclear if the suspensions were stirred . Furthermore , eggs were suspended in filtered secondary wastewater effluence , which may have absorbed a considerable amount of UV light , for which there was no correction applied . However , some inactivation would still have been expected . It is possible that some repair occurred ( it is unclear if the samples were kept in the dark following exposure ) or the accelerated development may be a result of the increase in temperature , which was not measured during the experiments , and has previously been shown to increase the rate of development in Ascaris spp [30] . An early study by Nolf compared two species of soil-transmitted helminth , and found that Trichuris trichiura was less sensitive to UV light ( unknown wavelength ) than A . lumbricoides [60] . Non-standard units were used to measure the extent of the exposure to UV light which means this paper cannot be compared to other studies , and there have been no further studies using Trichuris spp support this . The hookworm A . caninum appeared to be the most sensitive soil-transmitted helminth studied but the fluence was not recorded in these experiments . The initial log reductions achieved were relatively low , only 0 . 38 after five minutes exposure , however exposed larvae were not able to survive for as long as controls . Larvae exposed for five minutes did not live more than five days , whereas 52% of larvae exposed for 30 seconds were able to survive five days or more [36] . Unlike Ascaris spp and Trichuris spp , Ancylostoma spp larvae hatch from the egg in the environment , not inside the host , possibly explaining why this species is more sensitive to UV light . No studies were carried out using Ancylostoma spp eggs . Taenia taeniaeformis eggs were very resistant to UV light , requiring 720 mJ/cm2 to achieve a 0 . 65-log reduction in the number of cysts recovered from the host when compared to control eggs . However , only one study investigated Taenia spp and there are few details of how the fluence was calculated . Furthermore , there was a notable difference in the number of cysts recovered from the controls in each of the experiments ( 4–30% ) and it is unclear why this occurred . In the same study only 30 mJ/cm2 was required for 3-log reduction when the embryophore had been removed , suggesting that as with the eggshell for Ascaris spp , the embryophore is key to Taenia spp resistance to UV light [34 , 41 , 55] . The importance of the eggshell is less clear for other helminths in this review due to the lack of studies . A flow-through reactor with excimer lamps at 222 and 282 nm achieved a 0 . 92-log reduction in Opisthorchis felineus eggs in wastewater at a fluence of 25 mJ/cm2 , suggesting it is relatively sensitive to UV light . However , a very small sample size was used , and it is unclear how the fluence was determined . Only two experiment samples were tested and the number of eggs in the samples prior to UV exposure was not known , only one control sample was tested to calculate the log-reductions [32] . Hymenolepis diminuta ova that were exposed to UV light for a minimum of 30 minutes were unable to develop into cysts . At 15 minutes exposure one cyst was able to develop , though this was deformed [29] . No infections developed in mice injected with 500 exposed Echinococcus granulosus eggs that had been exposed to UV light ( unknown wavelength ) for 24 hours . Yet , when mice were injected with 2 , 000 exposed eggs , 75% were able to develop infections , although significantly less eggs were able to develop into cysts than in control mice ( 0 . 15% compared to 0 . 7% ) . This was probably due to the proportion of viable embryos in each of the doses [33] . Of the trematodes , only one study used miracidia of Fasciola gigantica . There was a significant reduction in cercariae shed from snails when they were infected with one exposed miracidium , compared to one control miracidium , even at very short exposure times less than 70 seconds [61] . The effect of UV light on Schistosoma spp has been the most widely studied and it is the most sensitive helminth in this review . The majority of papers were immunization studies , investigating the use of UV-attenuated cercariae to produce a vaccine against human schistosomiasis . In these experiments cercariae were exposed to a fluence high enough to cause damage and prevent development into adult worms , but that still allowed penetration of the host’s skin . The focus was therefore on the worm burden , and details of the exposure methods were often limited . In the immunization papers a 1-log reduction in worm burden was achieved with fluences of 5–14 mJ/cm2 [25 , 26 , 43 , 62–68] or exposure times of less than one minute [39] . The direct effect of UV light on cercariae was first studied by Krakower in 1940 who found 45 minutes exposure to a mercury lamp ( unknown wavelength ) was required to kill the whole sample . Shorter exposures were still able to cause damage , making the cercariae less motile than the control samples , though they were able to recover from their injuries within 30 minutes and survived for as long as the controls , suggesting schistosome cercariae have some repair potential [44] . Standen and Fuller found that only four minutes was required to kill 100% of S . mansoni cercariae in their study , but the mercury lamp used was very near to the sample ( 2-cm ) and it is unclear if the authors controlled the water temperature [69] . Older mercury lamps are known to have produced a lot of heat and cercariae are inactivated within minutes at 45°C and almost instantly at temperatures above 50°C [70] . Ghandour and Webbe studied the effect of UV light on the ability of S . mansoni and S . haematobium cercariae to penetrate skin . There was a significant increase in mortality during skin penetration when cercariae were exposed for 5–20 seconds , even though they did not appear to be harmed . 10–11% of exposed cercariae were unable to penetrate at all compared to 2–3% of the control sample [37 , 38] . Another study found that short exposure times caused a reduction in the motility of cercariae , but this only became apparent four hours after exposure [39] . Cercariae penetrate skin through enzyme activity and mechanical action , a combination of motility reduction and inhibition of enzymes may have prevented cercarial penetration . Two studies used scanning electron microscopy ( SEM ) to examine the physical damage caused by UV light to S . mansoni . Mohamed showed that adult worms developed from irradiated cercariae had lost their spikes and suffered from torn tubercles and lesions , causing sexual anomalies and sterility , possibly explaining the reduction in fecundity of worms derived from irradiated cercariae in other studies [39 , 64 , 71] . Later Dajem & Mostafa used SEM to examine the damage on the surface of cercariae and discovered that irradiated samples appeared to be physically the same as control cercariae , suggesting the damage observed in adult worms is either a result of mutagenic effects of UV light which only appear later in development , or as a result of the hosts immune response to irradiated cercariae [40] . Another study found that UV exposure modified the structure of molecules on the surface of S . mansoni cercariae , even though no morphological changes occurred . This may have caused an enhanced immune response by the host [72] . Significantly more male S . mansoni worms were able to develop from irradiated cercariae in one study , suggesting that males may be more resistant to UV light than females [73] . This also may explain the reduction in fecundity observed in other studies , but further research is required to confirm this [39 , 64] . S . mansoni and S . japonicum were shown to be equally sensitive to UV light , suggesting the inactivation mechanism is the same in both species [66] . Only one study used S . haematobium cercariae , which were found to be slightly more resistant that S . mansoni cercariae , however no statistical analysis was performed [38] . Prah and James found there was no difference in the response of S . mansoni and S . haematobium miracidia to UV light from mercury source lamps . Experiments in this study were repeated with 1% turbid water , and a 15 . 4% reduction in the rate of movement of miracidia was observed , compared to a 60 . 3% reduction when distilled water was used [45] . However , it should also be noted that distilled water has been shown to kill schistosome cercariae , and it may have a similar effect on miracidia [74] . Whilst many different suspension media have been used , this was the only study in the review that investigated the impact of turbidity or the water matrix on UV disinfection . If UV or solar disinfection is to be used effectively for household and community scale water treatment this aspect requires further research , preferably using water samples collected from the environment . If water collected from local waterbodies is of particularly poor quality ( e . g . high turbidity , iron , or organic matter content ) , consideration may need to be given to pre-treatment , such as filtration or sedimentation . With the recent introduction of UV-C LED technology into the water sector , UV disinfection could be a realistic option for sustainable water treatment in low-income regions in the near future , to provide safe water supplies for water contact activities such as bathing , laundry , and to improve hygiene . Compared to bacterial and viral pathogens there has been little research into the effectiveness of UV light at inactivating helminth eggs or larvae , which are endemic to many developing countries . The majority of studies in this review investigated the effect of UV light on either Schistosoma spp or Ascaris spp , and many were immunization studies used for developing UV-attenuated vaccines , with a focus on the host response to irradiated larvae or eggs , not complete inactivation of the target organism or applications to water treatment . There were limitations to almost all of the studies , the most significant being the lack of a standardized procedure for calculating the UV fluence to which samples were exposed . 68% of studies were carried out before the industry standard protocol for fluence measurement was published in 2003 [22] . In the SODIS studies , experiments were carried out using very small amounts of water which is not representative of how disinfection will take place in practice . Very few studies considered the impact of water quality or accounted for the absorbance of UV by the water column or the effect of shielding caused by suspended particles and other organisms . Mercury lamps are known to produce considerable amounts of heat and it is not clear which of the studies controlled the water temperature . In some studies the fluence was not recorded at all and only one study investigated the repair potential . The methods for determining the viability of larvae or eggs varied , even between papers using the same genera , and this resulted in large ranges in the fluence response , most notably for Ascaris spp . Furthermore , the survival percentage of control samples was not stated in all studies and assumptions were made to calculate the log reductions presented in this review . These limitations make it difficult to directly compare the studies , however some conclusions can be drawn . All helminths included in this review could be inactivated by UV light at certain fluences and wavelengths , but the number of species studied was limited . Helminths which hatch from the egg in the environment were generally more sensitive to UV light than species which stayed in the egg until after they had infected the host . Studies found that eggs were much more sensitive to UV when the shell or embryophore had been removed , suggesting they play a key role in the resistance to UV light for some species . Fluences in excess of 80 mJ/cm2 were required to achieve a 1-log inactivation of Ascaris spp and Taenia spp eggs , over twice the current minimum fluence required by some European countries for the treatment of publicly supplied drinking water [75 , 76] . UV disinfection may therefore not be the most efficient form of water treatment for these helminths . UV disinfection may be particularly effective against Schistosoma spp which was consistently the most sensitive to UV light in this review , however further experimental research is required using the standard fluence measurement protocol . This systematic review has demonstrated that evidence exists to suggest that UV disinfection is effective against some helminths , but the data covers a limited number of species and is insufficient to produce detailed recommendations for household or community scale UV or solar disinfection of water in endemic regions . To aid the design of these water treatment systems we recommend the following for future studies on UV disinfection of WASH-related helminths: | Helminth infections are currently controlled by mass administration of anthelmintic drugs which are effective at treating the diseases but cannot prevent reinfection . As we work to eliminate these diseases , complimentary control methods such as improving access to water , sanitation , and hygiene will be crucial to reduce re-exposure and cut transmission . UV disinfection is a widely used form of water treatment but it is often seen as incompatible with low income regions . Recently developed UV-C LEDs and SODIS offer alternative sources of UV light that may be more suitable for this context , but there is little guidance about how we can use this technology to prevent helminth infections . We carried out a systematic review to establish which helminths are more sensitive to UV light and identify the areas which need further research . This will enable the production of design guidelines for household and community scale UV water treatment , so that the WASH community will be able to take full advantage of the recent developments and standardizations in UV disinfection technology . | [
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| 2019 | Ultraviolet sensitivity of WASH (water, sanitation, and hygiene) -related helminths: A systematic review |
Approximately 10% of genes in the human genome are distributed such that their transcription start sites are located less than 1 kb apart on opposite strands . These divergent gene pairs have a single intergenic segment of DNA , which in some cases appears to share regulatory elements , but it is unclear whether these regions represent functional bidirectional promoters or two overlapping promoters . A recent study showed that divergent promoters are enriched for consensus binding sequences of a small group of transcription factors , including the ubiquitous ets-family transcription factor GA-binding protein ( GABP ) . Here we show that GABP binds to more than 80% of divergent promoters in at least one cell type . Furthermore , we demonstrate that GABP binding is correlated and associated with bidirectional transcriptional activity in a luciferase transfection assay . In addition , we find that the addition of a strict consensus GABP site into a set of promoters that normally function in only one direction significantly increases activity in the opposite direction in 67% of cases . Our findings demonstrate that GABP regulates the majority of divergent promoters and suggest that bidirectional transcriptional activity is mediated through GABP binding and transactivation at both divergent and nondivergent promoters .
Since the discovery that more than 10% of the protein-coding genes in the human genome are located on opposite strands with transcription start sites less than 1 kb away from each other , there has been considerable interest in determining why such an arrangement exists [1–3] . Studies in mammals and more distantly related organisms have shown that these divergently transcribed gene pairs have highly correlated expression patterns [3–6] . However , neighboring , nondivergently transcribed genes also often have correlated expression patterns [5 , 7 , 8] . This raises the question of whether the regulation of divergent gene pairs is due to some unique aspect of their paired arrangement or merely a consequence of their proximity . A likely contributor to coregulation of divergent gene pairs is the less than 1-kb-long intergenic region between divergent transcription start sites of an annotated gene on the plus strand and another on the minus strand . Efforts to determine whether differences exist between these promoters and those that do not lie between start sites for two annotated divergent genes ( referred to here as general promoters ) have yielded mixed results . As a group , divergent promoters exhibit increased colocation with CpG islands , a paucity of TATA elements , and an enriched subset of transcription factor binding sites [3 , 9] . These features suggest that the functional behavior of divergent promoters is distinct from that of general promoters . Despite these broad differences , when the function of promoters from the two groups was tested by transient transfection of luciferase reporter plasmids into human tissue culture cells , 48% of general promoters were capable of directing transcription in both directions at similar levels , which we refer to as balanced bidirectional activity , compared with 66% of divergent gene promoters [10] . Another study using integrating lentiviral vectors designed for gene therapy demonstrated that several general promoters were capable of transcribing marker genes in both directions , suggesting that general promoters can be capable of bidirectional transcription in a genomic context [11] . This raises the question of whether bidirectional activity is the default state for certain promoters in the absence of directional “repressors . ” Despite some distinctive sequence characteristics , no clear functional features differentiate divergent from general promoters . Our recent analysis of 376 promoters showed that five motifs are over-represented in divergent gene pairs [9] . These motifs correspond to the binding sites of the transcription factors Nrf-1 , CCAAT , YY1 , Sp1 , and GA-binding protein ( GABP ) . GABP , an ets family transcription factor , was of particular interest because the presence or absence of its binding site in a set of divergent promoters explained much of the variation in activity observed for a set of reporter deletion constructs [9] . Additionally , the targeted disruption of a GABP site in a 30-bp fragment that we showed directs bidirectional transcription in a luciferase assay abrogated activity in both directions . Analysis of GABP motif-containing promoters from divergent pairs by chromatin immunoprecipitation ( ChIP ) led us to estimate that up to 57% of such promoters are bound by GABP , suggesting that GABP is an important regulator of divergent gene pairs . GABP is a key transcriptional regulator of myeloid , cellular respiration ( reviewed in [12] ) , and ribosomal protein genes [13] . GABPα has also been shown to be required for both cell cycle progression [10] and early embryogenesis [14] in mouse . GABP , also known as NRF-2 , is unique among ets-family transcription factors as it functions as a heterodimer composed of an α and a β subunit . The α subunit , encoded by the GABPA gene , contains the ets DNA-binding domain and is widely expressed from a divergent promoter that it self-regulates [15] . The β subunit , encoded by an unrelated gene , GABPB2 , contains the transcriptional activation domain as well as four ankyrin repeats necessary for dimerization with the DNA-binding subunit . Tandem GABP sites have been shown to be capable of initiating transcription in the absence of other cis-elements [16] , indicating that GABP is a potent transcriptional activator . When two recognition sites were present in the same orientation , GABP-initiated transcription was mapped to the more 5′ of the two GABP sites . This property of GABP-dependent transcription , coupled with the observation that many divergent promoters have multiple GABP sites , could explain why overlapping transcription start sites have been observed at some divergent genes [17] . These aspects of GABP biology , along with the presence of one or more of its binding sites in most divergent promoters , suggest that GABP regulates bidirectional transcription . In this paper , we report our studies of the relationships between GABP , divergent gene promoters , and bidirectional transcriptional activity . First , we used ChIP to measure GABP occupancy at 412 randomly selected promoters from divergent and general genes in three cell types . We then compared binding results for a subset of these promoters in a single cell type to the transient promoter activity in both directions for a representative promoter fragment to test whether binding and bidirectional transcriptional activity are correlated . On the basis of these results , we assayed whether the introduction of a single GABP site into a nondivergent promoter was sufficient for bidirectional transcriptional activity . Our data indicate that GABP not only binds the majority of divergent promoters , but that GABP binding correlates with bidirectional transcriptional activity and that the introduction of a GABP site into a promoter fragment is sufficient for such activity . These findings provide a mechanism for the regulation of bidirectional transcription at divergent and general promoters by GABP .
We performed ChIP followed by quantitative PCR ( QPCR ) in Jurkat , K562 , and HeLa cells with a monoclonal antibody that specifically recognizes GABPα to determine the fraction of divergent and general promoters bound by this factor . We tested for GABP enrichment in ChIP QPCR assays of 121 divergent and 50 general promoters that we previously studied [3] . We also tested 241 experimentally verified , nondivergent promoters identified in the ENCODE Project [18] . We used a cutoff of 3-fold enrichment to classify a promoter as bound; this threshold corresponds to more than three standard deviations from the mean enrichment of five negative control fragments . In all three cell types , a majority of divergent promoters were bound by GABP; 98/113 ( 87% ) , 91/115 ( 79% ) , and 73/117 ( 62% ) promoters were bound in Jurkat , K562 , and HeLa cells , respectively ( Figure 1 ) . By comparison , 58/234 ( 25% ) , 67/230 ( 29% ) , and 50/275 ( 18% ) of general promoters were bound in those same cell types . In all three cell types , the fraction of promoters from divergent genes bound by GABP was significantly higher than for nondivergent genes ( p < 0 . 0001 for all cell types; χ2 ) . Overall , 105/121 ( 87% ) of the divergent promoters were bound by GABP in at least one cell type , which was significantly greater than 89/291 ( 31% ) general promoters that were bound in at least one cell type ( p < 0 . 0001; χ2 ) ( Table S1 ) . As was previously noted [3] , divergent promoters contain an increased percentage of CpG dinucleotides relative to general promoters . To exclude the possibility that GABP binds preferentially to CpG-rich promoters and not necessarily to divergent promoters , we compared binding at promoters of divergent and general genes using a subset of our data with similar CpG composition ( see Materials and Methods ) . For divergent promoters , 82/96 ( 85% ) , 74/96 ( 77% ) , and 61/97 ( 63% ) were bound in Jurkat , K562 , and HeLa respectively . For the CpG matched set of general promoters , the fraction bound was 41/96 ( 43% ) , 45/96 ( 47% ) , and 34/97 ( 35% ) . Although the set of general promoters with matched CpG content exhibited a greater fraction of bound promoters than the complete set , divergent promoters still exhibited a significantly greater fraction of promoters bound by GABP ( p < 0 . 0001 for all cell types; χ2 ) . We also determined whether divergent and general promoters are bound differently by GABP across the tested cell types . We considered only those promoters that were bound in at least one cell type and had binding information for all three cell types . From this set , we then counted the number of promoters bound in all three cell types , in all pairwise combinations , and those that were bound in only single cell types . We then compared the number of promoters bound in each subset of cell lines for divergent promoters and general promoters and found the differences to be statistically significant ( p = 0 . 0011; χ2 ) ( Table S2 ) . Accordingly , we depict the data for divergent and general promoters as separate Venn diagrams ( Figure 2 ) . We found that GABP bound the majority ( 69% ) of divergent promoters in all three cell types . Only 48% of bound general promoters were bound in all three cell types , and the number of general promoters bound in a single cell type was larger than the number of divergent promoters for two of the three cell lines . We conclude from the analysis of three cell lines that general promoters were bound by GABP in a more cell-specific manner , while divergent promoters were more frequently bound in all tested cell types . Given that ets-family transcription factors all recognize very similar binding sites [19] , we tested another ubiquitously expressed ets-family member , ETS1 , by ChIP to exclude the possibility that divergent genes were not regulated more generally by ets factors . We tested for ETS1 enrichment at a subset of divergent and general promoters exhibiting a range of GABP enrichment values and cell-line specificity . For divergent promoters , ETS1 bound 19/44 ( 43% ) , 3/44 ( 7% ) , and 1/47 ( 2% ) of targets in Jurkat , K562 , and HeLa cells , respectively , using the same 3-fold enrichment cutoff as was used for GABP ( Table S3 ) . For the same targets , GABP bound 33/44 ( 75% ) , 25/44 ( 57% ) , and 24/47 ( 51% ) promoters in those three cell types . We compared the number of promoters bound or not bound by the two factors independently in each cell type and found that GABP bound significantly more promoters than ETS1 ( p = 0 . 0024 for Jurkat , p < 0 . 0001 for K562 and HeLa; χ2 ) . We looked for overlap between ETS1 and GABP binding at the same promoter and found 17/44 ( 37% ) , 3/44 ( 7% ) , and 1/47 ( 2% ) divergent promoters were bound by both factors in the three tested lines . For general promoters , 18/33 ( 55% ) , 19/32 ( 59% ) , and 18/34 ( 53% ) of selected targets were bound by GABP in the three tested cell lines , compared to 9/33 ( 27% ) , 2/32 ( 6% ) , and 2/34 ( 6% ) promoters that were bound by ETS1 . For all three cell lines , GABP bound a significantly greater number of general promoters than ETS1 ( p < 0 . 0001; χ2 ) . Despite ETS1 binding to a large number of divergent promoters in Jurkat cells , GABP bound a greater number of targets than ETS1 for all tested cell lines and promoter types . A previous study reported that many general promoters are capable of bidirectional transcriptional activity in a transient reporter assay [3] . This finding in combination with our observations on the broad binding distribution of GABP prompted us to explore the relationship between bidirectional transcriptional activity and GABP binding . We used the raw data from 145 promoters tested for luciferase activity in both orientations in the previous study and generated similar data for an additional 88 promoters in HeLa cells . Bidirectional transcriptional activity was previously reported by taking the log2 ratio of luciferase activity in the forward direction over the reverse direction ( log2 Fwd/Rev ) . As the forward direction was arbitrarily chosen for divergent promoters , we chose to reflect expression activity as the log2 ratio of the higher luciferase activity over the lower of the two orientations for a given promoter ( log2 H/L ) . All promoters used in this study demonstrated activity above the mean plus three standard deviations of a set of negative controls in at least one direction and no general promoters were observed with reverse activity greater than forward . We tested for an association between GABP binding and promoter activity in both directions by first classifying each promoter as bound ( ChIP enrichment greater than 3-fold ) or unbound . We then scored each promoter for whether its representative fragment was capable of directing luciferase activity above background in both directions or only one . We considered all promoters together , regardless of annotation , and compared the number of promoters that were or were not bound and did or did not demonstrate luciferase activity in both directions above background ( Table 1 ) . We found that GABP binding and transcriptional activity above background in both directions were highly dependent and statistically significant ( p < 0 . 0001; χ2 ) . Previously , the majority of divergent promoters were observed to direct luciferase expression to similar levels in both directions ( observed log2 H/L ratio of less than 2 ) , a phenomenon we call “balanced bidirectional transcription . ” To test whether GABP binding and balanced bidirectional activity are associated , we performed another categorical analysis , this time classifying promoter activity as balanced bidirectional or otherwise . For comparison , the percentage of all promoters with a log2 H/L ratio of less than 2 was lower than the percentage of promoters with activity above background in both directions ( 57% versus 80% ) . For promoters of divergent genes ( n = 117 ) , 80% exhibited balanced expression while 93% demonstrated promoter activity above background in both directions , compared to 34% and 64% of general promoters ( n = 116 ) , respectively ( Table S4 ) . This more stringent definition of bidirectional activity was also significantly dependent upon GABP binding ( p = 0 . 0103; χ2 ) . Thus , GABP binding is very strongly associated with transcriptional activity above background in both directions as well as with balanced bidirectional activity . Lastly , we examined the correlation between GABP enrichment at each promoter region and its promoter activity ratio ( Figure 3 ) . In addition to high GABP enrichment values ( median = 7 . 12 ) , the majority of divergent promoters ( bottom panel ) demonstrated low log2 H/L ratios ( median = 1 . 02 ) , indicating similar levels of luciferase activity in each direction . General promoters showed a much broader range of expression ratios ( median = 2 . 81 ) , as well as a trend for lower GABP enrichment values ( median = 1 . 66 ) . Across all promoters examined , GABP binding and log2 H/L are significantly anticorrelated ( Spearman's ρ = −0 . 1773 , p = 0 . 0067 ) . Thus , regardless of the promoter's annotation , promoters bound by GABP ( high enrichment values ) tended to exhibit balanced bidirectional transcriptional activity ( low log2 H/L ratio ) . Having shown that GABP binding and bidirectional transcriptional activity are correlated , we tested whether the introduction of a GABP site into a promoter is sufficient to induce bidirectional transcriptional activity . We selected six functional promoters that were not bound by GABP in any tested cell type and had little or no expression in the reverse direction , and introduced a single , strict consensus GABP site , CCGGAAGTG , into each promoter through site-directed mutagenesis . Four of six promoters ( 67% ) demonstrated significantly increased luciferase activity in the reverse direction ( Figure 4 ) . The average increase in the reverse direction for these promoters was 4 . 5-fold . At two of these four significantly changed promoters , the increase in reverse activity yielded a log2 H/L ratio indicative of balanced bidirectional activity . In contrast , the minimum log2 H/L ratio before the introduction of the GABP site was 3 . 6 . Notably , only one of the six promoters demonstrated a significant increase in forward transcriptional activity . Of the two promoters without significant increases in expression , one exhibited significantly decreased expression , while the other was unchanged . In the majority of tested promoters , the addition of a single GABP site was sufficient to increase transcriptional activity in the reverse orientation .
We tested 121 and 291 randomly selected promoters from divergent and general genes , respectively , by ChIP in three cell lines to determine the percentage of genes from each class that were bound by GABP . Using an enrichment threshold of 3-fold for classifying a promoter as bound , we found that 86 . 8% of divergent promoters and 30 . 6% of general promoters were bound in at least one cell type . The significantly larger fraction of divergent promoters bound by GABP was also observed when comparing a subset of the two promoter types with matched CpG content . Furthermore , most divergent promoters were bound in all three cell types , suggesting that GABP regulates these promoters in a wide variety of tissues . This is not surprising given the broad expression of GABP and its tendency to regulate ubiquitously and broadly expressed genes , a class to which many divergent gene pairs belong [3 , 12] . However , a small fraction of divergent genes , as well as a large number of other genes , showed cell line specific binding , so the fraction of promoters regulated by GABP could increase with additional cell type sampling . Collectively , these studies suggest that GABP regulates the vast majority of divergent gene pairs as well as a large number of general genes . The finding that GABP binds to such a large percentage of divergent gene promoters is surprising , but not unexpected . Our previous study estimated that the transcription factor binds to 57% of divergent gene promoters based upon observed binding frequencies , using a 5-fold enrichment cutoff , for promoters containing a high , medium , or low scoring motif [9] . In this study , we examined a larger number of promoters without first scanning for GABP motifs , tested an additional cell type and used a less conservative but still stringent cutoff . Applying a binding cutoff of 5-fold to our current data decreases the fraction of bound promoters from 87% to 77% for divergent promoters and from 31% to 20% for general promoters; neither change would alter our conclusions . That GABP binds to such a large fraction of both divergent and nondivergent promoters is also not surprising given that its binding site is one of the top ten most common sequence motifs in the human genome [20] . The broad expression of many ets-family transcription factors and the similarity in their binding sites led us to test whether ETS1 , another ubiquitously expressed family member , might also be involved in the regulation of divergent genes [19] . Interestingly , we found considerable overlap between GABP and ETS1 binding at both divergent and general genes in Jurkat cells . This is perhaps not surprising , as ETS1 is the most highly expressed ets-family member in this cell line [19] . However , for all tested cell lines , GABP bound a larger fraction of divergent promoters than did ETS1 , and only in Jurkat cells did the latter factor bind more than 10% of targets . In addition to any functional differences conveyed by the binding of these ets transcription factors , it would appear that GABP regulates divergent promoters on a widespread basis at a basal level with ETS1 and/or other family members , perhaps serving to modify this activity in a cell-specific manner . In light of the observation that many general promoters are capable of balanced bidirectional transcriptional activity , we explored the relationship between GABP binding and bidirectional transcriptional activity at all promoters [3] . We found that GABP binding was significantly associated with promoter activity above background in both directions ( p < 0 . 0001; χ2 ) and with balanced bidirectional transcription ( p = 0 . 0103; χ2 ) , supporting a model whereby GABP directs bidirectional transcription . Furthermore , GABP enrichment was significantly anticorrelated with the log2 ratio of promoter activity for each direction of the corresponding promoter fragment ( Spearman's ρ = −0 . 1773 , p = 0 . 0067 ) . The low correlation coefficient can be partly explained by recognizing that we would not expect a perfect correlation between the degree of enrichment and a lower ratio of activities in each direction . Although the sum of motif occurrence scores correlates with the degree of enrichment [9] , there is no reason to expect that the strength or amount of GABP binding would lead to more balanced transcriptional activity . Nonetheless , these data strongly suggest that GABP binding directs bidirectional transcription at divergent and general promoters alike . Despite strong statistical evidence that GABP binding and bidirectional activity are both correlated and associated , we observed a number of unbound promoters that demonstrated balanced bidirectional activity as well as examples of bound promoters with activity primarily or only in one direction . Many of the former cases likely represent promoters that are bound in vivo but were not scored as bound because they fell below 3-fold enrichment threshold . HeLa cells , in addition to having the lowest percentage of bound divergent promoters that are bound by GABP , had enrichment values on average half that of those observed in K562 and Jurkat cells for those promoters that were bound in all three cell types ( unpublished data ) . In addition , there are almost certainly mechanism ( s ) for bidirectional activity that are not dependent on GABP binding . For example , five adjacent Sp1 sites were capable of directing bidirectional transcription in vitro [21] . We compared Sp1 and GABP binding at the set of promoters from our previous study [9] and were not able to observe a significant correlation between factor binding in either Jurkat or K562 cells ( unpublished data ) . Nonetheless , it will be interesting to see the degree of overlap between GABP binding and that of Sp1 and other factors with binding sites over-represented among divergent promoters . There are several possible interpretations for GABP bound promoters that do not conform to our definition of balanced bidirectional transcription . First , not all divergent promoters have log2 H/L expression ratios of less than 2 . Although this definition of balanced bidirectional transcriptional activity encompasses the majority of divergent promoters , it may exclude many biologically relevant bidirectionally active promoters . In addition , unknown regulatory element ( s ) may modify the bidirectional activity conveyed by GABP binding . For example , TATA elements are known to convey tissue and start-site specificity and may also influence the directionality of transcription [22] . We tested for a correlation between predicted TATA binding site log-likelihood scores and log2 H/L ratio among all promoters in this study and observed a significant , positive relationship ( Spearman's ρ = 0 . 2601 , p < 0 . 0001 ) , although , in another study , the introduction of a TATA and/or Inr element into the mouse thymidylate synthase promoter had no effect on its bidirectional activity [23] . In general , it is clear that GABP binding confers bidirectional activity at a large number of promoters , but this activity may be modified in a number of ways on a gene-by-gene basis . In this study , we observed balanced bidirectional transcriptional activity at 34% of general promoters . One possible explanation for this observation is that a strong promoter contains elements sufficient for leaky expression in the other direction when reversed in our reporter plasmids . To investigate this possibility , we looked for a correlation between activities in each direction for general promoters and found a significant relationship ( Spearman's ρ = 0 . 5883 , p < 0 . 0001 ) . However when we compared the luciferase activity values for general promoters that were categorized as having balanced bidirectional activity and those that did not , there was no significant difference ( p = 0 . 3419; Mann-Whitney U-test ) . This suggests that although there may be some expression in the reverse direction due to a strong promoter , this was not sufficient to result in balanced bidirectional activity . Another likely explanation is that the approximately 500-bp promoter fragment we cloned may not have contained all the regulatory sequences necessary for its proper function in the genome . Doubtless other transcription factor ( s ) and/or boundary elements such as insulators play a role in controlling the level and direction ( s ) of promoter activity in their endogenous genomic context . Finally , many of the general promoters for which we observed bidirectional activity may actually belong to an unannotated divergent gene pair . A subset of these may prove to be functional bidirectional promoters with a protein-coding gene in one orientation and some other type of transcript in the other . Several recent studies have reported that a much larger percentage of the genome is transcribed than was previously thought , much of this occurring outside of annotated genes [17 , 24 , 25] , and it will be interesting to see whether these transcripts are in any way specifically initiated or are merely an unintended consequence of GABP regulation of promoter function . While GABP may be responsible for the majority of balanced bidirectional activity , the regulation of directionality at promoters needs more research . Finally , having shown that GABP is correlated with bidirectional transcriptional activity , we tested whether the introduction of a single GABP site into a promoter with no annotated reverse transcript , little to no promoter activity in the reverse direction and no evidence for GABP binding might be sufficient to induce such activity . Four of six such promoters produced significant increases in reverse luciferase activity after the introduction of a GABP site . Furthermore , for at least one of the promoters that did not show a significant increase in either direction , NM_001697 , it is likely that we inadvertently disrupted other sequences necessary for promoter function , as there were significant decreases in activity in both directions . We cannot explain why the introduced site was not capable of directing balanced transcriptional activity in all cases , although it is worth noting that not all divergent promoters exhibit balanced activity . Even in the case of the small change observed in the reverse direction for AF161466 , the fold increase was 1 . 9 , which is likely to be biologically relevant for a dosage sensitive gene product . These results are particularly striking given that GABP sites were introduced without regard for site orientation or proximity to the start of transcription . Previous work demonstrated the ability of two tandem GABP sites to drive transcription in either orientation [16] , but our study is the first report to our knowledge to show that a single site can drive transcription in both directions in a functional promoter . Given that previous studies have shown that GABP sites might be required for bidirectional transcriptional activity [9 , 15] , these results argue very strongly that a GABP site can be both necessary and sufficient for bidirectional transcriptional activity . While our data do not suggest how the paired gene arrangement came to exist , they are consistent with a role for GABP regulation in maintaining this relationship . In this model , unknown forces would bring two critical genes together , each with its own promoter , and GABP would reinforce this relationship through its ability to direct balanced , bidirectional transcriptional activity . Given its ability to be sufficient for such activity , the presence of a GABP site could make other factor binding sites redundant over evolutionary time . Thus , the short intergenic region between the gene pair would be free to accumulate mutations , each gene losing its independent regulators and becoming part of an inseparable , truly bidirectional promoter . This supports the conclusion that the promoters of divergent gene pairs do not consist of two overlapping promoters , and that in addition to the previously noted characteristics of these promoters , their bidirectional character is in large part conveyed by GABP binding . Analyses of transcription start site distributions in species from fish to mammals have revealed that although a bimodal distribution , indicative of an abundance of divergent gene pairs , seems to exist only in mammals , 83 divergent pairs have been maintained in proximity and orientation as far separated from humans as Fugu [2 , 4] . A search of HomoloGene ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=homologene ) indicated that although GABPα has an ortholog in Drosophila , the most distant GABPβ ortholog has been observed in Danio rerio ( Fugu is not currently represented in HomoloGene ) . The coincident emergence of conserved divergent gene pairs and a GABP heterodimer capable of bidirectional transcriptional activity reinforces the finding that GABP is the major regulator of divergent genes . In summary , we showed that GABP binds the majority of divergent promoters and is correlated with and sufficient for bidirectional activity . We have established its role as a major regulator of divergent genes , which carry out a variety of activities critical for the function and survival of many different cell types . In addition , GABP binds a large number of nondivergent genes , and further study will be needed to ascertain whether its ability to promote bidirectional transcription genes has widespread biological consequences through the generation of novel and/or noncoding transcripts .
ChIP was performed as described [9] in HeLa , K562 , and Jurkat cells ( ATCC ) using either a mouse monoclonal antibody recognizing GABPα , sc-28312 , or a polyclonal rabbit antibody against ETS1 , sc-350 ( Santa Cruz Biotechnology ) . We tested for GABP enrichment by quantitative PCR of amplicons designed to the promoters of 121 random divergent gene pairs and 50 general genes from a previous study [3] , as well as 241 general genes from ENCODE regions [18] . Primers for quantitative PCR as well as corresponding average enrichment values for each target in each cell type can be found in Dataset 1 . ETS1 enrichment was assayed for a subset of these targets , 48 divergent and 36 general promoters , using the same primers . Promoters showing greater than 3-fold enrichment relative to the average of five negative controls were considered bound . We then compared the number of GABP bound and unbound promoters for divergent and general promoters for each cell type individually using χ2 analysis in the Prism 4 . 0c ( GraphPad Software ) software package . We also determined the number of promoters that were bound in at least one cell type and compared this to those that were bound in no cell types for each type of promoter . We also compared the number of promoters that were and were not bound by GABP and ETS1 again for each cell line independently . We considered only promoters with binding information for both factors in each cell type . However , as divergent and general promoters were not randomly sampled for ETS1 , we did not compare the fraction of promoters bound for each type of promoter . We attempted to extract the largest possible set of CpG-content matched divergent and general promoters from our dataset . We calculated CpG percentage based upon the cloned promoter sequence , and then for each cell type , averaged CpG content for promoters for which QPCR data was available . Promoters from each group were then removed until the average CpG content for the two groups was within 0 . 1% for equal sample sizes . For divergent promoters , the average CpG contents were 31 . 2% , 31 . 1% , and 31 . 0% for Jurkat , K562 , and HeLa , respectively . For general promoters , CpG contents were 31 . 1% , 31 . 1% , and 31 . 0% for promoters sampled from those three cell lines . The 241 general promoters tested for GABP ChIP enrichment were previously cloned into the plasmid pGL3 Basic ( Promega ) and verified to be functional promoters as part of the ENCODE project [18] . We amplified these promoters , consisting of the sequence from approximately −500 to +50 bp relative to the start of transcription , by using common primers flanking the MCS ( Forward 5′-CATACGCTCTCCATCAAAACAAA-3′ , Reverse 5′-TTTATGTTTTTGGCGTCTTCCAT-3′ ) , digested them with MluI and BglII ( New England BioLabs ) , and then recloned them into a pGL3 Basic vector with a reversed MCS , yielding a luciferase reporter plasmid with a reversed promoter sequence . A total of 50 ng of each promoter construct was cotransfected with 5 ng of pRLTK ( Promega ) , a Renilla luciferase-expressing transfection control , using FuGene ( Roche ) . Transfections were performed in 96-well plates seeded approximately 24 h earlier with 5 , 000 HeLa cells in each well . Cells were lysed 24 h after transfection and assayed for luminescence on a Victor Light luminometer ( Perkin Elmer ) . The ratio of luciferase to Renilla luminescence was calculated for all fragments and compared to a panel of 24 negative controls . Sample ratios that were lower than the mean plus three standard deviations were considered to not be expressed above background and were recorded as 0 . 1 to permit the calculation of directional activity ratios . Only promoters with activity above background in at least one direction were subjected to further analyses . For 88 promoters we calculated the log2 ratio of the promoter orientation with the higher promoter activity over the lower ( log2 H/L ) . In all cases , the higher activity of the two orientations corresponded to the sense orientation relative to the annotated gene product . We also calculated log2 H/L ratios for 117 divergent and 28 general genes from a previous study [3] . The cloned promoter sequences , luciferase to Renilla ratios for each direction and the log2 H/L ratio for promoters in this study can be found in Dataset 1 . We plotted the activity ratio for each promoter against its corresponding GABP enrichment score and then performed a nonparametric correlation on the data using the Spearman method in the Prism 4 . 0c software package . We also categorized each promoter as either having luciferase activity above background in one or both promoter orientations . We then compared the number of promoters that were or were not bound by GABP and did or did not demonstrate luciferase activity above background using χ2 analysis in Prism 4 . 0c . We also performed a similar χ2 analysis , but this time compared the number of bound and unbound promoters with log2 H/L ratios below two , indicating balanced bidirectional activity , or above two . We selected six sequences with experimentally confirmed unidirectional activity and no evidence for GABP binding in any cell type . We then designed primers , using the web-based PrimerX tool ( http://bioinformatics . org/primerx/ ) , to introduce one to four substitutions around an existing GGAA sequence in the wild-type promoter resulting in a GABP consensus site , CCGGAAGTG . Mutagenesis reactions were performed with Quikchange mutagenesis kit ( Stratagene ) in accordance with the manufacturer's protocol . The mutant sequences , once confirmed by sequence in each orientation , were then transfected into HeLa cells three times independently in sextuplicate alongside wild-type controls as above . The original cloned promoter sequence , mutagenesis primers , and luciferase to Renilla ratios for each direction for mutated and wild-type promoters can be found in Dataset 2 . Luciferase to Renilla ratios were pooled across the three independent experiments and wild-type ratios were compared to those of the mutant using a two-tailed Welch's t-test in Prism 4 . 0c .
The RefSeq ( http://www . ncbi . nlm . nih . gov/RefSeq/ ) accession numbers for the genes and gene products discussed in this paper are: GABPA and ETS1 ( NM_002040 and NM_005238 , respectively ) . GABPB2 is listed under accession numbers NM_181427 , NM_002041 , NM_016655 , NM_005254 , and NM_016654 . | Surveys of the locations of genes in the human genome have revealed that a surprising number of genes , greater than 10% , have transcription start sites within 1 kb of one another on opposite strands . These divergent gene pairs , sometimes referred to as bidirectional genes , are common in organisms such as bacteria and yeast , but it is unknown why such an arrangement exists in large , mammalian genomes . Recently , it has become apparent that the promoters of these divergent genes are regulated by a subset of transcription factors , and we have focused on one of these , GA-binding protein ( GABP ) . We find that it regulates a large number of human genes , including the majority of divergent genes , and that its binding is associated with , correlated with , and sufficient for bidirectional transcriptional activity . Although clearly GABP is a major regulator of divergent genes , which carry out a variety of roles critical for the function and survival of the cell , these data also propose novel roles for GABP as a transcription factor . For example , the ability of GABP to promote bidirectional transcription may prove to be biologically relevant in generating many of the transcripts that have been observed outside of protein coding genes . | [
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| 2007 | The ets-Related Transcription Factor GABP Directs Bidirectional Transcription |
Cells trigger massive changes in gene expression upon environmental fluctuations . The Hog1 stress-activated protein kinase ( SAPK ) is an important regulator of the transcriptional activation program that maximizes cell fitness when yeast cells are exposed to osmostress . Besides being associated with transcription factors bound at target promoters to stimulate transcriptional initiation , activated Hog1 behaves as a transcriptional elongation factor that is selective for stress-responsive genes . Here , we provide insights into how this signaling kinase functions in transcription elongation . Hog1 phosphorylates the Spt4 elongation factor at Thr42 and Ser43 and such phosphorylations are essential for the overall transcriptional response upon osmostress . The phosphorylation of Spt4 by Hog1 regulates RNA polymerase II processivity at stress-responsive genes , which is critical for cell survival under high osmostress conditions . Thus , the direct regulation of Spt4 upon environmental insults serves to stimulate RNA Pol II elongation efficiency .
Cells sense and robustly respond to environmental fluctuations to maximize cell fitness . An increase in extracellular osmolarity provokes immediate cellular responses that are crucial for cell survival . These responses are mainly controlled by the High Osmolarity Glycerol ( HOG ) pathway [1 , 2] in which the p38-related Hog1 Stress-Activated Protein Kinase ( SAPK ) modulates almost any aspect of the cell physiology essential for cell survival under stress . In response to osmostress , there is a major change in the transcriptional pattern of the cell that is crucial for long term adaptation [3 , 4] . The coordinated expression of such stress-responsive gene reprogramming is accomplished by the regulation of several steps in mRNA biogenesis and mRNA fate through the Hog1 SAPK ( reviewed in [5 , 6] ) . This signaling kinase associates with chromatin [7 , 8] to specifically target RNA Pol II to stress-responsive genes in order to bypass the general down-regulation of gene expression that occurs during stress conditions [4 , 9] . Association of Hog1 with stress-responsive genes is strongly correlated with chromatin remodeling and increased gene expression [4 , 10–13] . The mechanisms by which Hog1 regulates initiation of transcription are well-described and involve direct phosphorylation of specific transcription factors [14 , 15] as well as recruitment of coactivators to osmo-responsive promoters [8 , 16 , 17] and chromatin modifying activities [12 , 18–20] . Hog1 also associates with the coding regions of osmo-dependent genes , where it acts as a transcription elongation factor specific for stress-responsive genes [21] . However , how this SAPK regulates elongation of stress-dependent genes still remains to be elucidated . Transcription elongation by RNA Pol II is a dynamic and highly regulated step in the gene expression cycle , where elongation accessory and/or chromatin remodeling factors play an essential role ( reviewed in [22 , 23] ) . The Spt5/Spt4 complex is a universally conserved RNA Pol II-associated factor with a pervasive role in transcription elongation ( [24 , 25] and reviewed in [26] ) . Whereas Spt4 is a small zinc finger protein , Spt5 is a large multi-domain protein consisting of an N-terminal acidic domain , a NusG N-terminal ( NGN ) domain , multiple Kyprides-Ouzounis-Woese ( KOW ) domains and a set of short repeats at its C-terminus ( CTR ) . Crystallographic studies have shown that Spt4 interacts intimately with the NGN domain of Spt5 in formation of the Spt4-Spt5 heterodimeric complex that serves to stimulate polymerase processivity [27–31] . Apparently , Spt4 might stabilize the elongating RNA Pol II , thereby allowing polymerase molecules to travel long distances through a gene without dissociating from the template [32] . Spt4 is also required for the transcription of GC-rich DNA sequences [33] and it has been shown to help RNA Pol II to traverse lengthy gene regions that encode polyglutamine repeats [34] . Here , we show that in response to stress the Hog1 SAPK phosphorylates Spt4 to regulate the transcriptional response . The targeting of Spt4 by Hog1 enhances RNA pol II processitivity at stress-responsive genes . Remarkably , the non-phosphorylatable mutant of Spt4 renders cells partially osmo-sensitive . Therefore , our results demonstrate that the HOG signaling pathway directly targets elongating RNA Pol II to modulate polymerase processivity in response to environmental insults .
To elucidate the role of Hog1 in transcription elongation , we tested whether Hog1 directly phosphorylates any of the RNA Pol II transcription elongation factors . We systematically purified a subset of 40 TAP-tagged proteins involved in transcriptional elongation and subjected them to an in vitro phosphorylation assay with active Hog1 ( S1 Fig ) . Of these proteins , we found 3 transcriptional-related factors ( Spt6 , Spt5 and Spt4 ) that were phosphorylated by Hog1 and followed up the phosphorylation event of the elongation factor Spt4 . We focused on Spt4 since it has a unique putative phosphorylation consensus site for the SAPK and it was suggested to be involved in the osmostress response [21] . To validate this result , we used GST-tagged Spt4 purified from Escherichia coli in an in vitro kinase assay with Hog1 that was activated in the presence of a constitutively active MAPKK allele ( Pbs2EE ) [15] . As shown in Fig 1A , Spt4 was phosphorylated only when it was incubated with activated Hog1 , indicating that it is a direct substrate for this SAPK . We next determined the sites of Spt4 phosphorylation by Hog1 . Spt4 contains the sequence Thr42-Ser43-Pro44 that coincides with the putative S/TP MAPK consensus site and combined mutations of Thr42 ( and the adjacent Ser43 ) to Ala resulted in total loss of phosphorylation by Hog1 ( Fig 1A ) . It is worth noting that these sites in Spt4 are extremely conserved throughout evolution , although in some cases the proline is not conserved ( Fig 1A ) . To assess Spt4 phosphorylation in vivo , endogenous TAP-tagged Spt4 was immunopreciptated from wild type and hog1Δ mutant cells that were subjected or not to osmostress , and Spt4 phosphorylation was analyzed by Western blotting using anti-pSer/Thr antibodies as described in [35] . As shown in Fig 1B , Spt4 phosphorylation increased upon stress and this phosphorylation was dependent on Hog1 since it did not increase in hog1Δ mutant cells . This Spt4 phosphorylation was specific to the Hog1 phosphorylation sites ( Thr42 and Ser43 ) since it was abolished in cells carrying the TAP-tagged mutant allele Spt4T42AS43A ( Fig 1B ) . These data indicated that Spt4 is phosphorylated in response to stress by Hog1 . It is worth noting that the Spt4 is already phosphorylated in the absence of stress , suggesting that other non-stress kinases might act on these sites in basal conditions . Of note , Spt4 might also be phosphorylated at other sites than Thr42 and Ser43 in basal conditions , given that there is still signal with the antibody anti-P Ser/Thr in the Spt4T42AS43A mutant . Cells lacking SPT4 have reduced expression of osmostress-responsive genes [21] . To further study the functional relevance of Spt4 phosphorylation by Hog1 for the osmostress response , we followed the transcriptional response of cells expressing the non-phosphorylatable allele Spt4T42AS43A upon osmostress . This mutant displayed reduced expression of representative stress-responsive genes ( STL1 , CTT1 , ALD3 or GRE2 ) versus wild type cells , but similar expression to that of spt4Δ cells ( Fig 2A and 2B ) , indicating that phosphorylation of Spt4 on these specific residues is relevant for the regulation of osmostress-dependent gene expression . It is worth noting that osmo-responsive gene expression of the untagged and TAP-tagged Spt4 strains was similar upon osmostress ( S2A and S2B Fig ) . Moreover , although the protein levels of the Spt4T42AS43A mutant were quite similar as compared to wild type Spt4 strain , Spt4 mutant expression was only slightly reduced ( 20% ) ( S2C Fig ) . The defect in transcription that was triggered by the non-phosphorylatable mutant of Spt4 was specific to stress , since , in contrast to the deletion of SPT4 ( spt4Δ ) , it did not affect the expression of GAL genes upon galactose addition ( Fig 2C and 2D ) . Of note , cells carrying the deletion of SPT4 have been shown to be sensitive to 6-azauracil ( 6-AU ) , which is frequently used as a tool to study the genetics of transcript elongation [24] . In contrast to spt4Δ cells , cells expressing the non-phosphorylatable Spt4 mutant could grow in the presence of 6-AU to the same extent as wild type cells ( Fig 2E ) , which indicated the functionality of the Spt4T42AS43A allele . These data confirm that the phosphorylation of Spt4 by Hog1 is relevant in the control of stress-responsive genes . To further characterize the effect of the Hog1-phosphorylation sites in Spt4 on osmostress-induced gene transcription , we characterized the genome-wide gene expression in response to osmostress by performing RNA-seq in wild type and Spt4T42AS43A cells . Under stress conditions ( 0 . 4M NaCl , 15 min . ) , expression profiles of 1507 genes were found to be significantly altered by at least 2-fold ( FDR< 0 . 05 ) in the wild type strain ( S3 Fig ) . Under non-stressed conditions the wild type and Spt4T42AS43A mutant displayed a very similar gene expression pattern , with only a few genes significantly altered in the mutant versus the wild type ( FDR < 0 . 05 and at least 2-fold difference ) ( Fig 3A ) . These results indicated that , in contrast to the deletion of SPT4 [34] , the unphosphorylatable Spt4 mutant had only minor effect on the overall transcriptional pattern in basal conditions . However , upon osmostress , expression of 556 genes was significantly altered in the Spt4T42AS43A mutant cells compared to wild type ( FDR < 0 . 05 and at least 2-fold difference ) ( Fig 3B ) . When the osmostress-induced transcriptional response was further analyzed , we observed that almost 50% ( 335 genes ) genes that are normally osmo-induced in wild type cells exhibited a significantly smaller induction in Spt4T42AS43A mutant cells ( Fig 3C ) . Notably , osmo-unchanged genes ( defined by log2 fold change significantly smaller than 0 . 5 , FDR < 0 . 05 ) did not show a comparable systematic difference between wild type and Spt4T42AS43A strains . These results led us to conclude that phosphorylation of Spt4 is required for proper activation of genes responsive to osmostress . We then assessed the association of RNA Pol II ( Rpb1 ) with representative stress-responsive genes in wild type and Spt4T42AS43A cells using ChIP analysis ( Fig 4A ) . As expected , RNA Pol II was recruited to the promoters and ORFs of STL1 and CTT1 only in response to osmostress . Cells carrying the Spt4T42AS43A mutation showed reduced binding of RNA Pol II to STL1 and CTT1 in response to stress compared to wild type , and the effects were particularly striking for the coding regions of those genes . These results are consistent with the fact that CTT1 and STL1 gene expression is reduced in response to stress in the non-phosphorylatable mutant ( Fig 2A ) . Of note , ChIP assays using a specific antibody that recognizes RNA Pol II Ser2-CTD showed that association of RNA Pol II phosphorylated at Ser2 with the stress-dependent CTT1 coding region was reduced in cells carrying the Spt4T42AS43A mutation compared to wild type ( Fig 4B ) . The complex of Spt5-Spt4 is required for efficient transcription elongation by RNA Pol II [24 , 25] . The sites in Spt4 that are phosphorylated by Hog1 are located close to the Spt5-binding domain ( Spt5 NGN ) and , when modeled , they appear to be accessible . Thus , it is formally possible that the phosphorylation of Spt4 resulted in a change in the affinity of the binding of Spt4 with Spt5 , or with the indirect binding of Spt4 with RNA Pol II to stimulate transcription processivity . This possibility was addressed by testing whether Spt4 wild type or the non-phosphorylatable mutant were differently immunoprecipitated with Spt5 ( Fig 5A ) or RNA Pol II ( Fig 5B ) in response to osmostress . Yeast cells expressing TAP-tagged Spt4 or Spt4T42AS43A and Myc-tagged Spt5 , each expressed from their genomic locus , were subjected to osmostress and Spt4 was immunoprecipitated using specific antibodies against TAP . As shown in Fig 5A and 5B , Spt4 wild type and its non-phosphorylatable version coprecipitated Spt5 and RNA Pol II upon stress to the same extent , indicating that Spt4-Spt5 and Spt4-RNA Pol II interactions are not affected by Hog1-phosphorylation of Spt4 . Next , we assessed Spt4 and Spt5 binding to osmo-responsive genes in wild type and Spt4T42AS43A cells by ChIP assays . As expected , Spt4 and Spt5 were recruited to the coding regions of STL1 and CTT1 only in response to osmostress . Association of Spt4 and Spt5 to osmo-responsive coding regions was impaired in the non-phosphorylatable Spt4 mutant when compared to wild type ( Fig 5C ) . In addition , we followed in the same cultures the association of RNA Pol II and found that it was reduced similarly ( Fig 5C ) . Therefore , it is conceivable that the association of Spt4 and Spt5 to chromatin is reduced in the non-phosphorylatable Spt4 mutant due to a decrease in RNA Pol II association . To analyze the role of Spt4 phosphorylation by Hog1 within the RNA Pol II elongation complex , we uncoupled the processes of transcription initiation and elongation in response to osmostress by driving the expression of an osmo-inducible gene with the LexA-VP16 activator . By this approach , the stress-dependent transcript levels can be attributed exclusively to an effect on elongation and not to changes in transcriptional initiation [21] . Specifically , we measured RNA levels of the CTT1 osmo-responsive gene when their expression was driven by the stress-independent LexA promoter and the LexA-VP16 activator ( Fig 6A ) . In this system , in which initiation of transcription is independent from stress and Hog1 , but the osmo-inducible coding region is regulated by stress in a Hog1-dependent manner , the amount of CTT1 mRNA still increases in response to stress . This induction is impaired in the Spt4T42AS43A mutant , suggesting that these phosphorylation events are required for a proper RNA levels upon osmostress . Mutants with impaired transcription elongation display lower efficiency in the gene expression of long compared to short transcription units [36] . Gene Length Accumulation of mRNA ( GLAM ) -ratios was previously used as an indirect estimation of RNA Pol II elongation . For instance , the spt4Δ mutant shows low GLAM-ratios [37] . To gain insight into the putative defects in stress-dependent transcription elongation of the non-phosphorylatable Spt4 mutant , we engineered an osmostress-inducible long transcription unit by fusing the STL1 gene ( 1710 bp ) with lacZ ( 4810 bp ) ( Fig 6B ) . This unit was expressed in wild type , Spt4T42AS43A or spt4 mutant strains and the GLAM ratios were determined by dividing STL1 expression of the long STL1-lacZ transcription unit versus the short endogenous STL1 transcription unit upon osmostress ( Fig 6B ) . spt4 deleted cells showed GLAM values below 0 . 5 in response to stress as expected from a mutant with defects in RNA Pol II elongation . Notably , the levels of the full-length mRNAs transcribed from STL1-lacZ upon stress were significantly reduced in Spt4T42AS43A as compared to the short mRNA from STL1 , suggesting that phosphorylation of Spt4 by Hog1 upon stress influences gene expression in a gene length-dependent way and is therefore important for transcription elongation . We then aimed to assess whether Spt4 phosphorylation by Hog1 changed the distribution of RNA Pol II along the transcribed STL1-lacZ in response to stress . For this purpose , the profile of RNA Pol II occupancy along STL1-lacZ was analyzed using anti-Rpb3 ChIP in wild type , Spt4T42AS43A and spt4 mutant strains ( Fig 7A and S4 Fig ) . In all three tested strains RNA Pol II associated with the 0 amplicon ( starting of the transcript region ) upon osmostress and this association reached a maximum at 7 . 5 minutes after induction . From this time-point onwards , the recruitment of polymerase decreased until it approximated background levels after 15 minutes . RNA Pol II occupancy along the other amplicons along STL1-lacZ increased proportionally over time in wild type cells , following the propagation of the transcriptional wave . spt4Δ showed significant lower levels of RNA Pol II binding along the transcription unit ( except for amplicon 0 ) compared to wild type , as expected from a mutant that affects RNA Pol II processivity [32] . Similarly , Spt4T42AS43A also displayed reduced levels of RNA Pol II binding along STL1-lacZ compared to wild type ( reduction of ~50% from amplicon 900 to the end of the transcript ) . We then assessed the RNA Pol II processivity of the two mutants relative to wild type . In the Spt4T42AS43A mutant , a defect in processivity was detected at the 5’ end of the gene body , whereas in spt4Δ a progressive RNA Pol II drop-off was observed throughout the whole transcription unit ( Fig 7B ) . The role of transcription is not essential for the short-term adaptation , but it is crucial for long term adaptation and for protection against future stress ( reviewed in [5] ) . Indeed , mutants that display impaired transcription at mild osmolarities display growth defects only under severe osmolarities . Thus , to assess the relevance of Spt4 phosphorylation and its control of transcription elongation for cell growth upon osmostress , we monitored cellular growth in the presence of high osmolarity ( 1 . 4 M NaCl or KCl ) . Cells lacking HOG1 or SPT4 show compromised cell adaptation to high osmolarity [21] . We therefore monitored the sensitivity of the Spt4T42AS43A mutant to high osmolarity . The non-phosphorylatable Spt4 mutant did not show growth differences compared to wild type on YPD in the absence of osmostress . In clear contrast , the growth of the Spt4T42AS43A cells was more sensitive to high osmolarities ( 1 . 4 M NaCl or KCl ) than that of the wild type strain . Of note , Spt4T42AS43A cells were as sensitive as spt4Δ mutant cells in this respect ( Fig 8 ) . Thus , alanine substitutions in phosphorylable residues of Spt4 confer similar osmostress sensitivity as deletion of SPT4 , consistent with Hog1 phosphorylation of these residues being important for cell growth upon osmostress .
The Hog1 SAPK has a pivotal role in regulating distinct steps in the gene expression cycle in response to osmostress . Although the mechanisms by which Hog1 triggers stress-dependent transcriptional initiation are well-described ( reviewed in [5 , 6] ) , those by which Hog1 contributes to the regulation of elongation remained to be explored . Transcription elongation by RNA Pol II is a dynamic and tightly regulated step that is facilitated by several transcription elongation accessory factors as well as chromatin remodelers ( reviewed in [22 , 23] ) . We previously reported that Hog1 interacts with components of the RNA Pol II transcript elongation complex such as Spt4 , Paf1 , Dst1 or Thp1 and recruits the RSC chromatin remodeler to stress-responsive genes [4 , 12] . These elongation factors and chromatin remodelers are important for transcriptional activation in response to osmostress and for cell ability to grow under such conditions [21] . Here , we showed that the stress-activated Hog1 directly phosphorylates the Spt4 elongation factor to regulate the activity of RNA Pol II in response to osmostress . Hog1 serves to bypass the general down-regulation of gene expression that occurs upon osmostress by targeting RNA Pol II and inducing chromatin remodeling at stress-responsive loci [4] . Here , we demonstrated that this signaling kinase also targets an elongation factor to govern the transcription of stress-responsive genes . Several evidences have pointed out the ability of spt4 deletion to affect gene expression selectively rather than affect general transcription . For instance , RNA-seq analyses indicated that only a small fraction of the yeast transcriptome is affected in the spt4 mutant [34] . It has also been reported that Spt4 is specially required for the expression of long genes [32] and for transcribing genes with high GC content [33] . Here , we presented evidences that Spt4 is also required for stress-responsive gene expression . The absence of phosphorylation of Spt4 by Hog1 does not result in major effects on gene expression compared to wild-type under non-stress conditions; however , the expression of stress-responsive genes is clearly impaired . Notably , many genes whose expression is compromised in the Spt4T42AS43A mutant are also Hog1-dependent genes , which indicates a specific regulation of Spt4 activity by phosphorylation by Hog1 . Although it is formally possible that the Thr42 and Ser43 amino acid changes altered the Spt4 function , the Spt4T42AS43A mutant specifically showed a defect in transcription in response to osmostress but not upon galactose addition . Moreover , in contrast with cells deficient for Spt4 activity , cells expressing the Spt4T42AS43A mutant did not display cell growth defects in the presence of 6-AU . Of note , the site in Spt4 that is phosphorylated by Hog1 is completely conserved from yeast to mammals , suggesting that this phosphosite might have a physiologically relevant functional role across all eukaryotic cells . Genome-wide localization studies showed that the Spt4 and Spt5 were hardly ever present at promoters , but that their association with gene loci were strongly increased downstream of transcription start sites , which largely mirrored the distribution of RNA Pol II [38 , 39] . This finding suggested that Spt5/Spt4 binding to genes may serve to promote the initiation-elongation transition by sterically enforcing the nucleic acid arrangement and preventing RNA release and reassociation of DNA strands [29] . Also , it has been described that there is physical and genetic interaction between the osmostress initiation transcription factor Hot1 and Spt5/Spt4 [40] . Indeed , the presence of RNA Pol II at osmo-responsive promoters was slightly reduced in the unphosphorylatable Spt4 mutant , suggesting that phosphorylation of Spt4 by Hog1 might also be important for this transition and a potential cause for the changes observed during transcription elongation . The defect of RNA Pol II processivity across the STL1-lacZ fusion in the Spt4T42AS43A mutant was limited to the beginning of the transcript compared to the spt4Δ , which showed a defect across all the transcript . This suggests that the phosphorylation of Spt4 by Hog1 upon osmostress modulated mainly early transcriptional elongation . Of note , it was shown that the role of Spt4/5 has also been involved in the transition of initiation to elongation , where they compete with the initiation factor TFIIE for its binding to RNA Pol II to stimulate processivity [41] . Thus , it might be that stress-dependent Spt4 phosphorylation stimulates RNA Pol II processivity in an early step of transcriptional elongation . Crystal structure , modelling and in vivo crosslinking studies of the conserved complex of Spt4 with its partner Spt5 showed that it binds to the coiled domain of RNA Pol II and encloses the DNA template to promote processivity [28 , 29 , 31 , 42] . However , by co-imunoprecipitation assay , we did not detect a change in the affinity of Spt4 for Spt5 or RNA Pol II when it cannot be phosphorylated upon stress . Of note , Spt5 is also targeted by Hog1 in vitro and a mutant strain carrying Spt5 mutations in the putative Ser and Thr MAPK consensus sites to Ala showed compromised cell growth to high osmolarity . However , we did not observe any additivity on osmostress sensitivity when we combined it with the Spt4 mutation . Thus , we focused in the regulation of Spt4 to understand the effect of the phosphorylation in elongation . Spt4 does not directly contact RNA polymerase , but it stabilizes RNA Pol II/template complexes by binding to the template externally to the transcription bubble [28 , 29] . It is known that spt4 and spt5 mutations affect the ability of elongating RNA Pol II to traverse the entire length of a gene [32] . The association of Hog1 with coding regions increases RNA Pol II density at osmo-responsive coding regions [21] . Thus , it is reasonable to assume that cells have evolved mechanisms , such as the direct regulation of Spt4 , to ensure proper polymerase processivity through those genes that should be activated rapidly . On the other hand , Spt4 phosphorylation by Hog1 may also function at a step distinct from elongation such as stability of target RNAs or RNA export . Although the Ras/PKA signaling pathway is somehow involved in modulation of the Spt5/Spt4 complex [43] , the regulation of this elongation complex by external cues has not been reported . The fact that the direct phosphorylation of Spt4 by a signaling kinase facilitates RNA Pol II processivity suggests that elongation can be modulated depending on external cues to increase transcription efficiency and maximize cell fitness .
All strains are based in the BY4741 ( MATa his3-Δ1 leu2-Δ0 met15-Δ0 ura3-Δ0 ) genetic background . Genetic deletions and taggings were performed on the corresponding native genomic loci and under the control of their own promoters , using a long flanking homology PCR-based approach [44 , 45] . To genomically introduce Thr42Ala and Ser43Ala point mutations into Spt4 ( YAS13 strain ) , a Spt4 ( T42A S43A ) -TAP::kanMX4 cassette was generated by PCR in a two-step reaction . The first PCR product was amplified using oligos OAS60 ( AGGGCTCGAAGGATGTACTG ) and OAS71 ( CCCTCGAAAGAAGGAGCA GCACATTCCATT ) with genomic DNA from the BY4741 SPT4-TAP::kanMX4 strain ( YAS3 ) as a template , yielding a mutagenic PCR product . This product was used as a primer in the second step together with oligo OAS76 ( AGGGTCATAGTAGTCAAAGG ) using YAS3 genomic DNA again as a template . The resulting Spt4 ( T42A S43A ) -TAP::kanMX4 PCR product was used for BY4741transformation; G418-resistant colonies were confirmed by PCR with oligos OAS60/OAS76 , by sequencing and by Western blotting with the peroxidase anti-peroxidase ( PAP ) antibody ( P1291 , Sigma ) . The strains BY4741 SPT4-TAP::kanMX4 ( YAS3 ) , BY4741 SPT4-TAP::kanMX4 hog1::URA3 ( YAS7 ) , spt4::kanMX4 ( YAS17 ) , hog1::URA3 ( YCS40 ) , SPT4-TAP::kanMX4 stl1::LEU2 ( YLS221 ) , SPT4-TAP::kanMX4 ctt1::LEU2 ( YLS262 ) , Spt4 ( T42AS43A ) -TAP::kanMX4 stl1::LEU2 ( YLS222 ) , Spt4 ( T42AS43A ) -TAP::kanMX4 ctt1::LEU2 ( YLS264 ) , SPT5-9myc::hphNT1 ( YSC29 ) , SPT4-TAP::kanMX4 SPT5-9myc::hphNT1 ( YSC30 ) and Spt4 ( T42AS43A ) -TAP::kanMX4 SPT5-9myc::hphNT1 ( YSC31 ) were made using standard procedures . Plasmids pGEX4T-Hog1 and pGEX4T-Pbs2EE ( PBS2 with Ser514-Glu and Thr518-Glu mutations ) were previously described [46] . SPT4 was PCR amplified from BY4741 genomic DNA with the oligos OAS22 ( GATCGGATCCTCTAGTGAAAGAGCCTGTAT ) and OAS23 ( GATCCTCGAGTTACTCAACTTGACTGCCATC ) , and was then BamHI/XhoI digested and cloned into pGEX-6P-1 . Spt4T42AS43A was amplified by PCR in a two-step reaction . The first PCR product was amplified from BY4741 genomic DNA with the oligos OAS40 ( AATGGAATGTGCTGCTCCTTCTTTCGAGGG ) and OAS23 . The resulting mutagenized PCR product was used as a primer for the second step together with OAS22 and using BY4741 genomic DNA as a template . The final PCR product was digested with BamHI/XhoI and cloned into pGEX-6P-1 . Both plasmids were confirmed by digestion and sequencing . The LexA-CTT1 construct , and the plasmid with the LexA binding domain fused to the VP16 transcriptional activator , have been described [21] , and were used for transformation of the indicated yeast strains . For the GLAM assay , the STL1 promoter and ORF ( -810 to +1710 bp ) were PCR amplified with the oligos OAS142 and OAS120 and cloned into pRS413 digested with XhoI/SalI . The resulting plasmid was digested with SalI/XmaI to clone the lacZ sequence ( 3100 bp ) from pSCH212 [47] that had been amplified with the oligos OAS121 and OAS122 . Finally , 430 bp from the STL1 3’ UTR were PCR amplified with the oligos OAS123 and OAS124 and cloned after XmaI/XbaI digestion . The plasmid was confirmed by sequencing . GST fusion proteins encoding Hog1 , the constitutively active Pbs2EE and the wild type or the mutant versions of Spt4 , were expressed in E . coli DH5α cells and purified using glutathione-Sepharose beads ( GE Healthcare ) in STET buffer ( 10 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 1 mM EDTA , 5% Triton X-100 , 2 mM DTT , 1 mM PMSF , 1 mM benzamidine , 2 mg ml-1 leupeptin , 2 mg ml-1 pepstatin ) . Hog1 ( 1 μg ) was pre-activated with 0 . 5 μg of Pbs2EE in the presence of kinase buffer ( 50 mM Tris-HCl pH 7 . 5 , 10 mM MgCl2 , 2 mM DTT ) and 50 μM of ATP . After 20–30 minutes incubation at 30°C , eluted wild-type Spt4 or mutant Spt4T42AS43A were added to the pre-activated mixture together with [γ-32P]ATP ( 0 . 1 mCi/ml ) and incubated for 30 minutes at 30°C . The reaction was terminated by adding 10 μl of 5X Laemli buffer and boiling the samples for 5 minutes . Labeled proteins were resolved by SDS-PAGE and detected by autoradiography . Total protein level was detected by staining with Coomassie Brilliant Blue . TAP-tagged Spt4 and Spt4T42AS43A cells were grown to mid-log phase on YPD and were then subjected to a brief osmostress ( 0 . 4 M NaCl for 5 minutes ) or were left untreated . For each condition , 100 ml aliquots were harvested by centrifugation and pellets were resuspended in 300 μl of buffer A ( 50 mM Tris-HCl pH 8 . 0 , 15 mM EDTA , 15 mM EGTA , 0 . 1% Triton X-100 ± 150 mM NaCl ) supplemented with protease and phosphatase inhibitors ( 1 mM PMSF , 2 μg ml-1 leupeptin , 2 μg ml-1 pepstatin , 1 mM benzamidine , 2 mM DTT , 10 mM sodium fluoride , 25 mM β-glycerophosphate , 1 mM sodium orthovanadate , 1 mM sodium pyrophosphate ) . Chilled glass beads were added , and cells were disrupted with a Fast-prep glass bead beater ( Millipore ) . Protein extracts ( 10 mg ) were incubated with rabbit IgG agarose ( Sigma ) beads for 2–3 hours at 4°C . Beads were washed 10 times with supplemented buffer A + NaCl , and once with 50 mM Tris-HCl pH 6 . 8 , boiled in SDS-loading buffer for 5 minutes and analyzed by 11% SDS-PAGE . Proteins were then transferred to PVDF membranes and phosphorylation was detected by immunoblotting with an anti-phospho Ser/Thr antibody ( BD Transduction Laboratories ) . Yeast strains were grown to mid-log phase in rich medium , immediately subjected or not to osmostress ( 0 . 4 M NaCl ) , and samples were taken at the indicated times . Galactose induction was performed by growing cells on YP 2% raffinose up to mid-log phase and shifting them to 2% galactose for the indicated times . For the expression analysis of genes under the control of the LexA promoter as well as for the GLAM assays , mid-log phase cells were grown in minimal medium for plasmid maintenance . Total RNA and Northern hybridization analyses were carried out according to standard procedures and expression of specific genes was detected by hybridization of the Northern blot membranes with labeled PCR fragments of CTT1 ( 1 . 7 kbp ) , STL1 ( 0 . 88 kbp ) , ALD3 ( 0 . 6 kbp ) , GRE2 ( 0 . 9 kbp ) , ENO1 ( 1 . 3 kbp ) , and GAL1 ( 0 . 88 kbp ) . Signals were quantified using a Typhoon 8600 phosphorimager ( Molecular Dynamics ) and autoradiographs were obtained on Carestream Kodak Biomax XAR film ( Sigma ) . In the case of the GLAM assay , the phosphorimaging analyses were performed in a STORM-840 imaging system ( GE Healthcare ) and quantified using GelQuant . NET software provided by biochemlabsolutions . com . Yeast strains were cultured to mid-log phase in rich medium and then subjected or not to osmostress ( 0 . 4 M NaCl ) for 15 minutes . Three biological replicates for each condition were performed . Total RNA was extracted by using the standard hot phenol and glass-beads protocol . Libraries were prepared using the TruSeq stranded mRNA sample preparation kit v2 ( Illumina ) according to the manufacturer’s protocol , and starting with 1 μg of total RNA for poly ( A ) -mRNA selection . Libraries were first analyzed on an Agilent Bioanalyzer using a DNA 1000 chip to estimate the quantity and check the size distribution . Libraries were then quantified by qPCR using the KAPA library quantification kit ( KapaBiosystems ) prior to sequencing with 50 bp single-end reads on Illumina's HiSeq 2500 with v4 sequencing chemistry . All RNA-seq experiments were run in triplicates . Quality control of raw sequencing reads was performed by FASTQC; all samples passed the imposed quality restrictions and were used for further analysis . RNA-seq reads were mapped to the sacCer3 reference genome ( obtained via UCSC goldenPath ) using TopHat2 v2 . 1 . 0 [48] with default parameters . Quantification was performed by featureCounts v1 . 5 . 1 [49] using the Ensembl R64-1-1 annotation and only considering uniquely mapping reads . Subsequent analyses were conducted using the statistical programming language R , and the DESeq2 package ( v . 1 . 14 . 1; [50] ) was used for library size normalization ( RLE ) and differential expression testing . Osmo-responsive genes were defined as genes that significantly exceeded an absolute log2-fold change ( log2FC ) of 1 after multiple testing correction ( FDR < 0 . 05 ) . In contrast , osmo-unchanged genes were defined as genes whose absolute log2FC was not significantly larger than 0 . 5 ( FDR < 0 . 05 ) . Results were visualized using the ggplot2 R-package ( ggplot2 . org ) . Data have been deposited in the Gene Expression Omnibus ( GEO ) database ( GSE98352 ) . ChIP was performed essentially as previously described [16] . Yeast cultures were grown in rich YPD medium to mid-log phase before exposing them to osmostress ( 0 . 4 M NaCl ) and samples were taken at the times specified . Anti-Rpb1 ( 8WG16 , Covance ) or anti-phospho RNA Polymerase II ( S2 ) ( Bethyl Laboratories; Cat . Number A300-654 ) antibodies were used for RNA Pol II ChIPs . Anti-Rpb3 ( ab81859 , Abcam ) monoclonal antibody was used for GLAM ChIPs . Anti-Myc ( 9E10 hybridoma ) was used for Spt5-Myc binding . Rabbit IgGs were used for TAP-tagged versions of wild-type Spt4 or mutated Spt4T42AS43A binding . Oligonucleotides to amplify regions of CTT1 ( -432/-302 for the promoter; +737/+836 for the ORF ) , STL1 ( -682/-583 for the promoter; +1477/+1575 for the ORF ) and TEL1 ( a telomeric region on the right arm of chromosome VI that was used as an internal normalizing control sequence for each DNA analyzed; TEL RTa: ACCACTCAAAGAGAAATTTACTGGAAGA and TEL RTb: CTCGTTAGGATC ACGTTCGAATC ) were used for real-time PCR analysis with Power SYBR Green PCR Master Mix ( Applied Biosystems ) , employing an Applied Biosystems ViiA7 detector . Locations are indicated by the distance respect to the correspondent ATG initiation codon . In GLAM ChIPs the primers used correspond to 0 , +900 , +1900 , +3000 , +4000 and +4700 after the ATG initiation codon of the STL1-lacZ transcription unit . To study Spt4 and Spt5 interaction , cells expressing a 9myc-tagged version of Spt5 and TAP-tagged versions of either wild-type Spt4 ( YSC30 ) or mutated Spt4T42AS43A ( YSC31 ) , as well as cells with only Spt5-9myc ( YSC29 ) as a negative control for TAP pull down , were grown to mid-log phase on YPD , subjected or not to osmostress ( 0 . 4 M NaCl for 10 minutes ) and harvested at 4°C . Protein extracts and pull down of TAP-tagged proteins ( 5 mg ) were performed as for the in vivo phosphorylation assays . Proteins were then transferred to PVDF membranes and Spt5-9myc was detected by immunoblotting with an anti-myc monoclonal antibody ( 9E10 hybridoma ) . For analysis of Spt4 and RNA Pol II interaction , we used cells expressing the TAP-tagged versions of wild-type Spt4 ( YAS3 ) or mutated Spt4T42AS43A ( YAS13 ) , as well as BY4741 , which was used as the negative control for TAP pull down . Osmostress and TAP purification was performed as indicated above and RNA Pol II was detected using an anti-Rpb1 ( 8WG16 , Covance ) monoclonal antibody . Yeast cultures were grown to mid-log phase in YPD and then diluted to OD660 = 0 . 05 in YPD , 1 . 4 M KCl or 1 . 4 M NaCl . The analyses were performed in quadruplicate . Cell growth was monitored ( 30°C , constant agitation , 1 . 5-hour time points ) at OD660 for 21 hours . Growth curves were analyzed using a Synergy H1 Hybrid Reader ( BioTek ) . Yeast cultures were grown to mid-log phase in YPD , then diluted to OD600 = 0 . 05 and cell suspensions were diluted using 10-fold serial dilutions in 96-well microtiter plates ( 200 μl/well ) . The cells were spotted onto YPD and SC plates containing 6-azauracil and growth was monitored and photographed at appropriate time points . Spt4 protein sequence alignments were made with Geneious software version 7 . 1 . 9 ( http://www . geneious . com , [51] ) . Data are reported as mean ± SD . Statistical significance was assessed using a Student’s t test for equality of means , two-tailed and equal variance assumed . | Cells coordinate various intracellular activities in response to sudden changes in the environment to maximize survival and cell fitness . Clear examples of this are the regulation of gene expression and cell cycle progression . Stress-Activated Protein Kinase ( SAPK ) signaling pathway acts as the central core for these responses . Major changes in gene expression are associated with cell exposure to environmental changes and several aspects of mRNA biogenesis appear to be targeted by SAPKs . Here , we describe a new cellular mechanism to tightly regulate the expression of the stress-dependent genes . Our data demonstrate that the SAPK signaling pathway impacts on core elements of the cellular machinery and directly targets elongating RNA Pol II by phosphorylating the Spt4 elongation factor . This novel finding changes the framework for the understanding of how cells modulate the transcriptional elongation process to increase gene expression efficiency and maximize cell fitness in response to external cues . | [
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| 2017 | Regulation of transcription elongation in response to osmostress |
The recent emergence of zoonotic diseases such as Highly Pathogenic Avian Influenza ( HPAI ) and Severe Acute Respiratory Syndrome ( SARS ) have contributed to dominant Global Health narratives around health securitisation and pandemic preparedness , calling for greater co-operation between the health , veterinary and environmental sectors in the ever-evolving One Health movement . A decade later , One Health advocates face increasing pressure to translate the approach from theory into action . A qualitative case study methodology was used to examine the emerging relationships between international One Health dialogue and its practical implementation in the African health policy context . A series of Key Informant Interviews ( n = 32 ) with policy makers , government officials and academics in Nigeria , Tanzania and Uganda are presented as three separate case studies . Each case examines a significant aspect of One Health operationalisation , framed around the control of both emerging and Neglected Zoonotic Diseases including HPAI , Human African Trypanosomiasis and rabies . The research found that while there is general enthusiasm and a strong affirmative argument for adoption of One Health approaches in Africa , identifying alternative contexts away from a narrow focus on pandemics will help broaden its appeal , particularly for national or regionally significant endemic and neglected diseases not usually addressed under a “global” remit . There is no ‘one size fits all’ approach to achieving the intersectoral collaboration , significant resource mobilisation and political co-operation required to realise a One Health approach . Individual country requirements cannot be underestimated , dismissed or prescribed in a top down manner . This article contributes to the growing discussion regarding not whether One Health should be operationalised , but how this may be achieved .
One Health acknowledges the close relationships between humans , animals and ecosystems , promoting the potential added benefits to each sector or species that emerge as a result of its operationalisation ( Figure 1 ) . Whilst attempts to categorically define One Health are many and varied , the general consensus that it promotes a transdisciplinary , collaborative “whole of society” approach towards global health in the 21st century remains key [1]–[4] . The unprecedented financial and political response to H5N1 avian influenza at the turn of the 21st century facilitated the development of global intersectoral alliances , creating a unique policy space for agencies , governments and institutes to collaborate under a large scale One Health banner for the first time [3] , [4] . There is currently a strong drive to maintain the momentum and alliances established during the Global Response to Avian Influenza ( GRAI ) , with advocates promoting One Health as an approach towards various other aspects of international and regional health governance . However in the absence of a specific disease threat , examples of national commitment to One Health are increasingly difficult to find , particularly in developing countries . The argument for inter-ministerial platforms to co-ordinate policy and action for zoonoses control is well founded . However whilst One Health is theoretically - and arguably economically - attractive , significant political will and state capacity is required to overcome existing institutional and financial barriers to its implementation; particularly in developing countries where numerous health and development priorities compete for attention and programmatic funding . Identification and critical analysis of current examples is required if One Health is to be perceived as anything other than an “attempt to grab funds on the tail-end of the avian influenza bonanza” [4] . Africa is a relevant continent for the examination of One Health policy , particularly for the control of endemic and “neglected” zoonotic diseases [5] . Although Asia has been the recent focus regarding high profile emerging zoonotic disease outbreaks , Africa has historically been home to some of the most striking examples of disease spill over from animals including HIV , Rift Valley Fever and Ebola . Additionally , it is estimated one third of Africa's agricultural Gross Domestic Profit is obtained through livestock production [6] . Whilst significant economic gains could be realised on the continent through control of production-limiting zoonoses including the trypanosomiases , brucellosis , cysticercosis and anthrax , the existing socioeconomic evidence available to promote concrete policy shifts towards multisectoral approaches is currently lacking . In addition to the socioeconomic evidence , documenting the successes and challenges of existing One Health platforms , as experienced by those driving disease control policies on the continent , is also urgently required . Through interviewing a selection of respondents currently at the forefront of policy development for zoonoses control in Uganda , Nigeria and Tanzania , attempts have been made to address this latter issue .
A preliminary review identified that despite the ubiquitous international promotion of One Health through various meetings , agreements , frameworks and pledges , relatively few examples of successful long term adoption of the approach existed , particularly in sub-Saharan Africa [7] . A qualitative case study methodology [8] was applied in three African contexts in order to understand how – or where – a One Health policy approach may be appropriate to the control of diseases of regional or national importance . A total of 32 Key Informant Interviews were held with key policy actors in Uganda , Nigeria and Tanzania , including officials from the Ministries of Health and Agriculture , academia and international research institutes ( Table 1 ) . These countries were chosen given they have reported higher than average burdens of endemic zoonotic disease [5] , and were key International Cooperation Partner Countries ( ICPCs ) of the European Commission's Integrated Control of Neglected Zoonoses ( ICONZ ) project through which the research was undertaken [9] . Individual interview respondents were selected using a snowball sampling technique; a type of purposive sampling whereby existing local networks direct the researcher to further potential participants [8] . Given the relatively “closed” doors and time constraints common to government officials across many African ministries , snowball sampling was deemed the most sensible - and in many cases the only available - technique to ensure that interviews were secured from a wide variety of sectors and ministerial levels . With the exception of one international researcher , interview respondents were all nationals of the three focus countries , representing several sectors and governance levels as outlined in Table 1 . Whilst the semi-structured interview approach allowed for a certain degree of flexibility to reflect respondents' areas of expertise and experience , several key themes exploring intersectoral collaboration in the context of disease control were used as a general interview guide ( Figure 2 ) . Verbal consent was obtained prior to the commencement of all interviews , which were then documented via handwritten notes and voice recordings if the respondent agreed . Resulting transcripts were then manually coded according to the various emerging themes and topics , from which several context-specific narratives were then developed . The resulting observations and recommendations , discussed in the remainder of this article , outlines the various personal experiences of those in the “driving seat” of disease control in the three countries , potentially increasing the understanding of how One Health's application can extend to a wider variety of diseases and national contexts outside the GRAI .
Human African Trypanosomiasis ( HAT ) , or “Sleeping Sickness” , is a Neglected Tropical Disease of significant public health importance across much of Africa , with Nagana the corresponding disease in livestock . Transmitted by the Glossina species of tsetse fly , trypanosomiasis manifests in humans as either an acute or chronic form caused by T . br . rhodesiense and T . br . gambiense respectively . Presently the only country with foci of both forms of this fatal human disease , Uganda has suffered from devastating epidemics and outbreaks since the beginning of the 20th Century . To date the two forms have been confined to separate geographical foci in Uganda , facilitating surveillance and treatment . More recently however , country-wide movements of infected cattle - an essential reservoir of the T . b . rhodesiense parasites responsible for acute human disease in Uganda - have fuelled fears of disease convergence [10] , [11] . The intersectoral approach required for HAT control “lies at the heart of African rural development” [12] , providing a relevant case study through which to examine One Health . On the 6th of February 2006 , Nigeria reported Africa's first case of H5N1 Highly Pathogenic Avian Influenza in a commercial poultry farm in Kaduna state . The political and financial backing for control was unprecedented - “the government was giving money before they were even asked to” ( Key Informant 1 , Nigeria ) - with an alleged USD $50 million credit received from the World Bank to commence activities [14] , [Key Informant 1 Nigeria] . Pressure from external agencies resulted in the “Nigeria Avian Influenza Emergency Control Preparedness and Response Project”; a three year action plan that promoted the added benefits of a One Health approach through its objective to minimise the threat of HPAI to humans whilst simultaneously promoting poultry production in the country [15] . Through evaluating the extent to which intersectoral partnerships have been maintained since completion of the project in 2009 , this case study examines where One Health may be headed now that the H5N1crisis is over . Rabies is widespread in Africa , contributing to an estimated 23 000 deaths per year despite the existence of an effective toolbox for control [18] , [19] . Many officials do not prioritise rabies , doubting the feasibility of its elimination through mass dog vaccination [19] . A major question in the recent flurry of One Health activity is how localised academic and scientific projects funded by international donors can move into wider policy spheres in the African context; the ongoing experience of rabies research in Tanzania is illustrative of this process . The critical message emerging from all three case studies is that One Health will not “just happen” . Broad institutional changes - and ownership of these changes across the various ministries , departments and interest groups with a stake in disease control – are required for One Health to become a widespread approach to health policy . Moreover , institutional change and ownership will not drive One Health forwards in the absence of sufficient funding . Where external donors are to be the main source of financial support for One Health operationalisation , the need to balance global health agendas with national ownership of change will become even more crucial . There is no “blanket approach” to One Health; individual country requirements cannot be underestimated , dismissed or prescribed in a top down manner by the international community . Although One Health promotes intersectoral collaboration through flexibility and “small ‘g’ governance” [22] , evidence from these three case studies suggest that achieving this in the absence of a global health emergency , political endorsement and nationally-owned financial commitment is at once both challenging , yet urgently required . | The One Health movement requires more robust evidence around its practical implementation if it is to truly become a way forwards for addressing health issues at the human , animal and ecosystem interface . The research in this paper discusses some of the recent successes and challenges with both Emerging and Neglected zoonoses in the sub-Saharan Africa context . Through speaking to various human and animal health practitioners and policy makers in Uganda , Nigeria and Tanzania , the authors have created three case studies highlighting the various successes of the approach to date , but also clarifying areas where the approach will take longer to implement , often as a result of the wide institutional and policy changes required in many countries . The authors conclude that whilst the “goodwill” is certainly there , the reality of planning , executing and budgeting for joint interventions – particularly at the national or regional level – proves in many cases more difficult than first thought . It is hoped however that through gaining better insight from those charged with the decision-making in these countries , One Health practitioners will be encouraged to build on the momentum through addressing some of the issues that arise with its implementation . | [
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| 2014 | One Health: Past Successes and Future Challenges in Three African Contexts |
Cryptococcus neoformans is a common life-threatening human fungal pathogen . The size of cryptococcal cells is typically 5 to 10 µm . Cell enlargement was observed in vivo , producing cells up to 100 µm . These morphological changes in cell size affected pathogenicity via reducing phagocytosis by host mononuclear cells , increasing resistance to oxidative and nitrosative stress , and correlated with reduced penetration of the central nervous system . Cell enlargement was stimulated by coinfection with strains of opposite mating type , and ste3aΔ pheromone receptor mutant strains had reduced cell enlargement . Finally , analysis of DNA content in this novel cell type revealed that these enlarged cells were polyploid , uninucleate , and produced daughter cells in vivo . These results describe a novel mechanism by which C . neoformans evades host phagocytosis to allow survival of a subset of the population at early stages of infection . Thus , morphological changes play unique and specialized roles during infection .
Unicellular organisms exhibit morphological changes under a wide variety of environmental conditions . In many pathogenic fungi , the ability to switch cell morphology is integral to the infection cycle . Dimorphic fungi , such as Blastomyces dermatitidis and Histoplasma capsulatum , grow in the environment in a hyphal form . When a susceptible host inhales spores , these fungi grow as yeasts . This change in morphology is induced by the high mammalian body temperature [1] , [2] , [3] . Other pathogenic fungi , such as Candida albicans and Coccidioides immitis , change to specific cell morphologies based on environmental cues or stage of infection [4] , [5] , [6] . Morphological changes in the pathogenic fungus C . albicans affect tissue tropism and dissemination . Hyphal cells are important in the invasion of host tissues , while yeast cells can easily disseminate through the blood and lymph systems to spread the infection [5] , [7] . Additionally , phagocytosis of yeast cells induces differentiation into hyphal cells [6] . Cryptococcus neoformans is an opportunistic fungal pathogen that is most commonly associated with disease in immunocompromised patient populations , such as HIV/AIDS patients , transplant recipients , patients with lymphoid disorders , chronic treatment with corticosteroids , or patients undergoing certain types of chemotherapies [8] , [9] , [10] . C . neoformans presents clinically as skin lesions , pneumonia , or meningitis [11] . Over 30% of the HIV/AIDS population in Sub-Saharan Africa present with cryptococcal meningitis and cryptococcosis is currently the fifth leading cause of fatalities in this region [12] . Infection with C . neoformans begins when desiccated yeast cells or spores are inhaled and lodge in the alveoli of the lungs . Cryptococcosis occurs when yeast cells disseminate to the bloodstream and ultimately penetrate the blood-brain barrier ( BBB ) [10] , [13] . While the exact mechanism for trafficking from the lungs to the central nervous system ( CNS ) remains unknown , interactions with host phagocytes and the endothelial cells of the BBB have been shown to be important in this process [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] . Morphogenesis in C . neoformans has primarily been observed as a result of pheromone signaling and mating [23] , [24] . There are two varieties of C . neoformans: neoformans and grubii . Historically , mating has been studied in vitro in var . neoformans even though the vast majority of human cryptococcosis cases are caused by var . grubii . C . neoformans has two mating types: a and α . Mating is initiated when pheromone ( a or α ) secreted by one mating type binds to the pheromone receptor , Ste3α or Ste3a respectively , of the other mating type to trigger a mitogen-activated protein kinase ( MAPK ) signaling cascade [23] , [25] . Pheromone signaling results in morphological changes in var . neoformans , including germ tube formation by mating type α cells and enlargement of mating type a cells [23] , [24] . Pheromone-induced MAPK signaling ultimately results in fusion of a and α cells followed by dikaryotic filamentation . Dikaryotic hyphae eventually give rise to basidia where nuclear fusion occurs and meiosis produces haploid spores [23] , [26] . In var . grubii , no in vitro morphogenesis in wild-type strains has been observed during early pheromone signaling , although hyphal formation and basidium production mimic that seen in var . neoformans [27] . In this study , we show that cell enlargement is observed in vivo in var . grubii , and that this cell enlargement can be regulated by pheromone signaling . Additionally , we show that these morphological changes in cell size affect pathogenicity by altering phagocytosis and dissemination to the central nervous system ( CNS ) . Finally , we characterized DNA content of this novel cell type to reveal that these enlarged cells are polyploid .
Pheromone signaling in C . neoformans is known to cause morphological changes including formation of conjugation tubes , dikaryotic filaments , and production of basidia and spores [23] , [24] , [26] , [28] . Mating type a cell enlargement has also been observed in confrontation assays [23] . Cell enlargement has been observed in both human and mouse specimens [29] , [30] , [31] , [32] . Thus , we systematically analyzed cellular morphology in various tissues of mice intranasally infected with var . grubii mating type a or α strains or mice coinfected with both mating types to determine the effect of pheromone signaling and mating type on in vivo cell morphology . Histopathologic tissue sections from the lungs , heart , spleen , liver , kidneys , and brain at 1 , 2 , 3 , 7 , 14 , and 21 days post-infection were examined for changes in cryptococcal cell morphology . Dramatic changes in cryptococcal cell size were observed in the lungs , although a few cells with increased cell size were also observed in the spleen and brain at late time points ( Figure 1A , Figure S1 ) . Most fungal cells in the lungs remained small ( 5–10 µm in diameter ) resembling yeast cells grown in rich medium in vitro . However , a proportion of the cryptococcal cells in the lungs were much larger . For ease of reference , we designated this group of enlarged cryptococcal cells as “titan” cells . These titan cells were >10 µm in diameter , with some cell sizes approaching 50 to 100 µm in diameter ( Figure 1A ) . Titan cell diameter measurements were based on actual cell body size and excluded capsule changes which were highly variable . Titan cells were observed as early as 1 day post-infection in the lungs , accounted for approximately 20% of the cryptococcal cells in the lungs by 3 days post-infection , and remained relatively constant throughout the rest of the infection ( Figure 1B , 1C; Figure S1 ) . Titan cells were occasionally observed in the spleen and brain but at low levels ( Figure S1 ) . In contrast , coinfection with both mating types resulted in an increase in titan cell production to almost 50% of the cells present in the lungs ( Figure 1B , 1C ) . Because pheromone signaling induces mating type a cell enlargement during in vitro mating of var . neoformans , we hypothesized that the increase in titan cell formation during coinfection was specific to mating type a cells . To test this hypothesis , we differentially stained a cells with AlexaFluor 488 ( green ) and α cells with AlexaFluor 594 ( red ) prior to intranasal inoculation of mice . Mice were sacrificed at 1–3 days post-infection , and unstained histopathological sections were examined for cryptococcal cell fluorescence ( Figure 2 ) . At 1 day post-infection , no difference in the proportion of a or α titan cells in individual or coinfections was observed ( data not shown ) . However , at 2–3 days post-infection , the proportion of mating type a titan cells in coinfections increased while the α titan cell proportion remained equivalent to the individual infections ( Figure 1C ) . Almost half of the stained mating type a cells in coinfected lungs had converted to titan cells . To further quantify titan cell formation during coinfection , cells were differentially stained green with AlexaFluor 488 prior to intranasal instillation with the following treatments: a only ( green ) , α only ( green ) , a ( green ) /α , or a/α ( green ) . At 3 days post-infection , bronchoalveolar lavage ( BAL ) was performed . The resulting mix of cryptococcal and mouse cells was immediately fixed and the proportion of green titan cells was determined by microscopic examination ( Figure 1D , see below ) . Similar to the tissue sections , approximately 20% titan cells were observed in the individual infections with no difference in titan cell formation between the two mating types ( p = 0 . 2 , Figure 1D ) . In the coinfections , mating type α titan cell formation remained at the basal level ( p>0 . 64 , Figure 1D ) while mating type a titan cell formation increased ( p<0 . 01 , Figure 1D ) . In the BAL samples , titan cell formation was increased to 37% of the fluorescently labeled a cells . Additionally , the proportion of titan cells in the tissue sections and the BAL samples was similar , suggesting that BAL samples obtained at 3 days post-infection provide an accurate representation of the cryptococcal cells present in the lungs . Cryptococcal cells can signal to cells of the opposite mating type using pheromones . Pheromone from one cell type binds to a G-protein coupled receptor , Ste3 , on the opposite cell type to trigger a MAPK signaling cascade that can alter cell morphology [23] , [25] . We examined whether the increase in titan cell formation during coinfection was due to pheromone signaling by mutating the STE3a pheromone receptor gene . The STE3a coding sequence was replaced with a nourseothricin ( NAT ) resistance gene by homologous recombination . The resulting ste3aΔ mutant lacks the receptor to recognize α pheromone , thus the pheromone signaling pathway is not activated and ste3aΔ mutant cells fail to mate with α strains . Two independent congenic mutants were generated: ste3aΔ#1 and ste3aΔ#2 . Both ste3aΔ::NAT mutant strains were sterile in mating assays with an α strain ( data not shown ) . Murine survival assays verified that the ste3aΔ mutants had equivalent virulence to the parental strain KN99a and no differences in mean survival time were observed between wild-type and ste3aΔ coinfections ( Figure S2 ) . To assess the role of pheromone sensing in titan cell formation , BAL samples were obtained from mice infected with fluorescently labeled ste3aΔ#1 only ( green ) , ste3aΔ#1 ( green ) /KN99α , or ste3aΔ#1/KN99α ( green ) ( Figure 1D ) . Average titan cell formation was 14% in the ste3aΔ#1 infection , which was lower than the average for wild-type a cells ( p = 0 . 03 ) , but similar to wild type α cells ( p = 0 . 22 ) . In contrast , no increase in titan cell formation was observed in coinfections with the ste3aΔ#1 mutant ( p>0 . 6 , Figure 1D ) . Thus , the increase in titan cell formation by mating type a cells during coinfection requires the Ste3a receptor . However , the presence or absence of the Ste3a pheromone receptor has little effect on the basal level of titan cell formation observed in individual infections . To examine the role of titan cell formation in pathogenicity , individual and coinfections with the wild-type and ste3aΔ mutant strains were compared . The Cryptococcus infectious cycle can be divided into three stages: an initial pulmonary infection ( lungs ) , dissemination ( spleen ) , and penetration of the CNS ( brain ) . Previous studies with C . neoformans var . grubii congenic strains showed no differences in virulence between the a and α mating types [26] . However , coinfection with both mating types simultaneously resulted in reduced a cell penetration of the CNS [33] . Interestingly , while a cell CNS penetration was reduced compared with α cells , both cell types had equivalent accumulation at the first two stages of infection . During coinfection , only mating type a cells displayed an increase in titan cell formation and a subsequent reduction in CNS penetration . Thus we hypothesized that pheromone signaling and the resulting increase in titan cell formation reduces a cell CNS penetration . To determine whether pheromone signaling affected dissemination to the brain during coinfection , we compared wild-type and ste3aΔ mutant strains for CNS penetration when coinfected with α ( Figure 3 ) . In both wild-type and ste3aΔ coinfections , the number of a and α cells recovered from the spleen and lungs was equivalent to the proportion of the two cell types in the initial inocula ( p>0 . 1 ) . These data show , even at late time points , alterations in titan cell production in response to pheromone signaling do not affect persistence of the cells in the lungs . However , a significant decrease was seen in the proportion of wild-type a cells recovered from the brain ( p = 0 . 001 , Figure 3A ) . In contrast , coinfections with the ste3aΔ mutants restored a cell accumulation in the CNS to levels equivalent to the initial inocula ( p>0 . 4 , Figure 3B , C ) . Both independent ste3aΔ mutants showed similar results . Together , these data suggest that pheromone signaling during a/α coinfection affects the pathogenicity of a cells by increasing titan cell formation which inhibits the ability of a cells to establish a CNS infection . An in vivo murine tail vein injection model was employed to determine whether coinfection disrupts CNS penetration by reducing a cell interactions with the endothelial cells of the BBB [34] , [35] . In this model , cells bypass the lungs and are injected directly into the bloodstream via the mouse tail vein . The cells then lodge in the small capillaries of the brain and cross the endothelial cell layer of the BBB . To test whether interaction with the BBB was directly affected by mating type or coinfection , a and α cells were fluorescently labeled and examined for their interactions with the BBB . Both cell types were able to traffic to the small capillaries of the brain ( Figure 4A ) and quantification revealed equal proportions of the two mating types in the capillaries ( data not shown ) . During coinfection , the two mating types were observed in close proximity approximately 25% of the time , consistent with random interactions between cells in a mixed population . The finding that cells of opposite mating type are found in close association would enable pheromone signaling to occur between them in the capillaries of the brain ( Figure 4B ) . Both mating types could induce phagocytosis by the endothelial cells of the BBB ( Figure 4C ) . Capsule structural changes are important for interactions with the endothelial cells of the BBB [35] . These structural changes can be characterized by alterations in anti-capsular antibody binding . The binding patterns to the cryptococcal capsule for two monoclonal antibodies , E1 and CRND-8 , recognizing distinct epitopes on the capsular polysaccharide were studied over time and found to be similar for both mating types . Cells observed in the capillaries shortly after inoculation and up to 6 hours post-infection exhibited only E1 antibody binding . In contrast , cells observed in the brain parenchyma were mostly labeled with CRND-8 , as described previously for KN99α [35] . No difference in the capsular antigen staining or the kinetics of capsular changes upon crossing of the BBB were observed between the a and α cells during interactions with the endothelial cells of the BBB – either alone or during coinfection ( data not shown ) . These data suggest that the inability of a cells to penetrate the CNS during coinfection is not due to innate differences between the two cell types or their interactions with the BBB itself , but instead may be due to an inability of the a cells to traffic appropriately from the lungs to the brain . One of the first lines of defense by the host immune system is phagocytosis and the resultant killing of pathogens by mononuclear macrophages and monocytes in the lungs . These host cells identify pathogens , phagocytose them , and either kill the pathogen outright via oxidative and/or nitrosative bursts or present antigens to T cells for further activation of the host immune response [36] . Recent studies suggest phagocytosis by monocytes or macrophages is important for subsequent CNS penetration [16] , [18] , [20] , [22] . Thus , we examined titan cell interactions with lung host immune cells . Fixed BAL samples were analyzed microscopically for yeast cell interactions with host phagocytic cells . Titan cells were never observed inside host phagocytes , presumably due to their large size . Engulfed small cryptococci were observed inside phagocytic host cells ( Figure 5A , B ) . No difference in phagocytosis was observed between mating types ( p = 0 . 82 , Figure 5D ) . The percentage of intracellular α cells during coinfection was similar to that observed in single mating type infections ( p>0 . 89 , Figure 5D ) . In contrast , a decrease in the percentage of phagocytosed a cells was seen during coinfection ( p<0 . 09 , Figure 5D ) . Phagocytosis was restored in the ste3aΔ#1 mutant ( p>0 . 53 Figure 5D ) . Interestingly , titan cells were often surrounded by one or more host immune cells ( Figure 5C ) . Yet complete phagocytosis of titan cells was not observed upon characterization of these cellular interactions by confocal microscopy ( data not shown ) . Taken together , these data indicate titan cell formation was negatively correlated with phagocytosis by host immune cells . Both macrophages and neutrophils employ oxidative and nitrosative bursts as a means of killing pathogens ( Janeway et al . , 2008 ) . Titan cell resistance to these stresses was characterized by comparing the growth of purified titan and small cells isolated by cell sorting of BAL samples . Both cell types showed equivalent growth in the absence of oxidative or nitrosative stress ( Figure 6A ) . Treatment with sodium nitrate ( NaNO3 ) slowed the growth of the small cell population compared to the titan cell population ( Figure 6B ) . Treatment with tert-butyl hydroperoxide ( TBHP ) resulted in killing of small cells , represented by a decrease in cell counts relative to the initial time point ( Figure 6C ) . In contrast , titan cells exhibited continued growth in the presence of these oxidative stresses ( Figure 6C ) . Similar results were observed with stabilized hydrogen peroxide treatment . Thus , titan cells are more resistant than normal cells to both oxidative and nitrosative stresses similar to those employed by cells of the host immune system . In yeasts , cell enlargement is often associated with either cell cycle arrest or increased DNA content [37] , [38] , [39] . Pheromone sensing in the model yeasts Schizosaccharomyces pombe and Saccharomyces cerevisiae is known to trigger a cell cycle arrest . We examined titan cells for their progression through the cell cycle by characterizing their ability to bud and produce daughter cells . In addition , we determined the DNA content of titan cells . Titan cells produced in vivo were obtained from BAL of mice with single or coinfections . The cells were immediately fixed and stained with DAPI . Microscopic examination of titan cells revealed a single nucleus ( Figure 7A ) . Analysis of titan cell nuclear structure by confocal microscopy and z-stack sectioning showed the nucleus had an elongated tubular shape instead of the classic round shape observed in smaller cells ( data not shown ) . Because of its elongated shape , only a portion of the nucleus was observed in each focal plane . Several stages of the cell cycle were identified . In early bud formation ( Figure 7B ) , titan cells had a nucleus in the mother cell while the daughter cell lacked a nucleus . The mother cell nucleus was observed at the bud site and entering the daughter cell ( Figure 7C ) . After nuclear division the mother and daughter each contained single nuclei ( Figure 7D ) . Finally , after cytokinesis was complete , individual nuclei were visible in the mother and associated daughter cell ( Figure 7E ) . Budding of the titan cells was readily observed from in vivo samples suggesting complete cell cycle arrest would not explain the increased titan cell size . Increases in cell size in plants , or gigantism , is often correlated with increased ploidy [40] . Because titan cells contain only one nucleus , we quantified their DNA content by flow cytometry and quantitative PCR . Fluorescently labeled cells from individual or coinfections were isolated by BAL and immediately fixed and stained with DAPI . The fixed cell suspensions were then analyzed using an imaging flow cytometer to define cell populations ( Figure S3 ) . Two distinct populations of fluorescent cryptococcal cells were identified: cryptococcal cells alone and cryptococcal cells inside host cells . Because phagocytosed cryptococcal cell size cannot be accurately measured with flow cytometry , only single non-phagocytosed yeast cells were examined further . The single non-phagocytosed yeast cells were then divided into three populations based on cell diameter: ≤10 µm , >10 µm but ≤20 µm , and >20 µm . The ≤10 µm cell population was designated as small cells of typical size for Cryptococcus . The group of cells >20 µm were designated as the titan cell population . The intermediate cell population , >10 µm but ≤20 µm , contained a mixture of small and titan cells , thus could not be accurately characterized by flow cytometry . Flow cytometry and cell sorting of 50 , 000 cells were used to obtain an accurate representation of the DNA content for each population ( Figure 8 ) . DNA content determinations were based on DAPI fluorescence in haploid cells grown in vitro in Dulbecco's modified eagle medium ( DMEM ) at 37°C and 5% CO2 ( non-titan-inducing conditions ) ( Figure S4 ) . The small cell population isolated from coinfected mice showed a prominent peak consistent with a majority of the cells in the population containing two copies ( 2C ) of DNA . These data would suggest that most of the small cell population in vivo were in G2 of the cell cycle ( Figure 8A ) . In contrast , the titan cell population showed two peaks consistent with 4C or 8C DNA content ( Figure 8A ) . No differences in titan cell DNA content were observed between the two mating types or in individual versus coinfections , indicating that titan cell DNA content was not altered by coinfection ( Figure S4 ) . Analysis of the in vivo samples suggested that both the small cells and titan cells could be undergoing active cell growth and replication , making characterization of titan cell ploidy difficult in these in vivo samples . To determine the ploidy of titan cells , we identified in vitro conditions that stimulated titan cell production . Titan cell formation was only observed in cryptococcal samples grown in spent media previously used to culture mammalian cells ( Figure S5 ) . Differences in titan cell formation were observed based on the media used , the temperature of incubation , and mammalian cell type . Optimal in vitro titan cell production was observed when cryptococcal cells were grown in spent DMEM derived from MH-S alveolar macrophages at 30°C . When grown to stationary phase for 5 days in this medium approximately 4% of the total population was titan cells . On average , titan cells generated in vitro were smaller than those observed in vivo , ranging from 15 µm to 30 µm in diameter . Due to the smaller size of the in vitro titan cells , the intermediate cell population ( >10 µm but ≤20 µm ) was included in the flow cytometric DNA content analysis ( Figure 8B ) . In contrast to the in vivo samples , the DNA content of the in vitro small cell population at 5 days was consistent with 1C cells , suggesting that the cells were in stationary phase ( Figure 8B ) . Cells grown to stationary phase in a standard growth medium were also 1C ( data not shown ) . The intermediate cell population had a single peak consistent with 4C cells and the larger titan cell population ( >20 µm ) had a single peak consistent with 8C cells ( Figure 8B ) . Thus , titan cells in stationary phase appeared to be either tetraploid or octoploid based on cell size . Quantitative PCR was used to determine the average copy number per cell of the chitin synthase 1 ( CHS1 ) gene as an additional molecular characterization of DNA content in the in vivo small and titan cell populations . Quantitative PCR was performed on the isolated DNA from three cell populations ( small , titan , control ) . This quantitative PCR analysis confirmed that the titan cells had increased CHS1 DNA content compared with the small cells ( p<0 . 001 , Figure 8C ) . Consistent with the flow cytometry results , the small cells had an average CHS1 gene copy number of 2 in the in vivo samples , suggesting that the majority of the population had a 2C DNA content . The titan cell population had an average CHS1 gene copy number of 5 , consistent with a 3∶1 ratio of 4C to 8C cells . Taken together , the CHS1 gene copy number and flow cytometry data suggest that titan cells are tetraploid and octoploid .
We characterized a novel cell morphology produced by C . neoformans , referred to as “titan” cells . These enlarged cells have been observed in the lungs of mice following intranasal instillation [29] and can be up to 100 µm in diameter . Titan cells are commonly seen in human clinical isolates [30] , [32] . As early as the 1970s , cells greater than 50 µm in diameter were observed in sputum samples of an infected patient [30] . Our studies demonstrate that titan cells are resistant to oxidative/nitrosative stresses and phagocytosis by host macrophages . We propose that alterations in phagocytosis are beneficial to cell survival in the lungs early in the infectious process but impede dissemination to the major site of disease in the brain . One fifth of the cells in mouse lungs were titan cells following an initial pulmonary infection . The level of titan cell production varied depending on inoculum size . Inoculation with 5×104 cryptococcal cells resulted in almost 30% titan cell formation . In contrast , inoculation with 5×106 cryptococcal cells resulted in approximately 15% titan cell formation . Differences in titan cell formation in response to cryptococcal cell density/burden in the lungs were also observed by Zaragoza and colleagues [41] . Titan cell formation was stimulated by coinfection with strains of opposite mating type . Analysis of titan cell formation in the two mating types revealed that only a cells increased titan cell production upon coinfection . Concomitant with this increased titan cell formation we observed a decrease in a cell accumulation in the brain . Interestingly , a cell hematogenous dissemination to other organs , such as the spleen , was unaffected by increased titan cell formation . Our molecular studies implicate the pheromone response MAPK signal transduction pathway as a regulator of titan cell production . The increase in titan cell formation and reduction in CNS penetration during coinfection was dependent upon the Ste3a pheromone receptor . Mutant strains lacking Ste3a , and therefore unable to sense the presence of pheromone , did not enhance titan cell formation during coinfection and exhibited BBB penetration equivalent to α strains . The clinical relevance of in vivo pheromone signaling and its effect on the infectious process cannot be determined without first understanding the prevalence of coinfections in humans . Irrespective of the biological significance of pheromone signaling , alteration of titan cell formation using the pheromone signaling pathway is a powerful tool to dissect the effect of titan cell formation on disease progression . The ste3a mutant strain exhibited only a slight decrease in the basal level of titan cell formation in the absence of α cells , suggesting that pheromone sensing is not the only pathway leading to titan cell production . The observation that pheromone signaling only modifies the level of titan cells suggests that identification of other signaling pathway ( s ) involved in titan cell formation will be key to understanding other biologically relevant signals that trigger titan cell formation . The observation that titan cells can be generated in vitro by culture in spent medium suggests that cryptococcal cells may sense the presence or absence of a compound in this medium . Titan cell production was predominantly stimulated by spent media from a macrophage cell line . In contrast , little titan cell production was observed in spent media from an endothelial cell line , suggesting the compound could be cell-type specific . An increase in cell size has also been observed in mice deficient in T cells and NK cells [22] . Differences in titan cell formation were also observed in different media and at different temperatures . Thus , we cannot rule out the possibility that the signal is an absence or unavailability of specific nutrients . Taken together , these data suggest there could be four or more signals leading to titan cell formation: host , temperature , nutrients , and pheromone . At least three possibilities could account for pathway interactions affecting titan cell formation ( Figure 9A ) . First , the pheromone signaling pathway may positively affect an environmental sensing pathway to increase the signal leading to titan cell production ( Figure 9A top ) . Second , pheromone signaling may inhibit a negative regulator of titan cell formation ( Figure 9A middle ) . Finally , the pheromone signaling pathway may be independent of the environmental sensing pathway leading to titan cell formation ( Figure 9A bottom ) . Mutant analysis has revealed that signaling pathways such as PKA , cAMP , and RAS can affect cell size in vitro [31] , [41] , [42] and may be involved in the environmental sensing or pheromone signaling pathways leading to titan cell formation . Titan cells have higher DNA content compared with smaller cells . Titan cell production may be a result of a cell cycle pause or increased DNA replication due to other mechanisms . In S . cerevisiae the cell cycle mutant , cdc24 , can be induced to produce yeast cells up to six times greater in volume than normal cells due to continued growth in the absence of cell division [39] . However , because titan cells are able to continue producing daughter cells , it is unlikely they are generated by a complete cell cycle arrest , as in the cdc24 mutant . An increase in DNA content from haploid to tetraploid has been implicated in increased cell size and morphology changes in S . cerevisiae [43] . This may be due to an increase in transcripts that regulate passage from G1 to S phase in the tetraploids [43] . C . neoformans titan cells do not appear to have the mitosis defects and lowered viability seen in S . cerevisiae tetraploid cells [44] , [45] . These data suggest C . neoformans may have a distinct cell cycle regulation that allows titan cell replication . An increase in DNA content may be necessary to generate and sustain titan cells . Zaragoza and colleagues [41] have shown that titan cells have altered capsule formation and cell wall composition . These processes , along with the need to sustain a large cell , may require increased transcriptional and translational capacity by the cell . Additional copies of DNA may facilitate this process . An alternative hypothesis is that the increase in cell size protects against the host immune system and that the increase in DNA content promotes rapid generation of daughter cells . This phenomenon is observed in other human pathogenic fungi . Pneumocystis and Coccidioides species also exhibit increases in cell size and nuclear content in the lungs of infected hosts . In Pneumocystis , many cells are in a trophic haploid state and reproduce by binary fission , yet a subset of cells is thought to undergo a sexual cycle to produce enlarged cysts [46] , [47] . It is hypothesized that the Pneumocystis sexual cycle involves fusion of two haploid trophic cells to produce a diploid cell . Although the exact signal for fusion is unknown , molecular analysis has revealed a MAPK pathway similar to the C . neoformans pheromone signaling pathway , including a homolog of the Ste3 pheromone receptor [48] , [49] . The resulting diploid cell undergoes meiosis followed by mitosis , forming a cyst containing eight nuclei [46] , [47] . As the Pneumocystis cyst matures , the nuclei develop into new trophic cells and are released into the surrounding environment [46] , [47] , [50] . In contrast to cryptococcal titan cells , Pneumocystis cyst formation produces β-glucan resulting in increased recognition and phagocytosis by host immune cells [47] . Coccidioides immitis also undergoes dramatic morphological changes in vivo [4] , [51] . Infection begins with inhalation of spores , or arthroconidia , from the environment that are roughly 2 to 4 µm in diameter . The arthroconidia produce round cells in which nuclear division gives rise to multinucleated cells called spherules [4] , [51] , [52] , [53] . The developing spherules range in size from 60 to100 µm or larger . Host immune cells , such as macrophages and neutrophils , are unable to phagocytose the Coccidioides spherules due to their large size , protecting the cell from destruction while it generates endospores [4] . Eventually the spherule ruptures , releasing the endospores , and the cycle starts again . In C . neoformans , titan cells are also protected from phagocytosis . Titan cells do not rupture but instead produce daughter cells both in vitro and in vivo by budding . Unlike Pneumocystis and Coccidioides where enlarged cells contain multiple nuclei , the cryptococcal titan cells appear to have a single nucleus . Interestingly , titan daughter cells are small , suggesting they may be haploid . These findings imply ploidy changes occur both during formation of titan cells and their daughter cell progeny . Ploidy changes can occur via sexual , parasexual , and endoreplicative processes . Further studies are necessary to determine the method by which ploidy changes occur in C . neoformans . Titan cell production was observed within 1 day post-infection , yet the proportion of titan cells present in the lungs plateau by 7 days post-infection and remain constant throughout the rest of the infection . The observation that titan cell production is only stimulated at early stages of infection implies titan cells may promote pathogenesis early in the infectious process but are dispensable later . Cryptococcus is acquired from the environment by inhalation of spores or desiccated yeast cells . Previous studies examining changes in transcription and subsequent up-regulation of virulence factors in response to changes in temperature or phagocytosis by macrophages show that the initial population of inhaled cells is unlikely to be prepared for survival and replication in the host [8] , [54] , [55] , [56] , [57] . Thus , the vast majority of cells in the initial inoculum are likely to be engulfed and destroyed by host mononuclear phagocytes . Titan cell production protects a subset of cells from phagocytosis , possibly due to increased size . While not tested directly in this study , the observed in vitro survival in the presence of oxidative/nitrosative stresses may also promote titan cell resistance to killing mechanisms utilized host immune cells . Thus , the titan cells are able to survive the initial host immune response . Pheromone-mediated titan cell production did not increase the prevalence of a cells in the lungs over time . These data show that titan cell formation does not enhance persistence in the lungs . In support of this conclusion , titan cell formation is not readily observed in the rat persistence model of cryptococcosis [58] , [59] , [60] , [61] , [62] , [63] , [64] . Yet , the C . neoformans var . neoformans strain most commonly used in the rat model readily generates titan cells in mice [41] , [63] . Because titan cells are readily observed in human tissue , where dormancy is thought to be important in the infectious process [11] , [65] , [66] , we cannot rule out the possibility that titan cells play a role in dormancy and/or reactivation in human infections . Dysregulation of titan cell production in the coinfection model did not affect hematogenous dissemination from the lungs to the spleen; yet increased titan cell formation was correlated with a significant decrease in dissemination to the CNS . Survival in macrophages has been shown to be important for trafficking to the CNS [18] , [19] . Additionally , recent studies have demonstrated that phagocytosed cryptococcal cells are more efficient at disseminating to the CNS than non-phagocytosed cells [16] , [20] . Thus , interactions with host phagocytes promote cryptococcal BBB penetration and subsequent neurological disease . Increased titan cell formation in the lungs during coinfection reduced phagocytosis which subsequently inhibited dissemination to the CNS . It is still unclear whether coinfections are a common occurrence in Cryptococcus pathogenesis , yet the effect of coinfection on titan cell production has allowed us to study this important morphological transition . Our data support a model in which titan cells are advantageous at early stages of the infection ( Figure 9B ) . The ability of titan cells to evade phagocytosis allows C . neoformans to establish the initial lung infection and overcome the initial immune response generated by resident macrophages ( Figure 9B top ) . Because severe cryptococcal infections are often seen in patients with T cell deficiencies , it is likely that the clearance of the initial infection is T cell mediated . ( Figure 9B middle ) . Our model predicts that phagocytosis of the daughter cells allows dissemination to the CNS , resulting in neurological disease ( Figure 9B bottom ) . Examples of cell morphology changes and cell surface alterations that are important in the infectious process can be found throughout the microbial world . In the protozoan pathogen Toxoplasma gondii , cysts are formed in response to elements of the host immune response , including IFNγ , pH changes , and nitrosative stress . These cysts are able to escape immune recognition and establish an asymptomatic chronic infection [67] , [68] . Uropathogenic Escherichia coli strains are known to undergo filamentation in response to TLR4 signaling by host immune cells [69] . During development of these bacterial colonies , a subset of the cells filament . The filaments are then able to evade phagocytosis by host neutrophils that are recruited to the area in response to infection [69] . Reovirus undergoes dramatic morphological changes in the host . The reovirus virion can be degraded by proteases revealing a stable intermediate subvirion particle ( ISVP ) . Although both viral morphologies are infectious , the virion is more restricted in host range , while the ISVP can infect a wider variety of cell types [70] , [71] , [72] . Similar to morphological changes in other microbes , titan cell formation alters the host-pathogen interaction in the lungs during early cryptococcal infection . By studying these host-pathogen interactions and the molecular triggers involved in C . neoformans titan cell production , we may gain general insight into how morphological changes can affect pathogenicity of microbes . Our studies highlight the complex morphological variations microbes deploy to avoid recognition and killing by the host immune system .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the appropriate committee . Experiments at the University of Minnesota were reviewed and approved by the university Institutional Animal Care and Use Committee ( IACUC ) under protocol number 0712A22250 . Experiments at the University of North Carolina – Chapel Hill were reviewed and approved by the university IACUC under protocol number 09-166 . 0 . Studies at the Institut Pasteur we reviewed and approved under protocol number CHSCT#03-344 . The congenic C . neoformans var . grubii strains KN99a and KN99α were used in this study [27] . Strains were stored as glycerol stocks at −80°C and grown at 30°C in yeast extract-peptone-dextrose ( YPD ) agar or broth medium ( BD , Hercules , CA ) . C . neoformans cells were cultured overnight in YPD broth . The resulting yeast cells were pelleted and resuspended in sterile phosphate-buffered saline ( PBS ) at a concentration of 1×106 cells/ml based on hemocytometer count . Groups of 6- to 8-week-old female A/J mice ( Charles Rivers , NCI , Frederick , MD; Jackson Labs , Bar Harbor , MA ) were anesthetized by intraperitoneal pentobarbital injection . Three mice per treatment per time point were infected intranasally with 5×104 KN99a , KN99α , or an approximate 1∶1 mixture of KN99a:KN99α cells in 50 µl PBS . The concentration of cells in the inoculum was confirmed by plating serial dilutions and enumerating colony forming units ( CFU ) . At 1 , 2 , 3 , 7 , 14 , or 21 days post-infection mice were sacrificed by CO2 inhalation . The heart , lungs , brain , kidneys , liver , and spleen were harvested , fixed in 10% buffered formalin , paraffin-embedded , sectioned , and stained with PAS ( periodic acid Schiff ) or H&E ( hematoxylin and eosin ) . Tissue sections were examined for cell size and morphology by microscopy . To examine fluorescently labeled cells in tissue sections , the yeast cells were incubated with AlexaFluor 350 ( blue ) , AlexaFluor 488 ( green ) , or AlexaFluor 594 ( red ) succinyl esters for 10–20 minutes at 25°C using the appropriate Protein Labelling Kit ( Invitrogen , Carlsbad , CA ) . Labeled cells were washed >3 times in sterile PBS to remove unbound dye . The cells were resuspended in PBS at a concentration of 1×108 based on hemocytometer count . Three mice per treatment ( a , α , or coinfection ) were infected intranasally with 5×106 fungal cells . The concentration of yeast cells in the inoculum was confirmed by plating serial dilutions and enumerating CFU and the proportion of a cells in the coinfection inoculum was determined by mating assay [27] . Infected mice were sacrificed at 1 , 2 , or 3 days post-infection by CO2 inhalation . Lungs were extracted and fixed as described above and unstained sections were examined for cell size , morphology , and fluorescence . Data presented are representative of three independent experiments with two or three mice per treatment per experiment . Two independent ste3aΔ mutant strains were generated by gene disruption as previously described [73] . The nourseothricin transgene ( NAT ) was used to replace the STE3a gene coding region . PCR was used to generate the 5′ ( KN0035 and KN0036 ) and 3′ ( KN0037 and KN0040 ) flanking regions containing linkers to a NATr cassette and overlap PCR generated the NAT insertion allele ( Table 1 ) . The mutant allele was introduced by biolistic transformation into KN99a to generate ste3aΔ#1 and into the spontaneous ura- strain JF99a [74] to generate ste3aΔ#2 . Transformed colonies resistant to nourseothricin ( 100 µg/ml ) were identified by PCR amplification and sequencing of PCR products spanning a region upstream of the 5′ flanking region into the NAT cassette ( KN0079 and KN0031 ) and from the NAT cassette to downstream of the 3′ flanking region ( KN0032 and KN0109 ) . Gene deletion was further confirmed by mating the mutant strains with KN99α on V8 , pH 5 media for >14 days at 25°C in the dark . The mutant strains were sterile . The ste3aΔ#2 was passaged on SD-ura media to isolate a URA+ revertant for use in virulence tests . Groups of 5–10 mice were infected with 5×104 cells in an approximate 1∶1 ratio of ste3aΔ#1:KN99α , ste3aΔ#2:KN99α , or KN99a:KN99αNAT . The actual proportion of a cells in the infecting inoculum was determined by growth on selective media . At 21 days post-infection , animals were sacrificed . The lungs , spleen , and brain from each animal were homogenized in 2–4 ml PBS and serial dilutions were plated on YPD for CFU enumeration . >500 colonies per organ were isolated and assayed for antibiotic resistance on YPD containing 100 µg/ml nourseothricin to determine mating type . KN99a and KN99α cells were fluorescently labeled as described above . Three mice per treatment were inoculated by tail vein injection with KN99a , KN99α , or an approximate 1∶1 ratio of KN99a:KN99α at a final concentration of 2×107 cells . At 1 day post-infection animals were sacrificed , perfused with 20 ml PBS then 20 ml 4% paraformaldehyde ( PFA ) . Brains were harvested , placed in 4% PFA then 40% w/v sucrose solution in PBS , frozen in isopentane and liquid nitrogen , stored at −80°C , and 50 µm sections were generated . For immunohybridizations , slides were washed in PBS for 15 min followed by incubation with 100 µl trypsin-EDTA ( Invitrogen ) at 37°C for 10 minutes . Slides were then washed in PBS containing 20% fetal calf serum ( Invitrogen ) for 10 minutes , blocked with PBS containing 20% FCS , 0 . 1% bovine serum albumin ( BSA ) and 0 . 1% triton X-100 ( Sigma , St . Louis , MO ) for 20 minutes , then washed with PBS containing 0 . 1% triton X-100 . Anti-collagen IV antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) was diluted to a 1/50 concentration in PBS with 0 . 1% BSA and 0 . 1% Triton X-100 . Antibody-treated slides were incubated overnight at 4°C followed by washing in PBS . Cy3 labeled goat anti-rabbit antibody was diluted to a 1/200 concentration and added to the slides . After 5 hours of incubation at 37°C , slides were washed three times in PBS for 15 minutes . Hoechst medium was diluted to a 1/500 concentration and added to the slides for 30 seconds . Slides were washed for 5 minutes in PBS and mounted in Vectashield mounting medium . Capsule antigen staining was as described in Charlier et al . , 2005 using the CRND-8 and E1 antibodies . Slides were imaged by fluorescence microscopy ( Zeiss Axioplan ) or by 2-photon confocal microscopy ( Zeiss LSM 510 equipped with a Coherent Mira 900 tunable laser ) with sections compiled as a projection or as a 3D rendering . Four mice per treatment were infected as described above with 5×106 AlexaFluor 488 labeled KN99a , KN99α , and ste3aΔ#1 , or an approximate 1∶1 ratio of one stained and one unstained strain . Infected mice were sacrificed at 3 days post-infection by CO2 inhalation . Lungs were lavaged with 1 . 5 mL sterile PBS three times using a 20 gauge needle placed in the trachea . For flow cytometry , cells in the lavage fluid were pelleted at 16 , 000 g , resuspended in 3 . 7% formaldehyde , and incubated at room temperature for 30 minutes . Cells were then washed once with PBS , resuspended in PBS containing 300 ng/ml 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen ) , incubated at room temperature for 10 minutes , washed with PBS , and resuspended in PBS . >500 cells per animal were analyzed for size and fluorescence by microscopy ( AxioImager , Carl Zeiss , Inc ) . Confocal microscopy ( LSM710 , Carl Zeiss , Inc ) and z-stack imaging ( AxioImager with Apotome , Carl Zeiss , Inc ) were used to examine interactions with host mononuclear cells . Images were analyzed using Axiovision and Zen software ( Carl Zeiss , Inc ) . Crescent shaped and other fluorescently-labeled cryptococcal cell fragments ( i . e . not round cells ) were observed within host mononuclear cells . These cell fragments were not included in the analysis . Twelve mice were intranasally infected with 2×107 cells in 50 µL PBS of an approximately 1∶1 ratio of KN99a and KN99α cells . At 3 days post-infection , mice were sacrificed by CO2 inhalation and BALs were performed . Cells were sorted by FACS using an iCyt Reflection cell sorter ( iCyt , Champaign , IL ) . Cells were sorted based on size using forward scatter ( FSC ) into small cell and titan cell populations . Purity of samples was checked by flow cytometry and microscopy . Samples were resuspended in Roswell Park Memorial Institute ( RPMI ) medium 1640 ( Invitrogen ) supplemented with 10% fetal bovine serum ( FBS ) ( ATCC , Manassas , VA ) , 4 . 5 g glucose/L ( BD ) , 1 mM sodium pyruvate ( Invitrogen ) , 0 . 01 M HEPES ( MP Biomedicals , Solon , OH ) , 5% penicillin/streptomycin ( Invitrogen ) and 0 . 05 mM β-mercaptoethanol ( Chemicon ) to a concentration of 2×104 cells per 100 µL . Samples were then treated with 10 mM NaNO3 ( Sigma-Aldrich , St Louis , MO ) , 3 mM H2O2 ( Walgreens Co . , Deerfield , IL ) , or 1 mM tert-butyl hydroperoxide ( TBHP ) ( Sigma-Aldrich ) . At 0 , 6 , 16 or 24 hours post treatment , 10 µL aliquots of each sample were plated onto YPD agar for CFU enumeration . Fixed BAL samples from 4 mice per treatment were generated as described above and analyzed using an ImageStream imaging flow cytometer and INSPIRE software ( Amnis Corporation , Seattle Washington ) . Briefly , images for 5000 cells per sample were collected and analyzed for single cells ( R1 ) , doublets ( R0 ) , or aggregates of cells ( Figure S3A ) . Only single cells ( R1 ) were used in our analyses because cell aggregates would misrepresent cell sizes . Single cells were further analyzed for AlexaFluor 488 fluorescence and DAPI staining ( Figure S3B ) . Due to the high nuclear content of mammalian cells , these cells had extremely high DAPI staining ( R2 and R3 ) . Non-phagocytosed yeast cells ( R5 ) we identified based on their low DAPI staining . Visual confirmation of cell size in the flow cytometry images was used to identify small and titan cell populations ( R6 and R7 ) , that each gate contained only the target cells , and that no contamination between the populations was observed ( Figure S3C ) . Data analysis and gating was performed using IDEAS software ( Amnis Corporation ) . Cryptococcal cells grown in vitro in YPD or DMEM to log or stationary phase were used as controls to identify haploid cells ( 1C ) and actively dividing cells ( 1C + 2C ) . To examine titan cell ploidy , fixed BAL samples from 4 mice per treatment were generated as described above . In vitro control samples were grown in YPD or Dulbecco's modified eagle medium ( DMEM , 37°C , supplemented with 10% fetal bovine serum ( FBS ) ( ATCC ) , 4 . 5 g glucose/L ( BD ) , 1 M sodium pyruvate ( Invitrogen ) , 0 . 01 M HEPES ( MP Biomedicals , Solon , OH ) , 5% penicillin/streptomycin ( Invitrogen ) and 0 . 05 mM β-mercaptoethanol ( Chemicon ) for 6 hours ( log phase ) or 5 days ( stationary phase ) . Spent DMEM or RPMI was collected from MH-S macrophages after 3–5 days culture at 37°C and 5% CO2 . Spent endothelial cell ( EC ) media ( complete EGM medium , Clonetics , San Diego , CA , USA ) was collected from human umbilical vein endothelial cells ( HUVEC ) after 3–5 day culture at 37°C and 5% CO2 . In vitro titan cells were grown in filter sterilized spent media at 30°C or 37°C for 7 days . In vitro and in vivo samples were fixed in 3 . 7% formaldehyde and stained with 300 ng/ml DAPI in PBS . Autofluorescence of non-DAPI stained fixed titan cells was measured and used to set the baseline for ploidy measurements . Cells were examined for cell size by forward scatter ( FCS ) and nuclear content by DAPI using an LSRII flow cytometer with FACSDiva software ( BD ) using gating defined by imaging flow cytometry . FCS cell sizes in each gate were verified by microscopy ( Zeiss Axioplan ) . Data presented are representative of three independent experiments with four mice per treatment . 50 , 000 cells per treatment were analyzed to determine titan cell formation in vitro . In vitro titan cell formation was variable from experiment to experiment but trends between treatments remained constant . Data presented are representative of five independent experiments . Because the absolute number of cells in each population and in each mouse differed , the DAPI fluorescence for each population was normalized to the number of cells in that population in order to clearly visualize peaks on a histogram representation of the data ( Figure 8 , Figure S4 ) . Cells were examined for cell size by forward scatter ( FCS ) and nuclear content by DAPI using an LSRII flow cytometer with FACSDiva software ( BD ) using the gating defined by imaging flow cytometry . FCS cell sizes in each gate were verified by microscopy to identify the ≤10 µm , >10 µm but ≤20 µm , and >20 µm cell populations ( Zeiss AxioImager ) . Data presented are representative of three independent experiments with four mice per treatment . 50 , 000 cells per treatment were analyzed to determine titan cell formation in vitro . In vitro titan cell formation was variable from experiment to experiment but trends between treatments remained constant . Data presented are representative of five independent experiments . Ten to fourteen mice were infected with 5×106 AlexaFluor 488-stained cells at an approximate 1∶1 ratio of KN99a:KN99α , as described above . Infected mice were sacrificed at 3 days post-infection and BALs were performed . BALs were pelleted and resuspended in 0 . 05% SDS in sterile water for 1 minute to promote host cell lysis . Cells were then fixed in 1 ml PBS containing 1% formaldehyde and incubated for 30 minutes at room temperature with mixing . Samples were incubated in 125 mM glycine for 5 minutes , centrifuged at 1500 g for 10 minutes , and the pellets were resuspended in ice cold TBS ( 20 mM Tris , pH 7 . 6 , 150 mM NaCl ) containing 125 mM glycine . Cells were washed once in TBS , resuspended in 1 ml PBS and the cell concentration was determined by hemocytometer count . Cell numbers were adjusted to 106 cells/ml , and 1% BSA was added to the fixed cell suspension . Cells were sorted using a FACSAria fluorescence activated cell sorter ( FACS ) using FACSDiva software ( BD ) . Small and titan cell populations were isolated by FACS using gating as described above . DNA was isolated from 106 cells from small , titan , and 37°C DMEM ( control ) cell populations . A portion of the control cell population was DAPI stained and the number of haploid and diploid cells in the population was determined by flow cytometry ( Figure S3 ) . Small cells were classified as ≤10 µm and titan cells were >10 µm . After sorting , the two cell populations were pelleted and resuspended in lysis buffer ( 50 mM HEPES , 140 mM NaCl , 1% Triton X-100 , 0 . 1% Sodium deoxycholate , 1 mM EDTA ) . The cell suspensions were transferred to tubes containing 0 . 3 mm glass beads and vortexed for six 5 minute cycles at 4°C . The bottoms of the tubes were then pierced with a hot 21-gauge needle . The tubes were placed into 15 ml conical tubes and centrifuged at 1500 g for 5 minutes at 4°C . The pellets and supernatants were combined and transferred to new tubes . These mixtures were centrifuged for 10 minutes at 10 , 000 g at 4°C and the supernatants transferred to clean tubes . After a further 5 minute centrifugation , the DNA crosslinks were reversed by adding 200 µl TE ( 10 mM Tris , pH 7 . 5 , 1 mM EDTA ) containing 1% SDS to the clarified supernatants and incubating for 6 hours at 65°C . Samples were then incubated 2 hours at 37°C with 250 µl TE containing 0 . 4 mg/ml proteinase K . After adding 55 µl 4 M LiCl , the DNA was extracted with 0 . 5 ml phenol and the DNA was precipitated with 100% ethanol . The DNA pellets were washed with 70% ethanol , dried , and resuspended in TE containing 1 . 5 µl RNase ( Ambion AM22886 ) . Samples were stored at −20°C until analyzed by qPCR with primers KN104 and KN105 for chitin synthase ( CHS1 ) ( Table 1 ) . Gene copy number in the control sample was calculated based on the known number of 1C and 2C cells present in that sample ( 1 . 4C ) based on flow cytometry . The small and titan cell gene copy numbers were normalized to the control sample . All analyses were performed using Analyse-It ( Analyse-it Ltd . , Leeds , England ) . Wilcoxon rank sum analysis was used to analyze differences in coinfection data and P-values <0 . 001 were considered significant . The Mann-Whitney U test was performed to analyze differences between survival curves and P-values <0 . 001 were considered significant . One-way ANOVA was used to analyze differences in titan cell production or phagocytosis and P-values <0 . 05 were considered significant for titan cell production experiments . P-values <0 . 1 were considered significant for phagocytosis experiments . | Cryptococcus neoformans is a common life-threatening opportunistic human fungal pathogen . C . neoformans grows as a budding yeast in vitro with typical cell sizes ranging from 5 to 10 µm in diameter . Early reports suggested the presence of enlarged cells in human infections yet the identity of these cells and their role in virulence remained uncharacterized . Changes in cellular morphology are also observed in the mouse inhalation model of cryptococcosis . These enlarged “titan” cells accounted for 20% of the cells in the lungs . Titan cell formation was found to be regulated by a G-protein coupled receptor ( GPCR ) signal transduction pathway associated with pheromone sensing . Analysis of titan cells revealed uninucleate , polyploid cells that reproduced by budding , suggesting alterations in the regulation of cell growth and mitosis . Titan cell formation also affected host-pathogen interactions by reducing phagocytosis by host mononuclear cells and was correlated with reduced dissemination within the host . These results describe a novel mechanism by which C . neoformans undergoes morphogenesis to evade host phagocytosis , leading to the hypothesis that titan cell formation allows survival of a subset of the population and plays a unique and specialized role during infection . | [
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| 2010 | Cryptococcal Cell Morphology Affects Host Cell Interactions and Pathogenicity |
Lack of surveillance systems and accurate data impede evidence-based decisions on treatment and prevention of enteric fever , caused by Salmonella Typhi/Paratyphi . The WHO coordinates a global Invasive Bacterial–Vaccine Preventable Diseases ( IB-VPD ) surveillance network but does not monitor enteric fever . We evaluated the feasibility and sustainability of integrating enteric fever surveillance into the ongoing IB-VPD platform . The IB-VPD surveillance system uses WHO definitions to enroll 2–59 month children hospitalized with possible pneumonia , sepsis or meningitis . We expanded this surveillance system to additionally capture suspect enteric fever cases during 2012–2016 , in two WHO sentinel hospitals of Bangladesh , by adding inclusion criteria of fever ≥102°F for ≥3 days , irrespective of other manifestations . Culture-positive enteric fever cases from in-patient departments ( IPD ) detected in the hospital laboratories but missed by the expanded surveillance , were also enrolled to assess completion . Costs for this integration were calculated for the additional personnel and resources required . In the IB-VPD surveillance , 5 , 185 cases were enrolled; 3% ( N = 171/5185 ) were positive for microbiological growth , of which 55% ( 94/171 ) were culture-confirmed cases of enteric fever ( 85 Typhi and 9 Paratyphi A ) . The added inclusion criteria for enteric fever enrolled an additional 1 , 699 cases; 22% ( 358/1699 ) were positive , of which 85% ( 349/358 ) were enteric fever cases ( 305 Typhi and 44 Paratyphi A ) . Laboratory surveillance of in-patients of all ages enrolled 311 additional enteric fever cases ( 263 Typhi and 48 Paratyphi A ) ; 9% ( 28/311 ) were 2–59 m and 91% ( 283/311 ) >59 m . Altogether , 754 ( 94+349+311 ) culture-confirmed enteric fever cases were found , of which 471 were 2–59 m . Of these 471 cases , 94% ( 443/471 ) were identified through the hospital surveillances and 6% ( 28/471 ) through laboratory results . Twenty-three percent ( 170/754 ) of all cases were children <2 years . Additional cost for the integration was USD 44 , 974/year , a 27% increase to the IB-VPD annual expenditure . In a setting where enteric disease is a substantial public health problem , we could integrate enteric fever surveillance into the standard IB-VPD surveillance platform at a modest cost .
Enteric fever ( typhoid/paratyphoid ) is a major cause of mortality and morbidity in many low and middle-income countries . In 2015 , it was estimated to cause about 178 , 000 deaths and 17 million illnesses; 85% of all cases occur in three countries—Bangladesh , India and Pakistan [1–3] . The primary causative agents of enteric fever are Salmonella enterica serovars Typhi ( typhoid ) and Paratyphi A , B , and C ( paratyphoid ) [4] . Enteric fever was one of the largest killers during the pre-antibiotic era , but case fatality rates have decreased from 30% to less than 1% with the use of effective antibiotics [4] . In recent years , however , there has been an increasing number of reports on the rise of antimicrobial resistance of Salmonella Typhi/Parathyphi [5] . If this trend is not interrupted , untreatable infections with case fatality rates much higher than those experienced in the last few decades are likely to occur . This evolving crisis calls for urgent guidelines for institution of effective treatment and prevention policies; however , a scarcity of accurate data on burden and epidemiological and antimicrobial resistance trends impedes evidence-based policy decisions . For example , despite the availability of the typhoid Vi polysaccharaide vaccines ( ViPS ) that provide protection in >2 years children in Bangladesh , where the main etiology of blood stream infections is Salmonella Typhi/Paratyphi A [6–8] , they are not used programmatically . Due to poor reception by the public and clinicians , two pharmaceutical manufacturers ultimately removed their products from market; consumption in 2013 was only about 2 , 500 doses ( personal communication ) . Patients and physicians largely rely on empirical antibiotic therapy . This lack of data primarily stems from a lack of robust surveillance for enteric fever in endemic areas , which are mainly in resource-poor countries that cannot afford to establish and/or sustain new surveillance programs for this disease [9] . Credible disease estimates can help policy-makers and government officials to prioritize interventions . Unlike for enteric fever , most countries already have , or are in the process of introducing , pneumococcal conjugate vaccine ( PCV ) and Haemophilus influenzae type b ( Hib ) vaccine to combat invasive diseases caused by Streptococcus pneumoniae and Hib , respectively . The decisions to implement these vaccines have been facilitated by surveillance data generated by the Gavi’s PneumoADIP and Hib Initiative and the World Health Organization ( WHO ) -coordinated Global Invasive Bacterial-Vaccine Preventable Diseases ( IB-VPD ) Surveillance Network . The IB-VPD surveillance assesses the burden of pneumonia , meningitis and sepsis in children ≤59 months . This surveillance system captures data on S . pneumoniae , Hib and Neisseria meningitidis ( from blood and cerebrospinal fluid , CSF ) and monitors long-term trends , including the impact of vaccines [10] . As of 2016 , the system is in place in 56 countries [11] . Notably , enteric fever is not part of this surveillance platform . Since 2009 , Bangladesh has been operating four high-performing WHO IB-VPD sentinel surveillance sites in Dhaka ( two sites ) , Chittagong and Mirzapur . Epidemiological data generated from these sites facilitated introductions of Hib and pneumococcal vaccines in Bangladesh [11] . In this study , we aimed to evaluate the logistics , cost and sustainability of leveraging the ongoing WHO-coordinated IB-VPD platform for enteric fever surveillance by broadening the inclusion criteria of the original surveillance .
The study was conducted in two urban hospitals , Dhaka Shishu ( Children ) Hospital ( DSH ) and Shishu Shasthya ( Child Health ) Foundation Hospital ( SSFH ) , which are sentinel sites of the WHO IB-VPD platform in Bangladesh . These are the two largest pediatric hospitals in the country . DSH provides primary to tertiary care to patients aged 0–18 years; it acts both as a primary point of care and the major referral hospital of the country . SSFH acts as the primary point of care for children aged 0–14 years . Together , they have 840 beds , 246 ( 29% ) of which are reserved for families who are unable to pay . The hospitals are located close to each other and primarily cater to the same catchment area of Mirpur , Dhaka . In Bangladesh , using the WHO-coordinated IB-VPD surveillance system , we monitor pneumonia , meningitis and sepsis in children under 5 years old admitted in the in-patient departments ( IPD ) using protocols described elsewhere [12] . In brief , IPD patients are assessed by research physicians and are considered eligible for WHO IB-VPD surveillance study if they meet clinical definitions for meningitis , pneumonia or sepsis ( WHO’s IB-VPD inclusion criteria , Table 1 ) [12] . Blood specimens are collected from eligible cases at the discretion of the attending physicians . Only cases from which a specimen is collected are enrolled . In January 2012 , we expanded the existing surveillance platform to include surveillance of enteric fever; based on WHO case definition , we set inclusion criteria for enrollment as fever of ≥102°F for ≥3 days with no clinical manifestations of pneumonia , meningitis or sepsis ( Table 1 ) [13] . Here we report our findings from this expanded surveillance between January 2012 and December 2016 . To test the success rate of capturing enteric fever cases using the expanded IB-VPD platform , we additionally enrolled all cases ( of any age and any clinical presentation ) from the IPD whose blood culture yielded growth of Salmonella Typhi/Paratyphi A in the laboratories but were not enrolled in the active hospital surveillance systems . Microbiology laboratories of the two hospitals are also the diagnostic laboratories that provide service to all patients . They receive and culture all blood specimens referred by physicians in the hospitals . Blood culture results of all cases were communicated to the study physicians by the laboratory staff as soon as they were available . Study physicians at DSH and SSFH reviewed these cases of Salmonella Typhi/Paratyphi A that were not captured through the IB-VPD or enteric fever surveillance systems and enrolled them in the study . Clinical information of these culture-confirmed enteric fever cases were collected through the study physicians’ assessment within 24 hours of laboratory confirmation from hospital charts [12] . Blood cultures were performed using standard methods as described earlier [14] . In brief , 2–3 ml blood was aseptically obtained and inoculated into trypticase soy broth supplemented with sodium polyethanol sulphonate ( 0 . 25% ) and isovitalex ( 1% ) . All blood collections were performed by trained hospital phlebotomists and stringent measures were taken to avoid contamination by skin flora during phlebotomy . Blood collection was only performed once per patient . Incubated blood culture bottles were sub-cultured on the 2nd , 3rd and 5th days of incubation . Identification of Salmonella Typhi/Paratyphi A isolates was confirmed after standard biochemical tests and agglutination with Salmonella species and serovar specific antisera ( Ramel , Thermo Fisher Scientific , USA ) . Antibiotic susceptibility tests were conducted and interpreted according to most the recent Clinical and Laboratory Standards Institute ( CLSI ) guidelines for each antibiotic . Disc diffusion methods were used for ampicillin , cotrimoxazole , chloramphenicol , ciprofloxacin , azithromycin and ceftriaxone . In addition , microbroth dilution method was performed for ciprofloxacin . All data were entered into EpiData ( The EpiData Association , Odense , Denmark ) and analyzed using Stata 13 ( STATACorp , College Station , TX ) . Data were analyzed to examine the distribution of culture-positive etiological agents of meningitis , sepsis , pneumonia and enteric fever cases , and their clinical and epidemiological features . Age distribution and antimicrobial susceptibility of enteric fever cases were also examined . No statistical tests were performed in this study . The cost of integrating enteric fever surveillance into the ongoing IB-VPD surveillance was calculated based on the wet-laboratory resources required to process 1 , 699 additional blood specimens and the additional personnel appointed . While calculating wet laboratory expenses , extra items required for culture and identification of Salmonella Typhi and Salmonella Paratyphi A using biochemical and serological tests with specific antisera were considered . Two research physicians ( 50% time each , one at each facility ) and two research assistants ( 50% time each , one at each facility ) were needed to aid in assessment of the additional cases that became eligible and were enrolled due to the additional enteric fever surveillance inclusion criteria . The protocols were approved by the ethics review committees of the Bangladesh Institute of Child Health , Dhaka Shishu ( Children ) Hospital , Bangladesh . Blood samples were collected as part of routine clinical care and written consent was obtained from parents or caregivers of all participants for other aspects of the study , including collection of data and use of specimens for additional laboratory analysis .
Based on the original inclusion criteria defined by the WHO-coordinated IB-VPD surveillance , 10 , 130 cases were identified with possible invasive bacterial diseases ( sepsis , pneumonia , meningitis ) ( Fig 1 ) . Amongst them , 5 , 185 ( 52% ) cases were enrolled on collection of blood specimen for culture; 171 ( 3 . 3% , 171/5 , 185 ) cases were culture-positive ( Fig 1 ) . Of the organisms detected , 55% ( 94/171 ) were either Salmonella Typhi ( 50%; 85/171 ) or Salmonella Paratyphi A ( 5 . 2%; 9/171 ) . The other predominant organisms were S . pneumoniae ( 29%; 49/171 ) and N . meningitidis ( 1 . 2%; 2/171 ) ( Fig 2 ) . A full list of organisms detected in this original platform are listed in S1 Table . Using the added inclusion criteria of ≥102°F for ≥3 days for enteric fever , a further 2 , 744 cases with suspect enteric fever were identified , who did not meet the clinical criteria of WHO-defined meningitis , sepsis or pneumonia ( IB-VPD surveillance ) . Blood specimens were collected from 1 , 699 ( 62% , 1699/2744 ) of them and a total of 358 ( 21% , 358/1699 ) specimens were culture-positive . Amongst the culture-positive cases , Salmonella Typhi was the predominant etiology ( 85% , 305/358 ) , followed by Salmonella Paratyphi A ( 12% , 44/358 ) ( Fig 1 ) . Thus , the additional case definition increased the number of all blood cultures performed by 25% {1699/ ( 1699+5185 ) }and this resulted in a five-fold increase in the detection of enteric fever cases ( from 94 to 443 in five years ) . Of the remaining nine culture-positive specimens captured with the additional inclusion criteria , six were cases of S . pneumoniae infections , two of non-typhoidal Salmonella infections and one of Escherichia coli infection ( Fig 2 , S1 Table ) . In all age groups seeking care at the in-patient departments of the hospitals , a total of 311 culture-confirmed enteric fever cases ( 263 Salmonella Typhi and 48 Salmonella Paratyphi A ) cases were identified in the laboratories that were not enrolled through the WHO IB-VPD surveillance or the added enteric fever surveillance . Amongst them , 9% ( 28/311 ) were from children 2–59 months old , the WHO IB-VPD age group . The combination of the IB-VPD and the added fever ≥ 102°C for ≥ 3 days in the case definition captured 443 cases in this age group . Thus , the proposed expanded surveillance , using the IB-VPD platform , captured 94% ( 443/471 ) of the blood culture-confirmed enteric fever cases among 2–59 months old in-patients . The remaining 91% ( 283/311 ) culture-confirmed enteric fever cases obtained in the laboratories were from children > 59 m . In total , 754 culture-confirmed typhoid and paratyphoid cases were enrolled from the in-patient departments in this study . Of them , 283 ( 38% ) cases were children aged > 59 m and hence only captured after obtaining laboratory results . Out of 754 total culture-positive typhoid/paratyphoid cases identified , 471 enteric fever infections were in the target IB-VPD population of 2–59 m old children . In this age group , 36% ( 170/471 ) of the infections occurred in the first 24 months of life . Children in their second year of life seemed most vulnerable with 27% ( 125/471 ) cases occurring in children aged 12–23 months ( Fig 3 ) . Antibiotic susceptibility was tested for all Salmonella Typhi ( N = 653 ) and Salmonella Paratyphi A isolates ( N = 101 ) . In total , 166 ( 25% ) Salmonella Typhi isolates were multi-drug resistant ( MDR , resistant to chloramphenicol , ampicillin and trimethoprim ) . No MDR Salmonella Paratyphi A strain was identified . While 578 ( 89% ) of Salmonella Typhi and 70 ( 69% ) of Salmonella Paratyphi A isolates were susceptible to azithromycin , all strains were susceptible to ceftriaxone . By contrast , 646 ( 99% ) Salmonella Typhi and 99 ( 99% ) Salmonella Paratyphi A isolates were non-susceptible to ciprofloxacin . Clinical manifestations of all 754 laboratory-confirmed typhoid and paratyphoid cases were assessed ( Table 2 ) . Of the 94 cases captured in the original WHO IB-VPD surveillance , 78% ( 73/94 ) had fever of ≥102°F for ≥3 days . Median duration of fever was five days . Other manifestations included vomiting ( 30% , 28/94 ) , convulsions ( 37% , 35/94 ) and inability to feed ( 17% , 16/94 ) . In the 349 cases captured with the added inclusion criteria for enteric fever , where only children with ≥ 102°C fever for ≥ 3 days were enrolled , the median duration of fever was six days and vomiting ( 37% , 130/349 ) was often observed . Overall , clinical manifestation of enteric fever cases identified by the IB-VPD surveillance appeared more severe than those identified based on expanded fever ≥ 102°C fever for ≥ 3 days criteria , most notably with convulsions ( 37% vs 4% ) and inability to feed ( 17% vs 0% ) occurring at a higher rate . Amongst the 28 2–59 m children enrolled after laboratory confirmation , who were neither captured by IB-VPD or enteric fever surveillance , physicians observed a fever of ≥102°F in only one case and the duration of fever was <3 days . Vomiting was the most common manifestation ( 32% , 9/28 ) . In the >59 months-old group ( N = 283 ) , fever of ≥102°F was observed in 249 ( 88% ) cases and the median duration of fever was six days . The most common accompanied clinical sign was also vomiting ( 41% , 117/283 ) . The additional cost for running the enteric fever surveillance leveraging the WHO’s IB-VPD platform was USD 19 , 374 per year for research physicians and assistants , in addition to the base expenditure of USD 167 , 765 of the platform . The wet laboratory work required additional reagents and resources of USD 25 , 600 per year . The total of USD 44 , 974 for added enteric fever surveillance is a 27% increase to the annual IB-VPD cost .
In South Asia , Salmonella Typhi/Paratyphi A comprise three-fourths of all isolates obtained from blood cultures of sick pediatric and general populations [6–8] . Enteric fever can be prevented by improving water , sanitation and hygiene and with effective vaccines . However , as it is a disease that disproportionately affects resource-poor communities , unless the vaccines are provided through the government health services , the new generation typhoid vaccine carries a risk of not protecting the most vulnerable children in the poorest countries . Moreover , it remains difficult for policy makers to make evidence-based decisions as historically only a few , small , sporadic studies have addressed disease burden . Recently , with investments like the Coalition against Typhoid by the Bill and Melinda Gates Foundation , there has been renewed interest among the global community , including industries , in this disease . The Sabin Vaccine Institute , with support from the Bill and Melinda Gates Foundation , established a hospital-based enteric fever surveillance network in Asia called the Surveillance for Enteric Fever in Asia Project ( SEAP ) , to enable systemic collection of data and fill knowledge gaps on impact of severe enteric fever . Another surveillance program , Typhoid Fever Surveillance in Africa Program ( TSAP ) , was completed in Africa in 2014 and its follow-on study , Severe Typhoid in Africa ( SETA ) that measures the severity and burden of enteric fever , is underway [15] . However , such comprehensive population-based surveillance systems are expensive and bear the risk of unsustainability . For example , the large multi-country study , Pneumococcal Vaccine Accelerated Development and Introduction Plan ( PneumoADIP ) , successfully demonstrated the burden of pneumococcal disease in developing countries and facilitated introduction of the appropriate vaccines [16] . Nevertheless , due to the high costs , this comprehensive surveillance system was not sustained . To sustainably monitor trends of enteric fever and the characteristics of its etiologies , sustainable and cost-effective surveillance systems are desirable . In Bangladesh , we have been able to establish a cost-effective integration of enteric fever surveillance within the ongoing WHO-coordinated IB-VPD surveillance system to generate data on another vaccine preventable disease . During 2012–2016 , in the IB-VPD surveillance , 55% etiologies of all blood culture positive cases of suspect pneumonia , sepsis and meningitis were Salmonella Typhi and Paratyphi A , despite the fact that IB-VPD does not aim to monitor enteric fever . Over the same time period , our attempt to identify additional enteric fever cases using added inclusion criteria of fever >102°F for ≥ 3 days increased capture of culture-confirmed enteric fever episodes in 2–59 m children from 19/year to 89/year . We report a total of 471 cases in this age group , where 94 were captured through the original IB-VPD surveillance and 358 through the added enteric fever surveillance . An additional 28 cases were found through analysis of laboratory results , which were missed by the inclusion criteria of the surveillance study . Overall , this integrated IB-VPD and enteric fever surveillances was able to capture 94% ( 443/471 ) of culture-confirmed enteric fever cases in 2–59 m children . This integration was sustainably managed for five years with no major hurdles and required an increase of 27% in cost; co-sharing of resources and personnel make the proposed surveillance a cost-effective approach . The data generated from this multi-layered and multi-year study corroborate with results from previous typhoid specific studies from the region . Previous studies in Bangladesh showed that more than 50% of typhoid cases occur in children under the age of 5 years , similar to the findings of this study [7 , 17 , 18] . The rate of multidrug resistance in recent years has been reported to be around 20% for Salmonella Typhi strains in Bangladesh with more than 90% non-susceptibility to ciprofloxacin [6 , 19] . Similar rates were observed in our surveillance . The surveillance system proposed here is not without limitations . Firstly , blood cultures were not performed for all suspect cases . This limitation is intrinsic to all IB-VPD sentinel site based surveillance systems since obtaining blood cultures is mainly dependent on the discretion of the treating physicians . To improve the blood-culture practice , our study team leveraged state-of-the-art laboratories and provided the test free of cost and the hospital authorities encouraged the treating physicians to advise blood culture . However , the large number of culture-positive cases ( 471 cases in 2–59 m patients and 283 cases in >59 m patients ) that were detected yielded large amount of information and indicate that the proposed integrated surveillance system can be used to generate high quality epidemiological data and monitor antimicrobial resistance trends . Secondly , previous studies performed in Bangladesh and other endemic countries of the region show that majority of enteric fever cases seek care at the out-patient departments of hospitals , where they are commonly treated using empirical therapy; as the proposed enteric fever surveillance rides on the ongoing IB-VPD surveillance , only in-patient cases can be captured . Despite missing a large proportion of cases , such a surveillance program will capture the most severe cases with higher likelihood of hospitalization . Moreover , the antimicrobial resistance trends learnt from documented cases in this surveillance can guide empirical treatment policies in out-patient departments . Thirdly , because the surveillance reporting is not population-based , it does not have a denominator and thus does not allow for incidence calculation . This can be overcome if the data can be linked to a denominator using the low-cost hybrid approach proposed by Luby et al [9] . This approach combines the existing laboratory diagnosis data conducted in healthcare centers with those from community-based surveillance of utilization of healthcare facilities ( study hospitals ) to generate incidence estimates . We are currently initiating such a combined surveillance to calculate incidence and generate data relevant to policy decisions . Additional resources are also being invested to follow-up patients to characterize outcome and estimate disease severity and case fatality rates . Furthermore , previous work on impact of pneumococcal conjugate vaccine has shown that with such large number of cases in sentinel sites , it is also possible to monitor vaccine impact in hospital-based surveillance systems [20] . Fourthly , the stated costs for the proposed integration were estimated based upon sites that have considerable experience in bacterial disease surveillance . It is unclear how much this experience and these costs would transfer to other sites . Overall , this study demonstrates that enteric fever surveillance can be sustainably and cost-effectively integrated into the original IB-VPD surveillance and the proposed integrated platform will fulfill the objectives of WHO for other invasive bacterial vaccine preventable diseases: ( i ) to collect data to describe epidemiology and estimate burden , ( ii ) to establish a surveillance platform in order to establish baseline rates of disease to measure impact after introduction of vaccines and ( iii ) to detect and characterize circulation bacterial types [10] . Establishment of a new and stable surveillance platform is time consuming and expensive and there is a high possibility of failure . With typhoid conjugate vaccines in sight we recommend that WHO considers the integration of enteric fever surveillance into the present IB-VPD platform . With such a system in place , areas with known high burden of enteric fever will benefit from better understanding of epidemiological characteristics of the disease and antimicrobial resistance trends for optimal empirical treatment . We also recommend this integration in areas where the burden of enteric fever is unknown , as this can act as a rapid and cost-effective way to monitor hospitalized children without major changes in infrastructure and or loss of resources if the disease is found to be absent . Data generated from such surveillance systems will help countries make evidence-based decisions on introduction of upcoming vaccines and prepare for evaluation of vaccine impact studies . By including enteric fever surveillance into the WHO-coordinated IB-VPD surveillance , robust data can be obtained about the true burden of one of the leading vaccine preventable diseases . | Typhoid/paratyphoid fever imposes a major global burden , specifically in low-and-middle-income countries ( LMICs ) . However , it is challenging to implement evidence-based decisions for treatment and prevention because of lack of data from comprehensive surveillance systems , which are often expensive and difficult to sustain . The WHO has established a global surveillance program called “Invasive Bacterial–Vaccine Preventable Diseases ( IB-VPD ) Surveillance” to capture sepsis , meningitis and pneumonia in under-five children in many LMICs . Data generated by this program have facilitated introduction of live-saving vaccines and development of treatment strategies . However , the program does not include typhoid/paratyphoid surveillance . We tested the feasibility and sustainability of integrating typhoid/paratyphoid surveillance into this program in two leading children’s hospitals in Bangladesh . By monitoring all patients with signs of typhoid/paratyphoid , we captured 471 laboratory-confirmed episodes in under-five children between Jan 2012 and Dec 2016 . Blood culture results from all in-patients revealed that the proposed expanded surveillance captures 94% of hospitalized typhoid/paratyphoid cases . Thirty-six percent ( 170/471 ) of 2–59 m cases were in children <2 years . Overall , age distribution and antibiotic resistance patterns were consistent with data generated from larger , expensive and typhoid-specific surveillance programs in the region , adding credence to the proposed integration . Adding typhoid/paratyphoid surveillance to an established invasive disease surveillance platform took advantage of existing infrastructure and resources and as such was easy and cost-effective to implement . We recommend that WHO considers similar integration in other countries; data generated from such surveillances will help countries make evidence-based decisions on introduction of upcoming vaccines and prepare for impact studies . | [
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| 2017 | Integration of enteric fever surveillance into the WHO-coordinated Invasive Bacterial-Vaccine Preventable Diseases (IB-VPD) platform: A low cost approach to track an increasingly important disease |
Neglected tropical diseases , including diseases caused by trypanosomatid parasites such as Trypanosoma brucei , cost tens of millions of disability-adjusted life-years annually . As the current treatments for African trypanosomiasis and other similar infections are limited , new therapeutics are urgently needed . RNA Editing Ligase 1 ( REL1 ) , a protein unique to trypanosomes and other kinetoplastids , was identified recently as a potential drug target . Motivated by the urgent need for novel trypanocidal therapeutics , we use an ensemble-based virtual-screening approach to discover new naphthalene-based TbREL1 inhibitors . The predicted binding modes of the active compounds are evaluated within the context of the flexible receptor model and combined with computational fragment mapping to determine the most likely binding mechanisms . Ultimately , four new low-micromolar inhibitors are presented . Three of the four compounds may bind to a newly revealed cleft that represents a putative druggable site not evident in any crystal structure . Pending additional optimization , the compounds presented here may serve as precursors for future novel therapies useful in the fight against several trypanosomatid pathogens , including human African trypanosomiasis , a devastating disease that afflicts the vulnerable patient populations of sub-Saharan Africa .
Subspecies of Trypanosoma brucei ( T . brucei ) are the causative agents of human African trypanosomiasis ( HAT , also known as African sleeping sickness ) in sub-Saharan Africa . Neglected tropical diseases , including parasitic trypanosomal illnesses like HAT , Chagas disease , and leishmaniasis , are responsible for the loss of an estimated 56 . 6 million disability-adjusted life-years across several regions , particularly in the world's poorest countries [1] . Preventative measures such as vector control are effective at decreasing the incidence of HAT; however , given infection , the current treatment options are not suitable on the whole , particularly once T . brucei has infiltrated the central nervous system [2] . First-stage treatments include pentamidine and suramin , drugs developed more than half a century ago . Unfortunately , these drugs have severe side effects . Pentamidine is associated with hypoglycaemia and hypotension , while suramin is associated with anaphylactic shock , neurotoxic signs , severe cutaneous reactions , and renal failure [3] . The most common treatment for second-stage HAT is melarsoprol , a highly toxic drug with a 3%–10% fatality rate [4] . The danger of treatment is compounded by the emergence of melarsoprol-resistant parasites , particularly in central Africa [5] . Eflornithine , another HAT treatment , is less toxic but only effective against the T . b . gambiense subspecies; additionally , eflornithine is more costly to produce than melarsoprol [6] . Given the weaknesses of current treatments , new drugs are urgently needed . Fortunately , recent studies of the trypanosomal editosome have revealed several new drug targets . In trypanosomatids , mitochondrial gene expression includes an extra RNA-editing step . As in other eukaryotes , mitochondrial DNA is transcribed into RNA . In trypanosomes and Leishmania parasites , however , a protein complex known as the editosome makes extensive uridylate ( U ) insertions and deletions following transcription , at times even doubling the length of the original RNA sequence [7]–[11] . After each cycle of U addition or deletion , a nick in the RNA remains; RNA editing ligase 1 ( TbREL1; TriTrypDB ID: Tb927 . 10 . 8210 ) , an essential enzyme in trypanosomes [12] , is one of two ATP-dependent editosome ligases responsible for religation . As the editosome is absent in humans , the proteins of this complex , including REL1 , are potential drug targets in all trypanosomatid pathogens . Recently Amaro et al . identified several novel TbREL1 inhibitors . The relaxed complex scheme , a virtual-screening methodology that accounts for full protein flexibility [13] , was used to identify five low-micromolar inhibitors from among the compounds of the National Cancer Institute Diversity Set I [14] . Unfortunately , these TbREL1 inhibitors were ineffective against whole-cell T . brucei , perhaps in part because they are too hydrophilic to cross lipid membranes ( unpublished work ) . Motivated by both the urgent need for novel trypanocidal therapeutics as well as the success of virtual screening against TbREL1 in the past , we here use the relaxed complex scheme to identify additional naphthalene-based inhibitors in hopes of finding compounds that can kill T . brucei . To this end , online databases of commercially available compounds were first searched for compounds similar to the inhibitors previously characterized . Following virtual screening , the most promising of these compounds were subsequently tested experimentally , revealing four novel TbREL1 inhibitors with unique naphthalene-based scaffolds , two of which have ALogP values that suggest reasonable lipophilicity . Analyses of the predicted binding modes of these active compounds , performed using an ensemble-based approach and coupled with computational fragment mapping experiments , suggest that receptor flexibility may play an important role in ligand binding .
To generate a library of compounds similar to the TbREL1 inhibitors characterized previously [14] , we performed online substructure searches of several databases of commercially available compounds , including Hit2Lead ( Hit2Lead . com , ChemBridge ) , the NCI/DTP Open Chemical Repository ( dtp . cancer . gov ) , Sigma-Aldrich ( sigmaaldrich . com ) , and ZINC [15] . Searches were performed using three structures similar to the core naphthalene scaffolds of known inhibitors: naphthalene-2-sulfonic acid , 2-naphthoic acid , and 2-nitronaphthalene ( Figure 1 ) . The compounds identified via online substructure searches were each docked into a 1 . 20-Å resolution crystal structure of the TbREL1 catalytic domain ( PDB ID: 1XDN ) [16] using AutoDock 4 [17] . Ligand files were processed with AutoDockTools 1 . 4 . 5 to merge nonpolar hydrogens with parent heteroatoms and to assign Gasteiger charges . AutoGrid affinity grids contained 86×72×78 points spaced 0 . 375 Å apart , centered on the TbREL1 active site , the ATP-binding pocket . Grid files were created for the following ligand atom types: A ( aromatic carbon ) , C , F , I , N , NA ( hydrogen-bond accepting N ) , Cl , OA ( hydrogen-bond accepting O ) , P , S , SA ( hydrogen-bond accepting S ) , Br , HD ( hydrogen-bond hydrogen ) , and e ( electrostatic ) . AutoDock parameters similar to those published previously by Amaro et al . [14] were used: population size 200; 5 , 000 , 000 evaluations; 27 , 000 generations; 100 runs; and cluster tolerance of 2 . 0 Å . All other AutoDock parameters were set to the default values . The correct docked pose was judged to be the lowest-energy pose of the most populated cluster . With the intent of rescoring the top hits from the initial crystal-structure screen in a way that accounts for full protein flexibility , we drew upon a previous study of TbREL1 molecular motions [18] . In brief , molecular dynamics ( MD ) simulations of TbREL1 were performed using NAMD 2 . 6 [19] . Four hundred receptor conformations were extracted from the MD simulations , one every 50 ps . QR factorization [20] was used to eliminate conformational redundancy , thereby reducing the number of representative structures from 400 to 33 [14] . These 33 TbREL1 structures are said to constitute an ensemble representative of the many protein conformations sampled during the MD simulation . The relaxed complex scheme ( RCS ) was subsequently used to rescore the top compounds from the initial crystal-structure screen [13] . AutoDock was used to dock each of the top inhibitors into the 33 protein conformations of the receptor ensemble using the same docking parameters described above . The ensemble-average binding energy of each ligand was computed by taking the simple mean , and the ligands with the best mean predicted binding energy were subsequently tested experimentally . To partition the ATP-bound trajectory [18] into a set of structures representing regions of decreasing conformational population density , RMSD clustering , distinct from the QR factorization described above , was performed [21]–[23] as implemented in the rmsdmat2 and cluster2 programs of the GROMOS++ analysis software [24] . Four hundred receptor conformations were extracted from the 20 ns ATP-bound MD trajectory , one every 50 ps . Clustering was performed on a subset of 24 residues that line the ATP binding cleft: 87–90 , 155–162 , 207–209 , 283–287 , and 305–308 . These residues constitute the 5 conserved motifs of the nucleotidyltransferase superfamily [25] , [26] to which TbREL1 belongs . The trajectory frames were first aligned by minimizing the RMSD between the alpha carbons of the 24-residue subset of each frame and the corresponding alpha carbons of the first frame . This least-squares alignment removed external translational and rotational motion so that subsequent RMSD calculations could focus on the internal conformational variability of the 24-residue subset . After varying the RMSD similarity criterion from 0 . 06 to 0 . 12 Å , a value of 0 . 085 Å was chosen , as this cutoff produced 8 clusters of protein conformations . The three most populated clusters comprised 93 . 5% of the trajectory . Computational fragment mapping ( FTMap , http://ftmap . bu . edu ) was utilized to identify druggable regions on the surface of TbREL1 . The FTMap algorithm [27] determines the energetically favorable binding regions of sixteen fragments along a protein surface ( Figure S1 ) via the following steps: ( 1 ) rigid body docking of fragments using a fast Fourier transform approach , ( 2 ) minimization and rescoring of fragment-protein complexes , ( 3 ) clustering and ranking of low-energy fragment-protein complexes , and ( 4 ) determination of consensus sites . Consensus sites are regions of the protein surface where low-energy fragment clusters of multiple fragment types co-localize; in previous studies using FTMap and its predecessor CSMap [28] , highly populated consensus sites were shown to correlate strongly with ligand binding hot spots identified via biophysical methods [27] , [29] , [30] . The top ranked compounds from the relaxed complex screen were obtained for testing in experimental assays . Compounds were provided by the Developmental Therapeutic Program at the National Cancer Institutes ( NCI ) of Health , Hit2Lead . com , and Sigma-Aldrich ( Table S1 ) . Compounds V1 , V2 , and V3 ( Figure 1 ) were provided by the NCI , and compound V4 was purchased from Sigma . All compounds were dissolved in DMSO or DMSO/H2O . The protocols for recombinant TbREL1 expression , purification , and assaying have been described previously [14] . In brief , recombinant full-length TbREL1 was expressed in Sf9 insect cells after infection with recombinant baculovirus and purified via a C-terminal tandem affinity purification ( TAP ) tag . To measure enzyme inhibition , 0 . 1 pmol TbREL1 was incubated with 1 . 8 µCi ( 30 nM ) [α-32P]ATP in assay buffer ( 25 mM KCl , 12 . 5 mM HEPES pH 7 . 9 , 5 mM Mg acetate , 0 . 25 mM DTT , 0 . 1% Triton X-100 ) for 5 min at room temperature and in the presence of varying concentrations of the potential inhibitors . The extent of protein adenylylation ( and therefore competition with ATP for binding to the active site ) was subsequently measured by SDS/PAGE and phosphorimaging . All reactions were done in at least triplicate , and IC50 values were calculated using the GraphPad Prism 5 software . The effect of the identified REL1 inhibitors on parasite growth was determined using the Alamar Blue assay , essentially as described by Räz et al . [31] . Briefly , T . brucei brucei cells ( strain s427 ) were seeded in 96-well plates at a density of 1×104 cells per ml in a volume of 200 µl , in the presence of varying concentrations of predicted inhibitors or DMSO alone . After 48 hours , 20 µl Alamar Blue ( Invitrogen ) were added to the cells and incubation continued for an additional 24 hours . Absorbances at 540 and 595 nm were measured using an ELx808 Microplate Reader ( BioTek ) , and EC50 values were calculated using the GraphPad Prism 5 software .
Previously , Amaro et . al identified several micromolar inhibitors of TbREL1 [14] . The top three inhibitors identified were all based on a naphthalene-2 , 7-disulfonate ( NDS ) scaffold . In silico docking provides insight into why this scaffold is amenable to TbREL1 inhibition ( Figure 2 ) . Similar to the adenine moiety of ATP ( the native co-factor ) , the NDS naphthalene group is able to form π-π stacking interactions with F209 . Additionally , one of the negatively charged NDS sulfonate groups interacts electrostatically with the positively charged R111 guanidino group at the active-site periphery; R111 also participates in electrostatic and hydrogen-bond interactions with the ATP polyphosphate tail . A hydrogen bond is formed between NDS and N92 , similar to the hydrogen bond formed with the O2' oxygen atom of the ATP ribose . Finally , docking suggests that the second of the two NDS sulfonate groups is buried deep within the binding pocket , displacing a water molecule that normally mediates a hydrogen-bond network between the ATP adenine N1 atom and R288 . This water displacement allows the sulfonate group to interact with the charged R288 residue directly . Unfortunately , these previously identified TbREL1 inhibitors were ineffective against whole-cell T . brucei , likely because they are too hydrophilic to cross lipid membranes ( A . Schnaufer , unpublished work ) . Interestingly , these compounds show similarities to the anti-trypanosomal drug suramin , which , although much larger , also has a negatively charged polysulfonated naphtyl group [14] . How suramin enters the cell is unclear , but both fluid-phase endocytosis and receptor-mediated uptake have been suggested [33] , [34] . Suramin both binds various serum proteins , which may facilitate uptake by the trypanosome cell [34] , –and inhibits a considerable number of enzymes , including dehydrogenases and kinases in various organisms and glycolytic enzymes in T . brucei [35] . This promiscuous binding may be in part attributable to the negatively charged sulfonate groups [35] . Additionally , the hydrophilicity these sulfonates impart likely impedes both suramin and the previously identified REL1 inhibitors from passively crossing inner cellular membranes to reach organellar targets such as mitochondrial proteins . In an attempt to identify additional naphthalene-based TbREL1 inhibitors with improved pharmacological properties , we searched several online databases of commercially available compounds for similar structures: naphthalene-2-sulfonic acid , 2-naphthoic acid , and 2-nitronaphthalene ( Figure 1 ) . These searches identified 588 compounds: 61 compounds from Hit2Lead ( Hit2Lead . com , ChemBridge ) , 394 from the NCI/DTP Open Chemical Repository ( dtp . cancer . gov ) , 87 from Sigma-Aldrich ( sigmaaldrich . com ) , and 46 from ZINC [15] . In all , the search identified 376 naphthalene-2-sulfonic acid compounds , 130 2-naphthoic acid compounds , and 85 2-nitronaphthalene compounds . Given its previous successful identification of TbREL1 inhibitors , AutoDock 4 . 0 was utilized for docking . Although the AutoDock scoring function sacrifices accuracy for speed as compared to more rigorous methodologies such as thermodynamic integration [17] , [36] , single-step perturbation [37] , and free energy perturbation [38] , AutoDock performs well [39] when compared to other docking programs such as DOCK [40] , FlexX [41] , and GOLD [42] . The 588 compounds identified through online substructure searches were first docked into a 1 . 20-Å resolution crystal structure of the catalytic domain of TbREL1 [16] . AutoDock placed 14% of the naphthalene compounds in the expected pose ( 26% of the 2-naphthoic acid compounds , 10% of the naphthalene-2-sulfonic acid compounds , and 8% of the 2-nitronaphthalene compounds ) , with the naphthalene portion of the ligand buried deep in the ATP-binding pocket and the electronegative group at the two position either interacting with R288 or with R111 at the active-site periphery . The preliminary docking to the TbREL1 crystal structure , while useful for eliminating those structures that were grossly incompatible with the TbREL1 active site , did not account for full protein flexibility . Aside from the inaccuracies inherent in docking scoring functions themselves , docking accuracy decreases further when protein and/or ligand flexibility are ignored . When a ligand approaches a protein receptor in solution , it does not encounter a single static protein conformation , but rather an ensemble of many different conformations . Often , a given ligand may only bind to a certain subset of all protein conformations sampled , depending in part on the varied side-chain positions of active-site residues . When multiple protein conformations are incorporated into a virtual-screening protocol , the hit rate can drastically improve; ligands that do not bind to the crystal structure may bind to other related protein conformations . Screening against these other conformations in principle reduces the false negative rate . Of the top-ranked 100 binders from preliminary crystal-structure screens , 45 shared significant structural similarity with the most potent compound identified previously by Amaro et al . [14] . In order to account for full protein-receptor flexibility , these 45 compounds , roughly corresponding to the top 7 . 5% of the library , were docked into 33 protein receptor conformations extracted from a MD simulation of TbREL1 [14] using QR factorization [20] . The 45 ligands were then reranked by their respective ensemble-average scores , and 12 of the top compounds ( Table S1 ) were subsequently tested experimentally . Prior to RNA ligation , a key TbREL1 lysine must first be adenylylated . To measure the inhibition of this first step of the reaction pathway , the formation of TbREL1-[32P]AMP was monitored via SDS/PAGE and autoradiography in the presence of predicted inhibitor . Triton X-100 ( 0 . 1% ) was added in order to prevent aggregate-based inhibition . Four compounds , V1 , ( E ) -7-benzamido-4-hydroxy-3- ( ( 5-hydroxy-7-sulfonaphthalen-2-yl ) diazenyl ) naphthalene-2-sulfonic acid; V2 , ( E ) -7-amino-4-hydroxy-3- ( ( 5-hydroxy-7-sulfonaphthalen-2-yl ) diazenyl ) naphthalene-2-sulfonic acid; V3 ( Di-J acid ) ; and V4 ( Mordant Black 25 ) , inhibited TbREL1 activity with IC50 values of 2 . 16±1 . 20 µM , 1 . 53±1 . 17 µM , 8 . 36±1 . 71 µM , and 1 . 59±1 . 1 µM , respectively ( Table 1 , Figure 1 ) . Additional information about the predicted binding poses of these four validated inhibitors can be found in Table S1 . An additional four compounds inhibited TbREL1 adenylylation with IC50 values between 10 and 100 µM; the exact values in these cases were not determined ( Table S1 ) . All other compounds did not show significant inhibition at 100 µM . Interestingly , the crystal-structure protein conformation used for the initial docking is likely itself suboptimal for the binding of the four inhibitors identified , as evidenced by the improvement in rank when an ensemble-average AutoDock score was used ( RankEnsemble ) instead of the crystal-structure score ( RankCrystal , Table 1 ) . In fact , only one of the four compounds , V1 , scored in the top twelve when all 588 compounds were docked into the crystal structure alone . V2 , V3 , and V4 , which ranked 20th , 31st , and 25th against the crystal structure , respectively , may not have been tested had the ligand set not been reranked by an ensemble-average AutoDock score . A direct comparison of the predicted binding energy of the four indentified inhibitors docked into the crystal structure ( AutoDockCrystal ) and docked into the optimal protein conformation from the ensemble ( AutoDockEnsemble/Best ) likewise demonstrates the importance of accounting for full protein flexibility; in all four cases , predicted energies of binding improved several kcal per mol when the optimal structure was used rather than the crystal structure ( Table 1 ) . In order to investigate why binding to the crystal structure was suboptimal , the crystal structure was compared to the optimal receptor conformation for each of the four ligands . By aligning the best-scoring MD-generated receptor structures to the crystal structure and visualizing both proteins and ligands , it is evident that in all four cases the crystallographic position of E60 prevented optimal binding . During the molecular dynamics simulation , however , E60 extends its contact with R111 ( initial contact distance 5 . 35 Å; final interaction distance greater than 11 Å ) . This movement opens a wide cleft that is favorably occupied by all four of the novel inhibitors ( Figure 3 ) . This unique binding mode , described in more detail below , would not have been identified had protein-receptor flexibility been ignored . To further explore the role that receptor flexibility plays in inhibitor binding , we grouped the frames of the MD trajectory into sets of geometrically similar conformations using an RMSD-based clustering algorithm . Each cluster contains a central structure , or centroid , whose structural characteristics and binding properties are representative of all cluster members . Similar to QR factorization [14] , [20] , RMSD clustering reduces the MD ensemble to a representative set of ( centroid ) conformations . However , unlike QR factorization , RMSD clustering provides an approximate idea of the probability of sampling a set of geometrically similar conformations based on the fraction of conformations contained within each cluster [21] . Assuming the conformations sampled along the inhibitor-bound trajectories are similar to those observed during the ATP-bound trajectory , the receptor-inhibitor interactions characteristic of the most populated clusters , which represent the most frequently visited system conformations , should contribute most to ligand affinity . Indeed , the representative protein structure that best accommodates V1 , V3 , and V4 from the QR-factorization ensemble , as judged by the AutoDock score , belongs to the most populated RMSD-based cluster . The protein conformation that best accommodates V2 belongs to the second most populated RMSD-based cluster . The conformations sampled by the MD trajectory were grouped into 8 clusters when an RMSD similarity cutoff of 0 . 085 Å was used; 93 . 5% of the trajectory conformations were contained in the three most populated clusters . The conformational variability among the centroids of the top three clusters suggests two dynamically distinct active-site regions . Deep within the inhibitor-binding cleft , where F209 forms π-π stacking interactions with the sulfonated naphthalene moiety of each inhibitor , the conformational differences among the centroids are modest , consisting of only subtle amino-acid side-chain shifts ( Figure 4A ) . Given the rigidity of this region and the similarity between naphthalene and the adenosine of ATP , the native TbREL1 substrate , we hypothesize that the naphthalene scaffold is highly complimentary to the modest conformational fluctuations observed at the deep end of the binding pocket . In contrast , conformational variability at the binding-site periphery near the solvent interface is much larger ( Figure 4B ) . As the predicted binding modes of the validated inhibitors initially suggested , the varied positions of E60 relative to R111 are particularly notable . In the centroid conformation of the first and second most populated clusters , a cleft is again seen between E60 , which is directed into bulk solvent , and R111 , which is directed toward the inhibitor binding site . The distances between E60 ( OE2 ) and R111 ( NH1 ) are 9 . 01 Å and 10 . 96 Å , respectively . These two open-cleft clusters represent 83% of the entire trajectory . In the centroid conformation of the third most populated cluster , representing 11% of the entire trajectory , the cleft is narrowed; E60 is directed downward , toward R111 , and the distance between E60 ( OE2 ) and R111 ( NH1 ) is only 7 . 14 Å , closer to the closed-cleft crystal-structure distance of 5 . 35 Å . As noted previously , all four novel inhibitors are predicted to occupy this previously uncharacterized cleft , suggesting that it is pharmacologically important . This new cleft also presents an opportunity to develop compounds with improved specificity over the related human DNA ligases . A structural and sequence alignment of the superfamily members [18] reveals key sequence differences in relative positions between REL1 and human DNA ligase ( PDB: 1X9N ) . In REL1 , residues I59-E60-I61-D62 line the newly revealed cleft and make contact with several of the bound inhibitors . In human DNA ligase 1 ( PDB 1X9N ) , the equivalent residues are M543-L544-A545-H546 . The strategic design of REL1 inhibitors to take advantage of the variable contacts in this area , particularly the exposed side chains of the residues lining the cleft , may present novel avenues to design compounds with increased selectivity for the trypanosomal enzymes . To explore the pharmacological importance of the E60-R111 cleft in greater depth , computational fragment mapping was carried out on both the centroids of the three most populated clusters as well as the crystal structure ( Figure 4C ) . Computational fragment mapping estimates the binding affinity of fragment-sized organic groups and clusters them into consensus-binding regions . These consensus-binding regions ( a . k . a . hot spots ) represent regions of receptor sites that are the principal contributors to the ligand-binding energy . Importantly , these computationally predicted sites have been shown to correlate well with fragment-binding hot spots as determined via biophysical experiments in numerous studies [27] , [29] , [30] . Fragment mapping confirmed that the TbREL1 active site can be divided into two regions , as two consensus sites were apparent . The first site , conserved among the centroids of the three most populated clusters as well as the crystal structure , is found deep in the inhibitor-binding cleft , where both the adenine of the native ATP substrate and the sulfonated-naphthalene moieties of the novel inhibitors bind . The conservation of this solvent cluster supports the pharmacological importance of this region and is in harmony with the predicted docking poses of the four novel inhibitors . The second consensus site is found in the previously uncharacterized E60-R111 cleft . Notably , while conserved among the three most populated clusters , this site is entirely absent in the crystal structure , likely because the closed E60-R111 cleft of that structure occludes solvent-probe binding . Naphthalene-based inhibitors docked into the crystal structure are predicted to interact only with the high-affinity region deep in the binding pocket; at the active-site periphery , binding to the high-affinity region in the E60-R111 cleft is impossible , and so the predicted binding affinity is less favorable . Hence , the fragment-mapping approach supports the presence of an additional pharmacologically relevant feature of the ATP binding pocket . It also helps to explain why those compounds eventually confirmed as genuine inhibitors were not initially ranked among the top-scoring candidates . While fragment mapping did reveal a high-affinity region in the E60-R111 cleft of the centroid representing the third most populated cluster , this region does not extend as far into the cleft as the corresponding clusters of the top two centroids . This fact , together with the narrower cleft width , may partly explain why none of the four novel inhibitors was predicted to bind to receptor conformations of the third most populated cluster . In order to analyze the predicted binding mode of the four confirmed TbREL1 inhibitors , the protein conformation from the ensemble generated by QR-factorization that gave the best AutoDock-predicted binding energy ( i . e . the “optimal receptor” ) was visualized together with the associated docked ligand . In all cases , the electronegative group at the naphthalene C2 position was buried deep within the active site , forming interactions with R288 , as expected . Additionally , three of the four ligands , similar to the three most potent TbREL1 inhibitors identified previously [14] , had hydroxyl groups in the naphthalene 4 position , suggesting that the hydrogen bonds formed with E86 and V88 are also critical to ligand binding ( Figure S2 , upper rows ) . A fourth ligand , V4 , had a hydroxyl group in the naphthalene 6 position , were it could form hydrogen bonds with the backbone carbonyl oxygen atom of V88 and the side-chain amino group of K87 . At the active-site periphery , all four of the confirmed inhibitors had secondary sulfonate groups that docked near the more positively charged side of the active-site periphery , opposite the R111 residue ( Figure S3 ) , where they interact with K307 , R309 , and K87 ( Figure S2 , bottom rows ) . In contrast , the peripheral , negatively charged sulfonate groups of previous NDS inhibitors , substituents of the naphthalene core itself , interacted principally with R111 . The new inhibitors do not entirely neglect R111 , however; all four compounds are predicted to participate in π-cation interactions with this residue . In addition to these electrostatic interactions , the four novel inhibitors are predicted to interact with other protein residues at the active-site periphery ( Figure S2 , bottom rows ) . In some ways , these interactions mimic the interactions between TbREL1 and its native substrate , ATP . V1 forms a hydrogen bond with the R111 guanidinium group , similar to the bond formed between R111 and the ATP gamma phosphate . V1 also forms a hydrogen bond with the E159 side-chain carboxylate group , similar to the bond formed with the ATP 2′ ribose hydroxyl group . V1 forms unique interactions with TbREL1 as compared to the substrate; V1 forms a hydrogen bond with the backbone carbonyl of Y58 , a residue that does not participate in ATP binding ( Figure S2A ) . V2 is predicted to participate in only one hydrogen bond at the active-site periphery . This bond is formed with the E60 side-chain carboxylate group , a group that does not participate in ATP binding ( Figure S2B ) . V3 and V4 are likewise predicted to form only one hydrogen bond at the active-site periphery , a bond with the I59 backbone carbonyl . This same backbone carbonyl forms a hydrogen bond with the 3′ hydroxyl group of the ATP ribose ( Figure S2C ) . Unfortunately , first-stage HAT treatments such as pentamidine and suramin have harsh side effects [3] , and second-stage treatments such as melarsoprol can be fatal . The pharmaceutical industry has been slow to develop novel trypanocidal therapeutics because HAT infections occur primarily in developing countries with little market appeal; indeed , the only novel trypanocidal therapeutic registered in the last 50 years is eflornithine [43] , a drug that is likely only available because it can also be sold as a topical cosmetic cream for the treatment of hirsutism in developed countries . Given the hesitancy of the pharmaceutical industry , in recent years academia has played an increasing role in HAT drug-discovery efforts ( e . g . [44] ) . Amaro et al . recently identified inhibitors based on a 4 , 5-dihydroxynaphthalene-2 , 7-disulfonate scaffold that target T . brucei RNA editing ligase 1 ( TbREL1 ) , a validated drug target in these organisms [12] . Unfortunately , these inhibitors , while effective against the TbREL1 protein , were ineffective in whole-cell assays . As Schrodinger's LigPrep software [45] suggested that at pH 7 . 0 the sulfonates of these compounds are negatively charged , we hypothesize that they are too hydrophilic to cross cellular and organellar T . -brucei lipid membranes and thus cannot reach their physiological target . The ALogP values of Amaro's S5 , V1 , and S1 compounds were −1 . 043 , −0 . 292 , and −0 . 778 , respectively ( Discovery Studio , Accelrys ) , likewise suggesting excessive hydrophilicity . Indeed , two of these three compounds , S5 and S1 , are too hydrophilic to be considered druglike [46] . Building on the previous work of Amaro et al . , we have developed additional TbREL1 inhibitors based on novel naphthalene scaffolds . The compounds proposed in the current work are also sulfonated naphthalenes; however , some of them are more hydrophobic than the naphthalene-based inhibitors identified previously . The ALogP values of V1 , V2 , V3 , and V4 are 0 . 492 , −1 . 039 , −1 . 112 , and 1 . 835 , respectively ( Discovery Studio , Accelrys ) , suggesting that two of the novel inhibitors , V1 and V4 , may even prefer a lipid environment . Indeed , V4 was effective against cultured T . brucei with an EC50 of 2 . 16 µM ( Table 1 ) . To what extent this trypanocidal effect can be attributed to inhibition of REL1 is currently under investigation . The hydrophobicity and specificity of these compounds , and their ability to reach the mitochondrial matrix , could be further improved by eliminating the charged sulfonate groups . In the virtual screen presented here , naphthalenes with carboxylic acids and nitro groups were included to see if the sulfonate groups could be replaced with less electronegative functional groups . Unfortunately , none of the compounds with carboxylate groups scored well enough to justify experimental testing , and the few nitro-group containing compounds that were tested failed to inhibit TbREL1 . Rather than replacing the sulfonate groups , a better strategy may therefore be to modify those groups in order to neutralize their charge . For example , replacing the sulfonate groups with sulfonamides , a similar functional group that is not charged , may decrease hydrophilicity while preserving important protein-ligand interactions . Both molecular docking and computational fragment mapping indicate that a new cleft revealed by the molecular dynamics simulations may play a role in the favorable binding of these four novel TbREL1 inhibitors . Furthermore , RMSD-based clustering indicated that this previously uncharacterized cleft persists for a majority of the MD trajectory . In the future , further drug optimization is needed . Three of the four novel compounds contain diazene linkers that may be hydrolysable in vivo . Furthermore , the nitrogen atoms of these linkers are not predicted to participate in hydrogen bonds with the protein; replacing one or both of them with carbon atoms may therefore decrease hydrophilicity without sacrificing compound potency . Additionally , some of the compounds contain other moieties like hydroxyl and amino groups that are not predicted to contribute to inhibitor binding . Perhaps these groups could likewise be eliminated . | African sleeping sickness is a devastating disease that plagues sub-Saharan Africa . Neglected tropical diseases like African sleeping sickness cause significant death and suffering in the world's poorest countries . Current treatments for African sleeping sickness either have high costs , terrible side effects , or limited effectiveness . Consequently , new medicines are urgently needed . RNA editing ligase 1 is an important protein critical for the survival of Trypanosoma brucei , the unicellular parasite that causes African sleeping sickness . In this paper , we describe our recent efforts to use advanced computer techniques to identify chemicals predicted to prevent RNA editing ligase 1 from functioning properly . We subsequently tested our predicted chemicals and confirmed that a number of them inhibited the protein's function . Additionally , one of the chemicals was effective at stopping the growth of the parasite in culture . Although substantial work remains to be done in order to optimize these chemicals so they are effective and safe to use in human patients , the identification of these parasite-killing compounds is nevertheless a valuable step towards finding a better cure for this devastating disease . | [
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| 2010 | Novel Naphthalene-Based Inhibitors of Trypanosoma brucei RNA Editing Ligase 1 |
The aim of this work is to elucidate how physical principles of protein design are reflected in natural sequences that evolved in response to the thermal conditions of the environment . Using an exactly solvable lattice model , we design sequences with selected thermal properties . Compositional analysis of designed model sequences and natural proteomes reveals a specific trend in amino acid compositions in response to the requirement of stability at elevated environmental temperature: the increase of fractions of hydrophobic and charged amino acid residues at the expense of polar ones . We show that this “from both ends of the hydrophobicity scale” trend is due to positive ( to stabilize the native state ) and negative ( to destabilize misfolded states ) components of protein design . Negative design strengthens specific repulsive non-native interactions that appear in misfolded structures . A pressure to preserve specific repulsive interactions in non-native conformations may result in correlated mutations between amino acids that are far apart in the native state but may be in contact in misfolded conformations . Such correlated mutations are indeed found in TIM barrel and other proteins .
Despite recent advances in computational protein design [1] , there is no complete understanding of basic principles that govern design and selection of naturally occurring proteins [2] . In particular , the physical basis for the ability of proteins to achieve an adaptation to a wide variety of external conditions is still poorly understood . While several attempts to design proteins with a desired fold were successful [1 , 3] , rational design of proteins with desired thermal properties is still an elusive goal . However , Nature apparently succeeds in doing so by “designing” proteins in hyperthermophiles that are stable and functional up to 110 °C . Thus , in the absence of the complete solution of the protein design problem , it is tempting to get clues from Nature as to how thermal properties of proteins can be modulated by proper sequence selection and which physical factors play a role in this process . A clear manifestation of thermophilic adaptation can be found in a highly statistically significant variation of amino acid compositions of proteomes between meso- and thermophilic organisms [4–7] . Recently , we showed that the total concentration of seven amino acids , I , V , Y , W , R , E , and L , is highly correlated with optimal growth temperature ( OGT ) of an organism ( R = 0 . 93 ) [8] . The total concentration of IVYWREL combination of amino acids serves as a predictor of OGT with mean accuracy of 8 . 9 °C [8] . In this work we seek a fundamental theoretical explanation as to why Nature requires an elevated concentration of both hydrophobic and charged amino acids to design hyperthermostable proteins . Our first goal here is to develop a minimalistic physical model of protein design that could help us to rationalize comparative proteomic analysis of thermo- and mesophiles . A crucial question is how to incorporate the environmental temperature in the model of protein design . Two factors may play a role . The first effect is due to fundamental statistical mechanics of proteins that posit that stable and foldable proteins should have an “energy gap” [9–12] . Specifically , the stability of the native state of a protein is determined by the Boltzmann factor exp ( −ΔE/kBT ) , where ΔE is the energy gap between the native state and lowest energy completely misfolded structures [11–15] . Therefore , to maintain their stability at elevated temperature , the thermophilic proteins should have a greater energy gap . In principle , the increase of the energy gap can be achieved by lowering the energy of the native state ( positive design ) , raising the energy of misfolds ( negative design ) , or both . Another factor that may affect protein thermostability is a possible dependence of fundamental interactions ( e . g . , hydrophobic forces ) on temperature . However , the temperature dependence of different types of interactions may be very complex , and it remains a subject of controversy as to how and to what extent it influences the stability of proteins [16–19] . Our approach to this complex issue is simple: consider first how far one can go based on purely statistical–mechanical analysis of protein thermostability without resorting to explanations based on temperature dependence of various interactions . Specifically , here we use the 27-mer cubic lattice model of proteins [20 , 21] . The model features 20 types of amino acids that interact when they are nearest neighbors on the lattice; interaction energy depends on types of amino acids involved . The potential is derived from known protein structures and is temperature-independent [22] . For this lattice model all compact conformations can be enumerated [20] and , therefore , exact statistical–mechanical analysis is possible . Previously , protein thermodynamics [9 , 23] , folding [24 , 25] , and evolution [11 , 26 , 27] were extensively studied by using this model . We simulate the process of thermal adaptation by the design of 27-mer sequences with selected ( at a given environmental temperature Tenv ) thermal properties [14 , 15] . The algorithm of design ( see Methods ) carries out simultaneous unrestricted search in conformational , sequence , and amino acid composition spaces . In our analysis we will focus on the amino acid composition of designed sequences as a function of the environmental temperature and we will compare the model findings with amino acid trends in real proteomes . Our main result is that thermal adaptation utilizes both positive and negative design . We show that by increasing the content of amino acids from both extremes of the hydrophobicity scale , thermostable proteins achieve exactly that goal: hydrophobic residues help with positive design while elevated concentration of charged residues helps to achieve stronger negative design . Further , we find an interesting and potentially important aspect of negative design: similar to positive design that strengthens certain native interactions , negative design can make specific non-native interactions strongly repulsive . This , in turn , may lead to emergence of correlated mutations between amino acids that are not in contact in native structure .
We design lattice model proteins with selected thermostability as a first step toward modeling thermal adaptation of organisms . There is a direct connection between OGT ( environmental temperature , or Tenv ) of an organism and the melting temperature of its proteins [28 , 29] . We used the P–design procedure to create model 27-mer sequences that are stable at selected Tenv ( see Methods ) . We designed sets of 5 , 000 model proteins for each Tenv in the range 0 . 3 < Tenv < 0 . 8 in Miyazawa–Jernigan dimensionless units . The average melting temperature <Tmelt> of lattice proteins is strongly correlated with Tenv ( see Figure S1 ) suggesting that the P-design procedure does work . It provides model proteins with desired stability in response to the increase of environmental temperature . The dependence of <Tmelt> on Tenv is close to linear and qualitatively matches the empirical linear relationship , Tmelt = 24 . 4 + 0 . 93 Tenv , between the average living temperature of the organism and melting temperature of its proteins [28] . As expected , the amino acid composition of designed proteins does depend on Tenv for which they were designed . To quantify the differences between “low-temperature” and “high-temperature” amino acid compositions , we plotted temperature dependencies of the fractions of hydrophobic ( LVWIFMPC ) , weak hydrophophobic and polar ( AGNQSTHY ) , and charged ( DEKR ) amino acids for designed lattice proteins ( Figure 1A ) and natural ( Figure 1B ) proteomes . Figure 1A shows a significant increase in the amount of charged residues ( red triangles ) and a slight increase in hydrophobic amino acids ( green squares ) at the expense of polar ones ( black dots ) . Remarkably , the results shown in Figure 1 suggest that increase of thermostability is accompanied by growth of amino acid content from both extremes of the hydrophobicity scale , adding both charged and hydrophobic residues . This observation is further highlighted in Figure 2 , which shows—amino acid by amino acid—how compositions of model proteins with Tenv in designed model proteomes for all 20 amino acids are ranked by their hydrophobicity according to the Miyazawa–Jernigan set of interaction parameters ( see Methods and Figure S2A and S2B for more detailed explanation ) . Figure 2 clearly shows that addition of amino acids to thermophilic model proteomes occurs from the extremes of the hydrophobicity scale while the middle is depressed . The content of charged ( Asp , Glu , Lys , Arg; DEKR ) and four of the hydrophobic ( Ile , Leu , Phe , Cys; ILFC ) residues is increased with temperature at the expense of other residues , mostly polar ones . This observation shows that combining amino acids with maximum variance in their hydrophobicity is crucial for creating hyperthermostable model proteins . We refer to this effect as the “from both ends of hydrophobicity scale” trend . For comparison , we analyzed the variation of amino acid composition in fully sequenced bacterial proteomes ( 83 species in total , see complete list , Table S1 ) of psycho- , meso- , thermo- , and hyperthermophilic prokaryotes ( habitat temperatures from −10 to +110 °C , see Table S1 ) . Importantly , amino acid composition of 83 natural prokaryotic proteomes reveals similar trends , an increase of the contents of hydrophobic and charged residues , and a decrease of the content of polar ones ( Figure 1B ) . For a more direct comparison of the predictions of our model with the properties of natural proteomes , in Figure 3 we plotted the temperature derivative of the fraction of each of the amino acids in designed lattice proteins against the corresponding temperature derivative calculated over the 83 natural proteomes . The observed positive significant correlation ( R = 0 . 56 , p = 0 . 01 ) suggests that generic physical factors captured by this simple statistical–mechanical model played a major role in shaping the amino acid composition patterns across a wide range of habitat temperatures . We hypothesize that the generic character of the “from both ends” trend that is universally observed in the model and in natural proteins is related to the positive and negative elements of design . In this case , one ( hydrophobic ) end of the scale is responsible for positive design while another ( hydrophilic ) end provides negative design . To test this hypothesis , we first studied how the energy gap between the energy of the native state and that of misfolded conformations for the designed model proteins depends on Tenv ( Figure 4 ) . Positive design is the major contributor to the effect ( the slope of the temperature-dependent energy decrease of the native state with growth of Tenv is −5 . 22; Figure 4 , black line ) , while the increase of the average energy of decoys with Tenv ( slope +1 . 64; Figure 4 , orange line ) is pronounced , but less significant . Nevertheless , the results presented in Figure 4 provide clear evidence that negative design works , along with positive design , in the selection of thermostable model proteins . The findings shown in Figure 4 demonstrate that indeed both positive and negative design act in enhancing thermostability of model proteins . However , the question remains as to how positive and negative design is related to the “from both ends” trend in amino acid compositions , as shown in Figure 2 . To address this question , we plot the number of contacts between amino acids whose content grows with Tenv , according to Figure 2 . Figure 5 shows how the average number of contacts ( per structure ) within both groups of amino acids , FILC and DEKR , in native conformations and in misfolded decoys , depends on Tenv . Remarkably , we see that in decoy structures the growth of the number of contacts occurs only within the “charged” group DEKR , some of which—according to the Miyazawa–Jernigan potential—repel one another . On the other hand , the number of contacts in the hydrophobic group in decoys do not change despite an overall increase of concentration of these amino acids in sequences designed at higher Tenv . This result shows that while strongly mutually attractive hydrophobic groups provide lower energies of native states for hyperthermophilic model proteins , the growth in concentration of “charged” ( DEKR ) groups mainly contributes to the negative design factor by raising average energy of misfolded conformations . Remarkably , the average number of contacts between hydrophobic groups ( FILC ) in misfolded conformations remain roughly the same in meso- and hyperthermophilic model proteins despite significant growth in overall concentration of these groups in hyperthermophiles . Therefore , the data shown in Figure 5 indicate that the “from both ends” trend in amino acid composition is directly related to positive and negative design in stabilization of hyperthermophilic model proteins . The data presented so far provide insight into averaged ( over many model proteins ) contributions to the energies of native conformation and decoys . However , a question arises whether negative design works by increasing “average” non-native interactions or by strengthening certain specific repulsive non-native interactions . Indeed , negative design may be based on introducing a few energetically disadvantageous non-native contacts that are persistent in many decoy structures , increasing their energy [2 , 30] . Therefore , non-native contacts responsible for negative design may well be specific for each sequence , making this effect more detectable if individual proteins are considered . The exact nature of the lattice model makes a detailed residue-by-residue analysis of the action of both positive and negative design possible . To this end , it is instructive to identify interactions , native and non-native contacts , between residues that play especially important roles in stabilization of the native state and destabilization of decoys . The key idea here is that such important interactions should be conserved in all sequences that fold into a given structure . While identities of amino acids that form such a contact may vary from sequence to sequence , the strength ( or energy ) of key native or non-native contacts will be preserved: it will be either strongly repulsive or strongly attractive for all sequences that fold into a given structure [31] . Therefore , to identify such key contacts , distributions of energies of native and non-native contacts in multiple sequences that fold into the same native structure should be considered . Such analysis can reveal not only conserved strong native contacts but also possible conserved strong repulsive non-native contacts . To investigate such possibility , we designed 5 , 000 lattice proteins that all fold into the same ( randomly chosen ) native structure . To achieve that , we used the design algorithm similar to P-design ( see Methods ) , but for a fixed native structure , and checked a posteriori that the target structure is indeed the native state for all 5 , 000 sequences . We designed a set of 5 , 000 mesophilic sequences at Tenv = 0 . 2 and 5 , 000 hyperthermophilic sequences that fold to the same structure but are much more stable ( Tenv = 0 . 8 ) . The concept of native and non-native contacts for our lattice model is illustrated in Figure 6A . It is a cartoon with a zoom-in into the contact matrix of the lattice structure used in simulations . The contact matrix of any compact lattice conformation contains all native ( green , total 28 in any structure ) and all possible non-native ( blue , total 128 ) contacts . All other contacts ( red ) are prohibited according to the properties of the cubic lattice . To identify important native and non-native contacts whose energies are conserved , we applied the following procedure . First , for each of the 5 , 000 sequences that fold into selected structure , we calculated energies of 28 native and 128 possible non-native contacts in this structure ( using the identities of residues and Myazawa–Jernigan potentials that were employed to design sequences ) . Next , for each contact we calculated average energy and its standard deviation over all 5 , 000 designed sequences ( see Figure 6B for illustration of this calculation ) . Contacts whose energy shows a very low standard deviation over all designed sequences are apparently the ones that are most important for stability . This procedure was carried out both for mesophilic sequences ( Tenv = 0 . 2 ) and for thermophilic sequences ( Tenv = 0 . 8 ) . The results are shown in Figure 7 , which presents standard deviation of interaction energies of each native and non-native contact over all 5 , 000 designed mesophilic sequences ( A ) and hyperthermophilic sequences ( B ) , plotted against the average ( over 5 , 000 designed sequences ) energy of that contact . The plot consists of 28 + 128 = 156 points , covering all native and all possible non-native interactions . The native state clearly defines conserved low- and high-energy native contacts ( shown in black ) in most of the sequences , as the standard deviation is the lowest at the extreme values of the energy . Conserved attractive interactions are in the protein interior , corresponding to the lattice analog of the hydrophobic core; apparently , they emerge due to the action of positive design . The non-native contacts ( red dots ) follow a different pattern , with only a few conserved attractive interactions , suggesting the diversity of decoy structures . What is surprising to see , however , is that energies of certain most-repulsive ( high-energy ) non-native contacts show a very low standard deviation , indicating that such contacts may be as important for protein stability as conserved native ones . Comparison of meso- and hyperthermophilic sequences shows clearly that emergence of strong and conserved attractive and repulsive interactions in key native and non-native contacts is directly related to sequence design that generates stable sequences: design of hyperthermostable sequences ( Figure 7B ) results in stronger and more conserved ( lower dispersion of energy ) attractive and repulsive specific native and non-native interactions . The only reason that repulsive energies of non-native contacts are conserved is that such contacts persist in certain frequent decoy structures and contribute to the widening of the gap between the native state and decoys . Such repulsive contacts are indirectly ( via the sequence ) related to a particular native state and are not numerous . Their role may be completely obscured in a “high-throughput” analysis where sequences with different native states are considered together , as in Figure 4 . Therefore , we conclude that negative design involves a very specific strategic placement of repulsive contacts in certain decoy structures . The results and analysis presented in Figure 7 have very important implications for real proteins . The requirement to conserve the energy of key contacts in multiple sequences that fold into the same structure implies that amino acids forming such contacts can mutate in a correlated way , for example by swaps . The observation that mutations may occur often as swaps to preserve specific attractive native and specific repulsive non-native interactions leads to a prediction of a peculiar dependence between frequency of amino acid substitutions ( as in , e . g . , BLOSUM matrices [32] ) and interaction energy between amino acids . Indeed , as illustrated in Figure 8 , a correlated mutation in the form of a swap can manifest itself in sequence alignment as a substitution between amino acids that are making the swap . The implication is that frequent substitutions will be observed between amino acids that strongly attract each other ( to preserve specific stabilizing native contacts ) . More interesting and perhaps more surprising , frequent substitutions are also predicted between amino acids that strongly repel each other ( to preserve specific non-native repulsive contacts ) . In other words , we predict that the scatter plot between elements of amino acid interaction energy matrix and substitution matrix will be non-monotonic with maxima at both extremes . We tested this prediction by plotting the dependence of elements of the substitution matrix BLOSUM62 [32] for 190 pairs of amino acids ( synonymous substitutions are excluded ) versus their interaction energy as approximated by the knowledge-based Miyazawa–Jernigan potential [22] ( Figure 9 ) . This analysis indeed reveals a non-monotonic shape: the parabolic fit in Figure 9A highlights the highly significant non-monotonic nature of the dependence . The striking feature of this dependence is that most frequent substitutions are observed not only between the most attractive amino acids but also between the most repulsive ones . One could argue , however , that the high frequency of substitutions between amino acids that repel each other may be a trivial consequence of conserved substitutions that preserve the charge ( R to K and E to D ) . However , a detailed inspection of the upper right part of the plot in Figure 9A shows that this is not the case ( Figure 9B ) . Indeed , frequent substitutions are observed between mutually repulsive amino acids with vastly different physical–chemical properties and encoded by very dissimilar codons , such as Serine to Asparagine , Glutamin to Arginine , etc . Several highly nonconservative substitutions show about “random”frequencies ( element of BLOSUM matrix close to zero , e . g . , for Asn to Lys ) , but this may be due to compensation of two opposite effects: suppression of highly nonconservative substitutions ( e . g . , that change charge ) and facilitation of correlated substitutions such as the ones in the form of swaps as illustrated here . Use of correlated mutations as predictors of spatial proximity of amino acids in the native structure has been proposed by many authors [33–36] . Indeed , statistical analysis shows that overall correlation between distance between amino acids and degree of correlation in multiple sequence alignments does exist [37] . However sometimes correlated mutations are observed between amino acids that are distant in native structure [33 , 38] . While sometimes such observations are discarded as false positives in the prediction algorithm [33] , our analysis predicts that indeed residues that are distant in structure but may form important repulsive contacts in misfolded conformations may exhibit correlated mutations as illustrated in Figures 8 and 9 . As an illustration of the significance of correlated mutations between amino acids that are far apart in structure , we consider a TIM–barrel fold protein triosephosphate isomerase . Guided by the results of statistical analysis shown in Figure 9 , we looked for pairs of residues with strong repulsion according to the Miyazawa–Jernigan potential , random or higher substitution rates between these residues according to the BLOSUM matrix , and highly correlated substitutions of these residues in two positions of the protein sequence in multiple sequence alignment for 7tim ( see Methods ) . To distinguish the effect that we seek from functional conservation , these residues should not be in contact in the native state and should not be involved in the functional site or in the protein–protein interactions We found correlated substitutions in the sequence of TIM–barrel fold ( 7tim , chain a ) , according to the physicochemical characteristic hydropathy [39] , by using the CRASP program , which “estimates the contribution of the coordinated substitutions to invariance or variability of integral protein physicochemical characteristics” [40] . Four pairs of residues ( Table 1 ) that have highly coordinated substitutions and repel each other according to the Miyazawa–Jernigan energy matrix were selected . None of those residues belong to the functional site of triosephosphate isomerase , and the protein itself is a single-domain protein not involved into protein–protein interactions [41] . Four pairs of polar and charged residues and one pair of charged residues were identified ( see Figure 10A and Table 1 ) . The shortest contact distance ( Cα − Cα , 8 . 7 Å ) is between charged Lys ( 84 ) and polar Gln ( 119 ) , which excludes the stabilizing interaction between them in the native structure . We found that correlated mutations between some of these amino acids occur as swaps in the TIM–barrel fold , possibly accompanied by conservative mutation , e . g . , surface Lys 120 and Gln 85 in 7tim swap to Gln 120 Lys 85 in Thermotoga maritima thermophilic ortholog of triosephosphate isomerase . Even more striking , the Gln85 , Lys 213 pair in 7tim ( distance in native structure 30 Å ) is replaced by Lys 85 , Asn 213 in 1b9b ( Figure 10B ) . This pair of residues shows a highly correlated substitution pattern in TIM–barrel multiple sequence alignment despite the fact that these are very distinct amino acids .
Stabilization of thermophilic proteins is achieved by negative and positive design working together , i . e . , the gap “opens” from both sides , decreasing energy of the native state and at the same time increasing the energy of misfolded conformations . This factor is responsible for the “from both ends of hydrophobicity scale” trend observed in model and real [8] thermophilic proteomes . In particular , our recent analysis of complete bacterial proteomes [8] revealed that proteomes of thermophilic bacteria are enriched in both hydrophobic residues ( IVYLW ) and charged ones ( ER ) , while all polar residues are suppressed . Discrepancies between different hydrophobicity scales [42] , the statistical nature of knowledge-based Miyazawa–Jernigan potential [22] , and limitations of the lattice model make it impossible to quantitatively compare the content of individual amino acids in lattice and natural proteomes or exactly predict the amino acid composition of thermophilic proteomes with very high accuracy from lattice model calculations . Nevertheless our lattice calculations are in semiquantitative agreement with data on natural proteomes , ( see Figures 1 and 3 ) and exhibit the same “from both ends of hydrophobicity scale” trend in amino acid composition adaptation in response to elevated habitat temperature . The knowledge-based Miyazawa–Jernigan potentials , derived from native structures of proteins , are certainly a crude approximation to real protein energetics [43] . A question arises as to whether our observations are generic or are due to the specific potential used to design model proteins . A detailed comparison of several potentials—all atom and group-based derived by different methods—was carried out recently in our lab [44] . Remarkably , we found that despite differences in detail all these potentials reflect the same dominant contributions to protein stabilization . It appears that dominant contributions to energy gaps in proteins come principally from two types of interactions: hydrophobic interactions and electrostatics [44] . Further , it was found that knowledge-based potentials derived using structures of meso- and thermophilic proteins are virtually indistinguishable ( KZ and ES , unpublished data ) . These observations suggest that the “from both ends of hydrophobicity scale” trend observed in model calculations and in real proteome is a robust phenomenon , reflecting basic physical principles of protein design , rather than a consequence of a specific potential set used in calculations . While positive design [45] is universally used in experiments , the role and omnipresence of negative design are still under discussion [46] . The main challenge in the study of negative design stems from the difficulties in the modeling of relevant misfolded conformations and energetic effects of mutations that destabilize them [46] . It was shown that charged residues can be effectively used in negative design [30] . Another indirect evidence of the contribution of charged residues to negative design emerges from site-directed mutagenesis , where mutations of polar groups to charged ones on the surface of a protein lead to protein stabilization even in the absence of salt–bridge partners of the mutated group [47–49] . In a series of experiments [47 , 48 , 50] , surface electrostatic interactions were shown to provide a marginal contribution to stability of the native structure , hence their possible importance for making unfavorable high-energy contact in decoys . An alternative view , proposed recently by Makhatadze et al . , suggests that long-range electrostatic interactions may contribute to stability of the native state [51] . However , at normal physiological conditions the range of electrostatic interactions is limited due to Debye screening and hardly exceeds 8 Å . Our simulations and proteomic analysis point to a possible role of some surface charged residues as contributing to destabilization of misfolded structures through a negative design mechanism . Positive and negative elements of design affect the evolution of protein sequences . The dependence of substitution rates in sequences of natural proteins ( BLOSUM62 substitution matrix ) on interaction energies according to knowledge-based Miyazawa–Jernigan potential has a peculiar nonmonotonic shape showing elevated substitution rates between residues that attract each other as well as between residues that repel each other . The physical reason for this phenomenon is the same as for the “both ends of hydrophobicity scale” trend: simultaneous action of positive and negative design . Upon substitutions , energy of attractive contacts in native states should be preserved as well as energies of specific repulsive contacts in misfolded conformations . Apparently both these factors act in concert to preserve the energy gap in proteins . Our study deepens an understanding of correlated mutations in proteins . With regard to native contacts , the fact that amino acids making strongly attractive native interactions should exhibit correlated mutations had been realized long ago . Several authors proposed to use correlated mutations as a tool to determine possible native contacts from multiple sequence alignment [33–36] . However , this suggestion is complicated by the observation that correlated mutations are often found between residues that have no obvious functional role and are distant in structure [33 , 38 , 52 , 53] . Using the double mutant technique , Horovitz et al . [33] suggested a relation between correlated mutations and energetic connectivity ( i . e . , nonadditivity of stability effects in double mutation cycles ) between corresponding amino acids . Green and Shortle [54] showed that amino acids that are distant in structure may indeed be “energetically coupled , ” attributing this effect to influence of mutations on the unfolded state of proteins , consistent with our findings . Lockless and Ranganathan [38] suggested that a “pathway of energetic connectivity” exists between distant residues that exhibit correlated mutations . Fodor and Aldrich [37] , however , examined several other proteins and argued against the “general principle of isolated pathways of evolutionarily conserved energetic connectivity in proteins . ” Here we show that negative design that destabilizes misfolded conformations of proteins may be responsible for correlated mutations between residues that are far apart in native structures . In this work , we developed a simple exact model of thermophilic adaptation and discovered fundamental statistical–mechanical rules that Nature uses in her quest to enhance protein stability . While many other factors , including dependence of hydrophobic and other interactions on temperature , certainly play a role in protein stabilization , the action of positive and negative design found and described here in a minimalistic model appears to be a basic universal principle determining evolution of sequences of thermostable proteins . A better understanding of fundamental principles of protein design and stability makes it possible to decipher peculiar signals that emerge in the analysis of meso- and thermophilic genomes and proteomes [8] and in many studies of correlated mutations in proteins [33 , 35 , 53] .
We use the standard lattice model of proteins as compact 27-unit polymers on a 3 × 3 × 3 lattice [20] . The residues interact with each other via the Miyazawa–Jernigan pairwise contact potential [22] . It is possible to calculate the energy of a sequence in each of the 103 , 346 compact conformations allowed by the 3 × 3 × 3 lattice , and the Boltzmann probability of being in the lowest energy ( native ) conformation , where E0 is the lowest energy among the 103 , 346 conformations , and Tenv is the environmental temperature . The melting temperature Tmelt is found numerically from the condition Pnat ( Tmelt ) = 0 . 5 . Note that if the energy spectrum Ei is sparse enough at low energies , the value of Pnat is determined chiefly by the energy gap E1 – E0 between the native state and the closest decoy structure that has no structural relation to the native state . To design lattice proteins , we use here a Monte-Carlo procedure ( P-design , [14 , 15] ) that maximizes the Boltzmann probability Pnat of the native state by introducing mutations in the amino acid sequence and accepting or rejecting them according to the Metropolis criterion . As this procedure takes the environmental temperature Tenv as an input physical parameter , and generates amino acid sequences designed to be stable at Tenv , it is an obvious choice for modeling the thermophilic adaptation . Initially , the sequence is chosen at random; the frequencies of all amino acid residues in the initial sequences are equal to 5% . At each Monte-Carlo step , a random mutation of one amino acid in a sequence is attempted and Pnat of the mutated protein is determined . The native structure is determined at every step of the simulation; generally , the native state changes upon mutation of the sequence . If the value of Pnat increased , the mutation is always accepted; if Pnat decreased , the mutation is accepted with the probability exp[− ( Pnat ( old ) − Pnat ( new ) ) /p] , with p = 0 . 05 ( a Metropolis-like criterion ) . We chose p = 0 . 05 so that the average melting temperature of designed proteins is higher than the environmental temperature ( see Figure S2 ) , in agreement with experimental observations [29 , 55] . The design procedure is stopped after 2 , 000 Monte-Carlo iterations . Such length of design runs is sufficient to overcome any possible effects of the initial composition of the sequences , so the amino acid composition of the designed sequences depends only on the environmental temperature Tenv . To relate the trends in amino acid composition with the physical properties and interaction energies of individual amino acids , we use hydrophobicity as a generic parameter characterizing an amino acid [42] . To characterize the hydrophobicity of amino acids in the simulations , we make use of the fact that the Miyazawa–Jernigan interaction energy matrix is very well approximated by its spectral decomposition [43] . Interestingly , it is sufficient to use only one eigenvector q , corresponding to the largest eigenvalue , so the interaction ( contact ) energy Eij between amino acids of types i and j reads Eij ≈ E0 + λqiqj [43] . In this representation , hydrophobic residues have the largest values of q , while hydrophilic ( charged ) residues correspond to small q . All sequences of TIM–barrel folds with length less than 300 amino acid residues were extracted according to the SCOP database description [56] . Identical sequences were excluded from further consideration . Remaining sequences ( total 39 ) were aligned against the sequence of the triosephosphate isomerase ( 7tim . pdb , chain a ) by using Kalign Web-server for multiple alignment of protein sequences ( http://msa . cgb . ki . se/cgi-bin/msa . cgi , [57] ) . Correlated substitutions in the multiple alignments were determined by using the CRASP program ( http://wwwmgs . bionet . nsc . ru/mgs/programs/crasp , [40] ) . The CRASP program gives the correlation coefficient between the values of physicochemical parameters at a pair of positions of sequence alignment . We chose hydropathy [39] as a physicochemical characteristic appropriate for establishing correlated mutations of interest . Only significant correlations , with the correlation coefficient higher than the critical threshold ( 0 . 311 ) , were considered . The complete genomes were downloaded from the National Center for Biotechnology Information Genome database at http://www . ncbi . nih . gov/entrez/query . fcgi ? db=Genome ( see Table S1 ) .
The accession numbers from the Protein Data Bank ( http://www . rcsb . org/pdb/ ) used in this paper are: TIM–barrel fold protein triosephosphate isomerase ( 7tim ) ; T . maritima thermophilic ortholog of triosephosphate isomerase ( 1b9b ) . | What mechanisms does Nature use in her quest for thermophilic proteins ? It is known that stability of a protein is mainly determined by the energy gap , or the difference in energy , between native state and a set of incorrectly folded ( misfolded ) conformations . Here we show that Nature makes thermophilic proteins by widening this gap from both ends . The energy of the native state of a protein is decreased by selecting strongly attractive amino acids at positions that are in contact in the native state ( positive design ) . Simultaneously , energies of the misfolded conformations are increased by selection of strongly repulsive amino acids at positions that are distant in native structure; however , these amino acids will interact repulsively in the misfolded conformations ( negative design ) . These fundamental principles of protein design are manifested in the “from both ends of the hydrophobicity scale” trend observed in thermophilic adaptation , whereby proteomes of thermophilic proteins are enriched in extreme amino acids—hydrophobic and charged—at the expense of polar ones . Hydrophobic amino acids contribute mostly to the positive design , while charged amino acids that repel each other in non-native conformations of proteins contribute to negative design . Our results provide guidance in rational design of proteins with selected thermal properties . | [
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| 2007 | Positive and Negative Design in Stability and Thermal Adaptation of Natural Proteins |
Wolbachia are the most widespread maternally-transmitted bacteria in the animal kingdom . Their global spread in arthropods and varied impacts on animal physiology , evolution , and vector control are in part due to parasitic drive systems that enhance the fitness of infected females , the transmitting sex of Wolbachia . Male killing is one common drive mechanism wherein the sons of infected females are selectively killed . Despite decades of research , the gene ( s ) underlying Wolbachia-induced male killing remain unknown . Here using comparative genomic , transgenic , and cytological approaches in fruit flies , we identify a candidate gene in the eukaryotic association module of Wolbachia prophage WO , termed WO-mediated killing ( wmk ) , which transgenically causes male-specific lethality during early embryogenesis and cytological defects typical of the pathology of male killing . The discovery of wmk establishes new hypotheses for the potential role of phage genes in sex-specific lethality , including the control of arthropod pests and vectors .
Wolbachia ( order Rickettsiales ) infect an estimated 40–52% of all arthropod species [1 , 2] and 47% of filarial nematode species [3] , making them the most widespread intracellular bacterial symbiont in animals . Concentrated in host testes and ovaries , Wolbachia primarily transmit cytoplasmically from mother to offspring [4 , 5] . In arthropod reproductive tissues and embryos , Wolbachia deploy cunning manipulations to achieve a greater proportion of transmitting females in the host population . Collectively , these strategies are categorized as reproductive parasitism . Male killing , or selective death of an infected female’s sons [6] , is one such form of reproductive parasitism [7 , 8] . It enhances the fitness of Wolbachia-infected females in three potential ways: ( i ) reducing brother-sister competition for limited resources [9] , ( ii ) reducing inbreeding [10] , and/or ( iii ) providing nutrients in cases where infected sisters cannibalize embryos of their dead brothers [10] . Male-killing Wolbachia are widespread in several major insect orders [11] and in pseudoscorpions [12] . In addition , male-killing Spiroplasma [13] , Rickettsia [10] , and Arsenophonus [14] occur in diverse hosts including flies [13] , ladybugs [10] , and wasps [14] . Male killing can have several significant impacts on host evolution [15–18] . For example , male death may lead to host extinction or reduce the effective population size of the host . As a consequence , theory specifies that fixation of deleterious alleles in host populations is more likely , and fixation of beneficial alleles is conversely less likely [19 , 20] . Male killing can also impose strong selection on hosts to counter the sex ratios shifts and lethality [16] . Evolutionary outcomes include mate preference between uninfected males and females [11] , a shift towards more mate-attracting behaviors by females or male mate choice [11] , and suppression of the phenotype [16 , 21–23] . As they manipulate arthropod reproduction to drive through host populations , Wolbachia are currently deployed in two vector control strategies: population suppression to reduce the population size of mosquitoes , and population replacement to transform mosquito populations that transmit pathogens to ones that cannot transmit pathogens [24 , 25] . In these cases , mosquitoes are released with Wolbachia that cause cytoplasmic incompatibility ( CI ) , in which offspring die in crosses between infected males and uninfected females . Notably , population genetic modeling demonstrates that male killing can be deployed in conjunction with population suppression techniques to speed up eradication or reduction of a target arthropod population and increase the likelihood of success [26] . However , the genetic basis of Wolbachia male-killing has remained a mystery for more than sixty years [27] and the causative gene of the Spiroplasma male-killing phenotype has only recently been reported [28] . Thus , potential vector and pest control applications of male killing have yet to be experimentally validated . In this study , we sought to determine the genetic basis of the male-killing phenotype in Wolbachia . Our previous comparative genomic , transcriptomic , and proteomic analyses identified two prophage WO genes , cifA and cifB , that underpin the induction and rescue of CI by wMel Wolbachia in D . melanogaster [29 , 30] . cifA and cifB reside in the newly characterized eukaryotic association module of prophage WO that is enriched with many sequences predicted to have eukaryotic functions and homologies [29 , 31 , 32] . Building on this previous analysis , we pursued characterization of genes that may also be responsible for male killing . Notably , Wolbachia can be multipotent because some strains induce multiple reproductive parasitism phenotypes ( e . g . , CI and male killing ) depending on the host background or environmental conditions [8 , 22 , 33 , 34] . For example , the wRec strain of D . recens causes CI in its native host , but it kills males when introgressed into the genetic background of its sister species , D . subquinaria [22] . Importantly , wMel and wRec share 99 . 7% nucleotide identity [35] , which raises the hypothesis that the CI-inducing wMel genome may also harbor male-killing genes . A long-standing question is whether multipotency is due to pleiotropy of the same gene ( s ) expressing different reproductive parasitism phenotypes or alternatively if different genes underpin the various forms of reproductive parasitism . We previously assessed several reproductive parasitism gene candidates in wMel Wolbachia for both male killing and CI , including cifA and cifB , and we ruled out their involvement in male killing [29] . However , other genes may still be involved . Although wMel is not known to naturally cause male killing , it is of interest because it is the native strain of the only host that is genetically tractable and is closely-related to a natural male killer , making it a useful system to test gene candidates for the phenotype . There are several expectations for a putative Wolbachia male-killing gene . First , we expect transgenic expression will recapitulate the embryonic cytological defects typically induced by male killing [36] . Second , native expression of the candidate gene will occur by the time male death naturally occurs in a given host [22 , 36] . Third , a male-killing gene would be shared across male-killing strains in Wolbachia but not necessarily absent from strains unknown to cause male killing . In other words , the gene may be more common than the phenotype because hosts frequently develop resistance to male killing , presumably due to the strong evolutionary pressure to avoid extinction [16 , 21 , 22 , 37 , 38] . As previously mentioned , Wolbachia can induce either male killing or CI in different hosts or rearing conditions [8 , 21 , 22 , 33] , which may be related to resistance in some hosts . Fourth , if there is a single gene that causes male killing in most or all cases , then the gene may rapidly evolve due to natural selection in diverse host backgrounds that suppress male killing . Here , based on genomic analyses , transgenic expression , and cytological characterizations in Drosophila melanogaster infected or uninfected by wMel Wolbachia , we report the discovery of a gene in the eukaryotic association module of prophage WO that is a candidate for male killing .
To generate a shortlist of male-killing gene candidates , we used the following criteria and assumptions: ( i ) universal presence in the genomes of male-killing strains wBif from D . bifasciata [27] , wInn from D . innubila [7] , wBor from D . borealis [39] , and wRec from D . recens [22]; ( ii ) genomic location in prophage WO because parasitic Wolbachia all have intact or remnant prophage WO regions with eukaryotic association module genes [32]; notably , the two previous parasitism genes , cifA and cifB , are both in this module of prophage WO , making it likely that other parasitism genes share a similar origin; ( iii ) exclusion of highly repetitive elements , including insertion sequence elements , reverse transcriptases of group II intron origin , and large serine recombinases that likely facilitate phage WO lysogeny; and ( iv ) exclusion of disrupted genes ( e . g . , early stop codons ) in one or more strains ( S1 Table for list of excluded genes ) . Table 1 shows seven candidate genes that fit these criteria . One of these genes , cifA , was previously evaluated by transgenic expression [29] , and it did not exhibit a biased sex ratio . Others include a predicted ankyrin repeat ( WD0550 ) , two Rpn genes ( recombination-promoting nucleases WD0297 , WD0627 ) , Phospholipase D ( WD1243 ) , and a hypothetical protein ( WD0628 ) . The remaining gene , WD0626 , was identified in the previous multi-omic analysis that uncovered the cif genes [29] . This candidate gene , hereafter denoted wmk for WO-mediated killing , is a putative transcriptional regulator in prophage WOMelB that is predicted to encode two helix-turn-helix ( HTH ) , XRE family DNA-binding domains ( NCBI conserved domains E = 5 . 9 x 10−11 , E = 6 . 5 x 10−10 ) . wmk in wMel has a single amino acid difference relative to its homolog in wRec . Due to the association of wmk with two different candidate gene analyses for reproductive parasitism and preliminary observations that transgenic expression associated with a sex ratio bias , we further assessed it as a putative male killing gene . Phylogenetic analyses indicate that wmk homologs are common in phage WO-containing Wolbachia including the above-mentioned male-killing strains ( S1 Fig ) , wBol from Hypolimnas bolina butterflies ( causes CI when male killing is suppressed ) [16 , 21] , and wCauB from Cadra cautella moths ( causes male killing in non-native host ) [33] , along with many strains not known to cause male killing ( S1A Fig ) . wmk is in the eukaryotic association module of prophage WOMelB , resides just a few genes away from the cif genes , and exists in multiple divergent copies in some strains ( S1B Fig and Fig 1 ) [32] . Phylogenetic analyses indicate that wmk sequence relationships do not cluster into typical Wolbachia supergroups ( S1A Fig ) , specifying independent evolution relative to the core Wolbachia genome . This finding is similar to that of other prophage WO genes including cifA , cifB , and the baseplate assembly gene , gpW [29] . It is attributable to the high rates of horizontal phage WO transfer between Wolbachia coinfections [40] . Similar to cifA and cifB [41] , wmk homologs are notably disrupted in the parthenogenesis-inducing Wolbachia strains wUni from Muscidifurax uniraptor wasps , wTpre from Trichogramma pretiosum wasps , and wFol from Folsomia candida springtails . The gene is also absent in the male-killing MSRO strain of Spiroplasma poulsonii , which contains the recently reported male-killing gene , Spaid [28] . Spaid has OTU deubiquitinase and ankyrin repeat domains and lacks direct homologs in Wolbachia [28] , indicating separate evolutionary origins of Spaid and wmk . In addition , genomic analyses suggest the full version of wmk in phage WO potentially originated from a fusion or duplication event with gene ( s ) in the non-prophage region of the Wolbachia chromosome . Indeed , homologs of the N-terminal XRE-family HTH domain occur in distantly related nematode Wolbachia strains ( wWb , wBm , wPpe ) and the sister genera Ehrlichia ( S2 Table ) that all lack prophage WO . To evaluate the function of wmk , we generated transgenic D . melanogaster flies that express codon-optimized wmk with the Gal4-UAS expression system because genetic editing of Wolbachia is not currently possible . We evaluated three other transgenes in a similar manner: WD0625 in prophage WO that encodes a putative MPN/Mov34/PAD-1 metalloprotease domain ( DUF2466 , NCBI conserved domain E = 3 . 85 x 10−41 ) because it is adjacent to wmk and may in theory be cotranscribed with wmk , WD0508 in the prophage WO-associated Octomom region that is another predicted transcription regulator with two XRE-family HTH DNA-binding domains ( NCBI conserved domains E = 1 . 70 x 10−9 , E = 1 . 99 x 10−11 , a homolog of wmk ) , and WD0034 , a non-phage , hypothetical protein-coding gene that is hereafter labeled ‘control gene’ and shares a transgenic insertion site with wmk . These three genes do not recapitulate CI [29] . In the experiments below , all transgenes were expressed in heterozygous flies under the control of an Act5c-Gal4 driver , which leads to ubiquitous transgene expression beginning with zygotic transcription ~2h after egg deposition ( AED ) . Genetic crossing schemes are described in the methods . To assess if wmk causes sex-specific lethality , we first quantified adult sex ratios in gene-expressing ( Act5c-Gal4; UAS-wmk ) flies using a ubiquitously-expressing actin ( Act5c ) driver . wmk transgene expression results in a significant reduction in the average male:female sex ratio ( number of males / number of females ) to 0 . 65 , or a 35% reduction in gene-expressing males ( Fig 2 ) . The sex ratio is approximately 1 in wild type flies and in transgenic flies that either do not express wmk ( CyO; UAS-wmk ) or express a control gene ( Fig 2 ) . All sex ratios represent a normal range of variance observed in previous experiments [28 , 29 , 42–44] . For example , natural male-killing Wolbachia strains cause variable offspring sex ratios that range from 0 . 5 to 0 ( all females ) in D . innubila [45 , 46] , and 0 . 2 to 0 in D . subquinaria [22] , although most cases are all female . For the three other prophage WO genes , transgenic expression in uninfected flies does not significantly change sex ratios ( S2A–S2C Fig ) , indicating the wmk-induced phenotype is not due to a generalized , transgenic artifact . Further , we explored whether another gene could be additionally involved . We tested dual expression of wmk and WD0625 , as they are adjacent and could potentially function together . Dual expression does not change the degree of male death ( S2A–S2C Fig ) , demonstrating it is not involved in the phenotype . In addition , ovarian transgene expression of wmk by the maternal triple driver ( MTD ) that loads product into developing oocytes [47] did not result in a biased sex ratio ( S2D Fig ) despite confirmed expression ( S2E Fig ) . The lack of phenotype under the MTD driver is likely due to insufficient transcript levels in the embryo as MTD is a germline-specific driver expressed in mothers before eggs are laid , whereas Act5c is ubiquitously expressed by the embryo itself . However , transgenic expression of wmk via the armadillo driver , which expresses genes ubiquitously beginning in embryogenesis , yields sex ratios that are similar to that of the Act5c driver ( S3A Fig ) , despite an order of magnitude reduction in expression level ( S3B Fig ) . These findings indicate that expression at Act5c levels is not necessary to induce the phenotype , and zygotic transcription ( ~2 h AED ) of wmk is required for the sex ratio effect . Thus , investigations so far have not revealed conditions that might alter the proportion of male death . Notably , this finding parallels the timing of embryonic mortality during early zygotic transcription in the D . melanogaster male-killer , Spiroplasma poulsonii , although it differs in that maternal expression does not recapitulate the wmk phenotype , while some aspects of the Spiroplasma phenotype can be recapitulated with maternal expression [28] . The wmk-induced change in sex ratio is also not consistent with other types of reproductive parasitism for several reasons . First , CI is not known to have a sex ratio bias except in haplodiploid species [29] . Second , the male lethality phenotype and transgene expression begin long after hallmark CI defects such as delayed histone deposition in fertilized embryos [48] . Third , an infected maternal background does not rescue the wmk phenotype , as would be expected if the phenotype were linked to CI ( S4A Fig ) . Fourth , neither wmk expression nor dual expression of wmk and WD0625 , a putative partner gene due to its adjacent location , causes or rescues CI when expressed with the nanos-Gal4 driver used in CI experiments for germline-specific expression [29] ( S4B and S4C Fig ) . Fifth , the bias in sex ratio cannot result from genetic males developing as females ( feminization ) because wmk expression does not increase the absolute number of females compared to controls ( S4D Fig ) . Finally , parthenogenesis ( virgin females produce all female offspring ) cannot explain the male lethality phenotype because transgenic expression occurs with a paternal chromosome present . Wolbachia–induced male killing occurs either during embryogenesis or larval development in Drosophila [22 , 36 , 45] . Embryonic cytological defects associated with Wolbachia male killing begin largely at the time of host embryonic cellularization ( ~2 . 5 h after egg deposition , AED ) and span abnormal nuclei distribution , chromatin bridging , and pyknosis in male embryos of D . bifasciata [36] . To determine if wmk transgene expression in D . melanogaster recapitulates the nature and timing of the defects , we stained DNA with propidium iodide in wild type ( WT ) embryos and in embryos expressing either wmk or the control transgene . We then monitored the defects in embryos ( only half of the embryos are expected to express the transgene , see methods ) . Several different defects were observed ( Fig 3A–3D ) . In embryos fixed 1–2 h AED , there was no significant difference in cytological defects of wmk-associated offspring compared to controls ( Fig 3I ) . However , in embryos fixed 3–4 h AED , cytological defects were enriched in wmk-associated embryos ( 28% ) relative to control gene ( 11 . 8% ) and wild type embryos ( 10 . 3% ) ( Fig 3J ) . Since significantly more defects occur in embryos fixed 3–4 h AED but not in those fixed 1–2 h AED , the male lethal defects could commence between 2–4 h AED . These results also indicate that cytological defects specifically occur soon after zygotic transcription of wmk , as only a zygotic driver , not a maternal egg loader , is able to induce the phenotype . In wBif-infected D . bifasciata , male embryos 15–20 h AED have several large defects including incompletely formed regions and lack of differentiation or segmentation [36] . To determine if the defects in early wmk-expressing embryos result in similar abnormalities later in development , we fixed sibling embryos 16–17 h AED . We discovered and assessed degraded embryos ( embryos with cloudy staining from degraded DNA and lack of distinct nuclei ) in wmk-associated offspring compared to controls . One category of degraded embryos had no visible cephalic furrow or segmentation similar to unfertilized eggs ( Fig 3E and 3F ) . These embryos occurred equally across all treatment groups at a low percentage similar to that of unfertilized eggs ( Fig 3E and 3K ) . This category likely represents decomposing , unfertilized eggs . A second degraded form exhibited a cephalic furrow that demarcates the head from the thorax ( Fig 3G ) , but it lacked other normally visible segmentation ( Fig 3H ) , similar to the lack of segmentation in infected embryos . There were approximately 10-fold more degraded embryos with a cephalic furrow in the wmk cross versus controls ( Fig 3K ) . This finding suggests the timing of death is soon after the commencement of the cephalic furrow formation , which occurs at approximately 3 h AED . As noted above , it is also approximately the time point when cytological defects are first observed ( Fig 3J ) . The furrow formation is largely complete by 4 h AED , and it is visible in the degraded embryos , suggesting most embryos reach this developmental time point before death . Though this furrow phenotype is not described in natural contexts , the literature demonstrates that there are highly defective areas in embryos later in development [36] . The furrow phenotype likely occurs in transgenic individuals because of consistent , strong expression of a transgene rather than natural expression levels that may vary in individuals due to differences in Wolbachia titer or gene expression . However , the lack of segmentation is known in natural contexts . Interestingly , the marked number of degraded cephalic furrow wmk embryos is proportional to the number of missing males in adult sex ratios ( Fig 2 ) . These results imply that the degraded embryos 16–17 h AED and the reduced sex ratios of surviving adults are the result of wmk-induced defects in early male embryos . Taken together , there are four key results: ( i ) wmk induces DNA defects 2–4 h AED , ( ii ) embryos arrest after cephalic furrow formation , ( iii ) embryos become degraded by late stages of embryogenesis , and ( iv ) embryonic defects lead to downstream reductions in sex ratios of surviving flies . Notably , the 2–4 h time window is when defects begin to significantly occur in D . bifasciata . The corresponding adult sex ratios for this experiment are shown in S5A Fig . Next , we confirmed that the cytological defects in embryos 3–4 h AED are male-biased using fluorescent in situ hybridization ( FISH ) with a DNA probe specific to the Y chromosome ( S6 Fig , expressing and non-expressing embryos , see methods ) . 40% of male wmk embryos exhibit defects versus 9% of female wmk embryos and 9–10% of WT and control gene embryos ( Fig 4A ) . In addition , while the embryonic sex ratios are not biased at 1–2 h AED , they are biased among viable ( non-degraded ) embryos fixed 16–17 h AED ( Fig 4B ) , as expected . The corresponding adult sex ratio of 0 . 68 was similar to the embryonic sex ratio ( S5B Fig ) , further indicating that male killing occurs during embryogenesis . These results specify that defects and degradation are enriched in males . To further determine the similarity in lethality between the transgenic wmk and natural infection phenotypes , we assessed embryos for an association between DNA damage and dosage compensation . In previous work , male D . bifasciata embryos infected with Wolbachia exhibited an accumulation of DNA damage in association with dosage compensation [49] . We assessed wmk-expressing and control embryos 4–5 h AED for the same association ( Fig 5 ) . Using the armadillo driver , we stained embryos with antibodies for pH2Av ( phosphorylated histone H2Av , indicative of DNA damage ) and H4K16ac ( acetylation of histone H4 at lysine 16 , primarily mediated on the X-chromosome by the male-specific dosage compensation complex or DCC ) . Males that express wmk have a greater number of pH2Av and H4K16ac punctae or foci than both wmk-expressing females and control gene-expressing males ( Fig 5A–5H ) . The higher number of H4K16ac punctae may potentially reflect increased DCC activity in wmk-expressing embryos . An example set of images for a control gene female is shown in S7 Fig . In addition , a significantly higher proportion of the two types of punctae overlapped ( Fig 5I ) . This suggests a mechanism of death related to DNA damage that is associated with dosage compensation , as with natural infections . Within males , there is a cohort of wmk embryos that have a higher number of H4K16ac and pH2Av punctae ( Fig 5G and 5H ) . Interestingly , this proportion ( ~40% ) is similar to the proportion of males that die according to adult sex ratios ( S3A Fig ) . In addition , the H4K16ac and pH2Av punctae often overlapped with chromatin bridging , which is another phenotype previously observed in D . bifasciata [49] . The overlap happened more frequently in wmk-expressing males than females or control gene-expressing males ( Fig 5J ) . Taken together , results demonstrate that DNA damage is accumulating at sites of dosage compensation activity in wmk-expressing embryos . To establish a native expression profile for wmk , we measured relative transcription in Wolbachia-infected embryos fixed 4–5 h AED , which is the estimated time of death of most wmk-expressing male embryos . In wMel-infected embryos , native wmk and control gene transcripts were approximately 10-fold lower than the highly expressed CI gene , cifA ( Fig 6A ) . There were no significant differences with either gene compared to the less abundant cifB gene transcript . Also , expression levels of the wmk and control transgenes are similar in uninfected D . melanogaster , and both are expressed significantly higher than native bacterial transcription of the same genes ( Fig 6B ) . Finally , D . bifasciata embryos infected with wBif male-killing Wolbachia showed a wmk-like expression profile similar to wMel , whereby the cifA homolog is expressed significantly higher than the wmk homolog ( Fig 6C ) . This suggests that differences in cifA vs wmk gene expression do not account for differences in reproductive parasitism phenotype where both CI and male killing can be induced by the same bacterial strain . Phenotypic differences may instead be determined by another factor such as host genotype . Phyre2 protein modeling [50] predicts that Wmk from wMel is globular and composed of α-helical secondary structures matching several transcriptional regulators , suppressors , and DNA-binding proteins ( S8A Fig ) . The best match to known protein structures , based on both alignment confidence and sequence identity , is the Salmonella temperate phage Rep-Ant complex , a dimerized DNA- and peptide-binding repressor [51] ( 99 . 8% homology confidence , 19% sequence identity , S8B Fig ) . Wmk may function similarly as a bipartite protein where the dimers are physically connected , especially considering that single HTH domains typically dimerize and act as transcriptional regulators across domains of life [52] . Further , predicted structures of the Wmk homologs in wBif ( S8C Fig ) , wInn/wBor ( same sequence , S8D Fig ) , and wRec ( S8E Fig ) are all very similar to the structure from wMel . Indeed , all exhibit a 5 α-helix bundle , connected by a long , flexible linker to another 4 α-helix bundle . This is despite wide variation in amino acid sequence ( e . g . , wBif Wmk has a 26 . 2% amino acid sequence identity to wMel Wmk , which represent the most distantly related protein pair ) . S6 Table shows amino acid pairwise percent identity between wMel Wmk and homologs from known male-killers . This similarity in overall protein structure , despite sequence divergence , suggests that the homologs may retain the same general function with target ( s ) that are possibly divergent across host species , such as different DNA sequences of homologous genes . Wmk may also have another function that accounts for structural conservation despite sequence differences across divergent hosts . To assess conservation in different regions of the protein , we also analyzed Wmk amino acid divergence across homologs , including that of wBif and all homologs in S1 Fig . There is relatively high sequence conservation overall across the protein ( S9A Fig ) , but there are two areas of high variability adjacent to the two HTH DNA-binding domains that may be important for functional differences across strains or hosts ( S9B Fig ) . In addition , although there is lower variation across DNA-binding regions relative to other parts of the protein , there is still variability that could account for differing abilities of homologs to cause a phenotype in one host versus another .
This study reports twelve key results supporting wmk as a male-killing gene candidate: ( i ) wmk recurrently associates with genomic screens for reproductive parasitism; it is on the shortlists of candidate phage WO genes in Wolbachia male-killers and CI-inducers [29] . ( ii ) The wmk gene is found in all sequenced male-killers including the reduced phage WO genome of wRec ( which retains ~25% of the full phage WO genome ) and the divergent phage WO genome of wBif . ( iii ) wmk is common , divergent in sequence , and located in the eukaryotic association module of phage WO that is enriched with sequences predicted or known to contain eukaryotic function and homology [32] . In this region , wmk is a few genes away from the two causative cytoplasmic incompatibility genes , cifA and cifB , that modify arthropod gametes [29] . ( iv ) Transgenic expression of wmk consistently induces a sex-ratio bias , but the phenotype does not recapitulate other forms of reproductive parasitism . ( v ) No sex ratio bias results from expression of other transgenes tested thus far under the same expression system , making the phenotype specific to wmk . ( vi ) Canonical DNA defects are recapitulated under transgenic expression at the same time in development as natural systems . ( vii ) wmk is naturally expressed in wMel and wBif embryos at the time the defects are known to occur in D . bifasciata . ( viii ) The Wmk protein is predicted to interact with DNA when DNA defects are a hallmark of Wolbachia male killing . ( ix ) wmk is unique to Wolbachia , and the Wolbachia male-killing mechanism has some unique phenotypic features compared to other male-killers . For example , the dosage compensation complex is not mislocalized in Wolbachia infection , but it is in Spiroplasma infection [13 , 49] . ( x ) The phenotype can be induced with drivers that yield approximately ten-fold variation in expression levels , indicating the highest Act5c levels of expression are not necessary for the phenotype . ( xi ) DNA damage is more common in wmk males than in controls and it is associated with H4K16ac , which parallels data in natural infections . ( xii ) Wmk’s predicted structure is conserved across arthropod hosts despite sequence divergence , indicating it likely has conserved function . Investigations into putative microbial male-killing genes have largely been hampered by an inability to culture or genetically manipulate intracellular bacteria and their mobile genetic elements . Recently , the gene Spaid in the endosymbiont Spiroplasma poulsonii was identified as a likely candidate underpinning killing of D . melanogaster males , possibly through misregulation of male dosage compensation [28] . Indeed , dosage compensation is an identified host target in Spiroplasma male killing [53 , 54] , and may be involved in Wolbachia male killing as well , although likely through a different method such as increased activity rather than mislocalization that is typical of Spiroplasma infection [49] . It also appears that the wmk-mediated mechanism of male death may involve dosage compensation , as it recapitulates H4K16ac associations with DNA damage , but this remains to be confirmed with further experiments . Interestingly , wmk males have slightly more H4K16ac than their control gene counterparts , raising the possibility that death is correlated with either accelerated or a greater amount of H4K16ac . Whether this is true and whether the dosage compensation complex is directly or indirectly involved both remain to be determined . Spaid is on a plasmid and has no homologs in Wolbachia , though it was previously noted that locus WD0633 in wMel has similar protein domains consisting of ankyrin and OTU domains [28] . However , WD0633 was not predicted here to be on the shortlist of candidates for Wolbachia male-killing due to its absence in wRec . wmk is also in the genome of a mobile element ( phage WO ) , likely originated in Wolbachia , and has no homologs in Spiroplasma . This indicates that there could be an emerging trend of endosymbiotic reproductive parasitism genes and candidates in mobile elements ( including the cifA and cifB phage WO genes for CI ) . Both Spaid and wmk exhibit independent origins from each other . This finding is consistent with arguments that differences in observed male-killing phenotypes and sex determination systems of affected hosts may be due to distinct male-killing genes and/or mechanisms [55] . Other male-killing candidate genes may also exist . If so , they could support the observation that male killing can independently arise in bacterial symbionts . Identification of additional genes and comparisons of their mechanisms is an important area of future work . Wmk is also a putative DNA-binding transcriptional regulator ( S8 Fig ) , which is notable in light of previous studies demonstrating Wolbachia’s ability to modulate host transcription to induce various phenotypes . For example , Spiroplasma [53 , 54] and likely Wolbachia [49] , kill males through the host dosage compensation complex , which is a critical mediator of transcriptional differences between male and female sex chromosomes . These reproductive parasites are therefore likely interfering with regulatory processes for host gene expression in males , which is a likely cause of male death . In addition , Wolbachia influences on host transcription have been implicated in the CI phenotype [56] and virus inhibition [57 , 58] . As wmk transgene expression similarly leads to DNA damage correlated with dosage compensation , it may follow a trend in the field of Wolbachia affecting the regulation or deregulation of host gene expression . If wmk is the causative agent of male killing , then the wMel genome could be multipotent and able to induce different phenotypes ( e . g . , CI and male killing ) either in other hosts or under different environmental conditions . This premise remains to be evaluated in future studies . Assuming wmk is a bona fide male-killing gene , then some patterns about multipotency emerge . First and as noted earlier , wMel and wRec from D . recens are very closely related Wolbachia strains and have a 99 . 7% genome-wide identity [35] . Importantly , wRec is a known multipotent strain that causes CI in its native host and male killing in a sister species [22] . While its genome has lost many prophage WO genes , it retains wmk and the cif genes that may underpin its multipotency , similar to wMel . Second , while CI genes and phenotype often correlate , wmk is not always associated with male killing . wmk and its homologs are present in all sequenced male-killers , and they are also common in many other strains not known to cause male killing ( Fig 1 , S1A Fig ) . In wMel and potentially other strains , lack of male killing in native hosts is possibly due to host resistance to male killing , as is likely in D . recens [22] . Importantly , host suppression of male killing is common [16 , 21 , 22 , 37 , 38] , presumably because of the evolutionary pressure on the host to develop a counter-adaptation that avoids extinction . Therefore , though the wmk gene is more common than the male-killing phenotype , this would be expected if the frequency of resistance is indeed high . It is also possible that male killing is a multilocus trait that requires another gene to induce the phenotype in its natural context . Moreover , differences in Wolbachia titers , and/or insufficient expression of native wmk within D . melanogaster may contribute to the lack of male killing by wMel , however this is unlikely given the similarly lowly-expressed wmk homolog in the wBif male-killing strain . Finally , wmk and the cif genes are similarly disrupted , degraded , or lost in parthenogenesis-inducing Wolbachia strains wUni from Muscidifurax uniraptor wasps , wTpre from Trichogramma pretiosum wasps , and wFol from Folsomia candida springtails . Therefore , multipotency is interestingly common for CI and male killing and will resultantly be rare in parthenogenesis strains . There is considerable amino acid sequence divergence in Wmk homologs across several arthropod orders that harbor male-killing Wolbachia . One potential reason for the divergence is that if a single gene kills many or all of these hosts in nature , a premise which remains to be evaluated , it may be divergent due to selection to target the varied genetic and cellular bases of sex determination in these hosts . Second , if there is a single gene behind the phenotype , it could explain the relatively high frequency of host resistance since hosts would counter-adapt to one gene product rather than multiple products . Under antagonistic coevolution , wmk would evolve to kill males , the host adapts to resist the male killing , and wmk would follow suit and adapt again , continuing the evolutionary arms race . Third and in addition to coevolutionary bouts of wmk adaptation and host counter-adaptation , pleiotropy or multiple functions of wmk could also explain the sequence divergence in wmk homologs , especially in hosts that do not exhibit male killing . Identification and further investigation of male-killing genes have relevance to translational applications in pest or vector control as male killing can theoretically be used in population suppression to crash target populations . Population modeling indicates that use of male killing in conjunction with other population-crashing techniques such as the Sterile Insect Technique ( SIT ) , where sterilized males are released to compete with fertile males , could decrease the time to crash the population and increase the chances of success [26] . In this context , male killing genes might be used to transform an endosymbiotic microbe or host to either add or enhance male-killing ability . Alternatively , a male-killing infection could be established in a host where one does not natively exist . These techniques may be desirable in cases of invasive species of disease-carrying mosquitoes or agricultural pests . Techniques like SIT can fail if males are not completely sterile or because of reduced mating competitiveness with fertile males [59 , 60] . Therefore , a two-pronged approach to simultaneously reduce viable matings in the wild ( SIT ) while killing off males ( male killing ) could in principle be used to more effectively crash populations prone to SIT failure on their own [26] , although this remains to be empirically evaluated . There are many remaining questions for the future , including ones that are important for understanding a male-killing gene’s role in host evolution and its potential in pest or vector control . First , is the wmk candidate gene in Wolbachia required for the phenotype in natural contexts ? In the absence of the ability to knock out genes , it cannot yet be absolutely stated if wmk is used by bacteria to kill males in nature . Therefore , in addition to the transgenic expression , phenotype recapitulation , and sequence analyses demonstrated thus far , knocking out these genes in their resident genome will be important to assessing a change in phenotype . Second , can wmk homologs from related symbiont strains kill males ? This will involve testing homologs in a genetically tractable host . Third , what is the exact mechanism of Wmk-induced male death ? As wmk is annotated as a transcriptional regulator , it may act by controlling host transcription in a way that harms males . In addition , results indicate that the mechanism may involve dosage compensation . Fourth , what is the reason that transgenic wmk expression does not kill all males ? Is it host resistance , inadequate expression patterns , divergence in host target or bacterial toxin gene sequence , or is another gene involved ? We have tested a likely gene partner ( WD0625 ) and multiple expression drivers ( Act5c , nanos , arm , and MTD ) to assess this , however no attempts so far have yielded answers . Finally , applications of male-killing bacteria or the genes to vector and pest control remain to be explored beyond population genetic theory [26] . The discovery of wmk-induced male death advances an understanding of the genes in the eukaryotic association module of prophage WO that interact with animal reproduction [29] . Moreover , male-specific lethality naturally occurs in many arthropods and has important influences on arthropod evolution [16 , 19 , 22 , 23 , 61 , 62] , such as modifying mate choice and selecting for male resistance to the phenotype [11 , 55] . Male killing may also serve as a means to enhance population suppression methods for vectors or pests [26] . Thus , assessing male-killing gene candidates advances an understanding of the tritrophic crosstalk between phages , reproductive parasitic bacteria , and animals as well as their potential in arthropod control programs [24 , 26] .
Most Drosophila experiments ( unless otherwise noted ) were set up with the following design . Crosses in each experiment were conducted by mating 10 female heterozygous Act5c-Gal4/CyO driver flies to 2 male homozygous transgene flies ( both uninfected , unless otherwise noted; switching the gender for each genotype does not alter the effect ) . The offspring of these crosses were used for all experiments , except where noted . As the Act5c-Gal4/CyO driver strain is heterozygous , when driver flies are crossed to homozygous transgene flies , half of the offspring express the gene ( those that inherit the Act5c driver gene that produces the Gal4 transcription factor ) , while the other half do not ( those that inherit the CyO chromosome , which does not produce Gal4 ) . Therefore , expressing males , expressing females , non-expressing males , and non-expressing females are expected in equal proportions under Mendelian inheritance . These four genotypes can only be visibly assessed in adulthood . Visually , embryos cannot be distinguished ( except when fixed for microscopy with the Y chromosome FISH probe , when sex can be distinguished ) , while larvae can only be differentiated by sex . Alongside several experiments , including the cytology in Figs 3 and 4 , sex ratios were measured concurrently . When flies were set up in the crosses described above , siblings were also set up in vials with CMY media . The protocol to measure sex ratios was then followed to obtain sex ratios side by side with these experiments . The results are in the extended data , where noted . The maternal triple driver ( MTD ) was tested by crossing this homozygous driver strain to homozygous transgene flies in the same design as above . This crossing leads to transgene expression in all offspring because the driver is homozygous . Females expressing the transgene in their ovaries ( MTD leads to targeted gene expression in the germline , specifically by loading embryos with the product ) were then crossed to WT flies . Offspring were then quantified to measure sex ratios . Putative Wmk domains were identified by a CD-SEARCH of NCBI’s Conserved Domain Database ( https://www . ncbi . nlm . nih . gov/Structure/cdd/wrpsb . cgi ) . For the full-length analysis ( S1A Fig ) , homologs were identified by a BLASTn of NCBI’s nucleotide collection ( nr/nt ) and whole genome shotgun sequence ( wgs ) databases . The sequences reported were reciprocal best BLAST hits with wMel wmk . Partial sequences and/or those located at the end of a contig were excluded from downstream analysis . For the comparative genomic analysis , wmk , cifA , and cifB homologs were identified by manual annotations of prophage WO regions within known male-killing strains . Homology was confirmed by translating each gene and performing a BLASTP search against wMel in NCBI . Only sequenced male-killing Wolbachia genomes in Drosophila were compared to demonstrate homologs clustering with gene synteny ( S1B Fig ) . For both phylogenetic analyses , sequences were aligned using the MUSCLE plugin in Geneious Pro v8 . 1 . 7 and all indels were stripped . Trees were built using the MrBayes plugin in Geneious and were based on the best models of evolution , according to the corrected Akaike Information Criteria ( AICc ) , as estimated by JModelTest and ProtTest v3 . 4 . 2 , respectively . The models each predicted the GTR+I+G model for S1A Fig and the JTT+G model for S1B Fig , respectively . wBif was excluded due to high sequence divergence . Protein modeling was performed with Phyre2 [50] . For the male-killer comparative genomics analysis , the entire wBif draft assembly was searched for prophage WO-like regions . Five WO-like islands were found , and the genes in these regions were annotated using the NCBI BLASTP and conserved domain database . We then performed a 1:1 BLASTP of the annotated genes against query genomes . If it was present in wBif , the wRec , wInn , and wBor genomes were searched for homologs , in the given order . If the gene was absent in one strain , it was marked as absent and excluded from further analysis . Genes were removed if they were: ( i ) absent in one or more of the strains ( wBif , wRec , wInn , and wBor ) , ( ii ) mobile elements ( including IS elements , reverse transcriptases of group II intron origin , or recombinases ) , ( iii ) disrupted genes ( frameshift with early stop codons ) in one or more of the strains , and , ( iv ) if the E-value was less than E-20 . See S1 Table for a list of all removed genes along with rationale for exclusion . The D . innubila Wolbachia genome was sequenced from a single wild-caught female . Briefly , D . innubila were captured at the Southwest Research Station in Arizona over baits consisting of store-bought white button mushrooms ( Agaricus bisporus ) . DNA was extracted using the Qiagen Gentra Puregene Tissue kit ( #158689 , Germantown , Maryland , USA ) . A genomic DNA library was constructed for several individuals using a modified version of the Nextera DNA Library Prep kit ( #FC-121-1031 , Illumina , Inc . , San Diego , CA , USA ) reagents [63] . DNA from an infected female was sequenced on a fraction of an Illumina HiSeq 2500 System Rapid-Run to generate 14873460 paired-end 150 base-pair reads . Reads were aligned to a draft D . innubila genome and all non-aligned reads were assembled de novo using Spades [64] . Those contigs blasting to other Wolbachia accessions were retained as putative Wolbachia genomic contigs . The Wolbachia genomes of wBif and wBor were sequenced from D . bifasciata ( line bif-F-MK [65] ) and D . borealis ( line PG05 . 16 [39] ) respectively . Following the protocol developed in Ellegaard et al . [66] , Wolbachia cells were purified from ~20 freshly laid ( less than 2 hours ) and bleach-dechorionated embryos by homogenizing them in phosphate-buffered saline solution ( PBS ) and conducting a series of centrifugation/filtration steps as explained in Ellegaard et al [66] . A multiple-displacement amplification was carried out directly on the bacterial pellet using the Repli-g midi kit ( Qiagen ) . The amplified DNA was cleaned with QIAamp DNA mini kit ( Qiagen ) . From each sample , both 3kb mate-pair and 50 bp paired-end DNA libraries were prepared and sequenced on a 454 Roche FLX ( Department of Biochemistry , Cambridge , UK ) and Illumina HiSeq2000 instruments ( The Genome Analysis Center , Norwich , UK ) respectively . The sequencing generated 203 , 565 and 239 , 485 454 mate-pair reads as well as 35 , 415 , 012 and 30 , 624 , 138 Illumina reads for wBif and wBor respectively . De novo hybrid assemblies combining 454 reads and a 10% subset of the Illumina reads were performed in Newbler ( 454 Life Sciences Corp . , Roche , Branford , CT 06405 , US ) . Contigs blasting to other Wolbachia accessions were retained as putative Wolbachia genomic contigs . Scaffolds were extended to fill regions with “N“s using GapFiller v . 1-11 [67] . The Wolbachia genome of D . innubila ( wInn ) was sequenced by the R . Unckless lab . The Wolbachia genomes of D . bifasciata ( wBif ) and D . borealis ( wBor ) were sequenced by the F . Jiggins lab . The genomes will be published by the respective contributors at a later date , and only the phage WO gene regions involved in this publication are publicly available ( the regions in Fig 1 ) . The Wolbachia transgene strains were generated as described previously [29] . WD0626 ( wmk ) and WD0034 ( control gene ) were both inserted into an attP site in the BSC8622 ( WT ) line of genotype y1w67c23; P[CaryP]P2 obtained from the Bloomington Drosophila Stock Center . WD0625 was inserted into the BSC9723 strain , with a genotype of y1M[vas-int . Dm]ZH-2A w*; PBac[y+-attP-3B]VK00002 . WD0508 was inserted into the y1M[vas-int . Dm]ZH-2A w*; P[CaryP]attP40 line . The genes were inserted into various strains to facilitate creation of strains that contain more than one gene homozygously . The Act5c-Gal4/CyO driver line is the same background as BSC3953 , which is y1w*; P[Act5C-GAL4-w]E1/CyO . The maternal triple driver ( MTD ) strain BSC31777 , genotype P[w[+mC] = otu-GAL4::VP16 . R]1 , w[*];P[w[+mC] = GAL4-nos . NGT]40; P[w[+mC] = GAL4::VP16-nos . UTR]CG6325[MVD1] , was provided by J . Nordman . The nanos-Gal4 strain used in S4B and S4C Fig was previously described [29] . The arm-Gal4 driver strain BSC1560 is w[*]; p[w[+mW . hs] = GAL4-arm . S]11 . The infected D . bifasciata flies were provided by G . Hurst and are infected with male-killing Wolbachia . The male-killing flies are maintained with males from a concurrently reared uninfected line also provided by G . Hurst . D . melanogaster were reared on 4% cornmeal ( w/v ) , 9% molasses ( w/v ) , 1 . 6% yeast ( w/v ) ( CMY ) media . The flies developed at 25°C at 80% humidity with a 12 h light/dark cycle . Virgin flies were stored at room temperature after collections . During virgin collections , stocks were maintained at 25°C during the day and at 18°C at night . Wolbachia-uninfected transgene or driver lines were generated via tetracycline treatment of infected lines as described previously [29] . D . bifasciata are maintained on CMY media at room temperature . To assess the ability of the gene candidates to alter sex ratios , twenty replicates of 10 uninfected , 4–7 day old female driver flies and 2 uninfected , 1–2 day old male transgene flies were set up in vials with CMY media . They were left on the media to lay eggs for 36 h at 25°C , at which point adults were discarded . Once the offspring emerged , they were scored for both sex and expression or non-expression ( if applicable ) , which was determined by presence or absence of the CyO wing phenotype as well as with eye color markers associated with Act5c-Gal4 and the transgene insertion . Any vials with fewer than 50 adult offspring were removed from the analysis , as this indicates either poor egg laying or abnormally low egg hatching ( average = 120 offspring ) . Extended data hatch rates ( S4B and S4C Fig ) were performed as previously described with the nanos-Gal4 driver [29] . The nanos driver was used to test induction of CI instead of Act5c-Gal4/CyO because it is expressed more specifically in the gonads where CI is induced [29] . For Figs 3 and 4 , eight stock bottles were set up per genotype , each with 60 uninfected , 4–7 day old Act5c-Gal4/CyO females and 12 uninfected , 1–2 day old transgene or WT males . Grape juice agar plates , made as described previously [29] , with a small amount of baker’s yeast ( Red Star ) placed on each bottle opening and fixed with tape . They were then placed with the grape plate down in a 25°C incubator overnight ( ~16 hr ) . The grape plates were then replaced with fresh plates and fresh yeast . The flies were then allowed to lay eggs in 1 h increments , replacing the previous plates with fresh ones each time . They were then allowed to sit at room temperature for 1 h ( embryos 1–2 h old ) , 3 h ( 3–4 h old ) , or 16 h ( 16–17 h old ) . Once they had reached the desired point in development , the embryos were fixed and stained , using a slight modification of the protocol outlined by Cheng et al . 2016 [13] . Briefly , the embryos were dechorionated in 50% bleach and fixed for 15 minutes in a 1:1 4% paraformaldehyde:heptane mixture while shaking on a tabletop vortexer at about 150 rpm . The solution was discarded , and the embryos were then devitellinized in a 1:1 heptane:methanol mixture by shaking vigorously for one minute . The solution was removed , and the embryos were placed in fresh methanol and stored at 4°C until the next steps were done , at least 16 h later . Then , the methanol was removed and the embryos were rehydrated in a series of methanol:water solutions , in the order of 9:1 , then 1:1 , then 1:9 , each for 15 minutes while mixing on a Nutator . They were then treated with 10 mg/mL RNase A ( Clontech Labs ) by incubating them at 37°C for 2–3 hr with enough RNase solution to cover the embryos . Once the RNase was removed , the embryos were washed three times for 5 min each in PBST ( 1X PBS , 0 . 1% Tween 20 ) , while mixing on the Nutator . They were then re-fixed in 4% paraformaldehyde for 45 minutes with mixing and were then washed or incubated with several solutions with mixing on the nutator . First , they were washed three times in saline-sodium citrate/Tween 20 buffer ( SSCT , 2X SSC buffer , 0 . 1% Tween 20 ) for 10 minutes each . They were then incubated with a series of SSCT/formamide solutions for 10 minutes each in the following order: 80% SSCT/ 20% formamide , 60% SSCT/ 40% formamide , 50% SSCT/ 50% formamide . Then fresh 50% SSCT/ 50% formamide was added and the embryos were incubated at 37°C for 1 h . The solution was removed , and the embryos were then hybridized with the Y-chromosome FISH probe . This was done by mixing 36 μL FISH hybridization solution ( 1g dextran sulfate , 1 . 5 mL 20X SSC , 5 mL formamide , to 15 mL with DNase-free water ) [68] , 3 μL DNase-free water , and 1 μL 200 ng/μL Y-chromosome FISH probe ( sequence 5’-AATACAATACAATACAATACAATACAATAC-3’ synthesized with Cy5 conjugated to the 5’end ( IDT ) ) using the sequence published by Cheng et al . 2016 [13] . Hybridization was done in a thermocycler by denaturing at 92°C for 3 min , followed by hybridizing at 37°C overnight ( ~16 h ) . Then , the embryos were again washed in a series of solutions on the nutator . They were done in the order of three 15 min 50% SSCT / 50% formamide washes , one 10 min 60% SSCT / 40% formamide wash , one 10 min 80% SSCT / 20% formamide wash , and three 10 min SSCT washes . They were then mounted on glass slides with ProLong Diamond Antifade ( Life Technologies , P36970 ) mounting media that contained 1 μg/mL propidium iodide ( Sigma Aldrich ) . Imaging was performed at the Vanderbilt University Cell Imaging Shared Resource ( CISR ) with a Zeiss LSM 510 META inverted confocal microscope . Images are of a single plane . Image analysis and preparation was done with ImageJ software . Image brightness and contrast were adjusted for visibility , but adjustments were applied equally across each whole image . For Fig 5 , a different fixing and staining protocol was used . Eight bottles were set up per genotype with 60 uninfected armadillo ( arm ) -Gal4 females crossed to 12 uninfected wmk or control gene males with a small amount of baker’s yeast ( Red Star ) placed on each bottle opening and fixed with tape . They were then placed with the grape plate down in a 25°C incubator overnight ( ~16 hr ) . The grape plates were then replaced with fresh plates and fresh yeast . The flies were then allowed to lay eggs in 1 h increments , replacing the previous plates with fresh ones each time . They were all aged to 4–5 h AED . Once they had aged to the desired point in development , they were fixed and stained using the protocol described in Hall & Ward [69] . Embryos were dechorionated for 2 min in 50% bleach and rinsed with water . They were then fixed with shaking in 1:1 4% paraformaldehyde to heptane at room temperature for 20 min . The bottom paraformaldehyde phase was removed and methanol was added in equal volume to the remaining heptane and embryos . They were then devitellinized by shaking vigorously for 20 s . Embryos were stored in methanol at 4°C until staining . Staining was performed by first removing the methanol and rinsing with 750 μL blocking solution ( Vector Laboratories Animal-Free blocking solution SP5030 ) . The embryos were then rinsed in 1X PBS twice . The PBS was removed and the embryos were permeabilized in 750 μL blocking solution for 30 min at room temperature with rocking . The blocking solution was removed and the embryos were rinsed with 1X PBS once . The embryos were then incubated with primary antibodies in 500 μL blocking solution overnight at 4°C with rocking . The antibodies included histone H2AvD pS137 antibody ( 1:100 , Rockland 600-401-914 ) , anti-acetyl-histone H4 ( Lys16 ) antibody or H4K16ac ( 1:100 , Millipore Sigma 07–329 ) , and Sxl antibody ( 1:20 , DSHB M18 ) . The Sxl antibody developed by P . Schedl was obtained from the Developmental Studies Hybridoma Bank , created by the NICHD of the NIH and maintained at The University of Iowa , Department of Biology , Iowa City , IA 52242 . In cases where primary antibodies were raised in the same animal , sequential staining was performed . After overnight staining with one antibody , the steps were repeated beginning with the initial blocking step for the second antibody . After overnight staining , the embryos were washed in 1X PBS three times at room temperature with rocking for 5 min each . They were then incubated with 750 μL blocking solution for 30 min at room temperature with rocking . The blocking solution was removed and the embryos were rinsed in 1X PBS once . The embryos were then incubated with secondary antibodies in 500 μL blocking solution at room temperature with rocking for 1 h out of the light ( all subsequent steps are also out of the light ) . The antibodies included goat anti-mouse IgG with Alexa Fluor 647 ( 1:500 , abcam ab150115 ) , goat anti-rabbit IgG with Alexa Fluor 594 ( 1:500 , Invitrogen A11037 ) , and goat anti-rabbit IgG with Alexa Fluor 488 ( 1:500 , Invitrogen A11034 ) . The embryos were then washed three times with 1X PBS at room temperature with rocking for 5 min each . They were then incubated with 750 μL blocking solution for 30 min at room temperature with rocking . The embryos were then rinsed once in 1X PBS . The embryos were then stained with 1μg/mL DAPI ( Invitrogen D1306 ) for 10 min with rocking at room temperature . Embryos were then washed three times in 1X PBS for 10 min each with rocking at room temperature . They were then mounted on glass slides with ProLong Diamond Antifade ( Life Technologies , P36970 ) mounting media . Imaging was performed using a Keyence BZ-X710 Fluorescence Microscope and all images are a single plane . Images were taken at 20X magnification . Quantification of punctae was done by manually focusing on several planes that encompassed all punctae and quantifying punctae with overlapping signals . Images were analyzed using Keyence analysis software . Image brightness and contrast were adjusted and dehazing software was used for visibility , but adjustments were applied equally across each whole image . Gene expression in embryos from Fig 6 was measured in each of four groups . Group 1 was generated in crosses between Act5c-Gal4/CyO uninfected females crossed to wmk uninfected males . Group 2 was generated in crosses between Act5c-Gal4/CyO uninfected females crossed to control gene uninfected males . Group 3 was generated by crossing y1w* infected females to y1w* uninfected males . Group 4 was generated by crossing wBif-infected D . bifasciata females to uninfected D . bifasciata males . Gene expression for S3B Fig was set up using two groups with either Arm-Gal4 or Act5c-Gal4/CyO uninfected females crossed to wmk males . For each group , 8 bottles were set up with 10 females and 2 males . A grape juice agar plate [29] with yeast was placed in each bottle . These were placed in a 25°C incubator overnight ( 16 h ) for D . melanogaster or kept at room temperature ( 23°C ) for D . bifasciata . Then , the plates were swapped with fresh ones . The flies were allowed to lay eggs for 1 h . The plates were then left at 25°C or 18°C for an additional 4 h to age them to be 4–5 h old ( the estimated time of male death in wmk crosses ) . Embryos were then gathered in groups of 30 ( each group from the same bottle ) and flash frozen in liquid nitrogen . RNA was extracted using the Direct-zol RNA MiniPrep Kit ( Zymo ) , DNase treated with DNA-free DNase ( Ambion , Life Technologies ) , cDNA was generated with SuperScript VILO ( Invitrogen ) , and RT-qPCR was run using iTaq Universal SYBR Green Mix ( Bio-Rad ) . qPCR was performed on a Bio-Rad CFX-96 Real-Time System . Primers are listed in S4 Table . Conditions were as follows: 50°C 10 min , 95°C 5 min , 40x ( 95°C 10 s , 55°C 30 s ) , 95°C 30 s . For each gene measured , a standard curve was produced with known concentrations alongside samples with unknown concentrations . Primers are listed in S3 Table . Differences in gene expression were done by calculating 2-Δct ( difference in ct values of two genes of interest ) . Confirmation of gene expression in adults from S2C and S2E Fig was done similarly . Samples were obtained by flash freezing adult offspring laid by siblings of the flies used in S2A Fig . Samples from S2B Fig were from pooled , whole-body extractions from three males of each genotype . Samples from S2C Fig were from pooled , whole-body extractions from three females of each genotype . Samples from S2E Fig were from pooled , dissected ovaries of six adult female siblings of flies of flies used in S2E Fig for each genotype . Samples were flash frozen in liquid nitrogen and then was processed ( RNA extraction , DNase treatment , and cDNA treatment ) as above . PCR was performed against positive controls ( extracted DNA ) , negative controls ( water ) , RNA , and cDNA . Gel image brightness and contrast were adjusted for visual clarity , but adjustments were applied equally across each whole image . Protein conservation was calculated with the Protein Residue Conservation Prediction Tool [70] . Amino acid sequences from S1 Fig along with the wBif Wmk homolog sequence were aligned using a MUSCLE alignment in Geneious Prime version 2019 . 1 . This alignment was uploaded to the prediction tool with the following settings: Shannon entropy scores , a window size of zero , and no sequence weighting . Conservation values were then input into GraphPad Prism version 8 for visualization . HTH regions were indicated using the amino acids predicted to be in the domains according to the NCBI annotation of wMel Wmk .
Statistical analyses were done using GraphPad Prism software ( version 5 or 8 ) or GraphPad online tools , unless otherwise noted . For comparisons among only two data categories , we used the two-tailed , non-parametric Mann-Whitney U test . For comparisons with more groups , a non-parametric Kruskal-Wallis one-way analysis of variance was used , followed by Dunn’s test for multiple comparisons , if significant . In cases of comparisons among groups where only a single measurement was taken per group ( such as cytology experiments ) , a Chi-square test was used . Exact tests used and other important information are listed in the figure legends of each experiment . | Male killing is an adaptive trait for bacteria that are maternally transmitted through host populations . Such bacteria are common in arthropods and resultantly have significant impacts on host population size , mating strategy , and evolution . Moreover , male-killing bacteria are under recent scrutiny as a symbiotic strategy for arthropod pest and vector control . Despite decades of research , the microbial genetic basis of Wolbachia-induced male killing remains elusive . Here we demonstrate that a single gene from the eukaryotic association module in prophage WO of Wolbachia is a candidate for male killing as it recapitulates many aspects of the phenotype when transgenically expressed in fruit flies . This discovery represents a step forward in understanding new roles of phage WO genes in shaping arthropod hosts and may inform the potential use of male killing in worldwide pest and vector control strategies . | [
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| 2019 | The phage gene wmk is a candidate for male killing by a bacterial endosymbiont |
A number of nonclassical MHC Ib molecules recognizing distinct microbial antigens have been implicated in the immune response to Mycobacterium tuberculosis ( Mtb ) . HLA-E has been identified to present numerous Mtb peptides to CD8+ T cells , with multiple HLA-E-restricted cytotoxic T lymphocyte ( CTL ) and regulatory T cell lines isolated from patients with active and latent tuberculosis ( TB ) . In other disease models , HLA-E and its mouse homolog Qa-1 can act as antigen presenting molecules as well as regulators of the immune response . However , it is unclear what precise role ( s ) HLA-E/Qa-1 play in the immune response to Mtb . In this study , we found that murine Qa-1 can bind and present Mtb peptide antigens to CD8+ T effector cells during aerosol Mtb infection . Further , mice lacking Qa-1 ( Qa-1-/- ) were more susceptible to high-dose Mtb infection compared to wild-type controls , with higher bacterial burdens and increased mortality . The increased susceptibility of Qa-1-/- mice was associated with dysregulated T cells that were more activated and produced higher levels of pro-inflammatory cytokines . T cells from Qa-1-/- mice also had increased expression of inhibitory and apoptosis-associated cell surface markers such as CD94/NKG2A , KLRG1 , PD-1 , Fas-L , and CTLA-4 . As such , they were more prone to cell death and had decreased capacity in promoting the killing of Mtb in infected macrophages . Lastly , comparing the immune responses of Qa-1 mutant knock-in mice deficient in either Qa-1-restricted CD8+ Tregs ( Qa-1 D227K ) or the inhibitory Qa-1-CD94/NKG2A interaction ( Qa-1 R72A ) with Qa-1-/- and wild-type controls indicated that both of these Qa-1-mediated mechanisms were involved in suppression of the immune response in Mtb infection . Our findings reveal that Qa-1 participates in the immune response to Mtb infection by presenting peptide antigens as well as regulating immune responses , resulting in more effective anti-Mtb immunity .
As the causative agent of tuberculosis ( TB ) , Mycobacterium tuberculosis ( Mtb ) is a continuing global public health concern that kills approximately 2 million people annually , with an estimated one-third of the world’s population infected with Mtb [1] . The emergence of multidrug-resistant Mtb , co-infection with HIV , and the limited efficacy of the Mycobacterium bovis Bacillus Calmette-Guérin ( BCG ) vaccine compound the need for further research on the immune responses to pulmonary Mtb infection , toward the development of more effective TB vaccines . Optimal protection against Mtb requires both CD4+ and CD8+ T cell responses . Most Mtb-specific CD4+ T cells produce Th1-type cytokines , including IFN-γ , TNF-α , IL-12 , and IL-2 [2] . Similarly , Mtb-specific CD8+ T cells are also potent producers of IFN-γ and TNF-α , cytokines crucial to anti-Mtb immunity [3 , 4] . While current Mtb subunit vaccine development has primarily focused on CD4+ and MHC Ia-restricted CD8+ T cell responses [5] , increasing evidence shows that non-conventional CD8+ T cells restricted by MHC Ib molecules can recognize distinct microbial antigens and contribute to protection against Mtb infection [6–8] . Like MHC Ia molecules , MHC Ib molecules can present antigens to CD8+ cytotoxic T lymphocytes ( CTL ) . However , MHC Ib molecules are less polymorphic than MHC Ia molecules , making them attractive targets for vaccine development . In particular , MHC Ib molecules CD1 , MR1 , Qa-1/HLA-E and Qa-2/HLA-G have been implicated in the host immune response against Mtb in mice and/or humans [6–10] . Group 1 CD1-restricted T cells specific to Mtb lipid have been detected in patients with active or latent TB infection and conferred protection against Mtb in human group 1 CD1 transgenic mice [11–15] . MR1-restricted mucosal-associated invariant T cells ( MAIT ) recognizing vitamin B metabolites were also shown to participate in anti-mycobacterial immunity [16 , 17] . In addition , Qa-2-restricted and other nonclassical MHC Ib-restricted CD8+ T Cells have been shown to provide protection against Mtb in mice [10] . Recently , 69 Mtb peptides have been shown to have predicted binding affinity to HLA-E , with most of these peptides inducing CD8+ T cell proliferation when presented by HLA-E to PBMCs from mycobacterial- responsive donors [8] . Multiple HLA-E-restricted Mtb peptide-specific CTLs from these donors have also been isolated [8 , 18–20] . Further , HLA-E tetramers have identified Mtb peptide-specific CD8+ T cells in PBMCs of TB patients at the highest frequency during active infection [18] , suggesting that HLA-E constitutes an important element in Mtb immunity . Like other MHC Ib molecules , HLA-E exhibits limited polymorphism; it has 3 protein variants , only 2 of which are detected in the human population with high frequency [21] . HLA-E has a structural and functional mouse homolog , Qa-1 ( H2-T23 ) [22 , 23] , and both human and mouse molecules have been shown to play diverse roles in the immune system . Both HLA-E and Qa-1 can present peptide antigens from intracellular pathogens such as Epstein Barr virus , cytomegalovirus , and Salmonella typhimurium to CD8+ T cells , resulting in activation of CTL activity against infected cells [23–25] . HLA-E and Qa-1 also both predominantly bind endogenous peptides derived from the leader sequence of MHC Ia molecules [26 , 27] . For Qa-1 , this single nonameric peptide is called Qa-1 determinant modifier ( Qdm ) . The Qa-1/Qdm or HLA-E/peptide complex serves as a ligand for CD94/NKG2 receptors , which are expressed mainly on NK cells and a subset of CD8+ T cells [28 , 29] . CD94 is primarily associated with the NKG2A isoform , forming an inhibitory receptor , but can also be complexed with NKG2C/E , forming activating receptors [23] . Ligation of Qa-1/Qdm with CD94/NKG2A results in inhibition of NK cell cytolytic activity [30] . As the presentation of Qdm and other leader sequence peptides is dependent on transporter associated with antigen processing ( TAP ) , the absence of HLA-E/Qa-1 bound to the endogenous peptides allows NK cells to detect and lyse abnormal cells [31 , 32] . CD8+ T cell responses to viral infection can also be dampened through the interaction of Qa-1/Qdm with CD94/NKG2A [33–36] . Lastly , Qa-1 is the restriction element for a subset of suppressor CD8+ T cells , called Qa-1-restricted CD8+ regulatory T cells ( CD8+ Treg ) [37] . Qa-1-restricted CD8+ Treg cells have been shown to suppress the development of murine experimental autoimmune encephalomyelitis ( EAE ) and other autoimmune diseases [38–40] . Further , Qa-1-deficient ( Qa-1-/- ) mice developed exaggerated CD4+ T cell responses upon viral infection or immunization with self-peptide compared to wild-type mice , due to a lack of Qa-1-restricted CD8+ Tregs [24 , 41] . Although CD8+ Tregs have not been well-studied in the context of HLA-E , a number of Mtb-specific HLA-E restricted CTL and/or regulatory CD8+ T cell clones have been shown to exhibit suppressive activity [8 , 18 , 20] . In addition , glatiramer acetate induced CD8+ T cells from human multiple sclerosis patients have HLA-E-restricted regulatory activity [42] . In summary , HLA-E and Qa-1 can present both self and foreign peptides and interact with various receptors , resulting in both activation and suppression of immune responses through a variety of mechanisms . Although Mtb peptide-specific , HLA-E restricted CD8+ T cells have been detected in humans , the overall contribution of HLA-E to the immune response to Mtb remains elusive , particularly whether it participates in inhibitory and/or immunoregulatory functions . In addition , whether murine Qa-1 can present Mtb peptides and its role in Mtb infection have not been investigated . In this study , we seek to extend and mechanistically dissect the findings from the human HLA-E studies to well controlled mouse TB models to better understand the diverse immunological functions of Qa-1/HLA-E in Mtb infection . We found that Qa-1 could present multiple Mtb peptide antigens to CD8+ T cells during aerosol Mtb infection , and Qa-1-/- mice were more susceptible to Mtb infection compared to wild-type controls . CD8+ and CD4+ T cells in Mtb-infected Qa-1-/- mice had more activated phenotypes and produced higher levels of pro-inflammatory cytokines , which have recently been associated with poor disease outcomes [43 , 44] . In addition , CD8+ and CD4+ T cells in Mtb-infected Qa-1-/- mice had increased expression of inhibitory and apoptosis-associated cell surface markers , correlated with a decreased ability to control bacteria . Together , our data suggest that Qa-1 participates in immune responses against Mtb through antigen-presentation and regulation of immune responses .
To determine if Qa-1 plays a role during Mtb infection , we first examined the surface expression of Qa-1 . Low-dose aerosol Mtb infection with 100–200 CFU/lung is the most commonly used aerosol Mtb infection model , as it closely mimics the natural route of infection and disease progression . A high-dose aerosol Mtb infection model ( 1000–2000 CFU/lung ) is also occasionally used to magnify phenotypic differences , particularly for mortality experiments [45] . We infected C57BL/6 ( B6 ) mice with either a low- or high-dose of virulent Mtb strain H37Rv and found that Qa-1 expression was upregulated in both types of infection . In low-dose Mtb-infected B6 mice , increased expression of Qa-1 was detected on leukocytes from the mediastinal lymph nodes at 2 weeks post-infection , which continued to increase over the course of the infection ( week 4 and 6 , Fig 1A ) . Qa-1 expression in high-dose Mtb-infected mice was also increased relative to naïve mice , and was upregulated on multiple cell types , including T cells , B cells and other antigen-presenting cells , such as macrophages and dendritic cells ( Fig 1B ) . These data suggest that Qa-1 may play a role in the immune response to Mtb infection . Studies have shown that HLA-E can present Mtb peptides to CD8+ T cells in human TB [8 , 18 , 19] . As the antigen-binding grooves of Qa-1 and HLA-E share a high degree of structural similarity , we hypothesized that some HLA-E-binding peptides could also bind to Qa-1 and elicit Qa-1-restricted CD8+ T cell responses during Mtb infection . To test this hypothesis , a panel of Mtb peptides were synthesized ( S1 Table ) and tested for binding to Qa-1 in a flow cytometry-based peptide competition assay . The panel consisted of 17 peptides; of these , 16 were known to bind to HLA-E [8] . As the Salmonella GroEL chaperone protein has been shown to bind to Qa-1 [23] , we also tested the Mtb GroEL homologue for binding to Qa-1 . Qa-1-transfected HeLa cells were incubated with biotinylated Qdm ( bio-Qdm ) peptide and increasing concentrations of unbiotinylated competing test peptide . Peptide binding was determined by the subsequent decrease in streptavidin-APC staining intensity on HeLa-Qa1 transfectants due to displacement of bio-Qdm by competing peptide . While unlabeled Qdm peptide was able to bind to Qa-1 and displace bio-Qdm peptide , the negative control OT-I peptide was unable to do so ( Fig 2A ) . Two representative Mtb peptides , P55 and P68 , showed dose-dependent competition with bio-Qdm peptide ( Fig 2A ) . Using this assay , we found six Mtb peptides that showed relatively high binding affinity to Qa-1 and were used for further study: P9 , P34 , P37 , P44 , P55 , and P68 ( Fig 2B ) . To assess whether these Qa-1-binding Mtb peptides are readily presented to T cells and involved in the immune response during Mtb infection , we performed a low-dose Mtb infection on Kb-/-Db-/- mice . Kb-/-Db-/- mice were chosen because they lack MHC Ia molecules and may have a higher precursor frequency of Qa-1-restricted CD8+ T cells . At 4 weeks post-infection , CD8+ T cells were purified from the spleen and stimulated in vitro with bone marrow-derived dendritic cells ( BMDC ) presenting individual Mtb peptides . IFN-γ-producing Mtb peptide-specific CD8+ T cells were quantified by ELISPOT assays . Of the 6 Mtb peptides tested , CD8+ T cells specific to peptides P55 and P68 were the most frequently and consistently detected while the response to peptide P44 was rather variable between experiments ( Fig 3A ) . The P55- and P68-specific IFN-γ responses could be blocked by the addition of a neutralizing anti-Qa-1 antibody ( Fig 3B ) , confirming that peptides P55 and P68 are presented by Qa-1 to CD8+ T effector cells during Mtb infection , resulting in the production of IFN-γ . These P55 and P68-peptide-specific CD8+ T cells can also be detected in Mtb-infected B6 mice ( S1A Fig ) . However , the magnitude of the response was lower than that found in Kb-/-Db-/- mice , and there was substantial variation in response between mice , likely due to a lower precursor frequency of Qa-1 restricted CD8+ T cells in B6 mice compared to Kb-/-Db-/- mice . Further , co-culture of CD8+ T cells from infected B6 mice with BMDC presenting P55 or P68 lead to an increase in the frequency of Annexin V expression on BMDCs relative to no peptide controls ( S1B Fig ) . This peptide-specific increase in BMDC apoptosis was also dependent on the expression of Qa-1 by BMDCs . Together , these data suggest that Mtb peptide-specific Qa-1-restricted CD8+ T effector cells are present in Mtb-infected mice and may be able to induce peptide-specific cytotoxicity . In addition to antigen presentation , Qa-1 has also been demonstrated to have immunoregulatory functions in viral infection and autoimmune disease models [35–37] . To determine what role Qa-1 plays in Mtb infection , we infected age-matched , sex-matched Qa-1+/+ and Qa-1-/- littermates with either low- or high-dose aerosolized Mtb . We found that high-dose Mtb-infected Qa-1-/- mice have a higher mortality rate compared to Qa-1+/+ mice . Qa-1-/- mice began to succumb to high-dose Mtb infection shortly after 4 weeks post-infection , and showed close to 50% mortality by 90 days post-infection ( Fig 4A ) . In contrast , no deaths in Qa-1+/+ littermates were found in this timeframe . In addition , high-dose Mtb-infected Qa-1-/- mice had an increased bacterial burden in both the spleen and lung compared to Qa-1+/+ mice , beginning at 3 weeks post-infection ( Fig 4B ) . These data indicate that Qa-1 is necessary for protection against high-dose aerosol Mtb infection . In contrast , there were no significant differences in bacterial burden between low-dose Mtb-infected Qa-1+/+ and Qa-1-/- mice from 2 weeks up to 12 weeks post-infection ( S2 Fig ) . Due to the significant phenotypes in bacterial burden and mortality , all later experiments were performed using high-dose Mtb infection . To investigate how Qa-1 provides protection against Mtb infection , we began by analyzing the total cell number and function of T cells in Mtb-infected Qa-1+/+ and Qa-1-/- mice . Flow cytometric analysis revealed no significant differences in the absolute number of CD8+ T cells , CD4+ T cells , or other types of leukocytes in the lung of Mtb-infected Qa-1+/+ and Qa-1-/- mice ( S3 Fig ) . Next , we analyzed antigen-specific IFN-γ production in Mtb-infected Qa-1-/- and Qa-1+/+ mice using intracellular cytokine staining . Despite having increased bacterial burden and mortality , we found that Qa-1-/- mice had a higher frequency and total number of Mtb antigen-specific , IFN-γ-producing lymphocytes compared to Qa-1+/+ mice ( Fig 5A and 5B ) in both the spleen and lung . Further , both CD4+ and CD8+ T cells in Mtb-infected Qa-1-/- mice contributed to this increased production of IFN-γ ( Fig 5C ) . Consistent with the enhanced IFN-γ production by T cells in Mtb-infected Qa-1-/- mice , we found that these mice also had increased frequencies of activated CD44hi CD62Llo T effector cells ( Fig 5D ) . Besides IFN-γ , lymphocytes isolated from the lung of Mtb-infected Qa-1-/- mice had increased production of pro-inflammatory IL-17A and TNF-α , as well as IL-10 , in response to in vitro re-stimulation with Mtb whole cell lysate compared to Qa-1+/+ mice ( Fig 5E ) . Neither Qa-1-/- nor Qa-1+/+ mice produced significant amounts of IL-4 and IL-13 during Mtb infection . Both IFN-γ and TNF-α are critical cytokines in anti-Mtb immunity; both activate macrophages to kill intracellular bacteria , with TNF-α also participating in regulating granuloma formation and structure [2] . However , over-production of IFN-γ has recently been shown to be detrimental to the control of Mtb in the lung [43] . Although the role of IL-17A in Mtb infection is not well studied , it appears to play an important role in the generation of protective immune responses in the lung during early infection [46] . Despite increased pro-inflammatory cytokine production and more highly activated T cells , the Qa-1-/- mice were unable to control Mtb infection as well as Qa-1+/+ mice , indicating that Qa-1 deficiency results in over-activation of the T cell response to Mtb infection . We next explored the mechanisms by which Qa-1 can downregulate immune responses during Mtb infection . Inhibitory NK receptors CD94/NKG2A have been shown to be upregulated on a subset of activated CD8+ T cells and possibly CD4+ T cells in response to infection [28 , 29] . Further , negative regulation of CD8+ T cell responses by CD94/NKG2A during poxvirus infection has been shown to prevent over-activation and subsequent apoptosis of CD8+ T cells , thereby enhancing the anti-viral immune response [36] . In naïve Qa-1+/+ and Qa-1-/- mice , the expression of CD94 and NKG2A , as well as activating NK receptor Ly49D , was not significantly different ( S4 Fig ) . During Mtb infection , we found that while lymphocytes from both Qa-1+/+ and Qa-1-/- mice upregulated CD94 and NKG2A expression , Qa-1-/- mice had significantly higher frequencies and absolute numbers of NK cells and T cells ( especially CD8+ T cells ) that expressed inhibitory CD94/NKG2A receptors compared to Qa-1+/+ mice ( Fig 6A–6C ) . Other members of the NKG2 family , such as NKG2C and NKG2E , also dimerize with CD94 to form activating receptors . As there is no antibody specifically for NKG2C or NKG2E , it was necessary to use the 20d5 antibody clone to detect NKG2A/C/E . Expression of NKG2A/C/E was virtually identical to that of NKG2A , indicating inhibitory NKG2A is the dominant form expressed in Mtb-infected mice , with very low expression of activating receptors NKG2C and NKG2E ( S5A Fig ) . In addition , we performed quantitative PCR and confirmed the low expression of NKG2C/E relative to NKG2A on T cells from Mtb-infected mice ( S5B Fig ) . The increased expression of CD94/NKG2A inhibitory receptor along with the increased production of IL-10 regulatory cytokine ( Fig 5E ) in Mtb-infected Qa-1-/- mice indicate that Qa-1-deficient mice have dysregulated immune responses during Mtb infection . As the expression of inhibitory CD94/NKG2A receptor was also increased on NK cells in Mtb-infected Qa-1-/- mice , we examined if there were differences in NK cell function between Qa-1+/+ and Qa-1-/- mice . We used a high dose , intravenous Mtb infection model known to stimulate NK cell function [47] and examined the cytotoxic potential and IFN-γ production of NK cells in Qa-1+/+ and Qa-1-/- mice . Qa-1-/- mice did not show any differences in the number of IFN-γ-producing NK cells or lysis of YAC-1 target cells compared to Qa-1+/+ mice ( S6 Fig ) . Further characterization of the surface phenotype of T cells in Mtb-infected Qa-1+/+ and Qa-1-/- mice showed that a high proportion of T cells in Qa-1-/- mice expressed several other inhibitory and apoptotic markers . T cells in Mtb-infected Qa-1-/- mice showed an increased frequency of KLRG1 and PD-1 expression ( Fig 7A and 7B ) . KLRG1 has been shown to be expressed on terminally differentiated CD8+ and CD4+ T cells that are able to produce cytokines upon antigen stimulation but have poor proliferation in viral and Mtb infection models [48 , 49] . PD-1 has been shown to downregulate immune responses through the induction of apoptosis and is often used to identify activated or exhausted T cells . Mtb-infected Qa-1-/- mice also had a larger number of T cells expressing FasL and CTLA-4 compared to Qa-1+/+ mice ( Fig 7C and 7D ) . FasL is both a marker for activated T cells as well as an important ligand for induction of activation induced cell death ( AICD ) . Similarly , CTLA-4 is expressed on activated conventional T cells , but also provides an inhibitory signal for T cell function when bound by CD80 or CD86 . In combination with the increased expression of inhibitory and apoptotic cell surface markers , both CD4+ and CD8+ T cells from Mtb-infected Qa-1-/- mice had a higher frequency of Annexin V+ cells in the mediastinal lymph node , indicating increased cell death ( Fig 7E ) . Lastly , we examined T cell-mediated macrophage killing of Mtb . Purified T cells from Mtb-infected Qa-1+/+ and Qa-1-/- mice were co-cultured with either Qa-1+/+ or Qa-1-/- bone marrow derived macrophages ( BMDM ) that were in vitro infected with Mtb . We found that T cells from Qa1-/- mice cultured with Qa-1-/- macrophages were the least effective in promoting BMDM to kill the intracellular Mtb ( Fig 7F ) . While Qa-1+/+ T cells cultured with Qa-1+/+ BMDM resulted in efficient bacterial clearance , Qa-1+/+ T cells cultured with Qa-1-/- BMDM were not as efficient at promoting bacterial killing . This indicates that the absence of Qa-1 expression on antigen presenting cells can cause dysregulation of immune function even in an in vitro setting . Taken together , our data demonstrates that aberrant activation of T cells from Qa-1-/- mice in turn leads to increased T cell death and an inferior ability of these T cells to mediate control of bacterial burden , which is in line with our mortality and in vivo bacterial burden experiments ( Fig 4 ) . Qa-1 has been shown to modulate immune responses through two different mechanisms: the activation of Qa-1-restricted CD8+ Treg cells , or the interaction of Qa-1/Qdm with inhibitory CD94/NKG2A receptors [24] . Thus , the dysregulated immune response in Mtb-infected Qa-1-/- mice was either due to a lack of CD8+ Treg suppression of T cell responses or a lack of Qa-1 binding to inhibitory CD94/NKG2A receptors on activated T cells . Unlike CD4+ T regulatory cells , CD8+ Tregs cannot be defined by any one set of cell surface markers or transcription factors . Recent studies from TB patient samples have identified suppressive CD8+ T cells expressing CD25 and FoxP3 [50 , 51] , among other markers . In addition , Qa-1-restricted CD8+ Tregs have been found to be enriched among CD44hi CD122+ Ly49+ CD8+ T cells in primarily autoimmune disease settings [52 , 53] . We used the markers from these human and mouse studies to potentially identify CD8+ Tregs in Mtb-infected mice . However , we found no differences in the number of CD25+ FoxP3+ CD8+ T cells or CD44+ CD122+ Ly49+ CD8+ T cells in infected Qa-1+/+ compared to Qa-1-/- mice ( S7 Fig ) , indicating that these markers may not be applicable in identifying Qa-1-restricted CD8+ Tregs in Mtb infection . To determine which mechanism was responsible for Qa-1-mediated immune regulation during Mtb infection , we instead turned to genetically modified Qa-1 mutant knock-in mice: Qa-1 R72A and Qa-1 D227K . The Qa-1 R72A mutation disrupts binding to NKG2A receptors , removing the inhibitory interaction between Qa-1/Qdm and CD94/NKG2A receptors but maintaining the ability to activate CD8+ Tregs [54] . The D227K mutation disrupts the interaction of Qa-1 with the CD8 co-receptor , resulting in mice that have no Qa-1-restricted CD8+ Tregs , but do have a functional interaction between Qa-1/Qdm and CD94/NKG2A [55] . Analysis of Mtb-infected Qa-1+/+ , Qa-1-/- , Qa-1 R72A , and Qa-1 D227K mice showed that Qa-1 R72A and D227K mutants have an increased frequency of IFN-γ-producing cells compared to Qa-1+/+ mice ( Fig 8A and 8B ) . In fact , no differences in the frequency of IFN-γ- producing cells were found among Qa-1-/- , R72A , and D227K mice . In addition , the frequency of NKG2A expression was increased on CD8+ T cells from Qa-1-/- , R72A , and D227K mice , as compared to Qa-1+/+ mice ( Fig 8C and 8D ) . Lastly , the frequency of activated CD44hi CD62Llo T effector cells was also increased in Qa-1-/- , R72A , and D227K mice as compared to Qa-1+/+ mice ( Fig 8E ) . These data show that T cells from both Qa-1 knock-in mutants had a phenotype similar to that of Qa-1-/- mice , indicating that both CD8+ Treg cells and inhibitory CD94/NKG2A receptors are involved in regulating immune responses during Mtb infection . Interestingly , NKG2A expression on NK cells in Qa-1 D227K mice was comparable to that of Qa-1+/+ NK cells , but significantly decreased from that of Qa-1-/- and R72A ( Fig 8C and 8D ) . The differential regulation of NKG2A expression on CD8+ T cells and NK cells observed in Qa-1 R72A and Qa-1 D227K mice suggests that the interaction of Qa-1 , NKG2A and CD8/TCR play a direct role in regulating NKG2A expression during Mtb infection .
Qa-1 has been implicated in the immune response in many different disease models , including intracellular bacterial infections , viral infections , and autoimmune diseases . Depending on the disease model , Qa-1 can function as an antigen presentation or immune regulation molecule . Similar to HLA-E , we found that Qa-1 can bind and present Mtb peptide antigens to CD8+ T cells during aerosol Mtb infection , resulting peptide-specific IFN-γ production and cytotoxicity to antigen presenting cells . In addition , our study demonstrates that Qa-1 provides protection against Mtb infection by restraining T cell immune responses , preventing T cell over-activation and death , leading to more effective bacterial clearance . Nonclassical MHC Ib molecules HLA-E and Qa-1 may be well suited to Mtb peptide vaccine development due to their low polymorphic character . In particular , HLA-E expression has been found to be enriched in phagosomes containing Mtb , which may facilitate loading of Mtb peptides onto HLA-E in infected cells [56] . Unlike MHC Ia molecules , HLA-E surface expression is not downregulated on HIV-infected human cells [57] , likely as a pathogen response to evade NK cell activity . This adaptation could be exploited by HLA-E-restricted CD8+ T cells , considering the high incidence of TB and HIV coinfection . Our study detected Qa-1-restricted CD8+ T effector cells specific to Mtb peptides P55 and P68 from Mtb-infected mice . As these two peptides were also among the most consistently detected HLA-E-restricted responses in human studies [8 , 49] , our findings indicate they may be promising targets for future vaccination studies . We attempted to immunize naïve mice with P55 and P68 peptide-pulsed dendritic cells to increase the frequency of these Mtb-specific T cells , but were unable to generate CD8+ T cells with consistent peptide-specific IFN-γ production upon restimulation from immunized mice . One issue for peptide immunization may be that HLA-E- and Qa-1-peptide complexes are significantly less stable than MHC Ia-peptide complexes at physiologic temperature , with rapid turnover of Qa-1 surface expression even when bound to Qdm [58] . Improved alternative immunization methods , such as the use of modified viral vectors to preferentially induce HLA-E-restricted CD8+ T cell responses [59] , may be needed to optimize both initial presentation of peptide and subsequent detection of peptide-specific responses . Although the role of CD4+ T regulatory cells in the context of modulating immune responses to pathogens has been well studied , CD8+ Tregs have not , particularly in Mtb infection . One reason for the relative lack of insight into CD8+ Treg function during infectious disease is because unlike CD25+ FoxP3+ CD4+ T regulatory cells , there are no universal markers for the identification of CD8+ Tregs . While a few studies have identified cell surface markers that enrich for CD8+ Treg populations from TB patient samples based on suppressive activity [50 , 51] , it is unclear how specific these markers are across different disease models , particularly for mouse models of infection . The best-known subpopulation of suppressive CD8+ T cells are the mouse Qa-1-restricted CD8+ Tregs . Qa-1-restricted CD8+ Tregs have primarily been studied in the context of autoimmune diseases such as EAE and systemic lupus erythematosus , where these CD8+ Tregs suppress autoreactive CD4+ T cells in an antigen-specific manner and protect against disease progression [24 , 41] . Knowledge of the restriction element of this major subset of CD8+ Tregs allowed for genetic depletion of these suppressive cells without needing to specifically identify them by surface phenotype . We were thus able to use mice deficient in either Qa-1-restricted CD8+ Tregs ( Qa-1 D227K mutant ) or the inhibitory NKG2A interaction ( Qa-1 R72A mutant ) to tease apart the regulatory contributions for both . Our data showed that , similar to Qa1-/- mice , T cells from both R72A and D227K mutants were more activated and had increased IFN-γ production compared to wild-type mice , suggesting both mechanisms are involved in regulating immune responses in Mtb infection . It is important to note that while Qa-1 D227K mutant mice are deficient in Qa-1-restricted CD8+ Tregs , they may also have impaired Qa-1-restricted CD8+ T effector cells . Further experimentation is needed to determine the total contribution of these CD8+ T effector cells to the anti-mycobacterial immune response compared to suppressive CD8+ T cells . Although our study is among the first to report participation of inhibitory NK receptors on T cells in the modulation of anti-Mtb immune responses [60 , 61] , inhibitory CD94/NKG2A has been implicated in a number of anti-viral T cell response studies . Blockade or deletion of NKG2A resulted in increased inflammatory cytokine production , inflammatory cell infiltrate at sites of infection , and tissue injury in influenza and adenovirus infection models [33 , 34] . Further , CD94/NKG2A expression is regulated by TCR engagement and cytokines , with IL-6 , IL-10 , and IL-21 upregulating expression and IL-4 , IL-23 , and IL-2 downregulating expression [62] . However , whether the overall effect of NKG2A-mediated immunoregulation results in decreased T cell function or more efficient response against pathogens appears to be related to the particular model being studied . While NKG2A engagement restricted CD8+ T cell cytotoxic ability against polyoma virus , potentially leading to more virus-induced tumors [35] , NKG2A prevented over-activation and apoptosis of poxvirus-specific CD8+ T cells , preserving their ability to respond to the pathogen [36] . In our model , the increased expression of inhibitory CD94 and NKG2A on T cells , combined with increased IL-10 production in Qa-1-/- mice are signs of an aberrant immune response that needs to be downregulated . As Qa-1-/- mice do not express the Qa-1/Qdm complex , however , they are unable to initiate the CD94/NKG2A regulatory signaling cascade and the T cells remain over-activated . Increased T cell activation has recently been shown to be an indicator of risk of TB progression in humans [44] . It is likely that the persistent over-activation of these T cells in our mouse model in turn leads to increased expression of other inhibitory and apoptosis-associated cell surface markers , increased cell death , and the inability of these T cells to control bacterial burden . Using Qa-1-deficient mice , our study is the first to describe the overall contribution of the Qa-1 molecule to the immune response against Mtb . We found that while Qa-1 is able to present Mtb peptides in infected Qa-1-sufficient mice , Qa-1-/- mice are less able to protect against Mtb infection compared to Qa-1+/+ littermates , due to aberrant activation and function of CD4+ and CD8+ T lymphocytes . Using genetically manipulated mouse models , we demonstrated that the Qa-1-mediated regulation of CD4+ and CD8+ T cells in Mtb infection is achieved through the interaction with inhibitory NK receptors on activated T cells and the presence of Qa-1-restricted CD8+ Treg cells . Given Qa-1’s antigen presentation and regulatory roles , it is possible that Mtb-specific Qa-1-restricted CD8+ T effector cells could have both cytolytic and regulatory functions . Indeed , a number of Mtb-specific HLA-E-restricted CD8+ T cell clones and polyclonal CD8+ T cells showed both cytolytic and suppressive capacity [8 , 49] . In the future , it would be of interest to determine how CD8+ Tregs and activating/inhibitory NK receptors participate in the human anti-mycobacterial immune response . We anticipate that our study will inform these future murine and human Qa-1/HLA-E studies , leading to a better understanding of protective immune responses against Mtb .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Animal Care and Use Committee of the Northwestern University ( Protocol number: IS00000985 ) . Kb-/-Db-/- on the B6 background were provided by Dr . James Forman ( UT Southwestern Medical Center , Dallas , TX ) and maintained in house . Qa-1-/- , Qa-1 R72A , and Qa-1 D227K mutant mice mice were provided by Dr . Harvey Cantor ( Dana-Farber Cancer Institute , Boston , MA ) . Qa-1-/- mice were crossed with C57BL/6 from Jackson Laboratories ( Bar Harbor , ME ) to generate Qa-1+/- , Qa-1-/- , and Qa-1+/+ littermates [41] . All mice were housed in a specific pathogen free environment at Northwestern University . Mtb H37Rv whole cell lysate was obtained through BEI Resources . Mtb peptides were synthesized by Peptide 2 . 0 ( Chantilly , VA ) . Mtb antigens were dissolved in either DMSO or PBS and stored as aliquots at -20°C . Qa-1 transfected HeLa cells were a gift from Dr . James Forman ( UT Southwestern Medical Center , Dallas , TX ) were incubated with 0 . 5 μM biotinylated Qdm ( bio-Qdm ) and either 5 μM or 10 μM of unbiotinylated competing Mtb or control peptide for 1 . 5 hours at 4°C . Cells were then washed with HBSS/2% FBS , stained with streptavidin-APC ( BioLegend , San Diego , CA ) , and flow cytometry was performed to determine the inhibition of bio-Qdm binding by competing peptide . Frozen aliquots of Mtb H37Rv were thawed and diluted in PBS with 0 . 05% Tween 80 . Mice were infected with either low-dose ( 100–200 CFU ) or high-dose ( ~1000 CFU ) of Mtb using a nose-only aerosol exposure chamber ( In-Tox Products , NM ) , equipped with a miniHEART nebulizer ( WestMed , Tucson , AZ ) , as previously described [11] . A day 1 CFU count was performed to determine the infecting dose . At indicated time-points after infection , bacterial loads in the lungs and spleens were determined by plating serial dilutions of tissue homogenate on Middlebrook 7H11 agar plates ( BD , Franklin Lakes , NJ ) , and colonies were counted after 2–3 weeks of incubation at 37°C . Single-cell suspensions were prepared from the lung , spleen and mediastinal lymph nodes by mechanical disruption in HBSS/2% FBS , followed by culture in complete RPMI media . Lung was digested with collagenase IV ( 1 mg/ml ) and DNase I ( 30 μg/ml ) ( Sigma , St . Louis , MO ) for 30 min . at 37°C prior to mechanical disruption . For ELISpot assays , CD8+ T cells were enriched using negative selection with biotinylated mAb specific to CD4 ( GK1 . 5 ) , CD11b ( M1/70 ) , and B220 ( RA36B2 ) ( BioLegend , San Diego , CA ) followed by streptavidin-conjugated magnetic beads ( Invitrogen , Carlsbad , CA ) . For RNA extraction , CD8+ and CD4+ T cells were isolated using positive selection with biotinylated mAbs specific to CD8β ( YTS156 . 7 . 7 ) , followed by streptavidin-conjugated magnetic beads ( Miltenyi Biotech , San Diego , CA ) or CD4 Microbeads directly ( Miltenyi Biotech , San Diego , CA ) , followed by purification via magnetic column ( Miltenyi Biotech ) . The purity and composition of enriched T cells were confirmed by flow cytometry . Bone marrow-derived dendritic cells ( BMDCs ) were derived from mouse bone marrow progenitors using GM-CSF and IL-4 ( PeproTech , Rocky Hill , NJ ) as previously described [11] . IFN-γ ELISpot assay was performed as previously described [11] , with some modifications . Briefly , multiscreen-IP plates ( Millipore , Bedford , MA ) were coated with anti-IFN-γ mAb ( AN-18 , BioLegend , San Diego , CA ) at 5μg/ml in PBS . Enriched CD8+ T cells from infected mice were incubated with MHC II-/- BMDCs with media alone or 5μM of Mtb peptide , in duplicate . To confirm Qa-1 restriction , BMDCs were pre-incubated with 2 μg/ml anti-Qa-1 blocking mAb ( 6A8 . 6F10 . 1A6 , BD , Franklin Lakes , NJ ) or mouse IgG1 isotype control ( clone MOPC-21 ) ( BioXCell , West Lebanon , NH ) prior to adding peptide and lymphocytes . After 18h incubation at 37°C , plates were washed using PBS/0 . 05% Tween 20 and developed using biotinylated α-IFN-γ mAb ( R4 . 6A2 , eBioscience , San Diego , CA ) , followed by streptavidin-conjugated alkaline phosphatase ( Jackson ImmunoResearch Laboratories , West Grove , PA ) and a BCIP/NBT substrate kit ( Bio-Rad , Hercules , CA ) according to the manufacturer’s instructions . Spots were counted using an ImmunoSpot reader ( Cellular Technology , Shaker Heights , OH ) . Cells were incubated with 2 . 4G2 Fcγ RII/RIII blocking mAb for 15 min , then stained with appropriate combinations of mAbs in HBSS/2% FBS for 30 min at 4°C . Monoclonal antibodies against mouse Qa-1 ( 6A8 . 6F10 . 1A6 ) , TCRβ ( H57-597 ) , NK1 . 1 ( PK136 ) , CD4 ( GK1 . 5 ) , CD8β ( YTS156 . 7 . 7 ) , CD11c ( N418 ) , CD11b ( M1/70 ) , F4/80 ( BM8 ) , Ly6G ( 1A8 ) , CD44 ( 1M7 ) , CD62L ( MEL14 ) , PD-1 ( 29F . 1A12 ) , KLRG1 ( 2F1/KLRG1 ) , B220 ( RA36B2 ) , CD94 ( 18d3 ) , NKG2A ( 16A11 ) , NKG2ACE ( 20d5 ) , NKG2D ( C7 ) , Ly49D ( 4E5 ) , FasL ( MFL3 ) , CTLA-4 ( UC10-4B9 ) , FOXP3 ( 150D ) , CD25 ( PC61 ) , CD122 ( TM-β1 ) , and Ly49C/F/I/H ( 14B11 ) with different fluorochrome conjugates were purchased either from BioLegend , eBioscience , or BD Bioscience ( San Diego , CA ) . Transcription factor staining was performed using the FOXP3/Transcription Factor Staining Set ( eBioscience , San Diego , CA ) according to manufacturer’s directions . Annexin V staining was performed after lymphocytes were incubated for 5 hours in culture media at 37°C . Annexin V staining was done in Annexin V Binding Buffer ( BioLegend , San Diego , CA ) according to manufacturer’s directions . Flow cytometry was performed with a FACS CantoII ( BD Biosciences , San Jose , CA ) and analyzed using FlowJo software ( Tree Star , Ashland , OR ) . Lymphocytes from the spleen and lung of infected mice were stimulated with unpulsed or Mtb whole cell lysate-pulsed ( 10 μg/mL ) BMDCs . After two hours of incubation , 5μg/ml Brefeldin A ( Sigma , St . Louis , MO ) was added and cells were cultured for an additional 16 hours . Cells were then stained for cell surface markers , fixed with 4% paraformaldehyde , permeabilized with 0 . 2% saponin , and then stained with APC-conjugated α-IFN-γ ( BioLegend , San Diego , CA ) . Flow cytometry was performed as described . Lymphocytes from Mtb-infected mice were incubated with Mtb whole cell lysate ( 10 μg/mL ) for 40 hours at 37°C . Supernatants were harvested and the cytokines IL-17A , IFN-γ , TNF-α , IL-4 , IL-13 and IL-10 were detected using individual mouse cytokine Cytometric Bead Array ( CBA ) flex sets ( BD , San Jose , CA ) , per manufacturer’s directions . Flow cytometry was performed as described . T cells from the spleens of Mtb-infected mice were purified via negative selection , using biotinylated antibodies toward B220 and CD11b and Dynabeads ( Invitrogen ) . Bone marrow-derived macrophages ( BMDM ) were derived from mouse bone marrow progenitors grown in 20% L929-conditioned cell culture media for 7 days , then in vitro infected with Mtb at MOI 1 . 2x105 Purified effector T cells were incubated with 1x105 infected BMDC in antibiotic free media supplemented with 20% L929-conditioned media for 6 days . On day 6 , cells were lysed and the supernatant plated on Middlebrook 7H11 agar plates ( BD , Franklin Lakes , NJ ) for CFU determination . Figures are shown with mean ± SEM . When comparing experimental values from two groups of mice , two-tailed Student's t-tests were used . When comparing experimental values from multiple groups , one-way ANOVA was used . Mortality statistics were calculated using the Log-Rank test . Statistically significant differences are noted ( ***P < 0 . 001; **P < 0 . 01; *P < 0 . 05 ) . Statistical analysis was performed with GraphPad Prism software ( GraphPad , La Jolla , CA ) . | The disease tuberculosis ( TB ) is caused by the microbe Mycobacterium tuberculosis ( Mtb ) , and remains a major public health concern . More research is needed to understand the diverse immune responses against Mtb to develop better vaccines . Mouse Qa-1 and its human counterpart HLA-E are nonclassical MHC I molecules that can activate or inhibit immune responses in a variety of diseases . However , their role during the immune response to Mtb remains unknown . We found that Qa-1 can present Mtb peptides to activate CD8+ T effector cells during aerosol Mtb infection . Further , Mtb-infected mice that lacked Qa-1 ( Qa-1-/- ) had higher numbers of bacteria and died more often than infected mice that expressed Qa-1 ( Qa-1+/+ ) . The lack of Qa-1 results in over-activation of the immune response upon infection , which is less efficient in controlling Mtb . Using mice expressing different mutant forms of Qa-1 , we showed that Qa-1 can regulate immune responses against Mtb through the interaction with inhibitory CD94/NKG2A receptors as well as the activation of regulatory CD8+ T cells . We believe our study sheds light on the diverse mechanisms at play in generating protective immune responses against Mtb and will inform future mouse and human studies . | [
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| 2017 | MHC Ib molecule Qa-1 presents Mycobacterium tuberculosis peptide antigens to CD8+ T cells and contributes to protection against infection |
The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data . The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects . WikiPathways , an online collaborative pathway resource , is now available in the semantic web through a SPARQL endpoint at http://sparql . wikipathways . org . Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries . In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic , respectively . Each gene , protein , and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web . WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API ( https://dev . openphacts . org/docs ) to be used in various tools for drug development . We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources , such as the Open PHACTS API . The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web .
Pathway analysis and visualisation of data on pathways provide insights into the underlying biology of effects found in genomics , proteomics , and metabolomics experiments [1–4] . WikiPathways is a pathway repository where content is provided by the community at large [5 , 6] . In a given pathway , elements like genes , proteins , metabolites , and interactions are identified using common accession numbers from reference databases such as Entrez Gene [7] , Ensembl [8] , UniProt [9] , HMDB [10] , ChemSpider [11] , PubChem [12] and ChEMBL [13] . Multiple databases can be referenced to annotate an element of the same semantic type , e . g . Ensembl and Entrez Gene to annotate gene information . Even single studies sometimes use different reference databases to annotate experimental findings . It is common for bioinformaticians to spend valuable time dealing with data mapping issues that impede the actual data analysis and interpretation . In WikiPathways we use the open source software framework BridgeDb [14] , to help resolve different identifiers representing the same ( or related ) entities . Capturing a semantically correct description of biological entities and their connections across datasets is the broader challenge that we have to address . The semantic web provides an approach to define entities and their relationships . By explicitly defining these entities and relationships the semantic web can provide a network of linked data [15] . The Resource Description Framework ( RDF ) consists of two key components: statements and universal identifiers . Each statement is captured as a triple , consisting of a subject , a predicate , and an object . For example , the following triple defines the glucose molecule as being part of the glycolysis pathway: < Glycolysis > ︸ subject < Has member > ︸ predicate < Glucose > ︸ object The notion of a semantic web surfaces as you link across large sets of triples representing a vast number of objects and diverse types of concepts and predicates . The use of uniform identifiers , or URIs [16] , provides consistency when specifying subjects and objects . identifiers . org [17] , for example , provides a clearinghouse for a wide variety of URIs for biological entities in the life science domain . WikiPathways provides identifiers for all its pathways and identifiers . org provides the URI scheme to make these resolvable . Standardized URIs for predicates come from efforts such as the Simple Knowledge Organization System ( SKOS ) [18] . For example , our example triple above can be expressed in a more universal way as: < http : / / identifiers . org / wikipathways / WP 534 > ︸ subject < http : / / www . w 3 . org / 2004 / 02 / skos / core # member > ︸ predicate < http : / / identifiers . org / chebi / CHEBI : 4167 > ︸ object where each element is uniquely and universally resolvable to a defined concept ( glycolysis , “has member” , and glucose respectively ) . Of course , the more human readable information can also be explicitly added by describing the labels in RDF . But that information is also available by resolving the URIs . In order to contribute pathway knowledge to the semantic web , we have modeled the content of WikiPathways to form triple-based statements . The interactions and reactions curated at WikiPathways are particularly well-suited to enrich the overall connectivity of the semantic web . Pathways offer a meaningful context for relations between biological entities , such as proteins , metabolites and diseases that are otherwise defined in disparate databases . We report on the conversion process and the development of two new vocabularies essential in capturing the semantics behind pathway diagrams . Finally , we evaluate the use of the semantically linked pathway knowledge through specialized queries and third-party resources , showing how to link WikiPathways with disease annotations ( from UniProt [9] and DisGeNET [19] ) , with gene-expression values ( from Gene Express Atlas ) and with bioactive chemical compounds known to affect proteins that occur in pathways ( e . g . from ChEMBL ) .
There are existing standards to model various aspects of pathway knowledge , such as BioPAX [20] , SBGN [21] , MIM [22] , SBML [23] and SBO [24] . BioPAX and SBO are in fact already available in a Semantic Web-compatible language called OWL [25] . These standards provide valuable building blocks for our “WP” vocabulary that captures the biological meaning of pathways . However , not all of the graphical annotations , spatial information and other subtleties critical for the visual representation , the intuitive understanding and the usability for data visualisation of the curated content at WikiPathways are captured by these standards . Our “GPML” vocabulary directly reflects these features defined in the XML format , GPML , or Graphical Pathway Markup Language . For example , in GPML , all genes , proteins and metabolites are types of data nodes , which are rendered as a rectangular box with properties capturing among others its position , height , width , label , and external reference . For example: <DataNode TextLabel = “Glucose” GraphId = “dba83” Type = “Metabolite”> <Graphics CenterX = “279 . 0” CenterY = “468 . 0” Width = “112 . 0” Height = “20 . 0” ZOrder = “32768”> <Xref Database = “ChEBI” ID = “CHEBI:4167” /> </DataNode> In the GPML vocabulary , used for semantic representation of pathway diagrams , the markup elements and values are described as classes and properties , each with their respective URIs . <http://identifiers . org/chebi/CHEBI:4167> rdf:type gpml:DataNode . <http://identifiers . org/chebi/CHEBI:4167> rdfs:label “Glucose”@en . <http://identifiers . org/chebi/CHEBI:4167> gpml:graphId “dba83” . <http://identifiers . org/chebi/CHEBI:4167> gpml:ZOrder 32768 . … The GPML vocabulary , in its current form , is mainly instrumental in the representation of the spatial information captured at WikiPathways . However , as we will describe below it can also be used to convert pathway information from other semantic web resources into a format amenable to being rendered and curated at WikiPathways . Explicit mappings to external ( graphical ) ontologies are not added , however through plugins such as Pathvisio-MIM [26] mappings to graphical notations such as MIM or SBGN , are possible . In an analogous way , the WP vocabulary can be used to capture the biological relations from other pathways in such a way that they can be used in resources using this semantic layer of the WikiPathways RDF . We used this approach for example to make the relations from Reactome pathways available in the Open PHACTS discovery platform [27] starting from the converted pathways at WikiPathways . The WP vocabulary , focusing on biological meaning , issues URIs for biological concepts and disregards layout and other rendering details . Using URIs from this vocabulary allows stating that something is a Pathway , or that a DataNode is a chemical compound or gene product . The vocabulary also captures descriptive elements , such as labels , shapes and lines that help annotate and contextualize the pathway reaction details . The RDF generated consist of terms from the vocabularies developed in this context . This is done to be able to reflect the semantics used in the WikiPathways community . However , to allow integration with external pathway resources—which is the primary objective of this project—we need to link to external ontologies . For the subset of concepts in common with prior vocabularies , such as BioPAX , we utilize the SKOS data model to express a range of similarities from skos:exactMatch to skos:closeMatch [18 , 28] . With these vocabularies in place , the next step is the actual conversion of GPML files into triples using the GPML vocabulary . Then rules are applied to make the biological meaning explicit using the WP vocabulary . For example a directed interaction is captured in GPML as two “DataNodes” , a line and an arrowhead . The “DataNodes” have external references as properties . Rules are then applied to state that a line is a Directed Interaction , with a source and a target . Fig 1 contains an example of such a rule based reasoning query that issues triples with URIs from the WP vocabulary . WikiPathways pathways are regularly curated by a team of volunteers that evaluate their usability for analysis and tag the pathways as “curated” . WikiPathways contains 1000 pathways in the curated set across over a dozen species that convert to a total of 1 . 6 million triples . The triples are loaded in a SPARQL endpoint ( http://sparql . wikipathways . org ) , which allows semantic querying of the data with the SPARQL query language [29] . RDF , including new and updated pathways , is generated and tested regularly and can be delivered upon request . Updates of the RDF that is available for download and in the SPARQL endpoint are triggered by crucial events , such as Reactome or Open PHACTS data releases . This prevents discrepancies in quality control or curation , due to small differences between ( frequent ) releases . Example SPARQL queries and their plain language translations are given in Table 1 . A broad set of ∼50 queries is available on the help pages of WikiPathways [30] . A federated SPARQL query [17] enables querying over multiple SPARQL endpoints . With a variety of SPARQL endpoints available with data on disease annotations ( e . g . DisGeNET and UniProt ) , significantly expressed genes ( e . g . EBI Expression Atlas ) and drug-target interactions ( e . g . ChEMBL ) , knowledge from these remote SPARQL endpoints can be integrated . Example queries are given in Table 2 and on the help pages of WikiPathways [30] Different common analysis platform allow the integration of linked data for future analysis and visualization . One nice example of such a analysis platform is R , a widely used software environment for statistical computing and graphics . R has a SPARQL library [31] , which enables the import of linked data for further processing in R . This allows running common statistical tests or the creation of different visualization of linked data . We recently published an R library that interfaces R with PathVisio [32] and allows manipulation of pathways and data visualisation on pathways . Fig 2 shows up and down regulated genes in Diabetes Mellitus ( efo:EFO_0000400 , efo:EFO_0001359 , and efo:EFO_0001360 ) in the pathway diagram on insulin signaling in human [30] . This pathway diagram with color-coding parts indicating up- and down regulated pathway elements , was created by integrating knowledge from two geographically dispersed and independent resources , through a single SPARQL query embedded in a R script , which is available online [33] . A number of resources provide content from multiple pathway databases , including Pathway Commons [34] and NCBIs BioSystems ( http://ncbi . org/biosystems ) . While BioPAX in fact is RDF , the NCBI system is not . NCBI BioSystems uses NCBIs native identifiers: GeneId , ProteinId , CID . We thus have a resource with pathways from different origins that are already described in the same way . Since for WikiPathways content we know how the different entities in these resources map to the GPML and WP vocabularies we can now use that to produce RDF using these same ontologies for each of the other pathway resources present in NCBI BioSystems . In fact , we can do the same for Pathway Commons where this approach will lead to an improved version of RDF with explicit mappings to the WP vocabulary . We made a prototype script available on GitHub to be used for this type of conversions from BioSystems [35] . The semantically linked pathway data from WikiPathways RDF have also been integrated into the Open PHACTS discovery platform [27 , 36] . Open PHACTS delivers and sustains an open pharmacological space using semantic web standards and technologies . The Open PHACTS platform currently provide 51 API methods of which thirteen deliver pathway information ( https://dev . openphacts . org/docs ) . Other information collected in Open PHACTS describes other relationships like drug-target ( from ChEMBL ) and protein interaction ( from UniProt ) . Having this all in one resource combined with a set of mapping tools allows fast analysis across the domains . By combining Open PHACTS API calls one can , for instance , find all protein targets for a drug and then all pathways that contain these targets .
In collaboration with partners in the Open PHACTS project , we proposed guidelines for presenting data as RDF [37] , most of that can be considered as general guidelines to produce RDF in the biomedical domain . The guidelines consist of a prerequisite and 11 steps , covering the licensing ( step 0 ) , designing ( step 1–5 ) , implementation ( steps 6–9 ) , and presentation ( steps 10–11 ) of the data in the semantic web . In the work presented here we follow these steps: In the context of the semantic web , it is impractical to burden query writers with handling identifier mapping per resource and per query . Rather , the mapping results themselves need to become part of the semantic web . We applied two distinct approaches to addressing identifier mapping in our WikiPathways and Open PHACTS projects . We present a semantic web representation of WikiPathways together with vocabularies needed to cover the graphical pathway layout and the biological meaning and solutions to map between different identifier systems . The public availability allows rapid integration with other biological resources . The availability of two vocabularies allows to convert between different pathways resources . Different analytical tools now support the import of semantic web data , allowing integrated use of data from different resources with a single query . We demonstrate this with a federated query across multiple resources where the resulting differentially expressed genes for a disease where shown on a discovered pathway using PathVisio . The following resources are publically available as beta releases just like WikiPathways . They are maintained as part of the open source WikiPathways project | WikiPathways is a crowd-sourced online platform for biological pathways . It is based on the same underlying platform as Wikipedia . Pathways are saved as graphical images embedded in a set of meta data elements ( i . e . references , list of pathways elements , and context annotations ) . Pathways are used as proxies of biological knowledge in their role as descriptors of processes . Yet integrating these hubs of biological knowledge with other biological data resources remains challenging due to a cacophony of file formats , identifier systems , and hidden content . We show the application of the semantic web to enable a straightforward integration of heterogeneous biological data sources . We have taken high-quality pathways from a curated set from WikiPathways and converted the content into a data format native to the semantic web . Here , data is expressed as a set of statements where the statements are built upon a set of web addresses . Given the results , we successfully integrated external resources ( e . g . , EBI Expression Atlas ) and pathway content with a single query . | [
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| 2016 | Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources |
African swine fever ( ASF ) is caused by a large and highly pathogenic DNA virus , African swine fever virus ( ASFV ) , which provokes severe economic losses and expansion threats . Presently , no specific protection or vaccine against ASF is available , despite the high hazard that the continued occurrence of the disease in sub-Saharan Africa , the recent outbreak in the Caucasus in 2007 , and the potential dissemination to neighboring countries , represents . Although virus entry is a remarkable target for the development of protection tools , knowledge of the ASFV entry mechanism is still very limited . Whereas early studies have proposed that the virus enters cells through receptor-mediated endocytosis , the specific mechanism used by ASFV remains uncertain . Here we used the ASFV virulent isolate Ba71 , adapted to grow in Vero cells ( Ba71V ) , and the virulent strain E70 to demonstrate that entry and internalization of ASFV includes most of the features of macropinocytosis . By a combination of optical and electron microscopy , we show that the virus causes cytoplasm membrane perturbation , blebbing and ruffles . We have also found that internalization of the virions depends on actin reorganization , activity of Na+/H+ exchangers , and signaling events typical of the macropinocytic mechanism of endocytosis . The entry of virus into cells appears to directly stimulate dextran uptake , actin polarization and EGFR , PI3K-Akt , Pak1 and Rac1 activation . Inhibition of these key regulators of macropinocytosis , as well as treatment with the drug EIPA , results in a considerable decrease in ASFV entry and infection . In conclusion , this study identifies for the first time the whole pathway for ASFV entry , including the key cellular factors required for the uptake of the virus and the cell signaling involved .
ASFV is a 200 nm large DNA virus that infects different species of swine , causing acute and often fatal disease [1]–[3] . Infection by ASFV is characterized by the absence of a neutralizing immune response , which has so far hampered the development of a conventional vaccine . A strong hazard of ASFV dissemination through EU countries from Caucasian areas has recently emerged , thus making progress of knowledge and tools for protection against this virus urgent . Analysis of the complete DNA sequence of the 170-kb genome of the Ba71V isolate , adapted to grow in Vero cells , has revealed the existence of 151 genes , a number of enzymes with functions related to DNA replication , gene transcription and protein modifications , as well as several genes able to modulate virus-host interaction [4]–[12] . ASFV replicates within the host cell cytosol , although a nuclear step has been reported [13] , [14] . Discrete cytoplasmic areas are reorganized into viral replication sites , known as factories , during the productive virus cycle . Regarding this , we have recently described ASFV replication as fully dependent on the cellular translational machinery since it is used by the virus to synthesize viral proteins . Thus , during infection , factors belonging to the eukaryotic translational initiation complex eIF4F are phosphorylated , and then redistributed to the periphery of the ASFV factory . Furthermore , ASFV late mRNAs , ribosomes and mitochondrial network were also located in these areas [15] . Such phosphorylation events and redistribution movements suggest , first , a reorganization of the actin skeleton induced by ASFV , and second , virus-dependent kinases activation mechanisms . Several other critical steps of the infection , probably including virus entry and trafficking , might be also regulated by phosphorylation of key molecules targeted by the virus . As the first step of replication , entry into the host cell is a prominent target for impairing ASFV infection and for potential vaccine development . Endocytosis is a major pathway of pathogen uptake into eukaryotic cells [16] . Clathrin-mediated endocytosis is one of the best studied receptor-dependent pathways , characterized by the formation of clathrin coated pits of 85–110 nm in diameter that bud into the cytoplasm to form clathrin-coated vesicles . Relatively low size viruses , as Vesicular stomatitis virus , Influenza virus , and Semliki forest virus all enter their host cells using this mechanism [17]–[19] . On the other hand , the caveolae-mediated pathway is dependent on small vesicles termed caveolae ( 50–80 nm ) enriched in caveolin , cholesterol , and sphingolipid . It has been implicated in the entry of other small viruses such as Simian virus 40 [20] . Macropinocytosis is another important type of endocytic route used by several viruses to enter host cells . It is defined as an actin-dependent endocytic process associated with a vigorous plasma membrane activity in the form of ruffles or blebs induced by activation of kinases and Rho GTPases . This pathway involves receptor- independent internalization of fluid or solutes into large uncoated vesicles sized between 0 . 5–10 µm called macropinosomes [21] , [22] . In recent years , it has been reported that macropinocytosis is responsible for virus entry of Vaccinia virus ( VV ) [23] , [24] , Coxsackievirus [25] , Adenovirus-3 [26] , Herpes simplex virus [27]–[29] , and is required for other viruses to promote viral internalization after entry by some different endocytic mechanism [30]–[32] . Regarding ASFV entry , preliminary studies were reported many years ago by our lab describing this process as temperature , energy , cholesterol and low pH-dependent , and also showing that ASFV strain Ba71V enters Vero cells by receptor-mediated endocytosis [33]–[37] . However , the cellular molecules involved and the precise mechanisms for ASFV entry remain largely unknown . A recent paper [38] reported that ASFV uses dynamin and clathrin-dependent endocytosis to infect cells . However , it is noteworthy that this work employed the expression of ASFV early proteins as readout of virus entry , which is not equivalent to virus uptake , since several post-entry events could be involved in virus early protein expression . Hence , explanation of several controversial points , such as the larger size of ASFV ( 200 nm ) compared to the smaller size ( 50–80 nm ) of clathrin coated pits , or the existence of several other possible roles for dynamin in addition to virus entry [39] , are not discussed in that work . In the present work we have characterized the mechanisms of entry of ASFV-Ba71V and ASFV-E70 strains either in Vero or swine macrophages , as representative models for ASFV infection . By means of a combination of pharmacological inhibitors , specific dominant-negatives and confocal and electron microscopy , we show that ASFV is taken up predominantly by macropinocytosis . Therefore , we provide evidence , for the first time , that the ASFV entry requires sodium/proton exchanger ( Na+/H+ ) , activation of EGFR and PI3K , phosphorylation of Pak1 kinases together with activation of Rho-GTPase Rac1 and relies on actin-dependent blebbing/ruffling formation , all events fully linked with macropinocytosis activation .
Vero ( African green monkey kidney ) cells were obtained from the American Type Culture Collection ( ATCC ) and grown in Dulbecco's Modified Eagle's Medium ( DMEM ) supplemented with 5% fetal bovine serum ( Invitrogen Life Technologies ) . IPAM cells ( porcine macrophage-derived cell lines ) were kindly provided by Dr . Parkhouse ( Fundaçao Calouste Gulbenkian - Instituto Gulbenkian de Ciência , Oeiras , Portugal ) and grown in RPMI 1640 medium supplemented with 10% fetal bovine serum . Cells were grown at 37°C under a 7% CO2 atmosphere saturated with water vapour in a culture medium supplemented with 2 mM L-glutamine , 100 U/ml gentamicin and nonessential amino acids . The Vero-adapted ASFV strain Ba71V and isolate E70 were propagated and titrated by plaque assay on Vero cells , as described previously [40] , [41] . In brief , subconfluent Vero cells were cultivated in roller bottles and infected with ASFV at a multiplicity of infection ( MOI ) of 0 . 5 in DMEM 2% fetal bovine serum . After 72 h post infection the cells were recovered and centrifuged at 3000 rpm for 15 min and the cellular pellet was discarded . The supernatant containing viruses was clarified at 14000 rpm for 6 h at 4°C and the purified infectious virus was resuspended in medium and stored at −80°C . Vero cells were infected with Ba71V isolate and IPAM cells with E70 or Ba71V as indicated . The MOI used ranged from 1 to 3000 pfu/cell , as explained . Viral adsorption to cells was performed at 4°C ( synchronic infection ) or at 37°C ( asynchronic infection ) during 90 min ( or 60 min when indicated ) , followed by one wash with cold PBS , and a shift to 37°C to allow the infection until indicated times . Pharmacological inhibitors were prepared either in water or DMSO following the manufacturer's recommendation and used at the indicated concentration . 5- ethylisopropyl amiloride ( EIPA ) , Cytochalasin D ( Cyto D ) , Genistein , IPA-3 , Chlorpromazine ( CPZ ) , Dynasore ( Dyn ) and Nocodazole were purchased from Sigma . ±Blebbistatin , EGFR inhibitor ( 324674 ) and Rac1 inhibitor ( Rac1 Inh , NSC23766 ) was purchased from Calbiochem , and LY294002 ( LY ) from Echelon . Specific antibodies against Akt , phospho-Akt ( Thr308 ) , phospho-Akt ( Ser473 ) and PI3K p85 were purchased from Cell Signaling Technology; anti-Pak1 , anti- phospho-Pak1 ( Thr423 ) , anti-Rock1 and anti-β-actin from Santa Cruz Biotechnology , Inc . Rac1 was detected with a monoclonal antibody from Millipore , kindly provided by Dr . C . Murga ( CBMSO , Madrid , Spain ) . Monoclonal anti-p72 ( 17LD3 ) [42] was a kind gift from Ingenasa and polyclonal antibodies risen against p72 , p32 and most of the ASFV structural proteins ( anti-ASFV ) were generated in our laboratory . Alexa Fluor 594-WGA , TRITC- phalloidin , Alexa Fluor 488-phalloidin , Topro3 , anti-mouse Alexa Fluor-488 , anti-goat Alexa Fluor-555 and anti-mouse Alexa Fluor-555 were purchased from Invitrogen , and anti-rabbit , anti-mouse and anti-goat immunoglobulin G coupled to peroxidase from Amersham Biosciences . GFP-tagged versions of wild type forms of actin ( pEGFP-actin ) and Rac1 ( pEGFP-Rac1 ) were kindly provided by Dr . J . Mercer ( ETH Zurich , Institute of Biochemistry , Zurich , Switzerland ) and Rac1 mutant form ( pGFP-Rac1-N17 ) was a generous gift from Dr . R . Madrid ( CBMSO , Madrid , Spain ) . GFP-tagged versions of WT , AID , and T423E of Pak1 constructs were a gentle gift from Dr . J . Chernoff ( Fox Chase Cancer Center , Philadelphia , PA , USA ) and pEGFP-C2 was purchased from Invitrogen . To analyze ASFV uptake , Vero cells were pretreated with the pharmacological inhibitors listed above at 37°C for 60 min in serum free medium . Ba71V synchronic infection was carried out at a MOI of 10 pfu/cell in the presence of the drugs . After binding , cells were washed once with cold PBS , followed by the addition of containing drug medium , and infection was allowed to proceed for 60 min at 37°C . After infection , cells were fixed and prepared either for Fluorescence Activating Cell Sorting ( FACS ) or Confocal Laser Scanning Microscopy ( CLSM ) analysis . The specific effect of the drugs on virus entry and post entry steps was analyzed by incubation of the cells either 60 min before virus addition or 60 min after virus addition , and viral infection was allowed in the presence of the drugs at 37°C , in each case . Ba71V or E70 asynchronic infection was carried out for 16 or 48 h at a MOI of 1 pfu/cell or at a MOI of 5 pfu/cell to analyze viral proteins by Western blot or number of infected cells by CLSM , respectively . To analyze Akt phosphorylation upon ASFV infection , Vero cells were infected at a MOI of 10 pfu/cell and viral adsorption was allowed for 60 min at 37°C . Actin distribution analysis was performed at different times post infection since virus addition at 37°C at MOI 50 . Rac1 distribution and Pak1 phosphorylation was measured after synchronic infection at a MOI of 10 pfu/cell . At the indicated times , cells were prepared for Western blot or CLSM analysis . Vero cells were pretreated with DMSO or pharmacological inhibitors for 60 min at 37°C . The asynchronic infection was carried out at a MOI of 1 pfu/cell for 48 h in the presence of the inhibitors and the supernatant was recovered . The number of productive viral particles was titrated by plaque assays on Vero cells as described in [41] . Cells were grown on glass coverslips , serum starved for 24 h , infected synchronously ( MOI 50 ) and at the indicated times post infection , fixed in 2 . 5% glutaraldehyde and 4% paraformaldehyde in 0 . 1 M phosphate buffer ( pH 7 . 4 ) for 3 h at 4°C . They were washed three times in phosphate buffer , postfixed in 2% OsO4/water at RT for 60 min , washed in water , dehydrated in acetone , critical point dried for 2 h and coated with graphite-gold in a sputter coater . The samples were analyzed with a JSM-6335-F ( JEOL ) Field Emission SEM ( Electron Microscopy National Center , UCM; Madrid , Spain ) . Vero cells were serum starved 24 h and virus binding was allowed for 90 min at 4°C with Ba71V ( MOI 3000 ) . Cells were fixed with 2% glutaraldehyde and 4% paraformaldehyde in 0 . 1 M phosphate buffer ( pH 7 . 4 ) for 3 h at 4°C . Sections of infected cells were prepared as described [43] and analyzed in a JEOL 100B electron microscope . In order to study real-time live imaging of ruffles formation induced by ASFV infection , Vero cells were serum starved for 24 h and virus binding was allowed for 90 min at 4°C at MOI 100 . After binding , cells were washed with cold PBS and images were collected for 30 min with an Orca R2 digital camera ( Hamamatsu ) on a wide-field microscope ( LeicaDMI6000B , Leica Microsystems ) with controlled environmental chamber ( temperature 37°C and 5% CO2 humidified atmosphere ) . Images were captured with LAS AF version 2 . 6 . 0 software ( Leica Microsystems ) at a resolution of 1344×1024 pixels using a 20× , 0 . 40 NA objective with a 1 . 6× magnification-changer , and analyzed with Image J software . To analyze blebs formation , IPAM cells were infected synchronously ( MOI 50 ) and at different times post infection , fixed with paraformaldehyde 4% for 20 min . Images were taken with a ccd monochrome camera ( Hamamatsu ) on a invert microscope ( Axiovert200 , Zeiss ) using a 63× objective and analyzed with Image J program . Mock-infected or infected cells in the presence of pharmacological inhibitors were detached with trypsin-EDTA after 60 min post infection ( mpi ) , fixed with 2% paraformaldehyde for 30 min at 4°C and then permeabilized with PBS-Staining buffer ( PBS 1× , 0 . 01% sodium azide , 0 . 5% BSA ) 0 . 2% saponin for 15 min at RT . Detection of infected cells was performed by incubation with an anti-p72 monoclonal antibody ( 17LD3 ) ( diluted 1∶100 in PBS-Staining buffer 0 . 2% saponin ) for 20 min at 4°C , followed by incubation with an anti-mouse Alexa Fluor-488 ( diluted 1∶500 in PBS-Staining buffer , 0 . 2% saponin ) in the same conditions . Finally , 2×104 cells were analyzed in a FACSCalibur flow cytometer ( BD Science ) to determine the percentage of infected cells . All FACS analyses were performed at least in triplicate and displayed as the average percentage of infected cells relative to control infection in the absence of a pharmacological inhibitor . Error bars represent the standard deviation between experiments . Cells were grown on glass coverslips and , at indicated times post infection , were fixed with 4% paraformaldehyde for 20 min and permeabilized with PBS-0 . 2% Triton X-100 for 30 min at RT . Viral particles or infected cells were stained with an anti-p72 monoclonal antibody ( 17LD3 ) ( diluted 1∶250 in PBS-5% BSA ) for 60 min at RT , followed by incubation with an anti-mouse Alexa Fluor-488 or an anti-mouse Alexa Fluor-555 for the same time . Alexa Fluor-488 phalloidin ( dilution 1∶100 ) or TRICT- phalloidin and Topro3 ( dilution 1∶500 ) were used to stain actin filaments and nuclei of cell , respectively . Goat anti-Rock1 was used at a dilution 1∶50 . To analyze the virus binding to the cellular membrane , the viral adsorption was allowed for 90 min at 4°C ( MOI 10 ) and after 60 min from virus addition cells were incubated with Alexa Fluor 594-WGA for 30 min . Cells were washed twice with cold PBS-0 . 1% BSA Buffer and incubated with anti-p72 monoclonal antibody ( 17LD3 ) and Alexa Fluor-488 for 60 min at 4°C . Finally , cells were fixed with 4% paraformaldehyde at RT for 20 min . Samples were analyzed by CLSM ( Zeiss LSM510 ) with a 63× oil immersion objective . To investigate ASFV uptake as well as actin , Rock1 and Rac1 distribution , Z-slices per image were collected and displayed as maximum z-projection of vertical slices ( x–z plane ) and/or maximum z-projection of horizontal slices ( x–y plane ) . For presentation of images in the manuscript , LSM images were imported into Image J software for brightness and contrast enhancements . In all instances one image is representative of three independent experiments . ASFV uptake in the presence of inhibitors was analyzed automatically by a Macro algorithm from Image J program ( developed by CBMSO Confocal Microscopy Service , Spain ) in which Intermode threshold was used to count the number of virus inside the cells . Vero cells were serum starved for 24 h and pretreated with DMSO or EIPA . After 60 min at 37°C the cells were synchronously infected ( MOI 10 ) or treated with PMA ( 200 nM ) at 37°C for 30 min . Fifteen min prior to harvesting or fixation , cells were incubated with 0 . 5 mg/ml 10 KDa 647-dextran or 3 KDa Texas Red-dextran ( Invitrogen ) at 37°C . Dextran uptake was stopped by placing the cells on ice and washing three times with cold PBS and once with low pH buffer ( 0 . 1 M sodium acetate , 0 . 05 M NaCl , pH 5 . 5 ) for 10 min . Then , the cells were prepared for FACS or CLSM analysis . In FACS experiments dextran uptake was displayed as fluorescence mean of three independent experiments . Error bars represent the standard deviation between experiments . Cells without wash acid buffer were added as an experiment control . At indicated times post infection , cells were washed with PBS and lysed in RIPA modified buffer ( 50 mM Tris-HCl pH 7 . 5 , 1% NP40 , 0 . 25% Na-deoxycolate , 150 mM NaCl , 1 mM EDTA ) supplemented with protease and phosphatase inhibitor cocktail tablets ( Roche ) . The protein concentration was determined by a Pierce BCA Protein Assay kit based on the bicinchoninic acid spectrophotometric method ( Thermo Scientific ) . Cell lysates ( 15–50 µg of protein ) were fractionated by SDS-PAGE and electrophoretically transferred to an Immobilon extra membrane ( Amersham ) and the separated proteins reacted with specific primary antibodies . The antibodies used were the following: polyclonal anti-p72 ( dilution 1∶2000 ) , anti-p32 ( dilution 1∶1000 ) , anti-ASFV ( dilution 1∶3000 ) ; anti-Akt , anti-phospho Akt ( Thr308 ) , anti-phospho Akt ( Ser473 ) , anti-Pak1 and anti-phospho Pak1 ( Thr423 ) ( dilution 1∶500 ) ; anti-β-actin and anti-Rac1 ( dilution 1∶1000 ) . Membranes were exposed to horseradish peroxidase-conjugated secondary antibodies ( dilution 1∶5000 ) followed by chemiluminescence ( ECL , Amersham Biosciences ) detection by autoradiography . In all instances the figures are representative of three independent experiments . Vero cells were serum starved for 24 hours and treated with DMSO or LY294002 for 60 min at 37°C in serum free medium . Asynchronic infection ( viral adsorption for 60 min ) was carried out at a MOI of 10 pfu/cell in the presence of the drug at 37°C until indicated times . PI3K subunit p85 was immunoprecipitated from lysed cells and PI3-kinase activity was measured as PI ( 3 , 4 , 5 ) P3 production by ELISA activation kit , following the manufacturer's recommendations ( Kit#1001s Echelon ) . Vero cells were serum starved for 24 hours before synchronic infection at a MOI of 10 pfu/cell . The cells were washed once with cold PBS , shifted to 37°C and harvested at the indicated times post infection . Rac1 activation was measured with a G-LISA activation kit ( Kit #BK128 Cytoskeleton , Inc . ) and by immunoblotting after a Pak1-PBD-Agarose Beads ( Upstate ) pull down step as described following the manufacturer's recommendations . Bound Rac1-GTP was detected by incubation with an anti-Rac1 specific antibody followed by a secondary antibody conjugated to HRP and a detection reagent . The signal was read by measuring absorbance at 490 nm using a microplate reader and by autoradiography . To check if the EIPA inhibitor was specifically blocking virus entry and not a down-stream process such as early gene expression , we induced the fusion of the viral membrane with the plasma membrane ( PM ) by lowering the pH of the medium [23] . The cells were pretreated with EIPA for 60 min at 37°C in serum free medium . Viral adsorption was allowed at MOI 1 for 90 min at 37°C in neutral ( 7 . 4 ) or acid ( 5 . 0 ) pH . Cells were washed once with cold PBS and infection was allowed to proceed for 16 h at 37°C in the presence of the inhibitor in neutral pH . Samples were prepared for Western blot analysis . Vero cells were transfected with 1 µg of specific expression plasmids per 106 cells using the LipofectAMINE Plus Reagent ( Invitrogen ) according to the manufacturer's instructions and mixing in Opti-MEM ( Invitrogen ) in a 6-well plate . Cells were incubated at 37°C for 4 h in serum free medium , washed and incubated at 37°C . After 16 or 24 h post transfection the cells were infected at indicated MOI and either lysated and analyzed by Western blot , or fixed and prepared for CLSM analysis . In order to analyze the localization of p72 in the viral particle , we carried out an experimental procedure as described in [44] . In brief , purified virus was treated with different buffers ( Buffer 1 and 2 ) for 30 min at RT and the separate samples were centrifugated over sucrose ( 20% in PBS ) cushion in a Beckman Airfuge at 24 p . s . i . for 15 min . The supernatant ( SP ) and pellet ( P ) were analyzed by Western blot by incubation with anti-p72 monoclonal antibody ( 17LD3 ) . Buffer 1: 10 mM Tris pH 8 , 0 . 65 M NaCl , 0 . 5% octil β- D- Glucopyranoside ( Sigma ) ; Buffer 2: 10 mM Tris pH 8 , 0 . 65 M NaCl , 0 . 2% octil β- D- Glucopyranoside ( Sigma ) and 0 . 1% Dithiothreitol ( DTT ) . To check cell viability after treatment with inhibitors cells were dyed with Trypan Blue and dead cells were counted in hemocytometer as blue cells . After Western Blot analysis , bands developed by ECL chemiluminescence were digitalized by scanning and quantified with Fujifilm Multi Gauge V3 . 0 software . Data were normalized after subtracting background values and calculated as factors by their ratio against the highest or lowest positive value obtained . All quantifications represent the mean of three independent experiments . ASFV proteins in Swiss Prot database: p72: MCP_ASFB7; p32: P30_ASFB7; p17: P17_ASFB7; p12: P12_ASFB7 . Cellular proteins in ENSEMBL database: Pak1: ENSMMUG00000001387; Rac1: ENSFM00250000002337; β-actin: ENSMMUG00000012054; Akt: ENSMMUG00000001041; Rock1: ENSFM00540000717933 .
Macropinocytosis mainly differs from other endocytic processes in the requirement of extensive actin cytoskeleton restructuring and formation of blebs or ruffling in the cellular surface , through which the specific cargo enters the cell [22] . These rearrangements are coupled to an external-induced formation of plasma membrane extensions . Several viruses have been described to use macropinocytosis for entry , including Vaccinia virus [23] , [45] , [46] , Ebola virus [47] and Kaposi's sarcoma-associated herpesvirus [29] , [48] . Receptor-mediated endocytosis has been postulated in classic studies as the most likely mechanism for ASFV entry into Vero cells [33]–[35] . Yet the specific characteristics to further depict the viral entry procedure have not been elucidated . To analyze the possible perturbation of the cellular membrane induced by ASFV , the virus strain Ba71V was used to synchronously infect Vero cells at MOI 50 . To achieve this , we have analyzed by Field Emission SEM analysis ( FESEM ) the induction of ruffling and bubbles-like perturbations at 10 , 60 and 90 min after ASFV uptake . The results are shown in Figure 1A , where a maximum level of membrane perturbation similar to ruffles appears in ASFV-infected Vero cells between 10 and 60 mpi , decreasing after 90 mpi , indicating that ASFV-induced macropinocytosis is a transient event . On the other hand , Figure 1B shows that ASF virions internalize in Vero cells adjacent to retracting ruffles , thus indicating that the macropinocytic uptake of viral particles seems to occur as part of the macropinocytic process . Finally , we have analyzed in vivo in real-time the membrane protrusions observed during Ba71V infection in Vero cells . Figure 1C shows the sequence of images during the first minutes of the infection ( Video S2 ) , illustrating the ASFV-induced ruffling , and in concordance with the data shown in Figure 1A . For comparison to Mock-infected Vero cells , see Video S1 . To assess whether the ASFV entry also induces membrane perturbation in swine macrophages , the natural target cell of ASFV infection in vivo , the virulent strain E70 was used to synchronously infect IPAM cells at MOI 50 . As early as 10 mpi , strong membrane protrusions were observed by FESEM analysis ( Figure 2A ) . To better characterize these membrane rearrangements , IPAM cells were synchronously infected with E70 strain at MOI 50 , during 30 , 45 and 60 mpi . Next , IPAM cells were fixed and analyzed by optic microscopy . Figure 2B shows images compatible with blebs induced by ASFV infection in swine macrophages from 30 mpi . To prove this point , we achieved an additional experiment showing the inhibition of virus entry with different doses of blebbistatin , an inhibitor of blebbing and macropinocytosis [23] , [29] , [49]–[51] . Western blot analyses have shown that blebbistatin impairs the entry of the virus in IPAM cells , as the drug inhibits the expression of ASFV proteins when preincubated before virus addition . Hence , when blebbistatin was incubated 60 min after virus addition , a much lower inhibition of viral proteins was observed , thus indicating the role of blebbistatin on early steps of virus entry . Results are presented in Figure 2C . Last , by using a specific anti-Rock1 antibody as a marker of blebs [52] , we have shown that Rock1 colocalizes with virus particles on blebs in IPAM cells from 30 min after ASFV uptake ( Figure 2D ) , revealing the close relation between bleb and viral particle . Taken together , these data strongly indicate that ASFV induces a vigorous plasma membrane activity during the first steps of the infection , both in Vero and IPAM cells , well-matching with macropinocytosis-mediated entry . With the membrane perturbation pattern shown above , it was likely that ASFV was using macropinocytosis to enter cells . Macropinocytosis is dependent on the Na+/H+ exchanger [21] , and thus amiloride and its analogue 5- ( N-ethhyl-n-isopropil ) -amiloride ( EIPA ) are frequently used as the main diagnostic test to identify macropinocytosis because this drug has been shown to be specific to this endocytic pathway without affecting others [53]–[55] . Consequently , to further assess the involvement of macropinocytosis in ASFV entry , the effect of EIPA was investigated . When tested on Vero cells , EIPA had no significant cytotoxic effect as assessed by cell monolayer integrity and trypan blue cell viability assessment ( Table S1 ) . It has been previously described that after 60 mpi more than 90% of the ASF viral particles are located in the cell [34] . Furthermore , the viral uncoating does not completely occur before 2 hours post infection ( hpi ) [34] . According to these data , we measured viral uptake by using the specific antibody 17LD3 against p72 , the major protein of ASFV capsid [42] , [56] ( see Materials and Methods and Figure S1A , B and C ) . Interestingly , amounts of EIPA from 40 µM to 60 µM caused a significant reduction ( 60% ) in the uptake of ASFV infective particles after 60 mpi ( Figure 3A ) , suggesting that ASFV entry depends on Na+/H+ exchanger activity/function . To further visualize the effect of EIPA on virus uptake , Ba71V strain was added to Vero cells , previously treated with DMSO or 60 µM EIPA . Sixty min after infection , the cells were incubated with anti-p72 antibody 17LD3 to stain the virus . A confocal microscopy analysis revealed that there was a noticeable drop in virus particles incorporated into the cells incubated with EIPA , as compared to those incorporated into DMSO-incubated cells ( Figure 3B , bottom panels ) . Images were taken as a maximum z-projection ( x–y plane ) . For clarification , individual channels are shown in Figure S2A . Moreover , we also analyzed images of a maximum z-projection of vertical slices to determine whether viral particles could be imbibed into the membrane in the presence of the inhibitor . As shown in the Figure 3B upper panels , a different distribution of viral particles in the cells infected in the presence of EIPA , compared to that found in cells infected in the absence of the drug , was observed . This last data strongly suggests that in EIPA-treated cells the virus can bind to the membrane but is not able to internalize . This could be the explanation for the percentage of cells that were positive for 17LD3 antibody detected in Figure 3A . The total number of virus obtained in the confocal images was automatically quantified using a macro algorithm in the Image J program ( Figure S3 ) . In regard to this , it is also remarkable that , although a small amount of viral particles can still be detected inside the cells in the presence of EIPA , neither early , p32 , nor late ASFV proteins , p17 , p24 , p12 and p72 [57]–[60] could be detected by Western blot in the presence of the drug ( Figure 3C ) . Hence , it is likely that EIPA is mainly affecting virus uptake since when drug is added 60 min after virus uptake , it does not affect the viral protein synthesis ( Figure S4A ) . As expected , no viral factories detected by using anti-p72 antibody ( green ) and Topro3 ( blue ) for viral and cellular DNA , were found after EIPA treatment by confocal microscopy ( Figure 3D ) . Separate channels are shown in Figure S2B and a morphological detail of an ASFV factory is shown in Figure S1C . Consequently , viral production was also strongly inhibited by the drug ( Figure 3E ) . Finally , and to fully ascertain if EIPA was specifically blocking ASFV entry and not a downstream step , we performed the infection by using the acid-mediated fusion of plasma membrane . Briefly , in the presence of acid pH , endocytosis is subverted and virions fused with the plasma membrane and then directly carried into the cytosol . When an inhibitor blocks virus endocytosis , inhibition of viral protein synthesis in the presence of drug can be bypassed through fusion . If membrane fusion could not rescue viral gene expression , the blocking would most probably occur at a post-entry step [23] . By using this method , we find that when the viral adsorption is performed in the presence of EIPA in acidic pH , p72 viral synthesis is clearly recovered in relation to the infection developed at neutral pH ( Figure 3F ) . Next , we investigated the dextran uptake during ASFV infection , since it has been described that macropinocytosis activation induces a transient increase of this fluid phase marker [61] , [62] . To achieve this , Vero cells were treated with EIPA for 60 min and then infected synchronously with Ba71V for 30 min , or stimulated with PMA as a positive control . Fifteen minutes before stopping the infection , cells were pulsed with dextran and prepared for FACS analysis . As indicated in Figure 3G , ASFV infection induces dextran uptake during the virus entry and this action is inhibited by EIPA . Moreover , to reinforce the hypothesis that ASFV entry occurs mainly by macropinocytosis , we developed an experiment to assess the colocalization between the virus particles and the macropinocytosis marker dextran . These results are included in Figure 3H . All together , these data strongly indicate that ASFV induces activation of macropinocytosis to enter cells . Macropinocytosis is a very specific actin-dependent endocytic process since it depends on acting rearrangements to induce membrane ruffling formation , and inhibitors of actin microfilaments , such as Cytochalasin D ( Cyto D ) [63] , [64] , Latrunculin A [65] and Jasplakinolide [66] , are commonly used to inhibit this process . To demonstrate whether ASFV depends on actin to enter cells , we used Cyto D , which binds to the positive end of F-actin impairing further addition of G-actin , thus preventing growth of the microfilament [67] . Vero cells were pretreated with Cyto D at a concentration of 8 µM and ASFV uptake ( MOI 10 ) at 60 mpi was next analyzed by FACS . As shown in Figure 4A , the disruption of actin dynamics by the inhibitor reduced ASFV entry in a percentage of about 50% . To assess whether the drug impairs the synthesis of viral proteins , Vero cells were untreated or treated with Cyto D ( 4 µM ) and then infected with Ba71V , MOI 1 . After 16 hpi , we used a specific antiserum against both early and late ASFV proteins ( generated in our lab ) , to analyze viral protein expression . As expected , Cyto D treatment importantly reduced both the synthesis of p32 , one of the main ASFV early proteins , and the synthesis of p12 , p17 and p72 , three typical late proteins in the ASFV cycle ( Figure 4B ) . In agreement with this , both virus production and viral factories clearly diminished as shown in Figure 4C and 4D , respectively . However , it is noteworthy that even in the presence of Cyto D , a number of virions seem able to enter the cell and induce a productive infection , thus suggesting that the actin cytoskeleton is involved in ASFV entry and also in successive post-entry steps , as shown in Figure S4B . To further assess the importance of actin microfilaments in the first steps of ASVF entry , we examined whether ASFV infection causes rearrangements of actin cytoskeleton in Vero cells , by using phalloidin in confocal microscopy experiments . Data are presented in Figure 4E , showing the change of actin pattern after 10 and 30 min after virus uptake at MOI 50 . Furthermore , and to reinforce these data , Vero cells were transfected with pEGFP-actin plasmid ( kindly gifted by Dr . J . Mercer ) , and infected with Ba71V , MOI 50 . Figure 4F shows the redistribution in aggregates of GFP-actin in transfected Vero cells , which are similar to those observed when endogenous actin was analyzed . Not only that , but also , viral particles ( red ) are found together with the actin aggregates both in endogenous and ectopically expressed actin . Since it has been described that blebs and ruffles contain actin , Rac1 and cortactin [23] , [68] , it is likely that these actin spots correspond to membrane active places where ASFV-induced ruffling should occur , thus suggesting that actin dynamics is a very important factor to ASFV in the host cell to mediate cell-wide plasma membrane ruffling . Another component of the cytoskeleton that has been reported to be involved in several virus entry processes is the microtubules system , although the importance of microtubules specifically regarding the macropinocytosis pathway is controversial [69] . In respect to ASFV infection , whereas it has been reported that nocodazole ( a specific inhibitor of microtubules system [70] ) does not affect viral DNA replication [71] , a report from Health et al . [72] describes that nocodazole produces a decrease in the expression of p72 and p12 late proteins , but not in the early proteins of ASFV . To investigate whether the microtubule system has a role in ASFV entry , Vero cells were treated with different concentrations of nocodazole and then infected with ASFV at MOI 1 . Microtubule disruption had no effect on early viral protein synthesis and barely on late proteins synthesis such as p12 and p72 ( Figure S5 ) . Therefore , we conclude that the microtubules system is not likely significant for ASFV entry . Macropinocytosis is typically started by external stimulation . This stimulation is usually associated with growth factors that trigger activation of receptor tyrosine kinases ( RTKs ) . These molecules then activate signaling pathways that induce changes in the dynamics of actin cytoskeleton and disturb plasma membrane [21] . Among them , epidermal growth factor receptor ( EGFR ) has been connected with actin rearrangement and activation of Rho family GTPases , and its activation is known to trigger macropinocytosis [45] , [73] . Besides the membrane perturbations and actin remodeling observed following ASFV uptake , we have found that EGFR activation was essential for ASFV infection , since 324674 , the specific inhibitor of this receptor tyrosine kinase [74] , efficiently inhibited ASFV uptake in a dose-dependent manner as assessed by FACS experiments in Vero cells . Accordingly , ASFV entry relies on tyrosine kinases activity , as preincubation of the cells with genistein ( tyrosine kinase inhibitor [75] ) also inhibited ASFV infection ( Figure 5A ) . The PI3K/PDK1/Akt/mTORC1 pathway regulates vital cellular processes that are important for viral replication and propagation , including cell growth , proliferation , and protein translation [76] . Concerning macropinocytosis , it has been described that PI3K and its effectors induce the formation of lipid structures in ruffles and macropinocytic cups involved in cytoskeleton modulation [77]–[79] . In recent years , it has been reported that several viruses use the PI3K-Akt pathway to support entry into cells and early events of the infection [23] , [80] . In order to investigate the importance of this pathway on ASFV entry , we have developed , after different times of ASFV uptake , an ELISA test that directly measures the activity of PI3K by analyzing phosphorylation of its specific substrate PI ( 4 , 5 ) P2 . The results ( Figure 5B ) , show the increase of substrate phosphorylation from 5 min after virus uptake , reaching a maximum after 30 min of infection . Importantly , the presence of the PI3K inhibitor LY294002 ( LY ) [81] strongly impaired the kinase activation by the virus . It has been reported that Akt is the major downstream effector of the PI3K pathway and is commonly used as readout of PI3K activation [82] , since Akt phosphorylation has been considered to be a direct consequence of PI3K activation pathway [83]–[85] . To analyze the effect of virus uptake on Akt phosphorylation , Vero cells were serum starved for 4 h and then infected with Ba71V ( MOI 10 ) from 5 to 90 min . Figure 5C shows that Akt is phosphorylated from 5 min after virus uptake , reaching a maximum at 30 min . It has been established that Akt phosphorylation of Thr308 is a direct consequence of PI3K activation pathway [83] while phosphorylation of Ser473 depends on mTORC2 [84] , [85] . Since phosphorylation in both residues of Akt is required for its complete activation , we measured the ASFV-induced Akt phosphorylation with two different anti-phospho antibodies . Figure S6 shows that Akt is phosphorylated both in Thr308 and in Ser473 early after ASFV infection , suggesting that ASFV entry fully activates this pathway in the infected cell . To further investigate whether the PI3K activation observed early during ASFV infection involves mainly upstream steps , we pretreated Vero cells with LY at a concentration of 60 µM . Cells were then infected with Ba71V MOI 10 , and the virus uptake was analyzed by FACS at 60 mpi . Figure 5D shows that virus uptake decreased to about 45% in treated Vero cells in respect to DMSO-treated cells , indicating that PI3K activation is involved in the virus entry . Not only that , but we also found that the activation of this kinase has a key role in the consecution of infection since , as shown in Figure 5E , the presence of 20 µM LY severely impairs the synthesis of both ASFV early and late virus proteins . Recently , our group has described that ASFV regulates the cellular machinery of protein synthesis to guarantee the expression of its own proteins [15] . Since it has been reported that one of the main roles of PI3K is regulating the translational machinery through the PI3K-Akt-mTOR pathway [86] , the strong effect observed of LY on the ASFV protein synthesis is not surprising ( Figure S4C ) . Finally , and to confirm the role of PI3K on ASFV infection , we performed experiments to analyze the number of cells presenting viral factories in the presence of LY . As shown in Figure 5F , a dramatic decrease of infected cells was observed after 16 hpi ( MOI 5 ) when the infection was performed in the presence of the inhibitor . Similarly , virus production was diminished about 3 logs units by the effect of LY after 48 hpi ( Figure 5G ) . Since activation of Rac1-GTPase has been involved in the regulation of macropinocytosis by triggering membrane ruffling in the cell [87] , we investigated the activation status of Rac1 during the first steps of ASFV entry in Vero cells . Ba71V was used to synchronously infect cells ( MOI 10 ) , and Rac1 activation was measured with the G-LISA activation kit following the manufacturer's instructions . The results showed that Rac1 activation is a very fast and strong event during ASFV entry , reaching a maximum ( 2 . 5 fold ) at 10 mpi compared to mock-infected cells ( Figure 6A ) . It has been shown that Rac1 controls macropinocytosis by interacting with its specific effectors , the p21-activated kinases ( Paks ) , thus modulating actin cytoskeleton dynamics [88] , [89] . It is also known that Rac1 binds and activates Pak1 only under its Rac1-GTP active form . To confirm the results obtained by G-LISA , we further analyzed the Rac1 activation during ASFV entry by performing a pull down assay using Pak1-PBD-Agarose Beads , which carried the PBD-Pak1 ready to bind Rac1-GTP . As shown in Figure 6B , Rac1-GTP was found together with the pulled Pak1-PBD-Agarose Beads after 10 min post ASFV infection , slightly diminishing 30 min after the infection . This result further corroborates that ASFV entry induces the formation of the Rac1 active conformation . Since it has been described that Rac1 is contained in blebs and ruffles [22] , [23] , [90] and , as shown above , ASFV induces these type of the structures when it infects cells , we next analyzed the localization of Rac1 during the process of ASFV entry . To achieve this , Vero cells were first transfected for 24 h with pEGFP-Rac1 ( kindly given by Dr . J . Mercer ) and then infected with Ba71V , MOI 10 . As shown in Figure 6C , we found clusters of the GTPase as early as 10 min after infection . Accordingly with the experiments shown above , this effect was clearly perceptible at 30 mpi , demonstrating , first , that ASFV infection induces accumulation of active Rac1 in ruffling areas , and second , that this is an event that takes place mainly during ASFV entry . The effect of Rac1 inhibition on virus uptake was next investigated . Cells were pretreated with 200 µM Rac1 inhibitor [91] and the virus uptake was measured after 60 mpi by FACS analysis , using the specific antibody against the ASFV capsid protein p72 , as described in Materials and Methods . Figure 6D shows the dramatic decrease of virus uptake when the infection is performed in the presence of the pharmacologic inhibitor of Rac1 . Furthermore , we analyzed the effect on the ASFV uptake in the presence of the inhibitor by CLSM experiments , using the same conditions as above . The images were taken as a maximum z-projection of horizontal and vertical slices . As Figure 6E ( bottom panels ) indicates , a strong inhibition of virus uptake could be observed in the presence of the Rac1 inhibitor , since the number of ASFV particles in the cell ( green ) is visibly lower in the presence of the drug . Moreover , and as shown in the upper panels of Figure 6E , virus ( green ) colocalized ( yellow ) , with cortical actin ( red ) , indicating that the drug immobilizes the virions imbibed into the plasma membrane and impairs their entry into the cell . Separated channels are also shown in Figure S2D . Alternatively , and to reinforce the role of Rac1 on ASFV infection , we studied the level of ASFV protein synthesis in Vero cells previously transfected with the mutant pGFP-Rac1-N17 ( a kind gift from Dr . R . Madrid ) . The expression of the inactive form of Rac1 strongly inhibited the expression of the ASFV early p32 protein ( Figure 6F ) . As expected , the synthesis of viral late proteins was also affected by treatment with the inhibitor ( Figure S7 ) . Not only that , but also , when Rac1 inhibitor was added 60 min after virus addition , the level of viral protein synthesis observed was completely recovered , thus reinforcing the role of Rac1 in virus entry ( Figure S4D ) . Hence , the role of Rac1 on ASFV morphogenesis and virus production was investigated . To achieve this , Vero cells were treated with the Rac1 inhibitor and then infected during 16 h , MOI 5 . Cells were fixed and stained with anti-p72 to visualize the viral factories by CLSM and the percentage of infected cells in the presence or absence of the inhibitor was represented in the graph ( Figure 6G ) . As observed , the number of cells containing ASFV factories decreased about 65% in the presence of Rac1 inhibitor compared to the untreated controls ( separate channels are shown in Figure S2E ) . In line with these results , the viral production at 48 hpi decreased strongly when the activity of Rac1 GTPase was inhibited ( Figure 6H ) . Finally , since Rac1 has been reported to be an important component of ruffles [22] , [23] , [90] , we have used the Rac1 inhibitor to assess its involvement in the inhibition of these membrane perturbations and therefore , indirectly , the role of ruffles in ASFV uptake . To achieve this , we have performed FESEM assays in Vero cells treated with 200 µM Rac1 inhibitor during 60 min prior to virus addition . As shown in Figure 6I , Rac1 inhibitor strongly decreases the ASFV-induced ruffles , in accordance with the decrease in virus uptake ( Figure 6D ) , viral infection ( 6G ) and virus production ( 6H ) previously observed . Taken together , these results demonstrate the significant role of Rac1 on ASFV entry . The p21-activated kinase 1 ( Pak1 ) , a serine/threonine kinase activated by Rac1 or Cdc42 [89] is one of the most relevant kinases related to several virus entry processes since it is involved in the regulation of cytoskeleton dynamics and is needed during all the stages of macropinocytosis [88] , [92] , [93] . Among the different residues to be phosphorylated in Pak1 activation , the Thr423 plays a central role because its phosphorylation is necessary for full activation of the kinase [94] . To determine whether Pak1 was activated during ASFV entry , we first analyzed the phosphorylation on Thr423 in Vero cells synchronously infected ( MOI 5 ) with Ba71V . At different times post infection , samples were collected and analyzed by immunoblotting using an anti-phospho-Pak1 Thr423 antibody . As early as 30 mpi , phosphorylation of Pak1 could be detected , increasing until 120 mpi ( Figure 7A ) . IPA-3 has been identified as a direct , noncompetitive and highly selective Pak1 inhibitor . In the presence of IPA-3 , Thr423 phosphorylation is inhibited since the Pak1 autoregulatory domain is targeted by the inhibitor [95] . To assess the role of Pak1 activation in ASFV uptake , we measured by FACS analysis the p72 levels detected into the Ba71V-infected Vero cells ( MOI 10 ) after 60 mpi . As shown in Figure 7B , the p72 levels incorporated into the cells in the presence of 30 µM IPA-3 were significantly lower ( 70% ) than those obtained in the absence of the inhibitor . These results indicate that Pak1 activation is involved in the first stages of ASFV entry , since phosphorylation of the kinase occurs at very early times after virus addition , and even more importantly , the uptake of the virus into the host cells is strongly dependent of Pak1 activity . Apart from the role played by Pak1 in viral entry , the sensitivity of ASFV infection to IPA-3 was investigated in Ba71V-infected Vero cells by Western blot . Using specific antibodies against both early and late ASFV proteins , the effect of the inhibitor from 1 to 10 µM on viral protein synthesis was evaluated . Figure 7C shows the strong dose-dependent IPA-3 inhibition over the most important early ( p32 ) and late proteins ( p72 , p24 , p17 and p12 ) . To reinforce the role of Pak1 in ASFV entry , a similar experiment performed by incubation with IPA-3 during 60 min after virus addition is shown in Figure S4E . These data indicate that the drug is mainly affecting virus entry as it does not induce important inhibition on viral protein synthesis when incubated after virus uptake . Moreover , virus title was reduced 1 . 5 log units in cells pretreated with 5 µM IPA-3 and then infected with Ba71V ( MOI 1 ) in the presence of the inhibitor during 48 h ( Figure 7D ) . To corroborate the significant role of Pak1 during ASFV infection , we used different Pak1 constructs affecting Pak1 activation ( see Materials and Methods ) . Vero cells were transfected for 24 h with pEGFP , pEGFP-Pak1-WT , pEGFP-Pak-AID and pEGFP-Pak1-T423E ( all of them kindly gifted by Dr . J . Chernoff ) and infected for 16 h with ASFV at a MOI of 1 pfu/cell . As shown in Figure 7E , the constructs containing the Pak1 autoinhibitory domain ( AID ) inhibited p12 and p32 viral protein expression , whereas cells transfected with wild type ( WT ) form showed the same protein levels than infected control cells . It is noteworthy that constitutively active Pak1 construction T423E ( even although it was only shortly expressed in the transient transfection process ) induced a remarkable enhancement on the expression of the ASFV early protein p32 , indicating that increasing Pak1 activity intensifies the early protein synthesis , probably due to its effect on virus entry . Numeric values of these data are shown in Figure 7F . These data , together with those of Rac1 activation explained above , strongly supports our hypothesis of ASFV triggering the Rac1-Pak1 pathway during the virus entry . Dynamin is a cellular essential GTPase which plays an important role in cellular membrane fission during vesicle formation [96] . It is likely involved in Rac1 localization and function , since it has been shown that Rac1-dependent macropinocytosis is blocked by the dynamin-2 ( DynK44A ) dominant-negative [39] . Since , as we demonstrated above , Rac1 is important to ASFV entry , we have analyzed whether dynamin-2 pathway plays a role either in ASFV entry or infection . To achieve this , we first investigated the effect of Dynasore ( Dyn ) , a reversible inhibitor of GTPases activity [97] , over ASFV uptake . After 60 min of pretreatment with 100 µM Dyn , Vero cells were infected with Ba71V at MOI 10 and virus uptake was measured by FACS using the specific antibody against the capsid viral protein p72 . The result showed that treatment with Dyn partially inhibited virus uptake ( 35% ) ( Figure 8A ) . A higher effect of the inhibitor on ASFV entry could not be found by using different experimental conditions ( data not shown ) , further indicating the partial involvement of dynamine in virus uptake . Moreover , the role of clathrin-mediated endocytosis was examined in parallel using Chlorpromazine ( CPZ ) , which inhibits the assembly of coated pits at the plasma membrane and is considered a specific inhibitor of clathrin-mediated endocytosis [98] . Using parallel experimental conditions , and in contrast with the data obtained after treatment with Dyn , we observed that the virus uptake was not likely affected in the presence of 20 µM CPZ ( Figure 8A ) . These data indicate that whereas dynamine is to some extent involved in ASFV entry in accordance with its role in macropinocytosis [39] , clathrin is not related to ASFV uptake in Vero cells . In order to investigate whether other steps downstream ASFV entry were affected by Dyn and CPZ , Vero cells were separately pretreated with the inhibitors , and then infected with ASFV ( MOI 1 ) . At the indicated times after infection , the synthesis of both early and late ASFV proteins was analyzed by Western blot . The treatment with 100 µM Dyn strongly inhibited p72 and p32 expression from early times post infection ( Figure 8B ) , consequently indicating that dynamine is required for ASFV both early and late infection course . As Figure 8C shows , CPZ had a similar effect to Dyn both on ASFV early and late protein synthesis , in concordance with the data from Hernaez et al . [38] , in which the expression of the viral protein p32 depends on clathrin function . Higher amounts of CPZ could result in an inhibition of p72 , but this effect is likely due to the cytotoxic effect of the drug , as reported in Table S1 . Taken together , our data showed that whereas the effect of Dyn on viral protein synthesis is probably due to dynamine participation on ASFV entry events , the clathrin inhibition does not involve virus uptake , but only viral protein synthesis , thus indicating a role for clathrin function merely in post entry events . Future experiments are planned to more specifically study which are the ASFV post entry events regulated by clathrin . Finally , and as expected , both inhibitors had an important effect on viral production measured after 48 hpi ( MOI 1 ) in Vero cells ( Figure 8D ) .
Endocytosis constitutes an efficient way for viruses to cross the physical barrier represented by the plasma membrane and to pass through the underlying cortical matrix . Knowledge of the specific pathway of virus entry and of the precise mechanisms regulating is key to understand viral pathogenesis , since virus entry into host cell is the first major step in infection . Whereas there is ample evidence showing that ASFV enters cells through endocytosis in a pH-dependent manner and that saturable binding-sites on the plasma membrane mediate the productive entry of the virus into Vero cells and swine macrophages [33] , [34] , the specific endocytic and signaling pathways used by the virus are largely unknown . In this report , by combining different and independent approaches , we have achieved an exhaustive analysis of the ASFV endocytic pathway . We have obtained a precise picture of how ASFV enters the cell and have identified the main cellular proteins required . Careful assessment of specificity and functionality of each pathway was performed and correlated with infection and virus uptake . Many recent reports have shown that viruses can directly use macropinocytosis as an endocytic way for productive infection [21] , [23]–[29] , and also to promote the penetration of viral particles that enter by other endocytic mechanisms [31] , [32] . Macropinocytosis activation is related to significant cell-wide membrane ruffling mediated by activation of actin filaments . These structures may have different shapes: lamellipodia , circular-shaped membrane extrusions ( ruffles ) or large membrane extrusions in form of blebs . Here we have illustrated by FESEM that ASFV strain Ba71V induced prominent membrane protrusions compatible with ruffles after 10 mpi . Transmission electron microscopy images further support this result by showing that ASF virions internalize adjacent to retracting ruffles , likely indicating uptake of viral particles occurs as part of the macropinocytic process . Not only that , but also , we found that inhibition of Rac1 , an important component of ruffles , importantly impaired the ASFV uptake , thus involving the formation of these membrane perturbations in virus entry . Moreover , and in parallel to the data obtained in Vero cells , we found that the E70 virulent strain induced a type of membrane protrusion similar to blebs a few minutes after the infection of the swine macrophage line IPAM . This last result is important , since macrophages are probably the natural target cell of the infection in vivo and suggests that different macropinocytic programs can be used by different ASFV strains , as has been published for other virus as Vaccinia [45] . Because of this , we have carefully characterized these structures . First , we showed the inhibition of virus entry with different doses of blebbistatin , and second we demonstrated that Rock1 ( a marker of blebs [52] ) colocalized with virus particles on blebs in IPAM cells from 30 min after virus uptake . Apart from characteristic membrane perturbations , macropinocytosis is also distinguished from other entry pathways by features that include actin-dependent structural changes in the plasma membrane , regulation by PI3K , PKC , Rho family GTPases , Na+/H+ exchangers , Pak1 , as well as ligand-induced upregulation of fluid phase uptake . In this regard , our work demonstrates that EIPA , a potent and specific inhibitor of the Na+/H+ exchanger [23] , [53] , [54] , [99] , severely impairs ASFV infection and entry . By using FACS analysis we found that EIPA treatment caused a significant dose-dependent manner reduction ( more than 60% ) in the uptake of ASFV infective particles . Confocal microscopy analysis also revealed that there was an evident drop in virus particles incorporated into the cells incubated with EIPA . It is important to note that macropinocytosis is the only endocytic pathway susceptible to the inhibition of the Na+/H+ exchangers . Thus , these results strongly indicate the involvement of macropinocytosis in ASFV virus entry . Actin plays a central role in formation and trafficking of macropinosomes . Cyto D , which binds to the positive end of F-actin ( impairing further addition of G-actin and preventing the growth of the microfilament [67] ) , reduced ASFV entry by approximately 50% and inhibited the synthesis of both early and late viral proteins , together with viral morphogenesis . However , it is remarkable that virions that escape from the action of Cyto D induce a productive infection , thus suggesting that actin cytoskeleton is mainly involved in ASFV entry , although it could have a role in successive post-entry steps . Corroborating this hypothesis , we have observed that ASFV infection causes rearrangements of endogenous actin cytoskeleton in Vero cells as early as 10 min post infection . These data were reinforced by overexpression of GFP-actin that was concentrated in aggregates in virus-infected cells . Together , these data provide evidence for a role of actin in ASFV entry and suggest that the virus can actively promote localized actin remodeling to facilitate its uptake through macropinocytosis or a similar mechanism . The first reports describing the endocytic entry of viruses into their host cells presumed that incoming viruses took advantage of ongoing cellular endocytosis processes [16] . However , it is now clear that several viruses are not only passive cargo but activate their own endocytic uptake by eliciting cellular signaling pathways . The activation of these pathways significantly depends on the interaction of the virus with cellular receptors specific to the type and activation status of the host cell [100] , [101] . ASFV , as Vaccinia virus [21] , [45] , seems to belong to the viruses that actively trigger their endocytic internalization . In this respect , we have found that entry of ASFV is dependent on signaling through tyrosine kinases as EGFR , and activation of PI3K together with Rho-GTPases as Rac1 , which have been all described to be important regulators of macropinocytosis [69] . Concerning the function of the PI3K pathway , activation of this kinase early after virus uptake was confirmed by analyzing the phosphorylation of its specific substrate PI ( 4 , 5 ) P2 . Also , phosphorylation of both residues Thr308 and Ser473 of Akt was observed early after ASFV infection . Besides , pretreatment of Vero cells with the specific PI3K pharmacological inhibitor LY strongly inhibited virus uptake at 60 mpi . Not only that , but we also found that the activation of this kinase has an important role in the infection , since the presence of LY severely impairs the synthesis of both ASFV early and late virus proteins . In this regard , our group has recently described [15] that ASFV uses the cellular machinery of protein synthesis to express its own proteins . Since it has been reported that one of the main roles of PI3K is to regulate the translational machinery through the AKT-mTOR pathway [86] , the strong effect observed of LY on ASFV protein synthesis is very much expected . We have also demonstrated that Rac1 , a regulatory guanosine triphosphatase of Pak1 , was activated during ASFV entry . Rac1 protein belongs to the Rho family of small guanosine triphosphatases , a subgroup of the Ras superfamily of GTPases [102] . In the last years , several viruses have been described to target Rho-GTPases activation to enter the host cells , such as Vaccinia virus [23] , [45] , Ebola virus [80] , Echovirus [92] or Adenoviruses type 2 [103] , among others . Through interaction with its specific effector Pak1 , Rac1 modulates actin cytoskeleton dynamics and controls macropinocytosis [88] , [89] . Consistent with the data reported by Mercer and Helenius , 2008 [23] , showing that active Rac1 is contained in virus-induced membrane perturations , our results show that ASFV induces clusters of this GTPase as early as 10 min after infection . Hence , Rac1 accumulates in ruffling areas very early during the process of ASFV entry , suggesting that ASFV targets Rac1 to entry in host cells . In agreement with this hypothesis , a strong inhibition of virus uptake , in parallel with ruffle formation , was observed in the presence of the Rac1 inhibitor . Moreover , by performing CLSM experiments , we showed that the drug immobilized the virus particles imbibed into the plasma membrane , thus impairing their entry into the cell . Taken together , these results demonstrate the significant role of Rac1 on ASFV entry . Our data strongly contrasts with a recent study [104] , which reported that , although Rac1 is activated by ASFV infection , it is not involved in either ASFV entry or viral protein synthesis . In that study by Quetglas et al . [104] , Rac1 would be responsible of a downstream process that only affected viral production . The discrepancies about the role of Rac1 in ASFV entry and infection might be explained by the fact that the Rac1 inhibitor concentration used does not match with the amounts usually employed to analyze the role of Rac1 in virus uptake [80] , and it is likely too low to disturb ASFV entry or viral protein synthesis . Moreover , confocal microscopy images to measure ASFV uptake were taken as mid z-section , in contrast to our procedure that includes several z-sections that allow us to count the total virus particles inside the cells . Finally , important information regarding the effect of the dominant-negative Rac1-N17 on viral protein synthesis were not shown in that study , in contrast to our results described in Figure 6F . Therefore , the limitations of that work [104] make it difficult to reach any conclusions about the function of Rac1 on ASFV entry and infection . Furthermore , in support of our data , we should note that we have found an important role for Pak1 in Ba71V entry in Vero cells . Pak1 is a serine/threonine kinase activated by Rac1 or Cdc42 involved in the regulation of cytoskeleton dynamics and needed during all stages of macropinocytosis [88] , [93] , [105] . Our results indicate that Pak1 activation is involved in the first steps of ASFV entry , since phosphorylation of the kinase occurs at very early times after virus addition , and even more importantly , the uptake of the virus into the host cells is strongly dependent of Pak1 activity . However , our preliminary studies using the E70 strain did not show a clear effect of the Pak1-specific inhibitor IPA-3 on the synthesis of ASFV proteins ( data not shown ) , either in IPAM or in alveolar swine macrophages . These data suggest that ASFV may activate other different pathways in macrophages or that IPA-3 cannot be efficient enough to inhibit Pak1 if this kinase is constitutively activated in these cells [106] , [107] . Nevertheless , the synthesis of viral proteins was strongly inhibited in macrophages after EIPA and LY treatments , indicating that Na+/H+ exchangers and the PI3K pathway are involved in macropinocytosis-mediated ASFV entry into these cells ( Figure S8 ) . In conclusion , the involvement of the EGFR and PI3K , the nature of the signaling pathway , the involvement of Rac1 , Pak1 and Na+/H+ exchangers , and the actin-cytoskeleton rearrangements , all support a macropinocytosis-driven endocytic process for ASFV entry . In addition , ASFV caused significant induction of dextran uptake ( a specific fluid phase marker of macropinocytosis ) , and colocalization of the internalized ASF virus particles with dextran was also observed . The ASFV genome encodes several glycoproteins [108] , whose role in host-cell binding and entry has not yet been described . However , it has been shown that glycoproteins and lipids are required for several virus binding and entry steps to the host cells [23] , [109] , [110] . It has been also reported that cellular partners that bind to specific regions of viral glycoproteins translocate from intracellular compartments to regulate the susceptibility of different cells to the infection [111] . These kinds of mechanisms could explain the differences found among ASFV viral isolates and their ability to infect different host cells . Future experiments are planned to study the role of both ASFV glycolipids and the putative host partners involved in the mechanisms of ASFV entry and infection of different cell populations . Dynamin is a large GTPase that is involved in scission of newly-formed endocytic vesicles at the plasma membrane [112]–[114] . Although we have shown that dynasore partially inhibits virus entry , we have found no evidence for a role of clathrin in ASFV entry despite the use of multiple approaches . The fact that in our hands dynamin was only partially involved in ASFV entry further ruled out roles for clathrin or caveolae-mediated pathways , as both require dynamin activity . Therefore , our data contrast with a recent study concluding that clathrin-mediated endocytosis is the major entry pathway for ASFV [38] . The key concern about the conclusion of this work is that virus entry is merely measured by the synthesis of ASFV early proteins in the presence of chlorpromazine , and not by specific analysis of virus uptake . Moreover , it is important to note that whereas chlorpromazine disrupts clathrin-coated pits , it may also interfere with biogenesis of large intracellular vesicles such as phagosomes and macropinosomes [115] . Here , by combining different and separate strategies we have carried out a precise analysis of each key endocytic pathway concerned , obtaining , for the first time , a relatively complete description of the mechanism by which ASFV enters into a cell , including identification of several cellular molecules and routes . We have carefully evaluated the specificity and functionality of each pathway and correlated them with virus uptake and infection . Two different strains of ASFV , the virulent E70 and the virulent Ba71V , adapted to growth in Vero cells , have been used to study the virus entry mechanism either in swine macrophages or Vero , respectively . Several drugs were used to inhibit pathways , but specificity was evaluated by testing the function of the main pathways after treatment . Furthermore , highly specific dominant-negative mutants were used to confirm the data obtained by pharmacological inhibitors . More importantly , all throughout this work either a FACS-based or a confocal sensitive virus entry assays were used in discriminating blockage in virus entry versus blockage in downstream steps of the infection cycle . This is particularly relevant when using drugs that frequently affect multiple cellular functions in addition to specific entry . Overall , our data provide strong evidence that ASFV entry takes place by a process closely related to macropinocytosis , adding new and valuable information regarding endocytosis mechanisms in the context of ASFV entry ( plotted in Table 1 ) . The evidence presented demonstrates for the first time , that ASFV utilizes a macropinocytosis-like pathway as the primary means of entry into IPAM and Vero cells . However , we cannot state that virus entry occurs exclusively by this pathway , especially in swine macrophages . But our data clearly show that its disruption blocks the greater part of infection and particle uptake . Our work also indicates that clathrin-mediated endocytosis plays at most a minor role in ASFV entry . However , and in accordance with the data of Hernaez et al . [38] , we found that CPZ diminishes both ASFV early and late protein synthesis , together with viral production . Thus , our data demonstrate a role for clathrin function merely in post entry events . A strong hazard of ASFV dissemination from Sardinia and Caucasian areas to EU countries has recently appeared , thus making the progress of knowledge and tools for protection against this virus urgent . Infection by ASFV is characterized by the absence of a neutralizing immune response , which has so far hampered the development of a conventional vaccine . Therefore , our findings are relevant as they not only provide a detailed understanding of ASFV entry mechanism , but also identify novel cellular factors that may provide new potential targets for therapies against this virus . In parallel , further studies are planned to characterize viral factors that may interact with components of the macropinocytosis pathway , probably useful for vaccine development . | ASFV is a highly pathogenic zoonotic virus , which can cause severe economic losses and bioterrorism threats . No vaccine against ASFV is available so far . A strong hazard of ASFV dissemination through EU countries from Caucasian areas has recently emerged , thus making urgent to acquire knowledge and tools for protection against this virus . Despite that , our understanding of how ASFV enters host cells is very limited . A thorough understanding of this process would enable to design targeted antiviral therapies and vaccine development . The present study clearly defines key steps of ASFV cellular uptake , as well as the host factors responsible for permitting virus entry into cells . Our results indicate that the primary mechanism of ASFV uptake is a macropinocytosis-like process , that involves cellular membrane perturbation , actin polarization , activity of Na+/H+ membrane channels , and signaling proceedings typical of the macropinocytic mechanism of endocytosis , such as Rac1-Pak1 pathways , PI3K and tyrosine-kinases activation . These findings help understanding how ASFV infects cells and suggest that disturbance of macropinocytosis may be useful in the impairment of infection and vaccine development . | [
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| 2012 | African Swine Fever Virus Uses Macropinocytosis to Enter Host Cells |
Spain has one of the world’s largest pools of organ donors and is a global leader in terms of the number of transplants it performs . The current outbreak of leishmaniasis in Fuenlabrada ( in the southwest of the region of Madrid , Spain ) has involved 600 clinical cases since late 2009 ( prevalence 0 . 2% ) . It may therefore be wise to monitor the town’s transplanted population for Leishmania infantum; its members are immunosuppressed and at greater risk of infection and relapse following treatment . The present work examines the use of cytokine release assays to determine the prevalence of Leishmania infection in this population , and to confirm recovery following treatment for visceral leishmaniasis ( VL ) . The humoral and cellular immune responses to L . infantum were characterized in 63 solid organ transplant ( SOT ) recipients from Fuenlabrada , 57 of whom reported no previous episode of VL ( NVL subjects ) , and six of whom had been cured of VL ( CVL subjects ) . Seventeen subjects ( 12 NVL and 5 CVL ) showed a patent lymphoproliferative response to soluble Leishmania antigen ( SLA ) . Stimulation of peripheral blood mononuclear cell cultures and of whole blood with SLA led to the production of different combinations of cytokines that might serve to confirm Leishmania infection or recovery from VL and help prevent cured patients from relapsing into this serious condition .
In Spain , leishmaniasis is an endemic zoonosis caused by Leishmania infantum . The national mean annual incidence is 0 . 45 cases/100 , 000 inhabitants , similar to that traditionally reported for the capital , Madrid ( 0 . 5 cases/100 , 000 inhabitants ) [1] . A few years ago , an unusual , large outbreak of leishmaniasis was noted in the southwest of the Region of Madrid , and disease incidence in the town of Fuenlabrada is currently 21 . 54/100 , 000 . Since 2009 , more than 100 cases of visceral leishmaniasis ( VL ) have been recorded every year in Fuenlabrada; similar figures are expected for the coming years [2] . Immunosuppression is a major risk factor in the appearance of overt clinical leishmaniasis; it can also alter the presentation of the disease and response to treatment [3] . Indeed , the worldwide number of cases of VL in recipients of solid organ transplants ( SOT ) has quadrupled since the 1990s [4] ( 77% of those affected received a kidney transplant , the most commonly performed transplant operation ) . Most of these cases occurred in the Mediterranean basin , particularly in Spain , which has one of the world’s largest pool of organ donors and is among the foremost in performing SOTs . Very little is known , however , about the relationship between leishmaniasis and transplants , and the prevalence of asymptomatic leishmaniasis is unknown [5] . During the recent outbreak in Fuenlabrada , eight out of the total 130 SOT recipients residing in the area developed VL ( 6 . 15% ) ; the risk for these patients is therefore around 30 times that faced by immunocompetent individuals . Recently , molecular and serological analyses of liver transplant recipients in an area of Brazil where leishmaniasis is endemic revealed a high prevalence of asymptomatic infection [6] . However , measurement of the cell-mediated immune response is critical when estimating exposure to pathogens causing intracellular infections . Immunity to leishmaniasis is associated with a strong Th1-type immune response , as demonstrated by a positive delayed type hypersensitivity ( DTH ) reaction in the Leishmanin skin test ( LST ) , along with lymphocyte proliferation and the production of high levels of IFN-γ and TNF-α [7] following stimulation with soluble leishmania antigen ( SLA ) . T-cell-based interferon-γ ( IFN-γ ) release assays have been developed as alternatives to DTH tests [8] . For example , the whole-blood gamma interferon release assay ( IGRA ) has recently been reported capable of detecting subclinical infection in healthy individuals living in an area where VL is endemic [9] . Although some studies have been published on the use of IGRA in tuberculosis and cytomegalovirus infection [10 , 11] , the literature contains nothing on its use to detect Leishmania infection in SOT recipients . The aim of the present work was to test cytokine release assays as a means of determining the prevalence of Leishmania infection in SOT recipients , and to confirm recovery following treatment for VL . Assessing the exposure to Leishmania and the immunological memory of SOT recipients living in an area highly endemic for leishmaniasis should throw light on the infection rate in this population , help prevent those treated for VL from relapsing , and reveal the epidemiological features of this disease in the immunosuppressed within the context of an outbreak .
Sixty three SOT ( kidney , liver and heart ) recipients were enrolled in the present study . All were aged 18 years or older , had undergone transplant surgery between 2005 and 2013 at the 12 de Octubre University Hospital , and resided in the town of Fuenlabrada . Fifty seven subjects had experienced no previous episode of VL or compatible symptomology ( NVL subjects ) , and six had been cured of visceral leishmaniasis ( CVL subjects ) . Recruitment and sample collection were performed in accordance with Good Clinical Practice guidelines . The study was approved by the ethics Committee of the 12 de Octubre University Hospital . All subjects gave their written informed consent to be included in the study . Recipients of a graft from a non-heart beating donor ( 30% of all SOT recipients ) underwent induction therapy with intravenous ( IV ) rabbit anti-thymocyte globulin ( ATG-Fresenius ) ( 1 . 25 mg/kg daily for 5–7 days ) and a calcineurin inhibitor ( CNI ) from day 6 . Patients at high immunological risk received induction therapy with ATG for 1–3 days plus CNI from day 0 . Basiliximab ( 20 mg on days 0 and 4 ) was provided to patients at high risk of CNI-related nephrotoxicity owing to advanced age or pre-transplant comorbidities . Immunosuppression was maintained with tacrolimus ( 0 . 1 mg/kg daily ) , mycophenolate mofetil ( 500–1000 mg twice daily ) or mycophenolic acid ( 360 mg twice daily ) , and prednisone ( 0 . 5 mg/kg daily with progressive tapering beyond day 20 or 30 ) . Perioperative prophylaxis consisted of a single dose of 2 g of IV cefazolin . Trimethoprim–sulphamethoxazole ( 160/800 mg 3 times weekly ) or monthly IV pentamidine was provided as prophylaxis for Pneumocystis jirovecii pneumonia for the first nine months . Patients at high risk of cytomegalovirus disease were administered IV gancyclovir ( 5 mg/kg daily ) or oral valgancyclovir ( 900 mg daily ) for the first three months . Leishmania infantum antigen extract was prepared from promastigote stationary phase parasite cultures ( JPC strain , MCAN/ES/98/LLM-722 ) . SLA was obtained from parasites by washing in 1x phosphate-buffered saline ( PBS ) and centrifuging at 1000 g for 20 min at 4°C . The supernatant was removed and the pellet resuspended in lysis buffer ( 50 mM Tris/5 mM EDTA/HCl , pH 7; 1 ml for every 109 parasites ) . The latter was then subjected to three rapid freeze/thaw cycles followed by three 20 s 40W pulses with a sonicator , and centrifuged at 27 , 000 g for 20 min at 4°C . The supernatants were then collected , aliquoted and stored at -80°C until use . Protein quantification was performed using the Bradford method employing the Bio-Rad Protein Assay kit ( Bio-Rad , USA ) . Peripheral blood mononuclear cells ( PBMCs ) were isolated by density centrifugation through Ficoll-Hypaque ( Rafer , Spain ) . The collected cells were cultured in RPMI 1640 supplemented with 10% heat-inactivated foetal bovine serum , 100 IU/ml penicillin , 100 μg/ml streptomycin , 2 mM L-glutamine , 50 μM 2-mercaptoethanol , and 1 mM sodium pyruvate . They were then plated in 96 well-plates and kept with RPMI 1640 medium alone ( unstimulated ) or in RPMI 1640 supplemented with 10 μg/ml phytohaemagglutinin type M ( PHAM ) ( Sigma-Aldrich , USA ) ( positive control ) , or with SLA ( 10 μg/ml ) . All were kept in a humidified , 5% CO2 atmosphere at 37°C for 5 days . Cell proliferation was measured by bromodeoxyuridine incorporation using the Cell Proliferation Biotrak ELISA kit ( General Electric Healthcare Life Sciences , UK ) . The results are shown as a stimulation index ( SI ) for each mitogen or antigen . The cut-off for positive lymphoproliferation was calculated as the mean+3 SD ( standard deviation ) of the SI for 45 Leishmania-exposed but completely negative SOT subjects: SI = 2 . 76 . The supernatants of the in vitro cell cultures were collected and stored at -20°C for cytokine quantification . Blood samples ( 9–10 ml ) were collected from patients in tubes containing heparin . Aliquots ( 500 μl ) of whole blood were incubated in tubes with 10 μg/ml SLA or 5 μg/ml PHAM as a positive control . An additional tube for which no stimulation was provided was included as a negative control . After adding the antigens and mixing thoroughly , the tubes were incubated at 37°C for 24 h . After centrifugation at 2000 g for 10 min , the supernatant plasma were collected and stored at -20°C for cytokine analysis . IFN-γ , granzyme B , tumour necrosis factor-alpha ( TNF-α ) , interleukin ( IL ) - 10 ( IL-10 ) , IL , 5 , IL-17A , IL-2 and IL-4 were quantified in 50 μl of plasma and PBMC culture supernatants following PHAM ( 10 μg/ml , 120 h ) or SLA stimulation ( 10 μg/ml , 24 h ) , using the BD Cytometric Bead Array Human Flex Set ( Beckton Dickinson Biosciences , USA ) following the manufacturer’s instructions . Data were acquired using a FACSCalibur flow cytometer and analysed using the Flow Cytometric Analysis Program Array ( Beckton Dickinson Biosciences , USA ) . An enzyme-linked immunosorbent assay ( ELISA ) was used to detect antibodies to SLA . Briefly , 96-well plates ( NuncMaxisorp Immuno Plates , USA ) were coated with 100 μl/well of 10 μg/ml SLA and left overnight at 4°C . The plates were then washed three times with PBS , 0 . 1% Tween 20 ( PBS-T ) , pH7 . 4 and blocked with 200 μl/well of PBS containing 0 . 1% Tween 20 and 3% BSA for 1 h at 37°C . After washing with PBS-T , diluted blood plasma ( 1/200 in PBS-T ) was added ( 100 μl/well ) and incubated for 2 h at 37°C . The plates were then washed with PBS-T and 100 μl/well of 1/5000-diluted HRP-conjugated anti-human Ig ( Invitrogen , USA ) were added for 30 min at 37°C . All plates were then developed with 100 μl/well of Sigma Fast o-phenylene diamine dihydrochloride ( OPD ) tablets ( Sigma , USA ) for 20 min . The reaction was stopped with 50 μl/well of 2N HCl , and absorbance measured at 492 nm . All tests were performed in duplicate and the mean value recorded . The cut-off for seropositivity was calculated as described above at 0 . 132 . Immunofluorescent antibody titre ( IFAT ) analyses of plasma samples were performed using 2 × 105 L . infantum promastigotes in PBS per well ( MCAN/ES/98/LLM-722 ) . Subject plasma was assayed as two-fold serial dilutions from 1/20 to 1/640 in PBS to determine total IgG levels using fluorescein isothiocyanate-conjugated goat anti-human IgG ( Fluoline G ) ( BioMérieux , France ) diluted 1/200 . The threshold titre for positivity was set at 1:80 . Finally , rK39-ICT dipsticks were purchased from Leti Laboratories and the corresponding test performed following the manufacturer’s instructions . DNA isolation and real time PCR ( qPCR ) were performed as described by Cunha et al . [12] . Briefly , DNA was extracted from 100 μL of peripheral blood by conventional phenol-chloroform extraction and eluted in 100 μl sterile distilled water . For qPCR , 1000 nM of R223 and 500 nM of R333 primers ( Sigma-Aldrich , USA ) for the small subunit rRNA ( SSUrRNA ) sequence were used [13] . Total DNA was used as a template in touchdown qPCR reactions involving the LightCycler FastStart DNA Master SYBR Green I kit ( Roche Applied Science , Switzerland ) [14] . PBMCs ( 2x105 ) from all SOT recipients were cultured in Novy-MacNeal-Nicolle medium . The presence/absence of Leishmania was checked every week up to 4 weeks . Immunological data were tested for normality using the Shapiro-Wilk test . Means were compared using the Mann-Whitney U test . The Spearman's rank test was used to seek correlations between immunological variables . Significance was set at p<0 . 05 . All calculations were performed using GraphPad Prism 5 . 0 software ( GraphPad Software , USA ) .
The in vitro cell proliferation assay with PHAM stimulation revealed the functional capacity of the PBMCs to mount a response in all immunosuppressed individuals . Some 21 . 05% of the NVL subjects ( 12/57 ) mounted a positive lymphoproliferative response to SLA stimulation ( Fig 1 ) ( hereinafter referred to as NVLpl+ subjects ) . The IFAT results revealed just two of these same 57 subjects to have mounted a positive serological response; the ELISA and rK39-ICT tests were positive in one of these latter two subjects . This person ( i . e . , positive in all three serological tests ) also showed a strong lymphoproliferative response to SLA ( SI ≥14 ) . The subject who was serologically positive by IFAT alone showed no lymphoproliferative response and developed VL within a few days of testing ( treatment was provided within 2 weeks of the original blood sampling ) , and for that reason was excluded for further analysis ( S1 Fig ) . The SLA-stimulated PBMC cultures returned positive results for five out of six CVL subjects . IFAT detected anti-Leishmania antibodies in two of these subjects , while the ELISA and rK39-ICT tests detected the same in only one . ( Note: The single CVL subject who showed no lymphoproliferative response showed no cytokines in the supernatants of SLA-stimulated PBMC cultures or plasma [see below] , and produced no antibodies against Leishmania . This subject relapsed four months after sampling and was therefore considered not cured , and excluded from further analysis ) . No Leishmania DNA was detected in any blood sample from any SOT recipient . Absence of residual parasite DNA could be due to the reduced sample size , endemicity of the area , PCR sensitivity or episodic presence of parasite in blood . Neither were any NNN cultures positive for parasites . The supernatants of in vitro SLA-stimulated cultures of PBMCs from NVLlp+ subjects showed significantly more IFN-γ ( P<0 . 0001 ) , granzyme B ( P<0 . 0001 ) , TNF-α ( P<0 . 0001 ) , IL-5 ( P = 0 . 0004 ) , IL-10 ( P = 0 . 0413 ) and IL-17A ( P = 0 . 0030 ) than those of NVL subjects who did not respond to SLA ( hereinafter referred to as NVLlp- subjects ) ( Fig 2 ) . The supernatants of CVL subjects showed the same cytokine pattern as those of the NVLlp+ subjects: IFN-γ ( P = 0 . 0013 compared to the NVLlp- subjects ) , granzyme B ( P = 0 . 0025 ) , TNF-α ( P = 0 . 0025 ) , IL-5 ( P = 0 . 0455 ) , IL-10 ( P = 0 . 0088 ) , and IL-17A ( P = 0 . 0379 ) . No significant differences were found in the production of IFN-γ , granzyme B , IL-10 , IL-17A , IL-4 and IL-2 between NVLlp+ and CVL subjects , while TNF-α ( P = 0 . 036 ) and IL-5 ( P = 0 . 0040 ) were higher in NVLlp+ subjects . In the 12 NVLlp+ subjects , IFN-γ production correlated with that of TNF-α ( r = 0 . 853 , P = 0 . 004 ) , granzyme B ( r = 0 . 818 , P = 0 . 00093 ) and IL-10 ( r = 0 . 810 , P = 0 . 0012 ) . By way of example , Fig 3 shows the correlation between IFN-γ and granzyme B . Non-specific stimulation of the PBMCs with PHAM led to an efficient Th1 response that was similar in the NVLlp+ , NVLlp- and CVL subjects ( Table 1 ) . IL-10 production was however , significantly higher in the NVLlp+ subjects than in the NVLlp- subjects ( P = 0 . 0097 ) . SLA-stimulated whole blood from NVLlp+ subjects produced significantly more IFN-γ ( P = 0 . 0004 ) , TNF-α ( P = 0 . 0469 ) , and IL-2 ( P = 0 . 0028 ) than that of SLA-stimulated whole blood from NVLlp- subjects ( Fig 4 ) . In these NVLlp+ subjects , IFN-γ production correlated with IL-2 ( r = 0 . 616 , P = 0 . 018 ) ( Fig 5 ) . Similarly , CVL subjects with a positive lymphoproliferative response showed significantly higher concentrations of IFN-γ and TNF-α in plasma ( P = 0 . 0101 and P = 0 . 0467 respectively ) than the NVLlp- subjects . No differences in IL-10 concentration were seen between the three different groups of subjects ( Fig 4 ) . Stimulating whole blood with SLA for 24 h did not induce production of IL-17A , IL-5 or IL-4 ( S1 Table ) . No significant differences were found between NVLlp+ and CVL subjects . Interestingly , in the NVLlp+ subjects , a strong correlation was found between the lymphoproliferative capacity of SLA-stimulated PBMCs and the production of IFN-γ in SLA-stimulated whole blood ( r = 0 . 881 , P = 0 . 0002 ) ( Fig 6 ) . PHAM stimulation of whole blood led to similar levels of cytokine production in all subjects , with the exception of TNF-α , which was higher in the CVL subjects than in the NVLlp+ subjects ( P = 0 . 0061 ) ( Table 2 ) , and higher in the NVLlp+ subjects than in the NVLlp- subjects ( P = 0 . 0225 ) .
The present results highlight the need to use tests that detect the cell-mediated immune response when screening for asymptomatic subjects in areas with endemic leishmaniasis . They also show that PBMC cultures and/or whole blood assays can be used to search for leukocyte cytokine production as a marker of infection . The production of IFN-γ , TNF-α , granzyme B , IL-5 and IL-10 by SLA-stimulated PBMCs , and of IFN-γ , TNF-α and IL-2 by SLA-stimulated whole blood , could be used to indicate exposure to leishmaniasis , especially for patients subjected to induced immunosuppression . The combination of serological and cell-based tests could help determine the true size of the epidemic of leishmaniasis affecting Fuenlabrada , and indeed of those affecting other areas . | We have used cytokine release assays to determine the prevalence of Leishmania infantum infection in solid organ transplant ( SOT ) recipients living in an area where the organism is endemic following an outbreak . Some 21 . 05% of SOT recipients with no previous history of leishmaniasis had been in contact with the parasite; the risk of these individuals becoming infected by Leishmania is high , a consequence of their need to be maintained in an immunosuppressed state . The results indicate the usefulness of whole blood stimulation assays , and of IFN-γ/TNF-α analysis , for determining exposure to Leishmania and confirming cure from visceral leishmaniasis in SOT recipients . | [
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| 2015 | Cytokine Release Assays as Tests for Exposure to Leishmania, and for Confirming Cure from Leishmaniasis, in Solid Organ Transplant Recipients |
Outcome of host-pathogen encounter is determined by the complex interplay between protective bacterial and host defense strategies . This complexity further amplifies with the existence of cell-to-cell phenotypic heterogeneity in pathogens which remains largely unexplored . In this study , we illustrated that heterogeneous expression of pneumolysin ( Ply ) , a pore-forming toxin of the meningeal pathogen , S . pneumoniae ( SPN ) gives rise to stochastically different bacterial subpopulations with variable fate during passage across blood-brain barrier ( BBB ) . We demonstrate that Ply mediated damage to pneumococcus containing vacuolar ( PCV ) membrane leads to recruitment of cytosolic “eat-me” signals , galectin-8 and ubiquitin , targeting SPN for autophagic clearance . However , a majority of high Ply producing subset extensively damages autophagosomes leading to pneumococcal escape into cytosol and efficient clearance by host ubiquitination machinery . Interestingly , a low Ply producing subset halts autophagosomal maturation and evades all intracellular defense mechanisms , promoting its prolonged survival and successful transcytosis across BBB , both in vitro and in vivo . Ply therefore acts as both , sword and shield implying that its smart regulation ensures optimal disease manifestation . Our elucidation of heterogeneity in Ply expression leading to disparate infection outcomes attempts to resolve the dubious role of Ply in pneumococcal pathogenesis .
The complex battle between the microbial virulence strategies and the host’s counter-defensive mechanisms governs the ultimate outcome of infection . Traditional approaches involving the bulk population based model considers each interacting partner as a functionally homogenous entity , overlooking the stochastic variation in the gene expression between isogenic cells of a population . This stochasticity in virulence attributes results in functionally heterogeneous host and pathogen population subsets which dynamically interact with each other and are responsible for the genesis of different infection foci , triggering disparate infection outcomes [1] . During the course of disease progression , a pathogenic microbe needs to pass through several infection bottlenecks which include initial asymptomatic colonization to further invasion and finally crossing of cellular barriers to infiltrate deeper tissues for establishment of a protective niche . These transitions are accomplished by sensing the ever changing environmental cues and smart regulation of the virulence repertoire by the pathogen that facilitates evasion , manipulation or exploitation of various host immune defense mechanisms witnessed at different stages of infection . In addition to such kind of deterministic adaptation , stochasticity in virulence gene expression within the clonal population gives rise to simultaneous existence of professional subsets that play crucial roles at different stages of host-pathogen interactions , facilitating the establishment of successful infection and disease progression . In this study , we set out to explore the role of heterogeneous expression of a pore-forming toxin , pneumolysin by the meningeal pathogen Streptococcus pneumoniae during its transcellular passage through the blood-brain barrier ( BBB ) [2 , 3] , a crucial step in meningitis pathogenesis . Streptococcus pneumoniae ( SPN , pneumococcus ) , a Gram positive human pathogen , is the common cause of life-threatening invasive diseases such as pneumonia , sepsis and meningitis [4] . In order to cause meningitis , SPN must penetrate the BBB comprised of brain microvascular endothelial cells . Employing a variety of surface anchored proteins SPN attaches and penetrates the BBB [5–10] , however , little is known about the mechanistic details behind how SPN thwarts lysosomal killing during transcellular passage across brain endothelium . Following invasion into brain endothelial cells , SPN displays remarkable heterogeneity with respect to the infection outcome . While a majority of the invaded SPN gets killed in a lysosome dependent manner [11] , a minor subset of the invaded SPN recycles back to apical surface via recycling endosomes [3] . Yet , another subset of the intracellular SPN is neither killed , nor recycled back , but is transcytosed from the apical to the basolateral side , enabling further dissemination in the host [3] . However , the key virulence genes and their molecular mechanisms underlying these heterogeneous infection outcomes during pneumococcal passage across the BBB remain uninvestigated . Pneumolysin ( Ply ) , the cholesterol dependent pore-forming toxin secreted by SPN , promotes cell lysis and extensive tissue damage in the host [12] . This multifaceted toxin is involved in a gamete of other cellular activities , such as complement activation , DNA damage , prevention of opsonophagocytosis and induction of apoptosis [13] . However , despite being one of the major virulence factors , its role in pneumococcal pathogenesis remains controversial [14 , 15] . In this study , we demonstrate that differential expression of pneumolysin gives rise to stochastically different SPN subpopulations inside brain endothelium governing its fate—during transit through the BBB . We demonstrate that among the heterogeneous SPN population that invades the brain endothelium , majority of Ply expressing SPN succumb to intracellular surveillance mechanisms but a handful of low Ply expressing SPN which safely transcytose through the BBB to gain access into the brain , a critical event in pneumococcal disease progression .
All the experimental work on animals was done as per the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animals ( CPCSEA ) , India . The study protocol has been reviewed and approved by the Institutional Animal Ethics Committee ( IAEC , Reg . no . 48/1999/CPCSEA ) of Indian Institute of Science ( IISc , Bengaluru , India ) under the project number CAF/Ethics/380/2014 . All experimental protocols adhered to the Breeding of and Experiments on Animals ( Control and Supervision ) Rules , 1998 , and its amendments that were published , as a sub section ( 1 , 1A and 2 ) of Section 17 of the Prevention of Cruelty to Animals Act , 1960 ( 59 of 1960 ) under the notification of the Ministry of Environment and Forests , Govt . of India . Antibodies used in this study were against; LAMP1 ( Cell Signaling Technology , D2D11 ) , Galectin 8 ( R & D Systems , AF1305 ) , NDP52 ( Abcam , ab68588 ) , Pneumolysin ( Santacruz Biotechnology , sc-80500; Abcam , ab71811 ) , GAPDH ( Millipore , MAB374 ) , Ubiquitin ( Enzo Life Sciences , BML-PW-8810 ) , LC3 ( Cell Signaling Technology , 4108S ) , Phosphorylcholine ( Sigma Aldrich , M1421 ) . Antiserum against pneumococcal Enolase was kindly provided by Sven Hammerschmidt ( University of Greifswald , Germany ) . Fine chemicals used in this study include Bafilomycin A1 ( B1793 ) , 3-Methyladenine ( M9281 ) , Rapamycin ( R0395 ) , PYR41 ( N2915 ) and MG132 ( 474791 ) , from Sigma Aldrich . SPN strain R6 ( serotype 2 , unencapsulated ) and TIGR4 ( serotype 4 , encapsulated ) were obtained from Prof . TJ Mitchell ( Univ . of Birmingham , UK ) . Other encapsulated SPN strains , namely , D39 ( serotype 2 ) , Tupelo ( serotype 14 ) and A60 ( serotype 19F ) were generously provided by Prof . EI Tuomanen ( St . Jude Childrens Hospital , USA ) . All SPN strains were routinely grown in Todd-Hewitt broth ( THB ) supplemented with 1 . 5% yeast extract ( THY media ) at 37°C in 5% CO2 and when necessary the following antibiotics were used: kanamycin ( 200 μg/ml ) , spectinomycin ( 100 μg/ml ) , chloramphenicol ( 2 μg/ml ) . To construct the ply mutant strain ( Δply ) , ply gene was amplified from the genome of SPN and cloned in the pGEMT Easy vector ( Promega ) . This was further digested with BbsI/AvaI for insertion of a kanamycin resistance cassette . The recombinant vector was linearized with NotI and transformed into wildtype SPN strains ( R6 or TIGR4 ) using competence stimulating peptide 1 or 2 ( 100 ng/ml ) . Full length ply gene was cloned in between BamHI and SacI sites of the shuttle vector pIB166 [16] ( kind gift from Prof . Indranil Biswas , Kansas University Medical Centre , USA ) and used for complementation ( Δply:pPly ) . High ( WT:Ply-High ) , low ( WT:Ply-Low ) Ply expressing SPN strains were constructed using the principle as described earlier [17] . Briefly , following cloning of the ply gene between BamHI / XhoI sites in pBSK , specific silent point mutations were incorporated in the 5' region of ply mRNA by PCR amplification with ply-sdm1-F / ply-sdm1-R and ply-sdm2-F / ply-sdm2-R primer pairs . This manipulated the folding energies of the transcripts affecting their translation efficiencies and resulting in modification of Ply expression . Site directed mutagenesis was also performed to generate W433F variant of Ply , by PCR amplification of Ply ORF cloned in pBSK with ply-F-W433F / ply-R-W433F primers . Individually these ply variants were sub-cloned in BamHI/XhoI sites in a recombinant vector where upstream and downstream regions of ply gene were sequentially cloned between KpnI / XhoI and BamHI / XbaI sites following amplification with ply-up-KpnI-F2 / ply-up-XhoI-R2 and ply-dwn-BamHI-F2 / ply-dwn-XbaI-R primer pairs , respectively , from SPN genome . Finally , a spectinomycin resistance cassette ( Spr ) was cloned in the BamHI site of the recombinant vectors containing ply variant genes coding for either W433F , high or low Ply expression , respectively . The recombinant vectors were linearized with PvuII and used for transformation of WT SPN strain R6 or TIGR4 using either CSP-1 or CSP-2 , respectively , to generate high ( WT:Ply-High ) , low ( WT:Ply-Low ) and W433F ( WT:PlyW433F ) Ply expressing strains . To transcriptionally fuse gfp ( green fluorescent protein ) with ply gene , firstly ply gene was PCR amplified using ply-XhoI-F / ply-XmaI-R primer pairs and was cloned into the recombinant vector containing upstream and downstream regions of ply gene . Further gfp gene was amplified from hlpA-GFP fusion cassette ( kindly provided by JW Veening , University of Groningen , Netherlands ) using GFP-XmaI-F1 / GFP-BamHI-R primers and was cloned in XmaI / BamHI sites downstream of ply gene in the recombinant vector . Finally , a Spr cassette was cloned in the BamHI site of the recombinant vector containing the ply-gfp fusion cassette that was used for transformation of WT SPN ( R6 ) following digestion with PvuII and transformants were selected on spectinomycin containing plates . Capsule was deleted in WT TIGR4 strain using allelic exchange method . Briefly , dexB ( SP_0342 ) and aliA ( SP_0366 ) gene segments , present upstream and downstream of the capsule locus were sequentially cloned in SacI / BamHI and BamHI / XhoI sites in pBSK following amplification with dexB-F-SacI / dexB-R-BamHI and aliA-F-BamHI / aliA-R-XhoI primer pairs , respectively . A Spr cassette , was subsequently cloned in the BamHI site of the recombinant vector and the final construct containing Spr cassette flanked by dexB and aliA gene segments was used for transformation of WT SPN ( TIGR4 ) and transformants were selected on spectinomycin containing plates . SPN strains were made fluorescent following transformation with hlpA-tRFP and hlpA-GFP fusion cassettes ( kindly provided by JW Veening , University of Groningen , Netherlands ) [18] or by staining with DRAQ5 ( BD Biosciences ) or DAPI . All gene deletions and cassette insertions were verified by PCR amplification of the respective gene locus followed by DNA sequencing . Unless otherwise mentioned , SPN strain R6 and its derivatives were used for in vitro experiments while WT TIGR4 and its derivatives were used for in vivo experiments . Hemolysis assay was performed by adopting the protocol as described earlier [19] . Briefly , bacterial pellets obtained from mid-exponentially ( OD600nm ~ 0 . 4 ) grown cultures were lysed by sonication and crude extracts were collected following centrifugation ( 15000 rpm , 30 min , 4°C ) . Tenfold dilutions of the bacterial crude extracts ( final volume 100 μl ) containing equal amount of protein were incubated with 2% RBC suspension in presence of 10 mM DTT . After 60 min incubation at 37°C , 50μl PBS was added to these suspensions and following removal of unlysed RBCs by centrifugation ( 1500 rpm , 10 min ) , 100 μl of the supernatants were collected . Absorbance of the released hemoglobin in the supernatants were determined at 540 nm using a microplate spectrophotometer ( Thermo Fischer scientific ) . PBS and Triton X-100 ( 0 . 05% ) were used as negative and positive control , respectively . Flow cytometry of SPN cultures was performed as described earlier [20] . Briefly , SPN cultures grown till 0 . 4 OD600nm were washed with PBS . Approximately 2x106 bacteria were incubated with rabbit anti-Ply antibody ( 1:50 ) for 30 min at 4°C . Following washes with PBS , bacteria were incubated with secondary antibody ( 1:100 ) for 30 min at 4°C . Finally , bacterial cells were washed with PBS and subjected to flow cytometry using a BD FACS Aria-Fusion flow cytometer using forward and side scatter parameters to gate on atleast 10000 SPN . Results were analysed using FlowJo software ( version 9 . 3 ) and data was represented as percent of bacterial population positive for fluorescence markers . Human brain microvascular endothelial cells ( hBMECs , provided by Prof . KS Kim , Johns Hopkins University ) were routinely cultured in RPMI 1640 medium ( Gibco ) supplemented with 10% Fetal Bovine Serum ( Gibco ) , 10% Nu-Serum ( BD Biosciences ) , and 1% Minimum Essential Medium Non-Essential Amino acids ( Gibco ) at 37°C in 5% CO2 [21] . Stably transfected hBMECs overexpressing GFP-LC3 , YFP-Gal8 and mStrawberry-Ubq fusion proteins were constructed by transfecting hBMECs with pBABEpuro GFP-LC3 vector ( Addgene , 22405 ) , M6Pblast-YFP-LGALS8 [22] ( kindly provided by F Randow , MRC Laboratory of Molecular Biology , UK ) and pMRX-IRE-mStrawberry-ubiquitin [23] ( kind gift from T Yoshimori , Osaka University , Japan ) . For RNAi mediated gene knock down , siRNA directed towards galectin 8 ( Thermo Fisher Scientific , HSS106038 ) and scrambled siRNA ( Thermo Fisher Scientific , 4390846 ) were used to transiently transfect hBMECs . All transfections were performed using Lipofectamine 2000 reagent ( Thermo Fisher Scientific ) and selections were done in the presence of 1 μg/ml puromycin ( Sigma Aldrich ) and 2 μg/ml blasticidin hydrochloride ( HiMedia ) where necessary . Infection assays were performed as described earlier [7] . Briefly , fully confluent hBMEC monolayers were infected at a multiplicity of infection ( MOI ) of 1 ( for Δcps , Δcps Δply strains ) 10 ( for R6 and its derivatives ) and 50 ( for TIGR4 and its derivatives ) for 1 h and further incubated in culture medium containing penicillin ( 10 μg/ml ) and gentamicin ( 400 μg/ml ) for 2 h to kill the extracellular bacteria . After several washes with RPMI , cells were trypsinized ( 0 . 025% trypsin EDTA ) , lysed ( 0 . 025% Triton X-100 ) and serial dilutions of the lysates were plated on BHI ( Brain Heart Infusion ) agar plates for enumeration of bacterial colonies . Bacterial invasion efficiency was calculated as ( recovered CFU/initial inoculum CFU ) × 100% . To assess intracellular survival trend of bacteria , cell lysates were prepared similarly and spread plated at indicated time intervals following 1 h of infection and 2 h of antibiotic treatment . Surviving bacteria at different time points were enumerated and was represented as percent survival at indicated time points relative to 0 h ( post antibiotic treatment ) . For inhibition studies , hBMECs were pretreated with Bafilomycin A1 ( 100 nm , 1 h ) , 3-Methyladenine ( 1 mM , 24 h ) or PYR41 ( 45 μM , 1 h ) before infection with bacteria . hBMEC cells were infected with WT R6 and its Δply mutant ( MOI ~10 ) for 1 h followed by penicillin-gentamycin treatment as described earlier . At 12 h post infection , cell viability was assessed using MTT assay kit ( HiMedia , India ) according to manufacturer’s protocol . Uninfected cells were used as negative control while Triton X-100 ( 0 . 025% ) was used as positive control . hBMEC monolayers were infected with WT:Ply-High and WT:Ply-Low SPN strains ( MOI ~ 10 ) for 1 h followed by penicillin-gentamicin treatment as discussed earlier . After washing the monolayer with RPMI , penicillin ( 0 . 04 μg/ml ) containing media was added to inhibit bacterial replication . At regular intervals ( 4 h ) , the number of bacteria egressed in the culture supernatant were quantified by plating the medium on BHI agar plates . The number of egressed bacteria were expressed as percent egressed at indicated time points relative to number of intracellular bacteria at 0 h ( post antibiotic treatment ) . The animal experiments were performed by adopting the bacteremia-derived pre-meningitis model as described earlier [24] . Briefly , 6–8 weeks old female Balb/c mice ( Jackson Labs ) were injected intravenously though tail vein with 200 μl PBS containing 106 bacteria . Control mice were injected with sterile PBS . For survival studies mice were monitored at regular intervals for assessment of disease progression . For analysis of tissue bacterial burden , mice were anaesthetized by intraperitoneal injection of Ketamine and Xylazine at 14 h post infection . Blood was collected by retro-orbital puncture for quantification of pneumococci in the blood . Following blood collection , mice were perfused with sterile PBS in the left ventricle via the vena cava and the blood was allowed to perfuse out from perforation made in the right auricle , until complete flushing out of remaining blood . Brain , lungs and spleen were aseptically collected in PBS . One half of the brain was transferred in 4% paraformaldehyde for histology studies and the other half was further divided into two parts wherein one part was used to prepare homogenates for bacterial enumeration and the other part was transferred into TRIZOL for RNA isolation . Tissue homogenates ( prepared by bead beating using 1 mm glass beads ) or blood were serially diluted and plated on blood agar for enumeration of bacterial load . SPN proliferation in the systemic circulation of mice was determined by tail vein blood sampling ( 10 μl ) at selected time points post infection by making an incision with a surgical blade followed by serial dilution and plating on blood-agar for CFU enumeration . For immunofluorescence detection , hBMECs were grown on 1% collagen coated glass cover slips and infected with SPN strains as described above . At desired time points post infection , cells were washed with RPMI , fixed with 4% paraformaldehyde for 15 min followed by permeabilization using 0 . 1% Triton X-100 for 10 min and blocking in 3% BSA for 2 h , all in PBS at RT . Cells were treated with appropriate primary antibody in 1% BSA in PBS for overnight at 4°C . Next day , cells were washed with PBS and incubated with suitable secondary antibody in 1% BSA in PBS for 1 h at RT . Finally , coverslips were washed with PBS and mounted on glass slides along with VectaShield with or without DAPI ( Vector Laboratories ) for visualization using a Laser Scanning Confocal microscope ( Zeiss Axio-Observer Z1 ) under 40X or 63X oil objectives . The images were acquired after optical sectioning and then processed using Zen lite software ( Version 5 . 0 . ) . Blood smears of infected mice were prepared on slides and were processed similarly for immunofluorescence detection . Live cell imaging was performed on spinning disk and laser scanning confocal microscope ( Zeiss Axio-Observer Z1 ) . Transfected hBMECs grown on glass bottom petri dishes were infected with SPN as described earlier and time-lapse imaging was performed at multi-position along with optical sectioning under 63x oil immersion objective . Image analysis and processing was done on Zen lite software ( Version 5 . 0 . ) . For live/dead bacterial staining , tRFP-SPN infected hBMECs were treated with 1 μM Sytox blue for 5 min following permeabilization with 0 . 1% saponin in 1 mM MOPS/MgCl2 buffer for 10 min . Acidotropic dye LysoTracker Deep Red was used at 100 nM for 1 h before imaging to stain lysosomes . Glass bottom petri dishes containing stained and infected hBMECs were visualized under Laser Scanning Confocal microscope ( Zeiss Axio-Observer Z1 ) under 63X oil objectives . hBMECs were infected with bacteria for 1 h at MOI 50 as described above and following infection cells were washed with 0 . 1 M sodium cacodylate buffer , fixed with 2% glutaraldehyde ( Sigma ) and 2% paraformaldehyde in 0 . 1 M sodium cacodylate buffer ( Sigma ) . Cells were then washed with 0 . 1 M sodium cacodylate buffer and post fixed with 1% osmium tetroxide in 0 . 1 M sodium cacodylate buffer ( Sigma ) for 1 h at 4°C . Following subsequent washes with cacodylate buffer and distilled water , cells were then dehydrated in increasing concentrations of ethanol and propylene oxide and finally embedded in DER 332/732 resin ( Electron microscopy services ) by polymerization at 70°C . Ultrathin sections ( 70 nm ) were cut using a glass knife on a Leica Microtome ( Ultracut R ) and stained with 1% uranyl acetate and 0 . 1% lead citrate and viewed using a transmission electron microscope ( Tecnai 12BT , FEI ) at 120 KV . SPN cultures grown to 0 . 4 OD600 were lysed by sonication and crude extracts were collected following centrifugation ( 15000 rpm , 30 min , 4°C ) of the lysates . In case of hBMEC , monolayers ( infected or uninfected ) were washed several times with PBS and lysed in ice-cold RIPA buffer ( 50 mM Tris-Cl , pH 7 . 89 , 150 mM NaCl , 1% Triton X-100 , 0 . 5% Sodium deoyxycholate , 1% SDS ) containing protease inhibitor cocktail ( Sigma Aldirch ) , Sodium fluoride ( 10 mM ) and EDTA ( 5 mM ) . Total cellular proteins were collected following brief sonication and centrifugation of cell lysates . Proteins present in bacterial or hBMEC cell lysates ( 10 μg or 20 μg ) were separated on 8–15% SDS PAGE gels and were then transferred to nitrocellulose membranes ( Bio-Rad ) . Following blocking , incubation with appropriate primary and HRP-conjugated secondary antibodies , blots were developed using ECL substrate ( Bio-Rad ) . Brain tissues obtained from infected or control mice were fixed in 4% paraformaldehyde for 12 h at 4°C . Tissues were then dehydrated and embedded in paraffin . Paraffin-embedded brain tissue were sectioned ( 5 μm ) under a microtome and processed for hematoxylin and eosin ( H&E ) and Gram staining . Stained sections were analyzed under bright-field microscope ( Eclipse Ti , Nikon ) . Total RNA from mouse tissue was isolated using TRIzol reagent ( ThermoFisher Scientific ) as per manufactures protocol . For RNA isolation , tissues were homogenized in a bead beater in 1 ml TRIzol containing 0 . 1 mm glass beads . RNA was treated for 15 min with DNaseI to remove any DNA contamination and processed for reverse transcription . 5 μg of RNA was reverse-transcribed using SuperScript First-Strand Synthesis System ( ThermoFisher Scientific ) . The cDNA was further used for quantitative RT-PCR ( qRT-PCR ) for various genes . The sequences of the primers used are mentioned in S1 Table . Changes in the expression of genes relative to 18S rRNA or 16S rRNA ( internal controls for mice and SPN , respectively ) were analyzed by 2-ΔΔCt method [25] . All statistical analysis were done using GraphPad Prism software ( version 5 ) . Statistical tests undertaken for individual experiments are mentioned in the respective figure legends . Statistical significance was accepted at p < 0 . 05 .
To explore the direct role of Ply in pneumococcal intracellular lifestyle during trafficking through the BBB , we firstly performed an intracellular survival assay with the SPN WT and ply mutant ( Δply ) strains , which was developed in serotype 2 strain R6 via allelic exchange that lacked hemolysis activity ( S1A Fig ) . The Δply mutant strain exhibited higher survival ability particularly at later time points of 8 and 12 h , wherein it showed 2 and 3 . 5 fold higher survival efficiency compared to WT SPN ( Fig 1A ) without any observable cell death due to infection ( S1B Fig ) . This higher survival phenotype of Δply mutant was completely reversed back to that of the WT by ectopic expression of Ply from a complementing plasmid in Δply mutant strain ( Fig 1A ) . Similar phenotype was observed in case of both Δply and ΔcpsΔply double mutant in comparison to encapsulated WT serotype 4 strain TIGR4 and its capsule mutant strain , Δcps , respectively ( S1C–S1E Fig ) , suggesting a capsule/serotype-independent detrimental role of pneumolysin in intracellular survival of SPN during BBB trafficking . Next , we used a bacteremia-derived meningitis mouse model to study the role of Ply in BBB crossover [24] . Comparison of bacterial counts in different tissue homogenates and blood at 14 h p . i . , revealed that SPN TIGR4 Δply strain showed lower bacterial counts in all tested tissues as well as in the blood compared to WT TIGR4 ( S2A Fig ) , an observation which corroborated with earlier findings [10] . However , only the brain/blood ratio for Δply mutant was higher with respect to WT ( S2B Fig ) compared to other tissues ( S2C & S2D Fig ) . Higher brain/blood ratio in spite of lower pneumococcal burden in respective tissues highlights efficient BBB trafficking potential of Δply mutant compared to WT SPN strain . Examination of pro-inflammatory response by qRT-PCR revealed no significant difference in the transcript levels of pro-inflammatory cytokines such as , TNFα , IL-1β and IFNγ in the brain tissues of mice that were infected with either WT or Δply strain ( S2E Fig ) . We also did not observe the typical meningitis hallmarks , such as characteristic leukocyte infiltration in the brain tissue of mice infected with either WT or Δply strains ( S2F Fig ) . Thus , these observations for both histology and inflammatory response studies support our pre-meningitis animal model , which is characterized by an intact and non-leaky BBB , a prerequisite for investigating the role of Ply in pneumococcal BBB trafficking exclusively via transcytosis route . We next hypothesized that Ply , by virtue of its pore-forming ability , might puncture pneumococcus containing vacuoles ( PCVs ) leading to recruitment of cytosolic “eat-me” signal Galectin 8 ( Gal8 ) which binds to exposed glycans lining the luminal surface of bacteria containing vacuoles . This targets the bacteria residing inside the compromised vacuole towards anti-bacterial autophagy [22] . Therefore , we analyzed co-localization of WT and Δply SPN strains following internalization in hBMECs with Gal8 using confocal microscopy . Clear association of Gal8 with WT SPN was observed while Δply infected cells failed to show any such events ( Fig 1B ) . This association steadily increased from 1 h to 3 h p . i . and subsequently remained fairly constant ( Fig 1C ) . Moreover , the presence of capsule did not affect the outcome of association of encapsulated serotype 4 SPN strain TIGR4 or its Δply mutant with Gal8 ( S1F Fig ) . TEM analysis also displayed intermittent rupture in the vacuolar membrane enclosing most of the WT SPN ( Fig 1J–1L ) , further substantiating the role of Ply in loss of PCV membrane integrity . The role of pore-forming activity of Ply in loss of endosomal membrane integrity was also examined using a PlyW433F mutant , which though expresses Ply at similar levels to that of WT , but have completely lost the hemolytic activity ( S3A & S3B Fig ) . Surprisingly , SPN strain containing PlyW433F exhibited equal amount of Gal8 association compared to that of WT ( S3C Fig ) , suggesting that hemolytic activity can’t always be synonymously used for pore formation . Additionally , we observed no difference in intracellular persistence ability of WT:PlyW433F compared to WT SPN ( S3D Fig ) . Visualization of hBMECs infected with WT SPN under a confocal microscope revealed recruitment of both the autophagy receptor NDP52 and the classical autophagosomal marker LC3B on Gal8 marked PCVs ( Fig 1D & 1E ) . Approximately 97% of the Gal8 positive PCVs acquired GFP-LC3B at 3 h p . i . ( Fig 1F ) indicating faithful autophagic targeting of SPN residing within damaged vacuoles . Pneumolysin seemed to be the sole virulence factor responsible for autophagic targeting of SPN as association with GFP-LC3B is completely abolished in Δply mutant strain while WT SPN progressively recruited GFP-LC3B until 6 h p . i . which persisted till 10 h p . i . ( S4A & S4B Fig ) . However , we did not observe any significant difference in LC3 flux ( LC3I to LC3II conversion ) between WT and Δply strains at both 3 and 6 h p . i . ( S4D Fig ) . We further examined for the contribution of Ply induced autophagy in higher killing of WT SPN inside hBMECs . Intracellular survival of WT SPN in hBMECs pretreated with the autophagy inhibitor , 3-methyladenine , was found to be 3 . 8 and 2 . 6 fold higher compared to untreated cells at 6 and 12 h time points , respectively ( S4C Fig ) . Additionally , siRNA-mediated specific knock down of Gal8 in hBMECs ( knock-down efficiency ~60% ) ( Fig 1G ) resulted in significant decrease of LC3 association with WT SPN at 3 h p . i . ( Fig 1H ) and considerably improved intracellular recovery of the same following infection at both 6 and 12 h time points ( Fig 1I ) . Expectedly , we did not observe any perturbation in the intracellular persistence ability of the Δply mutant strain following Gal8 knock-down by siRNA ( S5A Fig ) . Collectively , these findings demonstrate that Ply indeed damages PCV membrane integrity inside brain endothelium which triggers recruitment of the autophagy eat-me signal Gal8 and drives intracellular SPN towards autophagic killing which seems to be detrimental for its trafficking across BBB . It is evident from the co-localization kinetics of SPN with Gal8 and LC3 that Ply induced marking of intracellular SPN for autophagic degradation begins as early as 2 h p . i . Our confocal microscopy analysis also revealed that approximately 63% of WT SPN co-localized with late endosomal marker , lysosome-associated membrane protein 1 ( LAMP-1 ) , by 1 h p . i . as compared to only 21% in case of Δply mutant strain ( S5B Fig ) . Moreover , pretreatment of hBMECs with Bafilomycin A1 ( 100 nM ) , a vATPase inhibitor used to halt vacuole acidification , resulted in significant increase in intracellular survival of WT SPN ( 3 fold at 4 h and 5 . 5 fold at 8 and 12 h ) compared to the non-treated control ( Fig 2A ) . These suggest the remarkable contribution of Ply in driving PCV maturation and targeting the SPN towards lysosomal degradation soon after invasion inside the brain endothelium . However , despite this , few WT SPN manage to survive up until 12 h and persistently co-localize with autophagosomes ( Gal8+ and LC3+ compartments ) till extended hours post infection ( 8 and 10 h p . i . ) ( Fig 1C & S4B Fig ) . To shed light on these paradoxical observations , we explored the maturation of SPN containing autophagosomes by evaluating the interaction of Gal8 positive SPN with LAMP-1 and LysoTracker , an acidotropic probe . Interestingly , we observed that WT SPN was confined within both acidic ( Lyso+ ) as well as non-acidic ( Lyso- ) Gal8+ LAMP-1+ compartments ( Fig 2B–2E ) . At 3 h p . i . , approximately 87% of Gal8 positive SPN was associated with LAMP-1 while only 32% of them showed LysoTracker accumulation ( Fig 2F ) . However , the persistence of similar statistics even at 9 h p . i . , implies that although Ply mediated pore-formation on PCVs targets few SPN towards Gal8 mediated autophagic degradation indicated by Sytox Blue staining , a dead cell stain ( Gal8+ Lyso+ Sytox+ ) , few SPN simultaneously persist inside non-degradative autophagosomes ( Gal8+ Lyso−Sytox– ) ( Fig 2G–2J ) , supporting their prolonged survival inside hBMECs observed earlier . Further , to gain deeper insights into the maturation kinetics of SPN inside autophagosomes , we monitored tRFP expressing WT SPN in YFP-Gal8 marked compartments in real time using time-lapse confocal microscopy from 3 h p . i . for extended hours . Surprisingly , we observed two different phenomena with respect to the fate of the SPN inside YFP-Gal8 compartments . In one of the phenomena , both tRFP signal of SPN and YFP signal of Gal8 positive compartments faded very slowly with time ( Fig 3A and S1 Movie ) , implying gradual degradation of SPN inside autophagosomes . While in the other phenomenon , early fading and collapse of Gal8 signal was accompanied with persistent SPN signal for longer duration , suggesting disruption of Gal8 positive compartments and escape of SPN into cytosol ( Fig 3B and S2 Movie ) . However , the eventual fading of SPN signal in the cytosol indicates its degradation following recognition and action by other host cytosolic defense mechanisms ( Fig 3C ) . Quantification of such events revealed that approximately 60% of SPN escaped from autophagosomes and subsequently underwent cytosolic degradation , while rest ( ~ 40% ) succumbed to the autophagic trap ( Fig 3D ) . Collectively these findings suggest that following internalization in brain endothelium , despite Ply mediated autophagic targeting of SPN , few pneumococci halt autophagosomal maturation before eventual degradation , while others manage to escape autophagy by translocating into the cytosol . Given that SPN escapes from autophagosomes , we speculated that Ply mediated extensive membrane damage might rupture PCVs leading to cytosolic invasion of SPN . Our transmission electron microscopic analysis displaying presence of WT SPN without any membrane limiting structures in the cytosol of hBMECs supported this hypothesis ( Fig 3E & 3F ) . On the contrary , Δply mutant strain was always observed sequestered in membrane bound vacuolated structures ( S5C & S5D Fig ) . The fact that Ply disrupts autophagosomes leading to escape of SPN into cytosol , propelled us to investigate the possible role of host ubiquitination machinery in combating SPN infection inside brain endothelium . Confocal microscopy showed clear co-localization of ubiquitin ( Ubq ) with WT SPN inside hBMECs while Δply mutant strain failed to show any such events , indicating the critical role of Ply in ubiquitination of SPN ( Fig 4A ) . This association between Ubq and WT SPN increased progressively up until 6 h p . i . and persisted for extended hours ( Fig 4B ) . Additionally , most of the ubiquitin positive SPN showed association with LC3-GFP ( Fig 4D & 4E ) . Inhibition of ubiquitination by pretreatment of hBMECs with PYR41 , an E1 ubiquitin activating enzyme inhibitor , not only improved the intracellular survival of WT SPN ( Fig 4C ) , but also significantly lowered its association with LC3 ( Fig 4F ) , further confirming the contribution of ubiquitin as another eat-me signal , in addition to galectin-8 , targeting SPN towards autophagic degradation . Expectedly , PYR41 inhibition of cellular ubiquitination did not have any effect on the intracellular recovery of Δply mutant ( S5E Fig ) , justifying our findings that Ply mediated damage is responsible for ubiquitination and subsequent degradation of intracellular SPN . Next , in order to determine the existence of any intersection between these two eat-me signals , we quantified SPN co-localization events with Gal8 and ubiquitin at different time points post infection . Confocal microscopic analysis revealed that as compared to Gal8+ Ubq−and Gal8+ Ubq+ SPN population , Gal8– Ubq+ population , which unequivocally represents free cytosolic bacteria that escaped from disrupted vacuoles inside hBMECs , significantly increased from 3 h p . i . to 9 h p . i . ( Fig 4G ) . To further determine the mechanism behind ubiquitin-mediated degradation of cytosolic SPN , time-lapse live-cell imaging was performed on hBMECs stably expressing LC3-GFP and Ubq-mStrawberry following infection with DRAQ5 stained WT SPN . Ubq–mStrawberry positive SPN ( negative for LC3-GFP ) , representing cytosolic bacteria , were tracked at 6 h p . i . and monitored for extended hours . Approximately 37% of ubiquitinated SPN displayed accumulation of LC3-GFP with time and eventually degraded while remaining associated with LC3-GFP , representing the contribution of ubiquitin-mediated autophagy in clearance of cytosolic SPN ( Fig 5A , 5B & 5E and S3 Movie ) . Surprisingly , we also observed that 63% of ubiquitinated SPN failed to recruit LC3-GFP and degraded while remaining associated with Ubq-mStrawberry ( Fig 5C , 5D & 5E and S4 Movie ) . Further , treatment with MG132 , a 26S proteasomal subunit inhibitor , significantly improved intracellular survival of WT SPN ( Fig 5F ) suggesting involvement of proteasome-ubiquitin system in promoting SPN clearance via an ubiquitin mediated autophagy-independent pathway . Collectively , these results indicate that Ply-mediated progressive damage on PCV membrane leads to recruitment of eat-me signals like galectin-8 and ubiquitin . Further damage triggers collapse of the autophagosomes and escape of SPN in cytosol where ubiquitination machinery targets them towards autophagy-dependent or autophagy-independent degradative pathways . Our results clearly demonstrated the existence of phenotypically heterogeneous SPN subsets during trafficking through BBB with regards to variable maturation inside autophagosomes and distinct localization inside brain endothelium ( vacuolated or cytosolic ) culminating in differential survival ability . To support this hypothesis , we analyzed for the existence of different sub-populations of WT SPN for their association with Gal8 , Ubq and LC3 markers inside hBMECs using confocal microscopy . Interestingly , we observed six different sub-populations of SPN inside hBMECs: Gal8+ Ubq−LC3- , Gal8+ Ubq−LC3+ , Gal8+ Ubq+ LC3+ , Gal8– Ubq+ LC3+ , Gal8– Ubq+ LC3– and Gal8– Ubq−LC3+ ( Fig 6 and S6A Fig ) . This prompted us to speculate that existence of heterogeneity in Ply expression among isogenic SPN population and the resulting variability in the extent of Ply mediated damage on PCV membrane , may give rise to all such spatio-temporally heterogeneous infection outcomes . Flow cytometric analysis with a SPN strain expressing the fluorescent reporter GFP , transcriptionally fused with the ply gene revealed a normal distribution curve for GFP fluorescence , representing wide variation in Ply expression from one cell to another ( Fig 7A ) . This recombinant SPN strain showed similar hemolytic ability as that of WT SPN ( S6B Fig ) . Moreover , the surface Ply expression assessed by anti-Ply antibody in this transcriptional fusion strain also exhibited heterogeneity by flow cytometry ( S6C Fig ) . Similar variance in Ply expression was observed when in vitro grown WT SPN culture was stained with anti-Ply antibody and checked with fluorescence microcopy ( Fig 7B ) , supporting the existence of low to high Ply producers within isogenic SPN population . Propelled by this finding of heterogeneity in Ply expression , we constructed two SPN strains ( WT:Ply-High and WT:Ply-Low ) by introducing specific point mutations in the 5' region of ply mRNA that manipulated the folding energies of the transcripts [17] , affecting their translation efficiencies and resulting in differential Ply expression , both intracellularly as well as on cell surface ( S7A–S7D Fig ) . We then quantified the intracellular SPN population subsets observed earlier ( Fig 6 ) in hBMECs following infection with WT:Ply-High and WT:Ply-Low strains at 3 h p . i . and 9 hp . i . ( Fig 7C & 7D ) . Classification of these subsets with regards to cellular location of SPN showed that vacuolated SPN population ( includes Gal8+ Ubq–LC3– , Gal8+ Ubq−LC3+ , Gal8+ Ubq+ LC3+ and Gal8– Ubq−LC3+ ) is significantly higher in case of WT:Ply-Low strain while vacuolar escaped SPN population ( includes Gal8– Ubq+ LC3+ and Gal8– Ubq+ LC3– ) is substantially higher in case of WT:Ply-High strain ( Fig 7E ) . Out of these subsets; Gal8– Ubq+ LC3– and Gal8– Ubq−LC3+ showed significant difference between the two strains as compared to all other subsets ( Fig 7C & 7D ) . The Gal8– Ubq+ LC3– which unequivocally represents the cytosolic SPN pool , was significantly higher in hBMECs infected with WT:Ply-High strain . Contrarily , substantially higher occurrence of the Gal8– Ubq−LC3+ subset was observed in case of WT:Ply-Low strain . This unique subset was devoid of association with any of the autophagy eat-me signals and predominated at both the time points ( 3 h p . i . and 9 h p . i . ) compared to all other subsets . We then compared the intracellular survival efficiencies of these two SPN strains . Significantly higher survival ability of the WT:Ply-Low strain was observed compared to WT:Ply-High strain at 12 h ( 3 . 1 fold ) time point ( Fig 7F ) , though they exhibited similar invasion efficiencies ( S7E Fig ) . Furthermore , WT:Ply-Low SPN strain egressed out of hBMECs in significantly larger numbers as compared to WT:Ply-High SPN strain , suggesting higher transcytosis efficiency of low Ply producing SPN ( Fig 7G ) . Collectively , these results suggest that the low Ply producing subset of SPN preferentially stays compartmentalized by minimizing damage on SPN containing autophagosomes ( predominated by Gal8– Ubq−LC3+ subset ) and shows prolonged survival and trafficking across the brain endothelium . We next investigated the role of heterogeneous Ply expression in BBB trafficking of SPN using the previously described bacteremia-derived meningitis mouse model of infection . Following infection via hematogenous route , WT SPN in the blood displayed enormous heterogeneity in Ply expression , wherein majority of them showed low to negligible Ply levels at 14 h p . i . ( Fig 8A ) . Examination of Ply transcript levels in the brain and blood of WT SPN infected mice further supported the abundance of low Ply expressing SPN in the blood ( Fig 8B ) . We next compared the contribution of WT:Ply-High and WT:Ply-Low strains ( in TIGR4 background ) to SPN CNS pathogenesis . Survival studies revealed significant early death of mice infected with WT:Ply-Low strain . The median survival time of the WT:Ply-Low infected mice ( 60 h ) was two-fold lower compared with WT:Ply-High infected mice ( 120 h ) ( Fig 8C ) . We also analyzed proliferation of different SPN strains in the mice blood at early time points during the course of infection . Insignificant difference in blood bacterial load between WT:Ply-Low and WT:Ply-High strains at all time points tested ruled out the possibility of sepsis induced early death of mice infected with WT:Ply-Low strain ( Fig 8D ) . We then compared the capability of WT:Ply-High and WT:Ply-Low strains to disseminate in various mice tissues . Assessment of bacterial load in mice tissues revealed higher SPN counts only in brain ( 6 . 69x103 vs 1 . 29x103 ) of animals that were infected with WT:Ply-Low strain , however no such difference was observed in case of and lungs ( 2 . 71x105 vs 1 . 78x105 ) , spleen ( 8 . 9x105 vs 14 . 1x105 ) and blood ( 1 . 86x106 vs 1 . 85x106 ) ( Fig 8E ) . Moreover , examination of tissue to blood SPN CFU ratios highlight significantly higher trafficking capability of WT:Ply-Low strain into CNS ( Fig 8F ) compared to other mice tissues ( Fig 8G & 8H ) . Further , we did not observe any leukocyte infiltration and there were no difference between inflammatory responses in the brains of mice infected with these two SPN strains ( Fig 8I & 8J ) though SPN was detected in the brain tissue by Gram staining of the brain sections ( S7F Fig ) . This suggests significantly higher transcytosis potential of WT:Ply-Low SPN compared to WT:Ply-High SPN strain into the CNS across an intact BBB . Collectively , we infer that dominance of low Ply expressing subset within heterogeneous SPN population in the blood not only promotes safe passage of bacteria across BBB but also contributes to pneumococcal CNS pathogenesis leading to early death of the host . Finally , we investigated the phenomenon of Ply heterogeneity in various clinical strains of SPN including D39 ( serotype 2 ) , TIGR4 ( serotype 4 ) , Tupelo ( serotype 14 ) and A60 ( serotype 19F ) . Interestingly , our flow cytometry analysis for Ply surface expression suggests strain specific differences in Ply levels . Our results demonstrate that irrespective of serotypes , Ply expression is heterogeneous in nature ( Fig 9A ) . Close scrutiny of the flow cytometry data also reflects that highly invasive SPN strains belonging to serotype 4 and 14 contain extremely low number of Ply producers ( TIGR4; 10 . 85% and Tupelo; 30 . 55% ) . While the sepsis causing serotype 2 strain D39 ( 64 . 5% ) and 19F strain ( A60; 76 . 15% ) which primarily colonizes human nasopharynx without causing invasive pneumococcal diseases ( IPD ) exhibited significantly higher Ply expressing population ( Fig 9B ) . This suggests differences in pneumolysin expressing bacterial counts across various SPN serotypes .
A critical trait of any pathogen is its capacity to translocate from the external environment into the host by breaching multiple barriers and reach protected environment such as the CNS for uninhibited proliferation [26] . Most bacterial infections of the CNS are restricted to the meninges and result from dissemination of the pathogens present in the bloodstream . Blood-borne bacteria that invade the CNS should not only have neurotropic attributes , but must also have developed specific mechanisms to circumvent host barriers , particularly BBB . In this study , we for the first time , portrayed a comprehensive picture of intracellular lifestyle of the meningeal pathogen SPN , and its fate during trafficking through BBB while focusing on the role of its major virulence factor , Ply . We first show that abrogation of Ply expression confers a significant survival and trafficking advantage to SPN across the BBB , both in vitro and in vivo . However , it is noteworthy that a minor subset of WT SPN still survives for longer period which restricts us from declaring Ply to be detrimental for intracellular survival of SPN . Existence of such a dubious role of Ply in pneumococcal intracellular survival propelled us to speculate that cell-to-cell heterogeneity in Ply expression of individual bacterium within the isogenic SPN population could possibly be responsible for the observed disparate infection outcomes . Our studies demonstrate that indeed heterogeneity in Ply expression gives rise to stochastic SPN subsets with variable fate and trafficking efficiency through BBB . To unravel the role of pneumolysin in the enhanced intracellular killing of SPN , we explored for the contribution of its most important attribute i . e . its pore-forming ability . Endosomal membrane damage induced by pathogen has been demonstrated to trigger anti-bacterial autophagy via recruitment of danger signal molecule , galectin8 [22] . The resultant induction of autophagy , an evolutionary conserved mechanism targeted to remove pathogens following cellular invasion [27–29] involves sequestration of the pathogen in the characteristic LC3-positive double-membrane compartment that finally fuses with lysosomes triggering pathogen clearance . We demonstrated that autophagic targeting of SPN via galectin-8 is strictly dependent on Ply expression and this significantly contributes to pneumococcal clearance inside the brain endothelium . However , despite the robust Ply-driven autophagic targeting of SPN , most of Gal8 positive PCVs , showing association with LC3 and LAMP1 , failed to accumulate LysoTracker till delayed hours post infection . It appears therefore that SPN persists inside non-degradative autophagosomal compartments for prolonged period , an assumption , which is additionally supported by our time-lapse analysis for Gal8-SPN association exhibiting slow degradation kinetics of SPN inside autophagosomes . On the basis of these observations we propose the existence of variable maturation kinetics of SPN containing autophagosomes , wherein few show rapid acidification and early bacterial degradation while others prevent acidification and resist their fusion with lysosome contributing to extended survival of a SPN subset inside brain endothelium . Numerous bacteria escape autophagy via the action of bacterial effector proteins that disrupt vacuolar membrane integrity promoting cytosolic invasion of pathogens [30 , 31] . Although SPN was shown to reside in the cytoplasm of microglial and epithelial cells , free of any limiting membrane [32 , 33] , the molecular mechanism underlying its eventful progression from the phagosome into cytosol of host cells remained unknown . Our TEM analysis displaying presence of WT SPN in ruptured endosomes and as free cytosolic bacteria , coupled with time-lapse imaging demonstrating pneumococcal escape from autophagosomes clearly implies that Ply mediated extensive damage leads to egress of SPN into cytosol . The cytosolic ubiquitination machinery is known to act as a cellular immune surveillance system promoting clearance of pathogens residing either inside damaged vacuoles or free in cytosol [34] . Although ubiquitination has been reported to result in degradation of SPN in host cells [35 , 36] , our findings showcased that ubiquitination of SPN also occurs strictly in a Ply-dependent manner . Interestingly , only few ubiquitin positive cytosolic SPN get cleared via autophagy pathway while a majority of them degrade in the cytosol in an autophagy-independent manner , most probably via a proteasome dependent mechanism . Collectively , our findings for the first time illustrated spatio-temporal heterogeneity in SPN population during trafficking through BBB . While majority of pneumococci undergo early degradation , a few remain unharmed and survive for longer . Some undergo autophagic degradation while others escape autophagy and translocate into the cytosol . Co-localization studies for SPN with the three host markers , Gal8 , Ubq and LC3 , whose association is strictly Ply-dependent , further substantiates this intracellular heterogeneity and demonstrates the simultaneous existence of different SPN sub-populations which could be correlated to differential expression of Ply within isogenic SPN population . Out of all the subpopulations , the unexpected emergence of the Gal8-Ubq-LC3+ subset was intriguing as it represented Ply dependent LC3 lipidation on PCVs that did not undergo sufficient membrane disruption to recruit the damage sensing markers , Gal8 and Ubq . Osmotic imbalances induced by H . pylori toxin , VacA has been known to cause such unconventional LC3 lipidation on intact phagosomes via a non-canonical autophagy pathway [37] . Since Ply has been shown to form variable-sized pores on biological membranes and this differential pore-forming ability is directly dependent on the monomer concentration of the toxin [38–40] , we surmise that extremely small ion-channel like pores formed by Ply may cause osmotic imbalances triggering LC3 lipidation on a subset of PCVs ( represented by Gal8– Ubq−LC3+ subset ) . Indeed the number of Gal8– Ubq−LC3+ subset was significantly higher in case of WT:Ply-Low SPN infection that also displayed improved intracellular survival as well as CNS transcytosis capability . This is in accordance with a recent finding that suggests membrane damage leads to exocytosis of bacteria containing vacuole from the host cell in an autophagy dependent manner [41] . On the contrary , in case of WT:Ply-High SPN infection , that comparatively showed lower intracellular survival , the number of Gal8– Ubq+ LC3– subset was significantly higher . This suggested that higher Ply expression triggers irreversible membrane damage leading to collapse of the autophagosomes and escape of SPN into cytosol from where it gets cleared by cytosolic ubiquitination machinery . Overall , these findings showcase a comprehensive picture of the stochastic pneumococcal population subsets interacting with different microbicidal defense pathways as a consequence of heterogeneous expression of pneumolysin inside the brain endothelium ( Fig 10 ) . Different serotypes of SPN have varying potential to cause invasive pneumococcal diseases and are majorly classified into either carriage ( 15B/C 19F , 23 , 35F , etc . ) or invasive ( 1 , 4 , 5 , 7F , 14 , etc . ) serotypes [42 , 43] . For years now , although the role of Ply in invasive pneumococcal diseases ( IPD ) has been widely explored [14 , 15] , its contribution to IPD still remains to be fully elucidated . While studies have shown its crucial role at different stages of disease progression ranging from nasal colonization to invasive diseases like pneumonia , bacteremia and meningitis [15 , 44 , 45] , discrepancies in the observations by various groups indicate controversial role of this toxin to pneumococcal pathogenesis [10 , 46] . Our study for the first time elucidated the functional role of heterogeneous Ply expression during pneumococcal BBB trafficking , a crucial step bridging the two important pathological conditions , bacteremia and meningitis . In addition to displaying prolonged intracellular survival , our results showcased higher transcytosis potential of low Ply producing SPN strain into the CNS in an in vivo mouse model for bacteremia derived pre-meningitis like condition . These findings were further substantiated by our pneumolysin expression studies across various SPN clinical strains , wherein the widely studied meningitis strain , TIGR4 belonging to the highly invasive serotype 4 [47 , 48] exhibited extremely low Ply expressing population as compared to the sepsis strain D39 ( serotype 2 ) and the carriage strain A60 ( serotype 19F ) . As high grade bacteremia is prerequisite for meningitis [49] , low Ply expression by serotype 4 strains not only ensures threshold bacteremic levels avoiding septic shock and early death of the host which is majorly seen in case of highly proliferating SPN D39 infections [50] , but also facilitates safe trafficking across BBB by escaping cellular defense pathways leading to CNS invasion and meningitis as demonstrated by us . Similarly , strains belonging to serotype 14 such as Tupelo , which are most prevalent etiologic agents of pneumococcal community acquired pneumonia ( CAP ) [51] , has Ply expressing population higher than TIGR4 but lower than D39 and A60 strains . Apparently , extremely low Ply levels are required for full virulence of SPN as a mutant strain ( PlyW433F ) exhibiting 0 . 1% hemolytic activity displayed near-maximum virulence when compared to WT SPN [52] . We further showed that SPN strains harboring such Ply variant retains its ability to disrupt PCV membrane , indicating differences in molecular mechanisms for pore-formation on vacuolar and red blood cell ( RBC ) membrane . On similar lines , it would be interesting to investigate the molecular mechanisms promoting the high invasive character of non-hemolytic serotype 1 SPN strain ST306 which is a globally disseminated strain causing various IPDs [53] . Virulence factors play both beneficial and detrimental roles during disease progression suggesting that the expression of these factors is finely tuned depending on local environment , promoting diversification of bacterial populations for efficient dissemination inside the host [54] . There is accruing evidence for the existence of such cell-to-cell variation in the expression of numerous bacterial factors [1 , 55 , 56] that impacts overall pathogen fitness and subsistence during its challenging journey inside the host . Lately , the bacterial quorum-sensing ( QS ) systems have been shown to contribute to the phenotypic heterogeneity of genes under their regulation that may result from the dual existence of QS-responsive and QS-non responsive sub-populations within the QS-activated isogenic population [12 , 57] . It has been suggested that heterogeneity in QS activation or the expression of QS responsive genes may serve as a bet-hedging survival strategy for pathogens which ensures atleast subsets of the heterogeneous population adopt phenotypes which promotes optimal pathogen fitness in the fluctuating environments encountered during disease progression inside the host [43 , 58] . Pneumolysin expression is reported to be regulated by one such QS system controlled by LuxS that has been shown to play crucial role in pneumococcal virulence and nasal colonization using mouse models of infection [51] . We speculate that this may be responsible for heterogeneous expression of Ply and subsequent genesis of stochastic pneumococcal subpopulations . It is evident from our studies as well as various earlier reports that success at different stages of pneumococcal pathogenesis are best promoted by varying levels of pneumolysin expression . Metabolically demanding early stages of biofilm formation during SPN colonization in nasopharynx [49] and heart [50] require high Ply expression , conversely low Ply activity imparts early growth advantage to SPN in blood [59] and also promotes prolonged survival and efficient trafficking across BBB , as shown by us . Thus owing to the complex role of Ply at various stages of pneumococcal pathogenesis , generation of phenotypic heterogeneous subsets seems to be a smart strategy adopted by SPN for deploying its most functionally versatile and crucial virulence attribute at various locations inside the host in an economical and proficient manner . We presume that adoption of a Ply-deficient strain to answer the role of Ply in SPN pathogenesis in earlier studies might not have served as an appropriate model to explain the complex and multifactorial role of this important toxin [10 , 46] . Indeed , our observation of heterogeneity in Ply expression accounts for these conflicting observations for contribution of Ply to pneumococcal pathogenesis . Having identified the contribution of heterogeneous Ply expression to one aspect of pneumococcal pathogenesis , BBB trafficking , it would be interesting to explore its role to its other facets like nasal colonization , pneumonia and bacteremia . On the basis of our findings and existing literature , we speculate that SPN strains best capable of regulating their Ply stochasticity invade deeper tissues inside the host and cause wide range of IPDs . Thus understanding the role and mechanism behind stochastic Ply expression in the different SPN strains ( carriage and invasive ) may not only provide clues for decoding the perennial mystery of clonal evolution that fosters global dissemination of pathogens but is also pivotal for development of novel treatment strategies to combat the diverse and lethal pneumococcal diseases . | Streptococcus pneumoniae , the Gram-positive diplococci , is the primary etiological agent of bacterial meningitis . In order to cause central nervous system ( CNS ) infections , SPN has to breach the blood-brain barrier , however , the pneumococcal determinants involved in this process remain unidentified . Here , we demonstrate that pneumolysin , a pore forming toxin secreted by SPN , plays a complex role in governing the fate of the pathogen while trafficking through BBB . Though presumed to be an important virulence attribute , the role of Ply in SPN pathogenesis , particularly in development of meningitis remained debatable . By revealing heterogeneity in Ply expression within isogenic SPN population , our findings attempt to resolve the uncertain role of Ply in SPN pathogenesis . We illustrate that heterogeneity in Ply expression gives rise to stochastically different pneumococcal subpopulations during BBB trafficking . This arises as a consequence of differential interactions of SPN with host microbicidal defense pathways like autophagy and ubiquitination machinery . Among the stochastic SPN population , a low Ply producing SPN subset not only shows improved persistence , but is also capable of successful transcytosis across the BBB resulting in CNS pathogenesis . Overall , our findings suggest that tight spatio-temporal regulation of Ply expression is key to SPN subsistence and dissemination within the host . | [
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| 2018 | Heterogeneity in pneumolysin expression governs the fate of Streptococcus pneumoniae during blood-brain barrier trafficking |
Natural populations are known to differ not only in DNA but also in their chromatin-associated epigenetic marks . When such inter-individual epigenomic differences ( or “epi-polymorphisms” ) are observed , their stability is usually not known: they may or may not be reprogrammed over time or upon environmental changes . In addition , their origin may be purely epigenetic , or they may result from regulatory variation encoded in the DNA . Studying epi-polymorphisms requires , therefore , an assessment of their nature and stability . Here we estimate the stability of yeast epi-polymorphisms of chromatin acetylation , and we provide a genome-by-epigenome map of their genetic control . A transient epi-drug treatment was able to reprogram acetylation variation at more than one thousand nucleosomes , whereas a similar amount of variation persisted , distinguishing “labile” from “persistent” epi-polymorphisms . Hundreds of genetic loci underlied acetylation variation at 2 , 418 nucleosomes either locally ( in cis ) or distantly ( in trans ) , and this genetic control overlapped only partially with the genetic control of gene expression . Trans-acting regulators were not necessarily associated with genes coding for chromatin modifying enzymes . Strikingly , “labile” and “persistent” epi-polymorphisms were associated with poor and strong genetic control , respectively , showing that genetic modifiers contribute to persistence . These results estimate the amount of natural epigenomic variation that can be lost after transient environmental exposures , and they reveal the complex genetic architecture of the DNA–encoded determinism of chromatin epi-polymorphisms . Our observations provide a basis for the development of population epigenetics .
Recent studies have shown that individuals largely differ in their epigenomic chromatin signatures . This finding makes tracking epigenetic marks in natural populations attractive , including investigating their possible contribution to the variation of common physiological traits . So far , epigenomic intra-species diversity has been primarily studied at the level of the methylome ( DNA methylation profile ) . Natural accessions of A . thaliana were found to differ in their methylation level at about 10% of all CCGG sites [1] and this variability was largely concentrated within genic regions [2] . In humans , numerous inter-individual differences of DNA methylation were also reported [3]–[6] and , importantly , the methylomes of monozygotic twins were shown to diverge during their lifetime [7] . Measuring this diversity at a genome-wide scale extended what had been observed earlier at individual loci in mice , where the level of transgene methylation was shown to strongly vary between laboratory strains [8] , [9] . However , natural epigenomic variability is not restrained to DNA methylation . DNase-seq profiles of cell-lines from human families revealed ∼10 , 000 sites that were polymorphic in their chromatin signature [10] and it is likely that a significant fraction of them is not associated with DNA methylation differences but with other regulatory hallmarks . Natural variability was also reported at the level of high-order chromatin structure , when distinct A . thaliana accessions were compared for their level of genome compaction in response to light [11] . Finally , histone acetylation profiles also varies , as we previously described in a comparison of two unrelated wild strains of S . cerevisiae [12] . Unlike DNA variants that are irreversible and therefore tractable , epigenotypes are thought to be largely labile ( i . e . able to change their state ) on time scales ranging from seconds to multiple generations [13] . When the spontaneous epimutation rate of DNA methylation was estimated in 30-generations mutation accumulation lines of A . thaliana , it was found to be several orders of magnitude higher than the rate of DNA variation [14] , [15] . Moreover , chromatin signatures not only change spontaneously but also in response to environmental conditions [16] . Various environmental factors have the potential to exert this effect . Temperature , for example , can induce dramatic epigenetic changes in plants . In the normal life cycle of many species , experiencing winter cold is essential for flowering later in spring: the FLC locus , whose expression prevents flowering , becomes silenced by a well-described mechanism after several weeks of vernalization ( for a review , see [17] ) . In addition , extreme and stressful temperatures may be experienced , in which case the chromatin state of A . thaliana repetitive sequences can change to alleviate their silencing [18]–[20] . The response to subtle temperature variations was also shown to depend on the proper incorporation of histone variant H2A . Z [21] . In addition , specific extracellular signals such as hormones in animals can also trigger chromatin reprogramming at target loci , and the pathways involved provide many routes by which chromatin can sense environmental conditions . To a broader extent , diet represents a set of factors able to induce epigenome modifications [22] . Feeding animals with altered amounts of methyl donors can induce methylome reprogramming [23] . Such treatments have illustrated how environmental conditions may stably print epigenotypes across generations . In mice for example , reprogramming was observed in adult offsprings of males that had been on specific diets [24] , [25] . In the particular case of chromatin acetylation , direct coupling between epigenetic signatures and energy metabolism ( obviously related to diet ) is known to happen at least at three levels . First , sirtuins are known to deacetylate histones and a number of other proteins in a NAD+-dependent manner [26] , [27] . Secondly , the level of Acetyl-CoA , which donates the acetyl group transferred to histones , can vary according to glucose availability and efficient metabolism [28] . And thirdly , carbonyl compounds can inactivate class I Histone Deacetylases ( HDAC ) by alkylation of two cysteine residues [29] . And beyond dietary effects , some environments contain natural HDAC inhibitors such as Trichostatin-A ( TSA ) produced by Streptomyces platensis , or butyrate , a natural product of the intestinal flora [30] . Thus , individuals may harbor personalized epigenomes because they have experienced a specific history of past environmental exposures or stochastic transitions ( Figure 1A ) . Alternatively , epi-polymorphisms can be influenced by DNA variations that modify chromatin regulations , either in cis ( i . e . locally ) or in trans ( i . e . distantly ) [31] . Well-known examples of cis-modifiers are transposon insertions [32] , [33] , whose regional effects on chromatin states have been the basis for extremely informative genetic screens in yeast ( as reviewed in [32] ) . In humans , several heritable disorders are caused by trinucleotide repeat expansions that perturb chromatin states locally [34] . One striking example is the non-coding repeat region of the FMR1 gene , where moderate expansions mediate hyper-acetylation of the locus and increased mRNA levels , resulting in Fragile X Tremor Ataxia Syndrome [35] , whereas larger expansions induce chromatin silencing , decreased gene expression , and Fragile X Mental Retardation Syndrome [36] . The very few known trans-acting genetic modifiers of chromatin states are sequence changes within chromatin modifying enzymes [6] , [11] , but other DNA polymorphisms may also act in trans by affecting the activity of upstream regulators of chromatin modifying machineries . The numerous examples of DNA-encoded chromatin differences suggest that individuals may harbor distinct epigenotypes simply as a result of their different genetic content ( Figure 1B ) . We previously identified thousands of yeast nucleosomes carrying differential levels of H3K14 acetylation between two wild S . cerevisiae strains ( BY and RM ) [12] . Following this previous study , we define here Single Nucleosome Epi-Polymorphisms ( SNEPs ) as the intra-species variations of the level of an epigenetic mark carried on a nucleosome . The polymorphic mark may be any histone post-translational modification or the incorporation of a histone variant . A SNEP for one such mark then corresponds to the preferential presence of the mark at one nucleosomal position in some individuals or strains as compared to others . Consequently , SNEPs of various epigenetic marks may be carried on the same nucleosome . By tracking H3K14ac SNEPs , we describe here both an experimental reprogramming experiment and the genetic architecture of H3K14 acetylation variation . The results show that some epi-polymorphisms are reprogrammed after a transient perturbation of chromatin states whereas others persist , and this persistence can , at least partly , be explained by genetic determinants encoded in the DNA .
We previously described 5 , 442 SNEPs corresponding to acetylation variation between two S . cerevisiae strains ( BY and RM ) . Here we assessed the stability of these epi-polymorphisms by transiently exposing the two strains to an extremely perturbing environment ( Figure 1C ) . We sought to distinguish three types of SNEPs: Persistent SNEPs , corresponding to initial inter-strain differences that remained significant after the perturbation; Labile SNEPs , corresponding to original inter-strain differences that significantly changed after the perturbation; and Induced SNEPs , corresponding to inter-strain differences that appeared after the perturbation . BY and RM cells were treated with high concentrations of TSA for 4–5 generations . As expected , this treatment caused a bulk increase of H3K14 acetylation in both strains ( Figure S1 ) . Cells were then washed and let grown for over 20 generations in standard medium lacking TSA . After this recovery period , the global level of H3K14 acetylation had returned to normal in both strains and we examined again inter-strain differences at single-nucleosome resolution , using chromatin immunoprecipitation and hybridization on whole genome tiling arrays , as previously described [12] . The protocol was applied on biological triplicates for each strain . Inter-strain acetylation ratios before treatment and after recovery were highly correlated ( Figure 2A , Spearman r = 0 . 7 ) . We performed two complementary statistical analyses on the data . First , we specifically searched for induced and persistent SNEPs . To this end , we applied our previously described SNEP detection algorithm ( NucleoMiner ) to the newly generated dataset ( see Methods ) . At a False Discovery Rate ( FDR ) of 0 . 0001 , we detected 2 , 379 SNEPs after recovery . Interestingly , 898 of them were new ones: for these nucleosomes , the level of K14 acetylation was not significantly different between the strains before treatment . They were unlikely false negatives , because detection power was higher in the initial search than after recovery from TSA ( 12 versus 6 microarrays used ) . Rather , these induced SNEPs illustrate that epi-polymorphisms may indeed result from new environmental exposures . Interestingly , 524 of the 898 induced SNEPs were ‘isolated’ , i . e . their two flanking nucleosomes were not SNEPs after treatment and recovery . Of these , 436 were initially in a context where neither of the flanking nucleosome was a SNEP . This specificity illustrates that SNEPs can be induced at precise nucleosomes and not necessarily on consecutive ones . Of the 5 , 442 SNEPs originally detected in normal conditions [12] , 1 , 481 were also significant post-recovery . All of them except one had the same directionality ( i . e . same strain showing increased acetylation ) before treatment and after recovery and these were therefore called ‘persistent’ ( Figure 2A , 2B ) . The remaining 3 , 961 initial SNEPs could be ‘labile’ , but many of them may simply be false negatives that escaped detection post recovery . We therefore applied a different test to reliably search for cases of lability: we tested for all nucleosomes if the inter-strain acetylation ratio had changed ( see Methods ) . This was the case for 4 , 484 nucleosomes ( FDR = 0 . 001 ) . Among these , 1 , 076 belonged to the list of nucleosomes containing initial SNEPs and we therefore qualified these SNEPs as ‘labile’ ( Figure 2A , 2B ) . These labile SNEPs did not represent cases of high experimental noise , as they were not necessarily those with low initial statistical significance ( Figure S2 ) . In conclusion , three different types of acetylation epi-polymorphisms ( induced , persistent and labile ) could be detected in large proportions . We previously reported that for ∼50% of SNEPs , no acetylation variation was detectable on their flanking nucleosomes [12] ( see Figure 2B for an example ) . Here we observed that these ‘isolated’ epi-polymorphisms globally had reduced persistence ( Figure 2C , Wilcoxon P<10−15 ) and contained more labile SNEPs than expected ( 51% versus 35% among non-isolated , P<10−15 , χ2 test ) . This suggests that epi-polymorphisms carried on specific single nucleosomes are less stabilized than those established on consecutive nucleosomes . The mechanism ( s ) by which labile SNEPs are established and lost remain unknown . However , when confronting our data to a published map of histone turnover rates [37] , we observed that labile SNEPs corresponded to nucleosomes of faster histone replacement , as compared to persistent SNEPs ( P = 0 . 003 , see Text S1 ) . This suggests that the increased dynamics of molecular replacement at these positions contributes to SNEP lability . In addition , we also observed an increased persistence among nucleosomes located within protein-coding genes or located within regions of conserved DNA sequence ( Figure S3 ) . The reprogramming experiment presented here was designed to test the stability of SNEPs and not the effect of treatment in each strain . Assessing precisely the amount of reprogramming within each strain would require a dataset where all samples prior and post treatment are processed in parallel , by the same experimenter , using common batches of reagents . This was not the case here , and confounding experimental factors would likely bias any statistical inference of reprogramming within each strain . However , we made interesting observations when inspecting the fold change of acetylation between the levels before treatment and the levels after recovery . First , the mean fold change across all nucleosomes was similar between the two strains ( Figure 2D ) . This is consistent with the similar levels of bulk acetylation seen on whole protein extracts ( Figure S1 ) . Secondly , the fold change in the BY strain presented elevated variability between nucleosomes , as compared to the RM strain ( high variance in Figure 2D ) . This higher variability does not correspond to higher experimental error in the BY samples , as the between-replicates variance was similar between the two strains ( Table S1 ) . This suggests that more nucleosomes were reprogrammed in the BY strain than in the RM strain . To specifically look at this possibility , we plotted the distribution of fold changes in the 1 , 076 labile SNEPs , where reprogramming occurred . This highlighted a strong asymmetry in BY , with a majority of labile SNEPs having gained acetylation in this strain ( Figure 2E ) . There are at least three possible interpretations of this . First , TSA may have imposed a stronger chromatin hyperacetylation in BY than in RM . Secondly , the BY strain may have recovered badly from treatment , with a chromatin remaining at an artificially high acetylation level despite the long recovery time . Alternatively , the BY strain may initially have had many nucleosomes with low levels of acetylation , which were reset to ‘normal’ levels by exposure to TSA . In the first two cases , the observed gain of acetylation is not expected to target specific nucleosomes . In contrast , in the latter case , the nucleosomes that were reprogrammed should correspond to those initially identified as poorly acetylated in BY . In other words , the presence of a SNEP before treatment should predict the treatment effect . To see if such a prediction could be made , we considered three classes of nucleosomes on the basis of observations made before treament only: those initially SNEPs as BY hypo-acetylated , those initially SNEPs as BY hyper-acetylated , and those not initially SNEPs . We then compared the extent of fold change between these three categories of nucleosomes . The classification was not predictive of the fold change in the RM strain ( Figure 2F ) , but it was highly predictive of the effect in the BY strain ( Figure 2G ) . This observation suggests that the BY strain possessed many nucleosomes that were initially hypoacetylated and predisposed to resetting at a higher level . We then investigated the genetic control of epi-polymorphisms . Using the maps of nucleosome positions previously generated for BY and RM [12] , we associated every nucleosome with the nucleotide region that overlapped its position in both strains ( see Methods ) . We then measured the level of acetylation of each of these regions in 60 meiotic segregants from the BYxRM cross [38] . This was done by culturing each segregant in standard laboratory conditions , and by performing single-nucleosome resolution chromatin immunoprecipitation as above . We defined one quantitative trait of acetylation per nucleosome , which reflected the abundance of the DNA region associated with the nucleosome in the immunoprecipitated material ( see Methods ) . Using these trait values , we searched the genome for Quantitative Trait Loci of acetylation ( aceQTL ) . A first scan was performed at a genome x epigenome scale . To do so , we selected 36 , 558 nucleosomes with H3K14ac heritability higher than 0 . 2 , and for each of these we searched the entire genetic map for linkage . Calculations were done using a convenient platform called eQTNMiner , which was originally designed for the Bayesian Inference of nucleotide-resolution eQTLs [39] . eQTNMiner reports linkage evidence as a Bayes Factor ( BF ) , which quantifies the relative support of the data in favor of the alternative hypothesis ( there is a QTL ) against the null hypothesis ( there is no QTL ) . We recorded linkages at various Bayes Factor thresholds , and computed empirical significance of each threshold by a permutation test ( Table S2 , see Methods ) . At BF = 1000 ( corresponding to FDR = 0 . 034 ) , we found significant linkages for a total of 2 , 418 nucleosomes ( Figure 3A ) . Of these , 77 were linked to 2 aceQTLs and all others to a single one . We then applied a second scan to specifically search for cis-modifiers . For each nucleosome , linkage was searched across DNA polymorphisms located within 5 Kb . We chose this distance as a compromise between the small physical size of a nucleosome ( 147nt ) and the usual large regions scanned for cis-eQTLs ( 10–50 Kb ) . At BF = 50 ( corresponding to FDR = 0 . 0007 ) , we found cis-linkages involving the control of 4 , 173 nucleosomes . There were 17% of SNEPs ( 908/5 , 442 ) for which an aceQTL could be found in at least one of the two scans . Given the rather small size of the segregating population examined , we can assume that some genetic linkages were missed . This fraction is therefore a lower-bound estimate of the ‘genetically encoded’ class of SNEPs . All further analysis was done on results obtained from the first scan only , as they reflect both cis and trans regulators , with effects strong enough to pass a stringent genome-by-epigenome significance level . The number of nucleosomes controlled by each trans-acting aceQTLs varied greatly ( Figure 3B ) . Seventeen loci , called ‘master-aceQTLs’ hereafter , were found to control more nucleosomes than expected by chance ( Table 1 , see Methods ) . One of them contained the locus controlling the cell mating type ( MAT ) , which encodes different transcriptional co-factors in BY ( MATα ) and RM ( MATa ) . Of the 148 nucleosomes controlled by this locus , 83 had a marked acetylation difference between BYα and RMa that could be detected without replicated experiments . To directly test if MAT accounted for the associated acetylation variation , we performed two additional ChIP-chip experiments on strains having reversed mating types ( BYa and RMα ) and we tested for acetylation variation between isogenic strains differing only at MAT . The expected difference was observed for 69 of the 83 nucleosomes tested ( p<0 . 01 , see Text S1 ) , which validated MAT as the responsible polymorphism underlying this master-aceQTL . As examples , epigenomic profiles at the KAR4 locus are shown in Figure 3C , where the control of three SNEPs by MAT is apparent . Only a small fraction ( 16% ) of the nucleosomes controlled in cis were proximal to elements known to affect nearby chromatin ( Ty transposons , rDNA , telomeres , HML and HMR loci ) . This suggests that many other causes for acetylation variability exist . Intuitively , trans-regulation could result from sequence variants targeting chromatin modifying enzymes . To examine this possibility , we analyzed relevant Gene Ontologies ( GO ) . We saw that eight of the seventeen master-aceQTLs did not contain any gene annotated to participate in chromatin regulation ( Table 1 ) . Among all 141 trans-acting aceQTLs , 63 contained a gene with relevant annotation , which corresponded to the number expected by chance only ( Text S1 ) . Thus , trans-modifiers of acetylation are not necessarily restricted to chromatin modifying enzymes but may include upstream molecular players . This conclusion is analogous to the previous observation that trans-acting modifiers of gene expression ( eQTLs ) do not necessarily correspond to transcription factors [38] . In some cases , the genetic control of chromatin acetylation had a complex basis , such as digenic regulations by antagonistic aceQTLs ( Figure 3D ) . This illustrates the quantitative nature of acetylation variation and reveals that subtle epigenomic variations can segregate as complex traits in natural populations . Acetylation of H3K14 is generally a mark of active transcription [40] . However , we previously described that higher acetylation of BY/RM SNEPs did not necessarily imply an increased expression of the overlapping gene [12] . This suggests that some SNEPs do not participate in transcriptional activation while others do . Thus , one would expect that only a fraction of the genetic regulations of acetylation are concordant with the genetic regulation of gene expression . We therefore examined the overlap between aceQTL and eQTL results , taking advantage of a transcriptomic dataset previously generated on the same strains and culture conditions [38] . This was done in two steps . First , inspection of master-aceQTLs showed that several of them ( including MAT ) corresponded to loci previously identified as master eQTLs [38] ( Table 1 ) . For example , the GPA1-S469I polymorphism targets a G-protein α subunit and underlies expression variation of many pheromone-responsive genes [38] . This polymorphism lies at an aceQTL affecting 24 nucleosomes that reside within or near these target genes . GPA1-S469I is therefore a likely regulator of both expression and acetylation at these loci . For similar reasons , a transposon insertion altering the HAP1 transcription factor is likely a regulator of both expression and acetylation of target genes ( Table 1 ) . However , the overall overlap between aceQTLs and eQTLs was only partial . For example , the AMN1 locus on chromosome II was previously linked to the expression level of 18 transcripts and was not detected as a master QTL of chromatin acetylation here . Conversely , 10 master-aceQTLs were located at positions not previously associated with major transcriptional variation [38] ( Table 1 ) . In a second step , we systematically compared aceQTL and eQTL linkages without restricting the analysis to master-aceQTLs . To do so , we reduced aceQTLs of the first scan to 2 , 530 non-redundant linkages ( i . e . pairs of one nucleosome and one genetic marker ) . For every linkage between a genetic marker m and a nucleosome ν , we examined if a significant eQTL could be found between m and a gene located within 10 Kb of ν . For 31% of aceQTLs , no such concordance could be found . Note that statistical power was much higher to detect eQTLs than aceQTLs because many more segregants were used . It is therefore unlikely that these cases corresponded to false negatives . Overall , the strength of linkage was poorly correlated between aceQTLs and eQTLs ( Figure 3E–3G , Figure S4 ) . We then used the same criteria as above to re-examine master-aceQTLs and classify them based on the fraction of their linkages that matched eQTLs ( Table 1 ) . Of the 17 master-aceQTLs , nine clearly corresponded to eQTLs , four had partial concordance and four did not affect the expression level of genes proximal to the target nucleosomes . Notably , expression of KAR4 was not affected by MAT alleles ( Figure 3G ) . Our genetic dissection therefore unravelled the coexistence of two types of H3K14 acetylation epi-polymorphisms , one type associated with transcriptional variation and one disconnected from it . Although it is difficult to precisely estimate their relative proportions , the results argue that in at least 30% of cases , genetic polymorphisms modulate chromatin acetylation without altering gene transcription levels . When epi-polymorphisms result from DNA-encoded regulatory variation , they should persist ( e . g . be maintained or return to their initial state ) across extreme environmental perturbations because their causative variants do . We sought to test this principle by comparing the results obtained on SNEP persistence through temporary TSA exposure with the genetic properties of aceQTL control . We first examined the success rate of aceQTL mapping when searching for regulators of ‘isolated’ or ‘non-isolated’ SNEPs . More aceQTLs were found for SNEPs carried on consecutive nucleosomes ( Figure 3H ) . Note that this enrichment does not imply that aceQTL targets are necessarily clustered: for 60% of nucleosomes controlled by an aceQTL , none of the flanking nucleosome was in linkage with the same aceQTL locus . However , finding more aceQTL for clustered SNEPs was concordant with the increased persistence of these SNEPs ( Figure 2C ) . We therefore directly examined if the presence of genetic regulators correlated with the level of environmental persistence . Accordingly , aceQTLs were found 4 times more often for persistent SNEPs than for labile SNEPs ( Figure 4A ) . Consistently , a Receiver Operating Curve applied to all SNEPs showed that persistence was strongly associated with successful aceQTL mapping ( Figure 4B ) . In addition , if high environmental persistence is explained by strong genetic control , then it should correlate with high genetic linkage score . We therefore represented the strength of genetic linkage as a function of environmental persistence , which confirmed the expected trend ( Figure 4C ) . Finally , genetic linkage results alone may sometimes not reflect the strength of genetic determinism . For example , if numerous QTLs with small individual contribution altogether control the acetylation value of a nucleosome , then none of them may be found despite a complete overall genetic determinism . Similarly , a complete control by epistatic or antagonistic genetic loci may not be detected . However , even in such complex genetic cases , the overall determinism can still be estimated by the genetic heritability of the acetylation trait in the segregating population . We therefore examined heritability itself , and found that increasing heritability values were unambiguously associated with gradual shifts towards higher persistence ( Figure 4D ) . Thus , genetic determinism was indeed correlated with elevated environmental persistence , regardless of the complexity of the underlying control .
This study reveals that H3K14ac epi-polymorphisms are not equally sensitive to environmental reprogramming . Some of them can be lost after temporary perturbations while others persist . This persistence clearly correlates with the presence of genetic determinants that encode epi-polymorphisms in the DNA . This genetic control is complex and resembles architectures previously described for eQTLs , with both cis and trans regulators and the presence of master regulators affecting numerous targets . Importantly , our results further highlight the quantitative nature of the variation of acetylation levels . At any given time , a nucleosome of a given cell is or is not acetylated . Thus , acetylation is sometimes considered a discrete variable . However , the average acetylation level of this nucleosome across a population of cells is quantitative , because it depends on the number of cells that carry the acetylation mark , which corresponds to an equilibrium state of the population resulting from many biochemical reactions . The fact that this level varies as a complex trait shows that aceQTLs change the proportion of cells that are acetylated at their target nucleosome . In other words , aceQTLs are genotypes that modify the probability that a given nucleosome is acetylated in a given cell at a given time . How this happens will probably remain unknown until new technologies are developped to interrogate single nucleosome states in single cells . Also , the quantitative variability studied here is different from several epimutations described in plants where strong silencing of large chromosomal domains is established by a combination of many molecular and structural changes . Whether the genetic control of chromatin variability also controls the level of nearby gene expression appears to be context specific . In humans , Gibbs et al . did not find any consistent overlap between expression Quantitative Trait Loci ( eQTL ) modifying the transcriptome of brain tissues and Quantitative Trait Loci modifying the methylome of these tissues ( methQTL ) [5] . In contrast , Bell et al . reported a clear consistency between eQTL and methQTL in HapMap lymphoblastoid cell lines [6] . Here we observed that about 70% of aceQTLs linkages overlap with eQTLs . The remaining fraction of aceQTLs could correspond to regulations of non-coding transcripts , which were not interrogated by our study . Alternatively , given our previous association between SNEPs and transcriptional plasticity [12] , it is possible that some aceQTLs modify the chromatin in a way that manifests only upon transcriptional stimulation . In other words , genetic modifiers could increase K14 acetylation of a locus , which would then become more responsive to transcriptional activation or repression upon specific conditions . In such cases , aceQTLs could participate in gene x environment interactions by creating epi-polymorphisms that personalize the way the genome responds to the environment . We proved that the MAT locus affects chromatin acetylation of many target loci . This locus determines the cell's mating type by dictating specific transcriptional programs . The MATα allele encodes two regulatory proteins: α1 , which activates α-specific genes , and α2 , which represses expression of a-specific genes . The MATa allele encodes the a1 protein only , which heterodimerizes with α2 in diploid a/α cells to form a repressor of haploid-specific genes . How specific transcriptional programs are established in a and α cells has been the focus of many studies , revealing the interplay with chromatin acetylation regulation at specific target promoters . In α cells , the cooperative binding of α2 and Mcm1 recruits the Tup1-Ssn6 repressor , which is known to interact with several histone deacetylases [41]–[43] . In a cells , α-specific genes are occupied by Sum1 which is known to recruit the NAD+-dependent histone deacetylase Hst1 to repress transcription [44] . In our study , some loci controlled by MAT displayed an epigenomic profile totally predictable given the known transcriptional control . This was the case for the BAR1 gene for example , which encodes a secreted protease specifically expressed in a cells . The chromatin signature of the entire locus was affected by the mating type . MATa strains displayed occupancy and acetylation intensities typical of highly expressed genes [40] , with a marked nucleosome-free region near the transcription start site , and a high and low level of H3K14 acetylation in the first and second half of the coding region , respectively ( Figure S5 ) . However , other loci controlled by MAT displayed unexpected patterns of chromatin variation . One such example was the KAR4 locus , which encodes two forms of a transcription factor essential for nuclear fusion during mating . The long form is expressed in mitotically growing cells , and the short form is induced in response to pheromone from a transcriptional site about 30 nucleotides downstream the first ATG [45] . Our study revealed marked differences between MATa and MATα growing cells in the 3′ part of the gene , which were not accompanied by differential transcriptional levels ( Figure 3C and 3G ) . How a cells maintain elevated H3K14 acetylation on two nucleosomes at the end of the KAR4 coding region remains to be identified . It is possible that a and α cells do not use the same strategy to maintain the locus transcriptionally active and responsive to pheromone . Comparing Ste12 , Tup1 , or Sum1 occupancy between a and α cells might reveal some differences in this region . Alternatively , DNA replication initiated downstream KAR4 , at the ARS304 site , could have an effect if its timing differs between a and α cells [46] . Another particular case of mating-type specific chromatin organization was the promoter of the SAG1 gene , which encodes the α-agglutinin specifically expressed in α cells . The repressed state of a cells corresponded to nucleosome occupancy downstream the TSS , and to hypoacetylation of H3K14 specifically at the -1 nucleosome ( Figure S6 ) . These three examples illustrate that the mechanism by which MAT alleles affect chromatin signatures at target genes is not simple: it can affect an entire locus ( BAR1 ) , or a specific set of nucleosomes in the promoter ( SAG1 ) or the 3′ region ( KAR4 ) . More generally , the fact that aceQTLs were not preferentially found at sites coding for chromatin modifying enzymes may seem counterintuitive: one could expect that DNA polymorphisms affect chromatin states by modifying the sequences of enzymes directly involved in chromatin regulation . However , protein complexes that regulate chromatin are themselves highly regulated , and any DNA polymorphism affecting these upstream regulators has the potential to induce chromatin modification indirectly . In fact , this is what happens with MAT alleles: they do not code for chromatin remodelling enzymes but they determine distinct recruitments of chromatin modifiers at specific sites . This observation is very similar to results from eQTL mapping , from which we know that genetic modifiers of gene expression do not necessarily reside in direct transcriptional regulators [38] . For example , the AMN1 , GPA1 , IRA2 and MKT1 yeast genes were all validated as eQTL players but they do not encode direct regulators of transcription [38] , [47] . These polymorphisms affect gene expression by perturbing regulatory networks upstream of transcriptional machineries . The results presented here suggest that aceQTLs likely follow a similar rule: causative polymorphisms may reside not only within chromatin modifying complexes but also in their upstream regulators . We show that a transient environmental change imposed by TSA treatment can reprogram a subset of H3K14ac epi-polymorphisms: numerous new SNEPs were induced , and numerous initial SNEPs were lost . An important consideration is that TSA imposed a perturbation but did not necessarily saturate the acetylation of H3K14 on all nucleosomes . In normal conditions , H3K14 acetylation levels result from a balance between the activity of histone acetyltransferases ( HATs ) and deacetylases ( HDACs ) . In S . cerevisiae , at least three HATs are known to acetylate Lysine 14 of Histone H3: Gcn5p [48] , [49] , Sas3p [50] , and Hpa2p [51] , and deacetylation of Lysine 14 can be attributed to HDACs of all three classes: Hos3p and Rpd3p of class I [52] , [53] , Hda1p of class II [41] and Sir2p of class III [54] . TSA is known to induce a bulk hyperacetylation by inhibiting the activity of a subset of these HDACs: while Rpd3p and Hda1p are sensitive , Hos3p and Sir2p remain active . Thus , the perturbation applied in our experiment did not necessarily saturate K14 acetylation on the entire chromatin . In addition to the direct effect of TSA on HDACs that deacetylate H3K14 , the treatment may have perturbed this lysine residue indirectly . The very slow growth in presence of TSA ( not shown ) suggests that cells profoundly reshaped molecular profiles during treatment , with possible consequences on the regulations of HATs and HDACs . The reprogramming observed preferentially corresponded to a gain of acetylation in the BY strain , with a majority of labile SNEPs corresponding to hypo-acetylated nucleosomes in the BY strain that returned to levels comparable to those of the RM strain . An intuitive interpretation of this asymmetry would be that TSA was more efficient to induce hyperacetylation in BY than in RM . The strains are probably not equally sensitive to TSA , given the two previously mapped QTLs of growth fitness in the presence of TSA that segregate in the BYxRM cross [55] . However , the possibility that BY suffered a more pronounced hyperacetylation does not explain why only a subset of nucleosomes were preferentially reprogrammed . Alternatively , the strains may differ in their recovering efficiency . Although after 20 generations all HDAC complexes are young enough to consider they never bound the chemical inhibitor , it is still possible that the chromatin of the BY strain did not fully return to equilibrium . Then again , why would an incomplete recovery target preferentially a subset of nucleosomes ? Our observation that the nucleosomes affected are largely those initially hypoacetylated suggests a third and complementary interpretation: the BY strain may have accumulated hypoacetylation ‘epimutations’ that were cured by the treatment . BY is a strain that has been maintained in laboratories for decades and is known to possess many deleterious mutations that would likely be counter-selected in the wild . Our results raise the possibility that it has also drifted at the epigenetic level , and it will be very exciting to test this hypothesis in future experiments . More generally , it will be essential to question the origin of the ‘labile’ SNEPs: those which gained but also those which lost acetylation in BY , and the few where the change happened in RM . Theoretically , the differences in these epigenotypes may have occurred any time between the initial divergence of the strains and the last hours before the stocks were frozen in our laboratory . In other words , our study identified their lability but not their origin and age . A related question is how stable are labile and newly induced SNEPs: how harsh a treatment is needed to reprogram them ? If some ‘labile’ SNEPs are old , they have been maintained for a long time and one would expect them to be stable unless extreme environmental perturbations are experienced , like in our TSA-based assay . Likewise , it is possible that additional SNEPs could have been modified if we had applied a stronger or longer treatment . As mentioned above , class III HDACs such as Sir2p are not inhibited by TSA , and other SNEPs would probably be called ‘labile’ if an inhibitor of sirtuins was used instead of TSA . In contrast , some ‘labile’ SNEPs may be very unstable and might also disappear after a prolonged but unperturbed culture . It will therefore be interesting to monitor the dynamics of SNEP appearance and loss in unperturbed conditions . A time-course experiment tracking the H3K14ac epigenome of one strain over long culture periods would help determine its stability . How and for how long were new SNEPs induced despite the fact that the treatment applied was the same for the two strains ? As mentioned above , this can possibly result from a difference in the way the strains respond to the treatment , and this difference might or not be genetically encoded . Although our experiments were not designed to address this , it is also possible that epi-polymorphisms arise stochastically in particular environments regardless of the genetic background . This has been suggested by a recent study where the methylome of isogenic mice fed with high levels of methyl precursors was tracked over generations [23] . This treatment was shown to increase inter-individual epigenome diversity , although the diet itself was common to all animals . Thus , induced epi-polymorphisms may reflect not only differences in the history of past environmental exposures , but also genetic or stochastic differences in the way individuals reprogram their epigenome in response to specific environments . We observed a clear correlation between environmental persistence and genetic control of acetylation variation . Importantly , the two datasets ( reprogramming and QTL mapping ) were generated and analysed independently: at different dates , by different experimenters , the former using the parental strains only and the latter using the segregants . Thus , we believe that this correlation truly reflects the robustness of DNA-encoded epi-polymorphisms to environmental reprogramming . However , our observations do not imply that all cases of epi-polymorphism persistence result from their anchoring in DNA . It remains entirely possible that specific cases have a purely epigenetic basis . For example , H3K14 acetylation may be more robust to environmental perturbation if it is accompanied by additional epigenetic marks that are commonly associated with it , such as H3K4 di- or tri-methylation , or H3K9 acetylation [40] . If such marks drive H3K14 acetylation and are not affected by the environmental change , then persistence is ensured without a DNA-encoded control . Given our observation that both labile and persistent epi-polymorphisms coexist abundantly in natural epigenomes , we emphasize the importance of the stability of epi-polymorphism in the current debate on whether and how epigenotypes contribute to evolutionary mechanisms . As outlined by B . Turner , this question is fundamental because epi-polymorphisms potentially enable environmental conditions to reprogram molecular events for a durable time . This way , “epigenetic processes might contribute to evolutionary change , at least in part by expanding the range of phenotypic variants on which natural selection can act” [16] . A key factor for selection to act is then the amount of time during which the ‘novel’ phenotypic variants ( those generated by chromatin changes ) are exposed . If too short , individuals with beneficial traits may not have time to expand in the population , especially if the phenotypic variants consist of small quantitative differences . Although our results did not link SNEPs to phenotypic traits , they suggest that the amount of time for selection to act may differ if the phenotypic variants result from labile or from persistent epi-polymorphisms ( Figure 5 ) . This duration depends on the stability of the new epigenotype and on the probability to encounter environmental conditions that change its state . If the epigenotype is robust to environmental perturbations , then the phenotype is exposed as long as other genetic or epigenetic modifiers of it are acquired . Natural selection is therefore more likely to act on phenotypic variants resulting from persistent epi-polymorphisms . Note that such high persistence can sometimes result from a full genetic control . In this case , the fact that epi-polymorphisms are involved no longer matters: selection acts on the genetic determinant regardless of the mechanism leading to the phenotype . Importantly , loci harboring DNA-encoded epi-polymorphisms may remain highly susceptible to epigenetic regulations: as mentioned above , SNEPs represent small quantitative differences of molecular regulations , and it is likely that they do not prevent from switching between radically different epigenetic states . Thus , the prolonged duration of DNA-encoded epi-polymorphisms does not necessarily impair fitness in fluctuating environments , where adaptation requires rapid and profound chromatin remodelling at critical loci . In addition , chromatin changes may reveal the effect of cryptic genetic variations . For example , a mutation occuring at a silenced locus can remain cryptic until silencing of the locus is alleviated . Such epigenetic alleviation might also be either labile or persistent , with different consequences on the cryptic variation: the phenotype and therefore the cryptic variation itself may be exposed to selection for a longer period of time if alleviation persists . Altogether , our observations provide a necessary basis for the upcoming development of population epigenetics , where epi-polymorphisms of natural populations will be interpreted and possibly associated to the variation of common traits .
Strains used were BY4716 MATα lys2Δ0 ( called ‘BY’ in text ) and BY4715 MATa lys2Δ0 derivatives of S288c , RM11-1a MATa leu2Δ0 ura3Δ0 hoΔ::KanMX wine strain [38] ( called ‘RM’ in text ) and its GY689 MATα leu2Δ0 ura3Δ0 hoΔ::KanMX amn1-A1103T derivative ( see Text S1 ) , and 60 meiotic segregants from BYxRM previously used for eQTL mapping [38] , [47] . Except for the TSA experiment , cells were grown to exponential phase in synthetic medium with 2% glucose ( SDall ) at 30°C as previously done for SNEP identification [12] and eQTL mapping [47] . ChIP–chip was performed at single-nucleosome resolution as previously described , using formaldehyde fixation followed by micrococcal nuclease digestion , anti-H3K14ac antibody ( Upstate #07-353 ) precipitation and hybridization on Affymetrix Whole Genome Yeast Tiling 4-bp resolution microarrays [12] . The following protocol was applied on 3 independent cultures for each strain . A 110 ml culture of SDall medium was inoculated at OD600 = 0 . 15 using an overnight starter culture and was grown at 30°C until OD600 reached 0 . 5–0 . 6 . 45 ml of the culture was pelleted and frozen for later Western blot analysis ( sample t1 ) . TSA ( Wako 204-11991 ) was resuspended in ethanol 50% at 45 . 5 mg/ml and 3 . 9 ml of this stock was added to the remaining of the culture ( final TSA concentration: 2 . 6 mg/ml ) . The culture was kept at 30°C for 3–4 doubling times ( OD600 = 3 ) and 20 ml was pelleted and frozen for Western Blot analysis ( sample t2 ) . Remaining cells were washed twice with 20 ml TBS1X [Tris 25 mM , NaCl 140 mM , KCl 2 . 5 mM , pH 7 . 4] and 1% of the suspension was added to 50 ml of SDall and incubated at 30°C . The next day , 10 microliters of the overnight culture was transfered to 50 ml fresh SDall medium and incubated at 30°C for 6 hours . This culture was used to inoculate 200 ml fresh SDall incubated at 30°C until OD = 0 . 9 . This procedure corresponded to about 20 generations post-treatment . 25 ml of the final cell suspension was pelleted and frozen for Western blot analysis ( sample t3 ) and the remaining of the culture was used for chromatin immunoprecipitation . A matrix of raw microarray hybridization intensities was considered that contained the BY ( 6 arrays ) , RM ( 6 arrays ) , BYart ( 3 arrays ) and RMart ( 3 arrays ) ChIP-chip values for all probes having a single perfect match on both BY and RM genomes . Here ‘Xart’ correspond to strain X after recovery from TSA treatment ( time t3 on Figure S1 ) . This matrix was quantile-quantile normalized using NucleoMiner [12] and for every probe , 6 independent values of LR = log ( BY/RM ) were derived as well as 3 independent values of LRart = log ( BYart/RMart ) . For all 58 , 694 previously aligned nucleosomes [12] , we extracted relevant probes according to their physical position on the genome . The window of extraction was defined by the overlapping region between nucleosomal position in BY and nucleosomal position in RM [12] , trimmed at both extremities by 12 nucleotides to avoid possible artefactual border effects . All probes having mid-position within this window were used to test against the null hypothesis of similar inter-strain difference before TSA treatment and after recovery ( LR = LRart ) . This was done by an analysis of variance ( ANOVA ) based on the model logratio∼tsa+probe , where tsa reflects whether LR or LRart is considered and probe reflects the probe index . Nominal P-values relevant to factor tsa were used to derive q-values that account for multiple testing , by using the QVALUE package [56] . 4 , 484 nucleosomes showed q<0 . 001 . Among these nucleosomes , 1 , 076 belonged to the list carrying original SNEPs and these SNEPs were called ‘labile’ . Note that testing for acetylation reprogramming for each strain separately would be difficult from our dataset because the two sets of experiments ( before treatment and after recovery ) were done at different dates , by different experimenters . We therefore preferred to use inter-strain log-ratios to ensure a consistent comparison of the inter-strain difference within each dataset . We specifically searched for persistent SNEPs by running NucleoMiner on the BYart , RMart ChIP-chip dataset , together with the previously described BY and RM nucleosome mapping experiments [12] . 2 , 379 nucleosomes showed differential H3K14 acetylation levels at FDR<0 . 001 ( nominal P-value<4 . 05×10−5 ) after recovery from TSA . Note that fewer biological replicates were used after recovery ( 3 for each strain ) than before treatment ( 6 for each strain ) , which explains the detection of fewer SNEPs ( 2 , 379 instead of 5 , 442 ) . The intersection between the two lists of SNEPs corresponded to 1 , 481 nucleosomes that were called ‘persistent’ SNEPs ( magenta dots on Figure 2A ) . For every nucleosome , persistence was defined as 1−|log2 ( RM/BY ) before tratment−log2 ( RM/BY ) post recovery| . We generated an extremely dense genetic map by inferring , at every SNP , a probabilistic genotype given the genotypes previously described at marker positions [47] ( see Text S1 ) . To avoid hybridization artefacts due to DNA polymorphisms , we considered only the microarray probes having a single perfect match on both BY and RM genomes . A dataset comprising 18 microarrays previously described ( nucleosome mapping data and H3K14ac profiling on BY and RM replicates ) [12] and the 60 ChIP-chip microarrays performed here on the BYxRM segregants was normalized by quantile-quantile normalization using the NMc2tab program of NucleoMiner with option -n lqq . We then computed estimates of nucleosome-level ChIP intensities . For every nucleosome , we considered signals from probes that were entirely contained in the overlap between nucleosome position in BY and nucleosome position in RM . These signals were further corrected by quantile normalization ( to account for probe effects ) and averaged using the eqmr-fdb command of eQTNminer [39] with option -m qnorm . For every nucleosome , heritability of acetylation level was then computed as h = ( varS−varE ) /varS , where varS is the variance across all segregants and varE the environmental variance , estimated by the pooled variance of parental replicates . We noticed that one BY experiment had very high variation , therefore varE was estimated by the pooled variance of 5 BY and 6 RM ChIP-chip experiments . Mapping of aceQTL was performed using the eqmr-ftr command of eQTNMiner [39] version 2 . 0 with default parameters on 59 , 936 nucleosomes ( nucleosomes contained in translocated regions were not considered ) . A first scan was performed at a genome x epigenome level . To do so , we selected 36 , 558 nucleosomes with H3K14ac heritability higher than 0 . 2 . This threshold was chosen arbitrarily in order to avoid multiplying tests on nucleosomes where linkage is unlikely to be discovered . For each of these , the entire genetic map was scanned for QTL using a Bayesian regression model , implemented in eQTNMiner [39] , which follows the framework of Servin and Stephens [57] . The effect of individual i genotype at SNP j ( gijk ) on the acetylation level of the k-th nucleosome ( yik ) is assumed to follow a purely additive linear model: yik = μ+ajkg×gijk+εijk , where μ is the mean acetylation level of that nucleosome for individuals with g = 0 , and where ajk is the additive effect of the minor allele at SNP j . The residual εijk is assumed to be normally distributed , with mean zero and variance 1/τ equal to the variance of acetylation levels within each genotype class . Let P0k denote the probability of the acetylation data Yk under the null hypothesis that there are no aceQTL controlling nucleosome k ( i . e . , ajk = 0 for all j ) . Similarly , let P1jk denote the probability of the acetylation data Yk under the hypothesis that SNP j is an aceQTL of nucleosome k . In this case , the effect size ajk is modelled as being drawn from mixtures of normal distributions centered on 0 ( see below ) . The Bayes Factor reflecting genetic linkage between SNP j and nucleosome k is defined as BFjk = P1jk/P0k and measures the relative support for the hypothesis that SNP j is an aceQTL of nucleosome k , versus the null hypothesis . As suggested by Servin & Stephens [57] , we assumed that the effect size ajk is drawn from mixtures of normal distributions centered on 0 with variance σa2/τ . Specifically , we assumed a mixture of 6 normal with σa2 = ( 0 . 05 , 0 . 1 , 0 . 2 , 0 . 4 , 0 . 8 , 1 . 6 ) , we computed a Bayes factor for each value of σa2 , and considered the mean Bayes factor as our summary statistics . We controlled the False Discovery Rate empirically by re-scanning 100 permuted datasets . On average , only 20 , 288 linkages were obtained at a Bayes Factor threshold of 1000 from a permuted dataset , while 592 , 368 linkages were obtained at this level from the actual data ( Table S2 ) . The list passing this FDR = 0 . 034 threshold was used for further analysis . Note that many of the 592 , 368 linkages reflect redundant genetic information between adjacent DNA polymorphisms . In total , aceQTLs were found for 2 , 418 nucleosomes . To roughly see how many nucleosomes were controlled by two or more aceQTLs , we reduced the 592 , 368 linkages to account for linked markers: for each target nucleosome , the best QTL marker was recorded and all markers located within 100 Kb of it were discarded . This procedure was then repeated until no significant additional linkages remained . This way , 2341 nucleosomes were linked to a single aceQTL , 77 nucleosomes were linked to 2 distinct aceQTLs , and no nucleosome was linked to three or more loci . A second scan was performed on all 59 , 936 nucleosomes to specifically detect cis-acting aceQTLs . Using the eqmr-expca command of eQTNminer [39] , we applied a Principal Component Analysis and observed that the first 10 principal components represented significant general effects ( as compared to eigenvalues obtained from permuted datasets ) that could shade specific regulations . We therefore corrected for these effects by applying an elastic net regression on these 10 axes as implemented in eqmr-fenet of eQTNminer . The residuals were then used as the corrected traits . For each nucleosome , we used eqmr-fcr to search for linkages between the trait and any DNA polymorphism located within 5 Kb on each side . False Discovery Rate was controlled empirically by running 100 permutations ( Table S3 ) . We observed 235 , 942 linkages exceeding a Bayes Factor of 50 while only 160 were seen at this threshold from permuted datasets ( FDR = 0 . 0007 ) . In total , cis-aceQTLs were found for 4173 nucleosomes . 668 of these nucleosomes ( = 16% ) were located within 20 Kb of a region known to affect nearby chromatin states ( telomere , retrotransposon , rDNA , HML or HMR ) . Finally , an aceQTL for nucleosome i and marker m found in the first scan was called trans-aceQTL if m was at least 50 Kb away from any cis-aceQTL found for i in the second scan . We determined which of the trans-aceQTLs affect the acetylation level of a significantly high number of nucleosomes . To do so , we first reduced the results to the best linkage scores in trans . For each nucleosome for which a trans-aceQTL was found , the marker M with highest linkage score was recorded and all significant linkages to markers close to M were discarded . If additional significant trans-aceQTLs remained for this nucleosome , the procedure was repeated . We then segmented the genome in 20 Kb bins and counted the number of reduced trans-aceQTLs in each bin ( Figure 3B ) . We tested for enrichment by considering deviation from Poisson distribution , as done before for eQTLs [38] , [47] . 25 bins were significantly enriched ( at least 9 target nucleosomes , P<0 . 05 after Bonferroni correction ) , which could be concatenated to 17 non-consecutive bins . We then searched each bin for the best candidate polymorphism regulating the set of target nucleosomes: for every nucleosome i having a trans-aceQTL in the bin , we computed at every SNP k the posterior probability Pi ( k ) that the SNP is causal . This probability can be directly computed from the output of eQTNminer . Let Bi , m be the Bayes Factor for linkage between nucleosome i and SNP m , and Si the sum of Bi , m across all m of the genome , then the probability is simply Pi ( k ) = Bi , k/Si . These probabilities were then sumed across all nucleosomes in linkage to the bin , and the best candidate was identified as the SNP maximizing this sum ( indicated as ‘Score’ in Table 1 ) . To compare aceQTLs with eQTLs in a consistent way , we generated a set of eQTL results using the same method and same genetic map as for aceQTLs . Gene expression data was extracted for 4 , 464 genes from the “glucose condition” of Smith and Kruglyak [47] . eQTNminer was used to scan the genome x transcriptome space , without the hierarchical models previously described [39] and using data from 109 segregants previously generated under the same glucose medium as here [47] . This produced 2 , 159 , 456 linkages with Bayes Factor exceeding 50 . One hundred permutations were run to control the FDR , which was 3 . 5% at this threshold . In total , eQTLs were found for 3 , 572 genes and the results were consistent with previous studies . To then determine if aceQTLs could be considered as eQTLs , we considered all significant aceQTLs of the first scan . To remove redundant linkages supported by adjacent markers , we reduced the results to the best scores as described above for reducing trans-aceQTLs , leaving 2 , 530 aceQTL linkages out of the 592 , 368 original ones . For each one linking acetylation of a nucleosome i to a genetic marker m , we then recorded the best eQTL score between m and any gene located within 10 Kb of i . In several cases , no eQTL was found at a very relaxed threshold ( Bayes Factor of 1 ) and this search was then labelled as ‘non-significant’ ( Figure 3E ) . We considered that an aceQTL was not an eQTL if the best score found was not more significant than P<0 . 00125 ( nominal value ) . This corresponds to the usual 0 . 01 threshold divided by 8 which is the average number of genes examined within 20 Kb of the yeast genome ( 4 , 464 * 20/12 , 000 ) . Following this criterion , 790 of the 2 , 530 aceQTL linkages ( 31% ) did not correspond to eQTL . Note that the detection power was much higher for eQTL than for aceQTL , as more segregants were used . Thus , it is unlikely that we missed relevant eQTLs at this relaxed threshold . We also addressed the reciprocal question of whether eQTLs were aceQTLs . Redundant eQTLs supported by adjacent markers were removed as above . For each eQTL found at FDR = 0 . 035 between a marker m and a gene g , we considered all aceQTL scores between m and any nucleosome located with 10 Kb of g and recorded the best one . When no aceQTL was found at the relaxed threshold of BF = 1 , then this search was called “non significant” ( Figure S4 ) . To estimate whether master trans-aceQTLs correspond to master eQTLs ( Table 1 ) , we proceeded as follows . For each master trans-aceQTL controlling the acetylation levels of a set of nucleosomes νi , let m be the best candidate polymorphism as defined above . For each nucleosome νi we examined all genes located within 10 Kb and asked whether at least one of them was a significant eQTL target of m . The fraction of nucleosomes νi for which this was the case was called “Fraction of nucleosomes matching an eQTL target” . Because many target nucleosomes νi were located close to each other , we also examined them as distinct target loci: Target nucleosomes νi that were located within 1 Kb of each other were grouped into a “locus” . For each locus , we counted the fraction of target nucleosomes for which a relevant eQTL linkage was found ( as above ) . This number was then averaged across all target loci to define the “Average fraction per locus” indicated in Table 1 . Finally , master trans-aceQTLs were classified as being ‘also eQTL’ , ‘aceQTL only’ or ‘partial’ based on whether this fraction was higher than 75% , lower than 25% , or in between , respectively . Figure 4B was obtained by sorting the 5 , 442 original SNEPs by their persistence across the transient TSA treatment ( defined above ) . A Receiver Operating Curve ( ROC ) was then built: ‘positive’ SNEPs were the ones for which at least one significant aceQTL was found , as this corresponds to the expectation of a genetic control underlying persistence; ‘negative’ SNEPs were those for which no aceQTL was found . Fraction of positives and negatives were computed at increasing persistence values . All ChIP-chip raw data is available from ArrayExpress ( http://www . ebi . ac . uk/arrayexpress/ ) under accession numbers E-MTAB-575 and E-MTAB-1025 . Additional processed data files are available from our web site: http://www . ens-lyon . fr/LBMC/gisv/snep/ | Chemical modifications of chromatin , such as DNA methylation , incorporation of histone variants , or post-translational modifications of histone proteins , constitute the “epigenome” and confer specific properties to genome functions . Epigenomes differ from one individual to another , opening the exciting perspective to decipher the origins of these differences and their impact on physiology . However , population epigenomics remains challenging because , unlike DNA mutations , epigenetic hallmarks are themselves regulated . They can change upon particular environmental conditions , they may be inherited epigenetically , or they may result from activities encoded in the DNA . Thus , estimating the stability of intra-species epigenomic variation and its dependence on DNA polymorphisms is essential . Using a chemical perturbation of yeast cells as an experimental model system , we found that acetylation variation was persistent at some nucleosomes and labile at other nucleosomes . By studying a segregating population , we mapped DNA polymorphisms that affected chromatin acetylation levels at numerous nucleosomes . Strikingly , nucleosomes showing persistent variation of acetylation corresponded to those for which acetylation was under genetic control . Thus , part of epigenomic variation is stabilized by a DNA–encoded determinism , and another part can be reprogrammed if environmental perturbations are experienced . These results provide a necessary basis for upcoming developments in population epigenomics . | [
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| 2012 | Genetic Modifiers of Chromatin Acetylation Antagonize the Reprogramming of Epi-Polymorphisms |
Type I interferons ( IFN ) are important for antiviral responses . Melanoma differentiation-associated gene 5 ( MDA-5 ) and retinoic acid-induced gene I ( RIG-I ) proteins detect cytosolic double-stranded RNA ( dsRNA ) or 5′-triphosphate ( 5′-ppp ) RNA and mediate IFN production . Cytosolic 5′-ppp RNA and dsRNA are generated during viral RNA replication and transcription by viral RNA replicases [RNA-dependent RNA polymerases ( RdRp ) ] . Here , we show that the Semliki Forest virus ( SFV ) RNA replicase can induce IFN-β independently of viral RNA replication and transcription . The SFV replicase converts host cell RNA into 5′-ppp dsRNA and induces IFN-β through the RIG-I and MDA-5 pathways . Inactivation of the SFV replicase RdRp activity prevents IFN-β induction . These IFN-inducing modified host cell RNAs are abundantly produced during both wild-type SFV and its non-pathogenic mutant infection . Furthermore , in contrast to the wild-type SFV replicase a non-pathogenic mutant replicase triggers increased IFN-β production , which leads to a shutdown of virus replication . These results suggest that host cells can restrict RNA virus replication by detecting the products of unspecific viral replicase RdRp activity .
The innate immune system is an ancient set of host defense mechanisms that utilize germline-encoded receptors for the recognition of pathogens [1] . This set of receptors , termed pathogen recognition receptors ( PRRs ) , binds to the pathogen's own structural or pathogen-induced molecules and triggers an anti-pathogenic cellular state through various signal transduction pathways . The set of molecules brought into the cells or induced by pathogens are called pathogen-associated molecular patterns ( PAMPs ) [2] . The number of different germline-encoded PRRs is limited; therefore , PAMPs represent unique structural signatures that are characteristic of several groups of pathogens [1] . In the case of RNA viruses , double-stranded RNA ( dsRNA ) and 5′-triphosphate ( 5′-ppp ) RNA are the most common pathogen-characteristic molecular structures recognized by PRRs . Viral RNA replicases generate 5′-ppp RNA and/or dsRNA in ample amounts during replication and transcription of viral RNA genomes . The presence of viral dsRNA in an animal cell is an indication of the pathogen invasion and is recognized by the innate immune system as a non-self entity , as vertebrate genomes do not encode RNA-dependent RNA polymerase ( RdRp ) activity . Recognition of viral dsRNA by specific PRRs leads to the induction of type I interferons ( IFN; e . g . IFN-α and IFN-β ) [3] , which promote an antiviral state of the cell by inducing several hundred genes expression [4] . In vertebrates , type I IFNs and several other cytokines mediate innate immune system signals that determine the type of response elicited by the adaptive immune system [2] . Currently , three PRR families have been identified as innate immune sensors involved in the detection of virus-specific components in cells: Toll-like receptors ( TLRs ) , retinoic acid-inducible gene I ( RIG-I ) -like receptors ( RLRs ) , and nucleotide oligomerization domain ( NOD ) -like receptors ( NLRs ) . Only TLRs and RLRs , however , are important for type I IFN induction . RLRs are the primary detectors of cytosolic 5′-ppp RNA and dsRNA generated by RNA viruses [3] . In addition to dsRNA [5] , host PRRs detect dsRNA with 5′-ppp ends [6] , single-stranded RNA ( ssRNA ) [7] , and viral genomic DNA [8] , [9] . Thus , type I IFN production is almost exclusively triggered by the recognition of viral nucleic acids . In fact , there seem to be only two exceptions . First , TLR4 receptors present on macrophages trigger type I IFN induction in response to lipopolysaccharide , which is not nucleic acid [10] . Second , TLR2 receptors present on “inflammatory” monocytes were recently reported to activate type I IFN in response to as yet unidentified components of DNA viruses [11] . For RNA viruses , however , it is believed that type I IFN is triggered exclusively by viral dsRNA [5] or 5′-ppp dsRNA [6] , [12] . Accordingly , the presence of viral nucleic acids in a host cell is the absolute requirement for RNA virus detection and type I IFN production . The main function for the viral RNA replicase is to drive the replication and transcription of viral RNA . Recently , however , several observations regarding an unusual extra property of positive-strand RNA virus replicases have been reported . In particular , the transient expression of the HCV replicase was shown to activate the IFN-β promoter in several human cell lines [13] , [14] , and IFN-β promoter activation was also observed for the human norovirus replicase [15] . Moreover , transgenic mice expressing the replicase of Theiler's murine encephalitis virus ( TMEV ) were resistant to infection by this virus and showed increased basal IFN levels [16] . Therefore , the expression of the viral replicase in the absence of a replication-competent viral genome can activate the IFN-β promoter . However , the mechanism and role of this innate immune response activation on the viral life cycle have not been determined . In this report , we use Semliki Forest virus ( SFV , Alphavirus ) as a model to study the innate immune response of host cells to infection by a positive-strand RNA virus . Alphaviruses are single-stranded RNA ( ssRNA ) viruses that replicate in the cytoplasm . SFV RNA is translated into a replicase polyprotein , which consists of four multifunctional , non-structural viral proteins ( nsP1 , nsP2 , nsP3 , and nsP4 ) . The replicase polyprotein is remodeled by the nsP2 protease through sequential cleavages to produce replicases with different specificities [17] . The core RdRp of the SFV replicase is represented by nsP4 , which contains a conserved catalytic GDD triad [18] . However , when nsP4 is expressed separately from the other replicase proteins , it cannot function as an RdRp [19] . It has been reported that the alphavirus nsP2 protein is absolutely required for the suppression of the host cell antiviral response either by inducing macromolecular synthesis shutoff or by targeting the catalytic subunit of the cellular RNA polymerase [20] , [21] , [22] . In addition , it was demonstrated that nsP2 expression efficiently inhibited IFN-induced JAK-STAT signalling indicating a shutoff-independent mechanism [23] . However , the infection of heterogeneous bone-marrow-derived dendritic cells ( BMDC ) by wild-type Sindbis virus ( SIN ) resulted in high IFN-β induction , no prominent shutoff , and self-limiting infection [24] . In addition , IFN-α/β is very potently induced within the first 12 hr post-infection in the serum of mice infected by wild type SIN [25] . Thus , a simple model in which nsP2 antagonizes IFN-α/β and promotes virus infection cannot fully explain the virus phenotypes in non-established cell lines and animals [26] . On the other hand , SFV replication is associated with the production of viral dsRNA replication intermediates that are thought to be the molecules used by the host to detect SFV infection , even though these intermediates are located inside of the membrane-bound replicase complexes . Therefore , similar to other viruses , SFV possesses mechanism ( s ) that result in the prevention and/or suppression of the antiviral IFN response . A non-pathogenic SFV4 mutant that contains a mutation that disrupts the nuclear localization sequence ( NLS ) of nsP2 ( SFV4-RDR ) has been reported to be deficient in suppressing the antiviral IFN response [27] , [28] , [29] , [30] . Here , we present evidence for a novel mechanism by which mouse embryonic fibroblasts ( MEFs ) detect SFV infection . This detection results in IFN-β induction and , in the case of MEF infection with a non-pathogenic SFV4-RDR , the shutdown of virus replication . The reconstitution of SFV replication by uncoupling the wild-type and mutant replicase expression from the viral RNA template led to the identification of the viral replicase as the enzyme responsible for IFN-β induction . Remarkably , wild-type and mutant SFV replicases were capable of IFN-β induction in the absence of viral RNA replication or transcription . IFN-β induction resulted from the production of IFN-inducing RNA ligands generated from cellular RNAs that could activate RIG-I and MDA-5 . Increased levels of IFN-β were induced upon the expression of the viral replicase with a mutation in the NLS of nsP2 , while inactivation of RdRp activity of nsP4 blocked the induction of IFN-β . These results indicate that during SFV infection , two concurrent processes are driven by the viral replicase . First , the viral replicase drives the replication and transcription of the viral genome . Second , the viral replicase generates PAMPs using host cell RNA as a template . Thus , the innate immune recognition of RNA virus infection is more complex than pattern recognition , which is based on the detection of invariant structures present in pathogens or viral replication intermediates . Furthermore , viruses must find ways to circumvent the increased efficiency of their recognition by utilizing powerful mechanisms targeting innate immune response . A failure to interfere with host cell detection mechanisms results in virus replication shutdown and elimination .
All previous studies on IFN induction by alphaviruses have been performed using either viral infection or transfection of viral replicons . It has been shown that wild-type SFV4 is capable of IFN-β induction upon infection of a host cell [27] , [31] , [32] . It is not clear whether this is valid for all alphaviruses as no type I IFN induction was observed in MEFs infected with wild type SIN [33] . In contrast it had been shown that transfection of cells with a SIN replicon , a modified alphavirus genome that replicates but does not produce virus due to the replacement of the viral structural genes with a heterologous sequences , also triggers IFN-β production [34] . Introduction of an RR649R→RD649R mutation into the NLS of SFV4 nsP2 resulted in a virus , termed SFV4-RDR , that induced almost seven times more IFN-β than the wild-type virus [27] . Hence , the replicase region is essential for triggering IFN-β induction and also for limiting that response . However , the exact mechanism ( s ) by which IFN-β is triggered during alphavirus replication and the reasons why the mutant virus induces more IFN production than the wild-type virus are poorly understood . We have previously demonstrated that SFV replicase that was expressed from a non-replicating mRNA specifically and efficiently replicated the SFV RNA template when the template was provided as a separate RNA molecule [35] , and this finding was later confirmed [36] , [37] . Therefore , the approach used in the current study was based on the uncoupling of replicase expression from the viral RNA template . For this purpose , codon-optimized DNA sequences encoding only the replicases of SFV4 and SFV4-RDR were inserted under the control of a heterologous promoter to produce the pRep and pRep-RDR plasmids ( Figure 1A ) . As a control , we generated a pRep-RDR/GAA plasmid containing two point mutations in the RdRp-specific catalytic motif [18] of the nsP4 protein ( GD466D467→GA466A467 ) that inactivate the RdRp activity of the viral replicase . For accurate reconstitution of viral replication , we also produced the pSFVminRluc plasmid encoding a minimal viral RNA template , which contained the coding sequence of the Renilla luciferase ( Rluc ) reporter flanked by cis-sequences that are required for efficient viral replication ( Figure 1A ) . To determine the basal level of Rluc activity expressed from the RNA template , we transfected mouse fibroblast COP-5 cells [38] with pSFVminRluc and compared the results to those obtained with co-transfection of COP-5 cells with pSFVminRluc and either pRep or pRep-RDR . As expected , the presence of functional replicase resulted in the accumulation of intracellular Rluc activity that exceeded the basal level obtained with the template alone . In contrast , no accumulation of Rluc activity above basal level was observed when COP-5 cells were co-transfected with pSFVminRluc and pRep-RDR/GAA ( Figure 1B ) . Consequently , the intact RdRp activity of the viral replicase was responsible for the amplification of Rluc activity . This clearly indicated that replication of the SFVminRluc RNA template was driven by the viral replicases expressed from the pRep and pRep-RDR plasmids . Although the replication of SFVminRluc by the wild-type SFV replicase was more efficient ( Figure 1B ) , we found that pSFVminRluc co-transfection with pRep-RDR induced an increase in IFN-β production that was an order of magnitude greater that that induced by co-transfection with pRep ( Figure 1C ) . There are at least three potential mechanisms by which pSFVminRluc co-transfection with pRep-RDR could lead to enhanced IFN-β induction . First , viral RNA replication products generated by Rep-RDR may be more abundant and/or more accessible to the host cell innate immune sensors than the viral RNA replication products generated by wild-type Rep . Second , the wild-type replicase of SFV may interfere with IFN-β production and/or signaling by multiple mechanisms [20] , [21] , [27] , [28] . Alternatively , Rep-RDR may have additional , non-viral RNA template targets in the host cell transcriptome . To determine the exact mechanism , we transfected mouse COP-5 fibroblast cells with pRep , pRep-RDR , and pRep-GAA and measured the amount of IFN-β in the cell culture medium at different time points . Unexpectedly , the expression of the SFV replicase induced robust IFN-β secretion by the transfected cells . Rep-RDR induced approximately 3–4 times more IFN-β production than Rep , whereas Rep-GAA did not induce IFN-β production ( Figure 1D ) . The expression of the SFV replicase subunits became readily detectable in a western blot assay only when pRep , pRep-RDR , and pRep-GAA were used at the highest dose , arguing against the possibility of over-expression ( Figure S1 ) . These results indicate that the SFV replicase is capable of inducing IFN-β in the absence of replication-competent viral RNA . Thus , the induction of IFN-β during SFV infection may not only be caused by the presence of viral RNA replication or transcription intermediates but may also result from additional intrinsic properties of the functional viral replicase . We next wanted to compare the kinetics of IFN induction in COP-5 cells transfected with either pRep-RDR or poly ( I:C ) , which is known to induce IFN-β [5] . To achieve the comparable IFN induction we needed to increase the SFV replicase expression level , which was accomplished by the exchange of the promoter in pRep-RDR and pRep-RDR/GAA ( Figure S2 ) . Poly ( I:C ) transfection resulted in steady , and at the highest dose , declining levels of IFN-β , whereas the SFV replicase expression triggered a delayed but very potent accumulation of IFN-β ( Figure 1E ) . To determine whether the expression of the SFV replicase in primary MEFs also leads to IFN-β induction , we transfected MEFs with pRep-RDR , pRep-RDR/GAA DNA , or poly ( I:C ) dsRNA . In the MEFs , SFV replicase expression induced IFN-β secretion , whereas Rep-RDR/GAA again failed to induce IFN-β production ( Figure S3 ) . Chloroquine treatment , which was used to avoid the TLR-9-dependent induction of IFN-β in response to DNA [39] , had no effect on IFN-β induction . Therefore , transfection of pRep-RDR into primary MEFs also triggers IFN-β production . Taken together , these results demonstrate that SFV replicase expression triggers the accumulation of previously unknown PAMPs , which induce an innate immune response . Several reports demonstrated that the transient expression of a single-subunit HCV replicase ( NS5B , nonstructural protein 5B ) activated the IFN-β promoter [13] , [14] , [40] , [41] . Hence , we wanted to compare the abilities of SFV and HCV replicases to trigger the IFN-β induction . Corresponding HCV replicase plasmids encoding active NS5B ( pNS5B ) and the NS5B with inactivated catalytic motif ( pNS5B-GND , GD318D→GN318D ) were generated . Transfection of pRep-RDR and pNS5B into COP-5 cells resulted in a similar and potent IFN-β production , whereas pRep-RDR/GAA and pNS5B-GND failed to induce interferon ( Figure 1F ) . Thus , the ability to induce IFN-β expression is a property shared by replicases of positive-strand RNA viruses belonging to at least two different virus families ( Togaviridae and Flaviviridae ) . RNA viruses can be recognized by either MDA-5 , RIG-I , or a combination of the two [3] . Therefore , we tested whether these RLRs would be involved in the detection of PAMPs produced by SFV replicase RdRp activity . We transfected COP-5 cells with siRNAs targeting the mouse Ddx58 , Ifih1 , and Dhx58 mRNAs , which encode the RIG-I , MDA-5 , and LGP2 proteins , respectively . As a control , we used dsRNA poly ( I:C ) preparations shorter than ∼1 . 5 kbp in length , as dsRNAs of this size have been shown to induce IFN-β primarily through RIG-I [5] . As expected , silencing the expression of RIG-I either alone or in combination with MDA-5 strongly inhibited IFN-β induction by poly ( I:C ) , which indicated that RIG-I is the primary poly ( I:C ) sensor in COP-5 cells ( Figure 2A ) . Silencing both RIG-I and LGP2 expression enhanced IFN-β production as compared to silencing RIG-I alone , whereas knockdown of LGP2 expression alone or in combination with MDA-5 resulted in increased IFN-β production . These observations are consistent with the role of LGP2 as a feedback inhibitor of antiviral signaling [42] and indicate that there is both competition and interplay between receptors for a common substrate [43] . We then transfected pRep-RDR into COP-5 cells treated with siRNAs and measured IFN-β production . Again , silencing the expression of RIG-I alone strongly inhibited IFN-β induction , whereas knockdown of both RIG-I and MDA-5 blocked IFN-β production completely ( Figure 2B ) . In this case , knockdown of RIG-I in combination with LGP2 did not enhance the IFN-β production as compared to the effect of RIG-I knockdown alone ( compare Figures 2A and 2B ) . Notably , knockdown of LGP2 alone also did not increase IFN-β induction , whereas silencing of both MDA-5 and LGP2 did increase IFN-β induction . Importantly , silencing the expression of RLR sensors by different siRNAs gave essentially the same results , indicating that observed effects were not due to off-target effects ( data not shown ) . The possibility that the siRNA transfections also affected the expression of the SFV replicase and thus contributed to the alteration of IFN-β production was also excluded , as the amounts of SFV nsP2 and nsP4 in different cell lysates were roughly identical ( Figure 2B ) . Furthermore , the amounts of the replicase proteins in the transfected cells never exceeded the amounts in the cells that were infected with the corresponding virus . Thus , this analysis also excluded any possibility that the IFN-β induction was an artifact that resulted from the transient over-expression of the viral replicase . Therefore , the production of novel PAMPs is a natural function of SFV replicase RdRp activity , and RIG-I is the major sensor for these products . Moreover , for PAMPs generated by the SFV replicase , these data suggest that MDA-5 serves as an auxiliary sensor that also contributes to IFN-β induction , albeit to a much lesser extent than RIG-I . It has been previously reported that the sensors for PAMPs generated during alphavirus infection include MDA-5 and RIG-I [31] , [32] . Therefore , we determined which RLR sensors would be important for the detection of the mutant SFV4-RDR . For this purpose , SFV4-Rluc and SFV4-Rluc-RDR reporter viruses containing the Renilla luciferase ( Rluc ) coding sequence were used; in these viruses , the Rluc reporter is inserted between the coding sequences of nsP3 and nsP4 , and this insertion does not interfere with correct replicase polyprotein cleavage . In addition , the expression of the reporter gene for these viruses is proportional to the RNA genome copy number [44] . Following transfection of the siRNAs , we infected MEFs with SFV4-Rluc-RDR at different multiplicity of infection ( MOI ) values . Analysis of the Rluc reporter activity indicated that transfection of the siRNAs had little or no effect on mutant SFV replication in MEFs ( Figure 2D ) . In contrast , knockdown of both RIG-I and MDA-5 decreased IFN-β secretion at every MOI tested ( Figure 2C ) . At the lowest MOI tested , it became clear that both RIG-I and MDA-5 were equally important for SFV recognition in MEFs ( Figure 2C ) . These results indicate that the same RLR sensors are involved in both the detection of infection by the SFV and novel products of its replicase RdRp activity . However , MDA-5 knockdown had relatively little effect on the recognition of PAMPs generated by the intrinsic RdRp activity of the viral replicase , whereas the contribution of MDA-5 to the recognition of infection by the virus was similar to that of RIG-I . Given that SFV replicase RdRp activity was absolutely necessary for the generation of PAMP structures to trigger the RIG-I pathway and IFN-β induction , we expected that these PAMPs may in fact be RNAs . Furthermore , the dominant role of RIG-I in the recognition of these PAMPs suggests that these potential RNAs must be shorter than the full-length dsRNA replication intermediates produced during viral infection . To address this question regarding the nature of these PAMPs , we isolated total RNA from COP-5 cells transfected with pRep-RDR , pRep-RDR/GAA and poly ( I:C ) and then separated the large RNAs fraction ( >200 nt ) from the fraction containing smaller RNAs ( <200 nt ) . Only the large RNAs from COP-5 cells transfected with pRep-RDR were capable of inducing IFN-β in MEFs , whereas large RNA fraction from either pRep-RDR/GAA- or poly ( I:C ) -transfected cells was incapable of inducing a comparable IFN response ( Figure 3A ) . Consequently , the RNA extracted from poly ( I:C ) -transfected cells , which was used for the secondary MEFs transfection , contained very little of the originally-transfected poly ( I:C ) . Therefore , this RNA was unable to trigger the RIG-I pathway following re-transfection . Furthermore , this also indicates that the contribution of the IFN-β , translated directly from corresponding mRNAs purified from initially transfected cells , to the IFN-β production in re-transfected cells is negligible . Even when transfected at a 10-fold molar excess as compared to the large RNA fractions , the small RNA fractions were unable to trigger comparable IFN-β activity . This observation also indicates that the antiviral endoribonuclease L ( RNase L ) , which has been reported to cleave host cell RNA to produce small RNA molecules ( less than 200 nt ) capable of triggering the RIG-I pathway [45] , is not involved in the detection of products of SFV replicase RdRp activity . Next , we transfected COP-5 cells with pRep-RDR and fractionated the total RNA into polyadenylated ( polyA+ ) and nonpolyadenylated ( polyA− ) RNA using oligo ( dT ) -affinity chromatography . The polyadenylated RNAs comprised approximately 3% of the total RNA extracted from the COP-5 cells . We then transfected naïve COP-5 cells with polyA+ , polyA− , or total RNA in stoichiometric amounts ( polyA+ RNA : polyA− RNA : total RNA = 1 : 32 : 33 ) and measured the IFN-β response ( Figure 3B ) . Approximately 90% of the IFN-β signal was induced by the nonpolyadenylated RNA , suggesting that this fraction contained the majority of the RNAs ( PAMPs ) generated by the SFV replicase RdRp activity . It has been shown that RIG-I recognizes dsRNA or dsRNA containing a 5′-ppp [5] , [6] . To determine the structural features of the IFN-β-inducing RNA generated by the SFV replicase , we treated RNA isolated from COP-5 cells with various ribonucleases ( RNases ) . At the concentrations used , RNase A did not digest model dsRNA , whereas ssRNA was efficiently degraded ( Figure 3C , upper and lower panels ) . RNase T1 is exclusively ssRNA-specific and does not degrade dsRNA , whereas RNase III should specifically digest only dsRNA unless used at a high concentration [46] , which we confirmed to be the case ( Figure 3C , compare upper and lower panels ) . When RNA extracted from the COP-5 cells transfected with pRep-RDR was treated with RNase III , this RNA lost the ability to induce IFN-β production ( Figure 3D ) . In contrast , RNase A had virtually no effect on the IFN-inducing activity of the RNA . Similarly , RNase T1 treatment of the RNA did not substantially alter its ability to induce IFN-β production ( Figure 3D ) . These results suggest that the IFN-inducing RNA extracted from SFV replicase-transfected cells is in the form of dsRNA . As expected , DNase I treatment did not have any effect on the IFN-inducing of activity of the RNA . Finally , when we treated the RNA with alkaline phosphatase , the ability of the RNA to induce IFN-β was destroyed , which indicated that the terminal phosphate structure was absolutely required for IFN-β induction . Taken together , these results suggest that the SFV replicase generates ligands for RIG-I that consist of non-polyadenylated RNA species larger than 200 nt containing dsRNA regions and a terminal 5′-phosphate , which is most likely a 5′-triphosphate . It has been shown that although the dsRNA containing SFV replicase complexes ( spherules ) are initially formed at the plasma-membrane , they are subsequently internalized [47] and localize to the cytoplasmic surface of both endosomes and lysosomes [48] . Moreover , expression of the viral replicase alone in type I IFN-deficient BHK ( baby hamster kidney ) cells resulted in the characteristic endo- and lysosomal localization of nsPs , although there was no spherule formation [37] . To determine whether the IFN-inducing RNAs produced by the SFV replicase associate with either endosomes or lysosomes , we performed subcellular fractionation of COP-5 cells transfected with pRep-RDR and pRep-RDR/GAA . The subcellular fractions containing various organelles were prepared from post-nuclear supernatants by flotation in sucrose step gradients . Subsequently , we extracted RNA from each of the fractions , and transfected these RNAs into naïve COP-5 cells . The RNAs extracted from each subcellular fraction of the pRep-RDR-transfected cells were capable of inducing IFN-β production; however , RNAs extracted from the endosomes and lysosomes were the most potent IFN-β inducers ( Figure 4A ) . Next , we determined whether the replicase-generated IFN-inducing dsRNA could be detected in transfected cells by immunofluorescence microscopy . For this purpose , we utilized the J2 monoclonal antibody , which recognizes dsRNA regions longer than ∼40 bp in length [49] . In this experiment , dsRNA-specific staining was detected in both pRep- and pRep-RDR-transfected COP-5 cells , whereas pRep-RDR/GAA-transfected cells contained no detectable dsRNA . Moreover , co-localization of dsRNA and the nsP1 of SFV was clearly detected ( Figure 4B ) . Due to the enriched IFN-inducing RNA in endosomes and lysosomes , we further analyzed the potential co-localization between SFV nsP1 and dsRNA with the lysosomal marker protein LAMP2 . The assay was performed using human rhabdomyosarcoma ( RD ) cells , as several mouse-specific LAMP2 antibodies produced a high background signal in COP-5 cells . Both dsRNA and LAMP2 showed co-staining with nsP1 , and dsRNA and LAMP2 also showed co-staining with each other ( Figure 4C ) . These results confirmed that the SFV replicase generates dsRNA structures and demonstrated that the dsRNA duplex region ( s ) is longer than ∼40 bp in length . Additionally , the endosomes and lysosomes are enriched in these IFN-inducing RNAs and consequently serve as the sites of SFV replicase docking and dsRNA generation . These findings indicate that our experimental system accurately reconstituted the conditions observed during actual SFV infection [37] , [48] . Infection with a virulent strain of SFV , SFV4 [50] , is cytotoxic for vertebrate cells and leads to the shutdown of cellular transcription and translation . This process has , at least in part , been attributed to the properties of nsP2 [22] , [51] . Approximately half of the nsP2 produced is transported to the nuclei of SFV4-infected cells [29] , and disruption of the nsP2 NLS by the RR649R→RD649R mutation attenuates the pathogenicity of the corresponding virus [30] . Previous studies with SFV4 and SFV4-RDR in cells deficient for the type I IFN response showed that both viruses grew to high titers with similar kinetics [27] , [30] . However , in cells with an intact type I IFN response , the kinetics of viral accumulation differed; although both viruses grew to high titers by 12 hr post-infection ( h . p . i . ) , the SFV4-RDR titer failed to increase further , whereas the SFV4 titer continued to increase until 24 h . p . i . [27] . These findings indicate that replication of the mutant SFV4-RDR was altered in cells with intact type I IFN signaling . The analysis of the SFV4 and SFV4-RDR replication kinetics in MEFs did not reveal any differences in viral RNA accumulation up to 7 h . p . i . [27] . To analyze the replication of these viruses in greater detail , MEFs were infected with SFV4-Rluc and SFV4-Rluc-RDR at an MOI of 1 , and the Rluc activity was measured . No significant difference in the replication kinetics of either reporter virus was observed ( Figure 5A ) . Under closer examination , however , by 24 h . p . i . , the replication of the mutant virus was almost entirely suppressed and only minor cytotoxic side effects were observed , whereas the decreased replication rate of the wild-type virus was associated with death . SFV4-Rluc-induced death of MEFs was also confirmed by the disappearance of abundant cellular protein that is recognized non-specifically by the nsP4 antibody ( Figure 5C ) . Thus , the same phenotype ( decrease of replication ) seems to stem from a completely different origin . Similar to previously published results [27] , we observed that MEFs produced considerably more IFN-β in response to infection with the mutant virus ( Figure 5B ) . The observed difference ( up to 40-fold ) , however , was nearly a magnitude larger than previously reported . These results suggest that the increased IFN-β production induced by SFV4-Rluc-RDR triggered an antiviral mechanism , which led to the restriction of viral replication in MEFs . During several independent experiments , four aspects of the results were highly reproducible . First , the peak level of viral replicase expression , as indicated by the induction of Rluc reporter activity and the production of replicase subunits , was achieved faster in response to SFV4-Rluc-RDR ( 12 h . p . i . ) than to SFV4-Rluc ( 15 h . p . i . ) ; however , the levels of viral expression were roughly equal ( Figures 5A , C , D ) . Thus , there are no major defects in replicase production and RNA replication of SFV4-Rluc-RDR . Second , for the mutant virus , IFN-β was detectable at earlier time points in comparison to the wild-type virus ( Figure 5B ) . Third , peak levels of secreted IFN-β were obtained after peak levels of the viral replicase nsP3 and nsP4 ( RdRp ) subunits were established ( Figures 5B , C , D ) . Fourth , after achieving the peak levels nsP3 and nsP4 of SFV4-Rluc remained at a relatively steady level , whereas the levels of nsP3 and nsP4 of SFV4-Rluc-RDR gradually decreased ( compare Figure 5A and 5B ) . Taken together , these results indicate that the viral replicase that induces IFN-β production is formed faster during infection with the mutant virus . Furthermore , there is a clear correlation between replicase accumulation and IFN-β production . However , after the peak levels of IFN-β are achieved , the replicase of the mutant virus is degraded . To test whether the increased IFN-β production that was induced by SFV4-Rluc-RDR triggered an antiviral mechanism leading to the restriction of viral replication , MEFs were infected with SFV4-Rluc-RDR at different temperatures . When MEFs were infected with SFV4-Rluc-RDR at 28°C , the IFN-β secretion was greatly delayed and clearly reduced as compared to that produced as a result of infection performed at 37°C ( Figures 5H and 5F ) . This difference is most likely because at 28°C , the RDR mutation does not completely prevent the nuclear localization of nsP2 [51] . This delay in the kinetics of IFN-β secretion allowed for the efficient replication and spread of the mutant virus ( Figure 5G ) . In contrast , when MEFs were infected with the mutant virus at 37°C , virus infection was restricted to the cells that were initially infected , and importantly , there was a complete suppression of viral replication at 24 and 36 h . p . i . for every MOI tested ( Figure 5E ) . Thus , MEFs restrict the replication of SFV4-Rluc-RDR in an IFN-β-dependent manner . To confirm the results obtained from the Rluc reporter activity analysis , we used northern hybridization approach to directly visualize the replication products . During SFV infection viral replicase first generates negative-strand RNAs ( 42S RNA − ) , which , together with the genomic RNA , forms dsRNA intermediate ( Figure 5I ) . The replicase then utilizes the negative-strand RNA genomes to produce both positive-strand RNA genomes ( 42S RNA + ) and subgenomic RNAs ( 26S RNA + ) . At 28°C , all RNA species produced by the SFV4-Rluc-RDR in MEFs accumulated in a time-dependent manner , indicating that there was efficient viral replication and spread of infection ( Figure 5J ) . In contrast , at 37°C , the viral RNAs of positive polarity were detectable at 12 h . p . i . but were subsequently unable to be detected , whereas the amount of the 42S RNA negative strand remained at a steady level ( Figure 5J ) . These results clearly demonstrate that the IFN-induced restriction of SFV4-Rluc-RDR replication is mediated by the destruction of the viral positive strands of RNA . To compare the amounts of PAMP structures generated during the infection with SFV4-Rluc and SFV4-Rluc-RDR , we infected MEF cells with these viruses at a MOI of 1 . The replication of each virus was analyzed at 12 h . p . i . by measuring Rluc activity . As observed in Figure 6A , both viruses replicated to the same extent , which is consistent with data from the previous experiment ( Figure 5A ) . Also consistent with the previous data ( Figure 5B ) , the production of IFN-β differed drastically ( Figure 6B ) , suggesting that the wild-type virus produces less IFN-β-inducing PAMPs . To test this hypothesis , we extracted total RNA from infected MEF cells and used it to transfect COP-5 cells . To prevent the generation of additional dsRNA PAMPs after the transfection of COP-5 cells , we treated RNA samples that were extracted from the MEFs with UV ( 2000 µJ/cm2 , 2 minutes ) . UV-treatment inactivated the infectious replication-competent SFV RNA , and as a consequence Rluc activity was fully abrogated in the RNA samples extracted from the infected cells ( Figure 6C ) . Remarkably , the UV-treated total RNA extracted from the MEF cells infected with both viruses induced almost identical amounts of IFN-β in the COP-5 cells at all doses tested ( Figure 6D ) . Thus , the wild-type and mutant SFV viruses generated equal amounts of PAMP structures during infection . Consequently , the wild-type virus , but not the mutant virus , efficiently blocks either the access of the RLR machinery to the PAMPs or disrupts host cell antiviral signaling ( see the Discussion ) . Next , we wanted to estimate the relative contributions of different RNAs generated by the viral replicase during the infection to IFN-β induction . To address this question , we needed to separate the viral RNA and its replicative dsRNA form from the dsRNAs of different origin . Here , we utilized the information resulting from the pioneering works on alphavirus RNA replication , which have shown that the only species of SFV-specific RNA of negative polarity detected in infected cells were the 42S ( - ) RNA strands [52] , [53] , [54] , [55] . In addition , negative strands of replicative forms of alphaviruses either contain polyU sequences that are shorter than the corresponding polyA sequence at the 3′-terminus of the 42S genomic positive strand [53] or do not contain polyU sequences at all [56] , [57] . In both cases the structure of SFV replicative dsRNA is ideal for purification by oligo ( dT ) -affinity chromatography similarly to both genomic ( 42S ) and subgenomic ( 26S ) single-stranded RNAs of SFV . Thus , polyA− RNA fraction , depleted from all types of SFV RNAs but containing the large majority of novel alphavirus replicase generated PAMPs ( Figure 3B ) , could be obtained . To address this question experimentally , we fractionated total RNA extracted from mock- , SFV4-Rluc- , and SFV4-Rluc-RDR-infected MEF cells ( Figure 6 ) using oligo ( dT ) -affinity chromatography . The polyA+ RNA comprised approximately 5 . 6% , 10 . 8% , and 8 . 1% of the total RNA extracted from the mock- , SFV4-Rluc- , and SFV4-Rluc-RDR-infected MEF cells , respectively . Consequently , SFV increases the ratio of polyA+ RNA to polyA− RNA by synthesizing its own polyadenylated positive-strand RNAs . Next , equal amounts of the RNAs ( polyA+ RNA : polyA− RNA = 1∶1 , mass ratio ) were resolved by performing electrophoresis on a native agarose gel and visualized by ethidium bromide staining . Only three major viral RNA species were present in the fraction containing the polyA+ RNAs that were isolated from the infected MEFs: 26S ( + ) strand , 42S ( + ) strand , and 42S ( ± ) dsRNA ( Figure 7A , lanes 3 and 5 ) . The double-stranded nature of the 42S ( ± ) RNA was confirmed by staining with acridine orange , as well as by its absence on a denaturing agarose gel ( data not shown ) . These viral RNA species were also present in the corresponding polyA− RNA fractions ( Figure 7A , lanes 4 and 6 ) , but in highly reduced amounts . We found that polyA+ RNA fractions contained approximately 15-fold more viral dsRNA than the respective polyA− RNA fractions ( Figure 7A; lanes 3 and 4 , 5 and 6 ) . The ratio of infectious units ( 42S ( + ) RNA genomes ) between the two RNA fractions obtained from SFV-Rluc infected cells was also established using an infectious center assay . The infectivity was found to be 4 . 3×105 pfu/µg ( plaque forming units per microgram ) and 3×104 pfu/µg for polyA+ and polyA− RNAs , respectively , thus confirming the approximately 15-fold difference . In addition , the high infectivity of the isolated RNA reflected the high quality of the RNA and lack of RNA degradation during purification and fractionation . The ( + ) strands of SFV RNAs have 5′ cap structure and , on their own , cannot serve as PAMPs . In contrast , the ( − ) strands of SFV genome RNA or the ( − ) strands of defective interfering RNA ( DI-RNA ) can be efficient PAMPs since they possess 5′-ppp structure . Therefore , we wanted to analyze which types of viral RNA ( − ) strands were present in both fractions of RNA . For this purpose , equal amounts of the RNAs ( polyA+ RNA : polyA− RNA = 1∶1 , mass ratio ) were resolved by performing electrophoresis on a denaturing formaldehyde agarose gel and transferred to a nylon membrane . Next , we shredded the labeled full-length positive strand RNA of SFV4-Rluc into pieces with length of 300–600 nucleotides and used the obtained mixture as probe for northern hybridization analysis ( Figure 7B ) . Only one major RNA species , corresponding to 42S ( − ) viral RNA , were detected in these experiments . The absence of additional major RNA species on the northern blot indicated that there were no incomplete or fragmented 42S ( − ) strands in cells infected with either SFV4-Rluc or SFV4-Rluc-RDR ( Figure 7B ) . However , a single discrete negative-stand RNA present in trace amounts was detected in the polyA+ fractions of infected cells; its smaller size suggested that it could represent the negative strand of DI-RNA . Importantly , northern hybridization analysis revealed that the viral RNAs of negative polarity were present in abundance in polyA+ fractions and were barely detectable in polyA− fractions . For the RNA isolated from SFV4-Rluc infected cells , we found that polyA+ RNA fraction contained ∼15-fold more viral 42S ( − ) RNA than polyA− RNA fraction; the difference for RNAs obtained from SFV4-Rluc-RDR infected cells was even more prominent ( Figure 7B ) . Moreover , for SFV4-Rluc infected cells , the nearly identical 15-fold excess of viral 42S ( ± ) dsRNA and 42S ( − ) RNA species in the polyA+ RNA fraction strongly suggested that the latter species were derived from the former and no free 42S ( − ) strands existed in infected cells . Collectively , performed experiments demonstrated that the viral ssRNAs and dsRNAs fractionated with a similar efficiency and were present in 15-fold excess in the corresponding polyA+ RNA fractions . Subsequently , we wanted to compare the potential of polyA+ and polyA− RNA fractions isolated from infected cells to induce the IFN-β . To block the possibility of additional PAMPs generation from infectious RNA , the polyA+ and polyA− RNA fractions were UV-treated as described above before being used in subsequent assays . The transfection experiments revealed that the virus-induced PAMPs were potent inducers of IFN-β expression , and at a dose of 1000 ng , both RNA fractions saturated the ability of the host cell to produce IFN-β . The same is true for 100 ng of polyA+ RNA from SFV4-Rluc-RDR infected cells . In contrast , an almost linear response of IFN-β production was observed for 100 ng and 10 ng of RNAs from SFV4-Rluc infected cells ( Figure 7C ) . Therefore , the abilities of these RNAs to induce IFN-β expression were compared using these two RNA quantities . Based on the results of the quantification , the polyA+ RNA contains 15-fold more viral RNA than equal amounts of the corresponding polyA− RNA . Hence , if the viral dsRNA and/or negative strand RNA are the only PAMPs that are generated in the course of infection , then the transfection with 10 ng of polyA+ RNA should induce approximately 15-fold more IFN-β compared with the transfection with 10 ng of the corresponding polyA− RNA . However , as is evident from the graph in Figure 7C , this was not the case . Instead , the transfection of COP-5 cells with polyA+ and polyA− RNA fractions from SFV4-Rluc infected cells resulted only in approximately a 2-fold higher IFN-β induction in response to the polyA+ RNA species compared with the polyA− RNA . For the RNAs that were obtained from MEF cells infected with SFV4-Rluc-RDR , transfection with 10 ng of polyA+ RNA induced approximately 6 . 5-fold more IFN-β compared with the polyA− RNA fraction , which again is less than the values deduced from the content of viral dsRNA . In addition to the major viral 42S ( − ) RNA species , a minor barely detectable and approximately six times faster migrating RNA species were observed in the polyA+ RNA fractions isolated from cells infected with SFV4-Rluc and SFV4-Rluc-RDR ( Figure 7B ) . It has been reported that during SFV infection DI-RNA species might be generated [58] , [59] . DI-RNA are viral deletion mutants that contain the RNA sequences important for replication but are unable to self-replicate and rely on virus replication machinery , similarly to our SFVminRluc RNA template [58] . The low abundance of this RNA and especially its enrichment in polyA+ fraction excludes possibility that this molecule may have significant role in IFN-β induction by polyA− RNA fraction . Nevertheless , the presence of DI-RNAs was subsequently analyzed using a highly sensitive strand-specific reverse transcription reaction on denatured RNA followed by PCR . This method allowed for the detection of negative strands of the DI-RNAs in all of the RNA samples from infected MEF cells; the length of the DI-RNA was between 1 . 5 and 2 kb ( Figure S4 ) . However , coherent with the results of northern blot analysis ( Figure 7B ) negative strands of DI-RNA were more abundantly present in polyA+ fraction . Furthermore , positive strands of DI-RNA were detected only in the polyA+ RNA fractions ( Figure S4 ) . Taken together these results confirm that DI-RNAs were efficiently removed from the polyA− RNA samples by oligo ( dT ) affinity chromatography and therefore could not contribute to IFN-β induction in response to this particular RNA fraction . Next , we wanted to address whether the RNA PAMPs present in the polyA− RNA fraction from the infected cells were similar to the PAMPs generated by transfection of pRep-RDR . Most importantly , it was essential to determine if this fraction contained RNA molecules that were generated by the degradation of viral dsRNA by either RNase L or some other mechanism and would therefore lack a polyA sequence . We subfractionated the polyA− RNA fractions according to RNA size on silica-columns ( Figure 7D ) and transfected the fractions into COP-5 cells as described above . Only the fraction containing large ( >200 nt ) RNAs induced IFN-β production , whereas smaller RNAs ( <200 nt ) , even when used in a 10-fold molar excess , failed to do so ( Figure 7E ) . Consequently , the IFN-β that was induced by the polyA− RNAs isolated from the infected MEF cells was not dependent on RNase L and , importantly , could not be attributed to the presence of degradation products from the viral dsRNA , which could not be detected by the northern hybridization analysis due to their small size ( Figure 7B ) . Taken together , these results demonstrate that polyA− RNAs isolated from SFV infected MEF cells contain large amounts of potent IFN-β inducers . These inducers are not viral RNAs and therefore must have been produced by the viral replicase using host cell RNAs as templates as it was observed in case of cells transfected with pRep . To further characterize PAMPs present in polyA− RNA fraction of SFV infected cells the importance of 5′-ppp structure and double-stranded nature of these RNAs were analyzed . We found that neither RNA denaturation nor γ- and β-phosphates removal from the RNA 5′-end alone did not diminish IFN induction substantially . Combination of RNA 5′-end phosphates removal with subsequent denaturation , however , resulted in almost complete loss of the IFN signal ( Figure 8A ) . This result indicated that polyA− RNA fractions of infected cells contained the mixture of PAMPs . Second , to analyze the properties of these PAMPs in a more detail , the SFV4-Rluc polyA− RNA fraction was resolved by performing electrophoresis on a native low melting agarose gel and different RNA species were excised and extracted . Subsequently , obtained RNA species were treated with various enzymes and transfected into COP-5 cells ( Figure 8B ) . RNase T1 treatment of the isolated RNA species did not have any effect on their capacity to trigger IFN-β , indicating that all analyzed fractions contained dsRNA . Alkaline phosphatase treatment , however , substantially reduced the IFN induction only for RNA species co-migrating with cellular 28S and 18S rRNA ( Figure 8B ) . Importantly , similar structural properties ( RNase T1 insensitivity , AP sensitivity ) were observed for the IFN inducing RNA generated by Rep expression in COP-5 cells ( Figure 3D ) . Finally , to prove that SFV replicase indeed transcribes host cell RNAs producing 5′-ppp dsRNAs in the context of viral infection we attempted to identify an example of such RNA . Since IFN-inducing RNAs co-migrated with 28S and 18S rRNAs ( Figure 8B ) it was obvious that multiple cellular RNAs are recognized and used by viral replicase . Therefore an identification of any of such RNAs represents a considerable challenge . It was recently demonstrated that RIG-I activation depends critically on 5′-ppp structure only for a short dsRNA ( ∼40 bp ) , whereas increasing the length of the dsRNA compensates for the 5′-ppp removal [60] . Therefore the previous results indicate that we were searching for a 5′-ppp RNAs ( Figure 8A–B ) , complementary to a polyA− host RNAs longer than 200 nucleotides and forming ∼40 bp dsRNA structure ( Figures 7E and 8B ) . Consequently , we had to adopt the recently described method used for micro RNA ( miRNA ) cloning , based on sequential linker ligations to 3′- and 5′-ends of the miRNA . In this procedure , the ligation of the 3′-linker is a very efficient process , whereas the efficiency of 5′-linker ligation may differ 100-fold depending on the target miRNA [61] . Several additional difficulties , however , were associated with cloning of viral replicase-generated 5′-ppp RNAs . First , while 5′-p miRNA may have some secondary and tertiary structure , they are essentially single-stranded RNA molecules , whereas the 5′-ppp RNAs generated by the viral replicase form dsRNA structures . Second , every ligation step in miRNA cloning is verified by the electrophoretical mobility shift and ligated miRNA can be directly purified , whereas due to the underrepresentation ( for example as compared to miRNA ) and/or heterogeneity of 5′-ppp RNAs generated by SFV replicase such manipulations were not possible . Consequently , both ligation reactions were performed in a single tube ( “one-pot synthesis” ) . Therefore the second and the most critical RNA denaturation step was not particularly efficient due to the presence of buffer and proteins . In addition , due to higher concentrations of reactants , intermolecular ligation of 3′- and 5′-linkers was clearly more efficient than ligation to desired RNA molecule . Furthemore , host cell small RNAs ( including miRNAs ) , could also interfere with the cloning of the replicase-generated RNAs due to the presence of a 5′-monophosphate ( α-phosphate ) . Taking into account all these above listed considerations a procedure illustrated in Figure 8C was developed . Briefly , we first depleted polyA− RNA samples from small RNA species by three rounds of purification on size-exclusion silica columns . Subsequently these RNAs from mock- , SFV4-Rluc- , and SFV4-Rluc-RDR-infected cells were incubated in the reaction buffer either in the absence ( negative control ) or presence of the RNA 5′ polyphosphatase . The purified RNAs were subjected to sequential ligation with 3′- and 5′-linkers ( Figure 8C ) . Because the 5′-ppp structure does not allow the ligation of the 5′-linker , no unique clones should be present in the control samples which were not treated with RNA 5′ polyphosphatase . After performing reverse transcription and PCR we detected a unique discrete amplicon of approximately 100 bp only for the RNA 5′ polyphosphatase-treated polyA− RNA fraction isolated from SFV4-Rluc-RDR-infected cells ( Figure 8C , lane 6 ) . In contrast , no specific amplification was observed for polyA− sample originating from SFV4-Rluc infected cell . Most likely this was caused by the inefficiency of the developed detection procedure . The results indicated that either a population or a single RNA of ∼30–40 nucleotides were successfully amplified . Subsequently , we excised the gel fragment containing this ∼100 bp amplicon and the corresponding fragments from remaining samples for DNA purification and cloning into a plasmid vector . As expected considerably more clones were obtained for RNA 5′ polyphosphatase treated sample from SFV4-Rluc-RDR infected cells . To avoid possible biases approximately half of clones , obtained for each probe , were sequenced . The analysis of obtained sequences revealed that the majority of the clones contained an amplified side-product of type 2 , likely present due to its trace amounts after gel purification ( Figure 8C , bottom ) . Unexpectedly , we found an additional type of clones that contained two copies of side-product of type 2 , linked by a bacterial sequence , suggesting that a recombination leading to duplication took place . Almost 30% of the clones containing this type of inserts were generated as a result of the specific amplicon cloning , whereas in other samples these inserts constituted only a small fraction ( 2–8% ) . Consequently , conventional cloning is not an efficient approach for the analysis of amplicons representing the products of viral replicase and other methods such as direct analysis of PCR products by deep sequencing should be used . Nevertheless , one unique clone was obtained for the RNA 5′ polyphosphatase treated sample from SFV4-Rluc-RDR infected cells , providing an example from the putative population of ∼30–40 nucleotide RNAs ( Figure 8D ) . This clone ( named R2 ) contained a 33-nt copy of RNA , which was perfectly complementary to mouse antisense non-coding mitochondrial RNA 1 ( ASncmtRNA-1 , GenBank: GU332589 . 1 ) , an equivalent of human ASncmtRNA-2 transcript ( GenBank: EU863790 . 1 ) [62] , [63] , [64] , [65] . The function of the human ASncmtRNAs is unknown , however it was demonstrated that these transcripts , co-migrating with the 18S rRNA and having a stem-loop structure , are down-regulated in tumor cell lines and tumor cells present in 17 different tumor types [63] . Furthermore , the possibility that R2 clone corresponded to a fragment of 16S rRNA encoded by mitochondrial precursor RNA ( GenBank: V00665 . 1 ) was excluded due to the following reasons . First , the presence of ∼30 nt RNA fragment corresponding to 16S rRNA 3′-terminus would indicate that the integrity of RNA had been compromised . This was , however , highly unlikely since no ribonuclease activity was detected in either of the components of the reaction mixtures used for RNA enrichment and tagging ( Figure 8C ) . Second , if R2 would have resulted from normal cellular RNA rather than from RNA containing 5′-ppp generated by SFV replicase , amplicons of corresponding size and similar clones should have been detected also for other samples , which was not the case . Third , R2 clone has an extra adenosine residue at its 3′-end ( …GUUA ) that is not present at the 3′ terminus ( …GUU ) of the major processed form of 16S rRNA [66] , [67] , indicating the absence of a perfect match between these sequences . Fourth , minor processed forms of the mouse 16S rRNA have GUUAU , GUUAUU , GUUAUUAGG , or GUUAUUAGGG sequences at their 3′-termini [66] , however , these sequences were not present in R2 clone either . Finally , it was demonstrated that a small fraction of 16S rRNA contained polyA tails [66] , [68] and was recoverable either by oligo ( dT ) -affinity chromatography [68] or by RT PCR [69] , indicating that the presence of the polyA tail consisting of a single adenosine in the 16S rRNA is highly unlikely . Moreover , the R2 clone corresponded to all PAMP criteria listed above – in addition to having expected length it was complementary to host polyA− RNA longer than 200 nt . Accordingly , as there is no cellular RdRp activity capable for synthesis of such RNA , these results strongly suggest that such RNA was synthesized by viral replicase . Moreover , the fact that we were unable to obtain this amplicon without the use of RNA 5′ polyphosphatase treatment indicated that this RNA had also 5′-ppp structure . Thus , an RNA molecule which represents an example of SFV replicase generated PAMP was experimentally identified . Based on our results ( Figure 8B ) , it is also logical to assume that it does not represent the only PAMP of this kind generated by SFV replicase and its relative contribution to observed IFN induction is currently unknown . This as well as identification of full spectrum of sequences of SFV replicase generated PAMP RNAs represents topic for additional studies .
To subvert the vertebrate innate immune system , RNA viruses have evolved many different strategies . One of the most efficient of these strategies consists of blocking access of host sensors to viral dsRNA or 5′-ppp RNA . In the case of negative-strand RNA viruses and viruses with a dsRNA genome , the viral particles contain a replicase complex with genomic RNA . By retaining both their genomes and replicase in partially uncoated viral particles , negative-strand RNA viruses ensure that viral 5′-ppp RNA is not accessible to RLRs and that no exogenous ( host cell ) RNA can be transcribed . However , in the case of positive-strand RNA viruses , the viral particles deliver only viral genomic RNA into the cell , which serves as the mRNA for the synthesis of the viral replicase [70] . Therefore , it remains possible that host cell RNA could be transcribed by the viral replicase of positive-strand RNA viruses . At a later time in infection cycle , positive-strand RNA viruses remodel host cellular membranes to accommodate their replication complexes and thereby shield their own 5′-ppp dsRNA [71] , [72] . In this study , we report a novel mechanism by which positive-strand RNA viral infection is sensed by the innate immune system . To demonstrate the importance of this mechanism for virus infection , we also describe a mechanism by which MEFs restrict the replication of a non-pathogenic SFV-Rluc-RDR . While others have shown that the mutant virus triggered increased IFN-β induction , we also demonstrated that this led to the destruction of positive-strand viral RNAs and the shutdown of viral replication in primary MEFs ( Figure 5 ) . Thus , IFN-induction is undoubtedly important for limiting SFV infection . Here , we delineate the mechanism that mediates this important antiviral response . To understand the mechanism by which alphaviruses trigger potent IFN-β production , we dissected the replication of SFV by decoupling the expression of the replicases of the wild-type and mutant viruses from their viral replication-competent RNA templates . First , it was shown that the replication of the short virus-like RNA template by these replicases triggered the production of IFN-β by COP-5 cells ( Figure 1C ) . However , it was subsequently found that these replicases were fully capable of IFN-β induction even when expressed in the absence of any virus or virus-like RNA template ( Figure 1D ) . In both cases , the mutant replicase induced increased IFN-β production , as compared to the wild-type replicase , and we observed that the viral replicase RdRp activity was responsible for IFN-β induction ( Figures 1D–E ) . Moreover , we found that only the viral replicase with intact RdRp activity generated IFN-inducing RNA , which triggered the IFN-β production ( Figure 3A ) . Our results strongly suggest that during SFV infection , two parallel processes are driven by the viral replicase; in the first process , the viral replicase drives the replication and transcription of the replication competent viral genome , whereas in the second process , the viral replicase drives the transcription of non-viral host cell RNA templates ( Figure 9 ) . The IFN-inducing activity of the viral replicase-generated RNAs was resistant to RNase A and RNase T1 , although this activity was sensitive to RNase III and alkaline phosphatase , which indicated that this ligand is a dsRNA containing at least one terminal phosphate ( Figure 3D ) . Our interference experiments indicated that RIG-I was the primary sensor detecting IFN-inducing RNA produced by the SFV replicase ( Figure 2B ) . This result also strongly suggests that this RNA contains a 5′-ppp . The immunofluorescence results indicated that IFN-inducing RNA generated by the SFV replicase contained duplex RNA regions exceeding 40 bp in length ( Figures 4B and 4C ) . Thus , SFV replicase transforms host cell RNA into PAMPs that are recognized and counteracted as non-self entities . This strategy of innate immune recognition of a viral replicase is different from pattern recognition as the replicase generates novel PAMP structures not uniquely associated with a pathogen . Detection of viral replicase RdRp activity by vertebrate host cells may represent an ancient mechanism for the recognition of non-self enzymatic function . The majority ( ∼90% ) of the IFN-β-inducing RNA generated by the SFV replicase was non-polyadenylated ( Figure 3B ) . In this regard , it is interesting to note that non-polyadenylated transcripts comprise almost half of the human and mouse transcriptomes , although the biological function of these transcripts remains unclear [73] . Therefore , it will be of significant interest to identify which non-polyadenylated transcripts encoded by the host genome are involved in viral replicase detection . Our results also exclude the possibility that SFV replicase-triggered IFN-β induction is mediated by the antiviral endoribonuclease RNase L , which has been reported to cleave host cell RNA to generate small RNAs with lengths less than 200 nt upon viral infection [45] . In fact , we found that host cell RNAs longer than 200 nt are modified by SFV replicase to induce IFN-β ( Figure 3A ) . Subsequently , identical characteristics were observed for the PAMPs present in the non-polyadenylated RNA fractions isolated from SFV-infected MEF cells ( Figure 7D , E ) . Consistent with previously published results obtained using bone marrow-derived dendritic cells [32] , we demonstrated that MEFs detect SFV using the MDA-5 and RIG-I sensors ( Figure 2C ) . We observed that detection of SFV4-Rluc-RDR heavily relied on both MDA-5 and RIG-I , whereas for detection of the viral replicase , RIG-I was the primary sensor ( Figure 2B ) . This discrepancy is likely due to the fact that long viral dsRNA replication intermediates are generated during SFV infection . It was previously demonstrated that MDA-5 and RIG-I detect long and short dsRNAs , respectively [5] , and these findings further support the idea that the viral replicase drives both viral RNA replication and transcription using host cell RNA templates . In both cases the predominant type of RNA ligand produced by SFV replicase dictates the primary type of sensor responsible for ligand detection and IFN induction . Due to codon-optimization , the mRNA encoded by pRep and pRep-RDR lacked any cis-sequences that are known to participate in SFV RNA replication; hence , the PAMPs generated in the pRep-RDR transfected cells were non-viral . More importantly , our study reveals that host cell RNAs were used as alternative templates by the viral replicase during virus infection , demonstrating for the first time that during positive-strand RNA virus infection , both viral RNAs and cellular RNAs that are modified by the viral replicase contribute to IFN-β induction . The latter conclusion is based on the following data . Fractionation of total RNA from infected primary MEF cells by oligo ( dT ) -affinity chromatography led to a 15-fold enrichment of the major viral RNA species in the polyadenylated RNA fraction compared with the non-polyadenylated RNA fraction ( Figure 7A , B ) . Consequently , if the viral RNA is the only PAMP present in infected cells , then the polyadenylated RNA fraction should induce a 15-fold higher level of IFN-β compared with an equal amount of the corresponding non-polyadenylated RNA fraction . Remarkably , this was not the case , as polyadenylated RNA fractions from wild type and mutant virus infected cells induced only approximately 2-fold and 6-fold higher levels of IFN-β respectively ( Figure 7C ) . These findings strongly suggest that non-polyadenylated RNA fractions isolated from infected MEF cells contain potent IFN-β inducers , most of which are not viral RNAs and were derived from the host cell RNA . Strikingly , this finding was especially compelling for the non-polyadenylated RNA species extracted from the MEF cells infected with the wild type virus . These results cannot be explained based on the current paradigm that type I IFN is triggered exclusively by viral dsRNA or viral RNA containing a 5′-ppp that are produced during the course of viral genome replication or transcription by viral replicases [5] , [6] , [12] , [74] , [75] , [76] . Our results indicate that wild type and mutant SFV generate roughly the same amount of PAMPs . However , the amount of IFN-β that is induced by these viruses in MEF cells differs drastically ( Figure 6 ) and , in the case of mutant virus , leads to the inhibition of viral replication ( Figure 5 ) . Obvious explanation to this conundrum is the presence of a single mutation in the nsP2 subunit of the viral replicase of SFV4-Rluc-RDR , which does not allow nsP2 to enter the nucleus of infected cell . It has been reported that the nuclear localization of nsP2 ( for both SFV and SIN ) is absolutely required for the suppression of the host cell antiviral response [20] , [21] , [27] , [28] . Consequently , it is thought that nsP2 must suppress the innate immune system of the host cell to inhibit the sensing of the viral RNA PAMP ( s ) and/or response to the PAMPs . However , there are several arguments that challenge this hypothesis . First , cytosol-accessible SFV genomic ( 42S [+] strand ) and subgenomic ( 26 [+] strand ) viral RNAs , which direct the synthesis of viral replicase and viral structural proteins , are not PAMPs because their 5′-ppp is shielded by a cap-structure [55] . Second , the PAMP represented by the dsRNA replicative form ( 42S [±] ) resides in a membrane-bound replication complex [48] , [77] . Third , the 42S ( − ) strand , which does not have cap-structure [78] and , thus , may represent a PAMP , most likely does not exist as a free molecule ( Figure 7B ) ; instead , this strand is confined to the replication complex as part of the dsRNA . Indeed , our data clearly indicate that this RNA does not exist as a free molecule , as it was fully protected in the SFV4-Rluc-RDR infected MEF cells and , unlike free positive-strand RNA , was not degraded during the restriction of viral replication ( Figure 5J ) . Thus , it appears that RLR sensors may have extremely limited access to viral PAMPs in infected cells . The only species that are presumably accessible to RLRs are DI-RNAs , which have a negative polarity and are double-stranded . However , although DI-RNA is presumably localized to the spherules , this has not been experimentally verified . DI-RNA may also contribute to IFN induction , but this contribution is likely to be minor , as their amount was found to be negligible compared with the full-length viral RNA species ( Figure 7B ) . Moreover , the amounts of SFV DI-RNA produced during both wild type and mutant virus infection are very similar ( Figure S4 ) and cannot explain the dramatic difference in IFN-β induction ( Figures 5B and 6B ) . Still , a mutation in nsP2 results in the rapid detection of SFV4-Rluc-RDR infection ( Figure 6B ) and the restriction of viral replication by the host cell ( Figure 5 ) . Consequently , there are PAMPs that are easily accessible to RLR sensors in infected MEF cells . Our study strongly suggests that these easily accessible PAMPs are generated by viral replicase unspecific activity on host cell RNAs . It is known that enzymatically active nonstructural viral proteins that participate in the formation of the viral replicase complex are produced in much larger amounts than are needed for virus RNA replication . This has been demonstrated for alphaviruses [36] , [47] and HCV [79] . It has been proposed that these viral proteins play roles in processes other than viral RNA replication [36] . For example , the NS3/4A protease of HCV cleaves mitochondrial antiviral protein , precluding HCV detection by the host cell [80] . Proteases of picornaviruses cleave multiple host factors , including eIF4G , precluding the translation of host cell capped mRNAs [81] . Though the list of the enzymatic activities of viral proteins that target host cell components is long and rapidly growing , it is clearly biased towards the activities that are beneficial for virus infection . At the same time , it is hard to believe that this process is one-sided . The host cells have also evolved to take advantage of some of these enzymatic functions specific to viruses . In this regard , the use of these activities to detect the presence of a pathogen is a clear possibility that has not been considered . There are many benefits to recognizing RdRp activity of viral replicases rather than viral RNA PAMPs . First , especially in the case of in vivo infections , not all of the cells are permissive for viral RNA replication . However , entry of viral RNA into such cells will trigger the production of viral replicase , the RdRp activity of which then can be detected by the host cell . Second , all RNA viruses inevitably produce divergent progeny , including many viruses that are replication defective , which are also packed into virions . As long as the defective RNAs are capable of expressing a functional replicase , the synthesis of nonviral PAMP RNAs will occur . In this study , we have developed a procedure that allowed us to selectively amplify the RNAs produced by the SFV replicase in the context of SFV4-Rluc-RDR infection ( Figure 8C ) . However , because of the inefficient conventional cloning approach only a single non-polyadenylated RNA used by SFV replicase was identified ( Figure 8D ) . It has recently been demonstrated by deep sequencing analysis that heterogeneous host cell RNAs ( 22 mRNAs , 3 non-coding RNAs , and 2 pseudogenes ) are modified by the HCV RdRp when expressed alone in the mouse liver [41] . Our data strongly suggests that non-polyadenylated RNAs co-migrating with 28S and 18S rRNAs are the primary targets for the SFV replicase ( Figures 3B , 7C , 7E , 8A , and 8B ) . We demonstrate that PAMPs produced from host cell RNAs are an important byproduct of alphavirus replicase ( nsP1/nsP2/nsP3/nsP4 ) RdRp activity during infection and that the nsP2 protease subunit is used to counteract the consequences of this activity . For HCV , the activity of NS5B RdRp is counteracted by NS3/4A protease [13] , [82] , [83] . To date , the replicases of HCV , norovirus , and TMEV have been shown to be capable of inducing IFN without the requirement of viral RNA replication [13] , [14] , [15] , [16] . Direct comparison of the IFN induction by SFV replicase and HCV RdRp showed that both replicases are very efficient PAMP inducers ( Figure 1F ) . Therefore , together with our study , these findings suggest that the activation of the innate immune response by the replicases of positive-strand RNA viruses may be a general property used by the host cells to counteract viral invasion . If so , the question of why viruses have not evolved a way for their replicase to be more specific for their own RNAs remains . The likely reason is that the increased specificity of the replicase is not beneficial for the survival and/or evolution of the virus . Alphavirus replicases possess an outstanding ability to recognize defective cis-elements , repair such defects and rescue the replication in their genomes [57] . Thus , even if it were possible to obtain a polymerase with higher template specificity , the cost would be too high . This theory parallels reports studying the error prone nature of the same viral RNA replicases . As it was elegantly demonstrated , the fidelity of the picornavirus RNA polymerase can be easily increased [84] , but this increased fidelity results in reduced virus fitness and virulence [85] . Therefore , as discussed above , viruses have instead armed themselves with safeguards that are effective against innate immune recognition irrespective of the origin of the PAMPs .
pRep was generated by inserting codon-optimized coding sequence of SFV replicase ( EMBL-Bank: HC198689 ) into either KS plasmid ( Stratagene ) or GTU eukaryotic plasmid expression vector [86] . To generate pRep-RDR , we introduced mutations C4129G , G4130A , and G4131C ( nucleotide numbering as in HC198689 ) leading to RRR→RDR mutation in nsP2 nuclear localization sequence of SFV replicase . To obtain pRep-RDR/GAA plasmid DNA , we introduced additional mutations A7424C and A7427C , inactivating the nsP4 RdRp catalytic centre ( GDD→GAA ) . pRep , pRep-RDR , and pRep-RDR-GAA contained intron sequence from rabbit beta-globin gene ( GenBank: V00882 . 1 ) , which was inserted into SFV replicase coding sequence . The expression of SFV replicase polyprotein was under control of either hEF1α/HTLV composite promoter ( increased expression ) or Rous sarcoma virus long terminal repeat ( RSV LTR ) . For the generation of pNS5B and pNS5B-GND , the coding sequence of NS5B Con1 genotype 1b ( GenBank: AJ238799 ) was used . To keep total DNA amount constant during dose-dependent IFN-induction experiments , transfections were performed with “stuffer” DNA plasmid vector encoding d1EGFP . Plasmid pSFVmin was constructed from two fragments . First fragment of SFV cDNA corresponding to the nucleotides 1–274 of SFV genome was placed under control of CMV promoter and cloned into KS plasmid ( Stratagene ) . Second fragment consisting form polylinker ( BstB1-PmeI-BglII-SpeI-NotI ) , complete cDNA copy of 3′ UTR of SFV4 with polyA sequence of 60 residues , hepatitis delta virus negative strand ribozyme and SV40 early transcription terminator was cloned immediately downstream of the first fragment . To obtain pSFVminRluc the coding sequence of Rluc was amplified by PCR and inserted to pSFVmin using SpeI-NotI restriction sites . In the resulting construct pSFVminRluc Rluc reporter is expressed in form of fusion protein containing 78 foreign aa residues at its N-terminus , first 63 of them representing the N-terminal region of nsP1 of SFV . The antibodies for mouse RIG-I ( R37 ) and LGP2 were purchased from Immuno-Biological Laboratories Co . , Ltd . The antibody for mouse MDA-5 ( AL180 ) was purchased from Alexis Biochemicals . The antibody for dsRNA ( J2 ) was purchased from Scicons . Antibody for β-actin ( C4 ) was purchased from Santa Cruz Biotechnology , Inc . The antibody for LAMP2 ( H4B4 ) was purchased from Abcam . SFV nsP2 and nsP4 antibodies were kindly provided by Dr . Tero Ahola , antibodies against nsP1 and nsP3 of SFV were made in-house . Secondary antibodies used in immunofluorescence were purchased from Life Technologies . Poly ( I:C ) and chloroquine were purchased from Sigma-Aldrich . RNase A , RNase T1 , and RNase III were purchased from Ambion . Alkaline phosphatase and DNase I were purchased from Roche Applied Science and Promega respectively . T4 RNA Ligase I and β-Agarase I were purchased from New England Biolabs . RNA 5′ polyphosphatase was purchased from Epicentre ( Illumina ) . COP-5 and RD cells were maintained in L-glutamine-containing IMDM medium supplemented with 10% fetal bovine serum ( FBS ) , and antibiotics . Primary MEF cells were purchased from Millipore ( EmbryoMax Primary Mouse Embryo Fibroblasts , Not Mytomycin C Treated , Strain CF1 , passage 3; Catalogue Number: PMEF-CFL ) and cultured in L-glutamine- , sodium pyruvate- , and high glucose-containing DMEM medium supplemented with 15% FBS , 0 . 1 mM β-mercaptoethanol , and antibiotics up to passage 6 . Transfection of DNA ( 0 . 2 µg/ml ) and RNA ( 0 . 2–0 . 4 µg/ml ) into COP-5 cells ( 0 . 25–1 . 0×106 per 60-mm dish ) was carried out using Lipofectamine 2000 ( Invitrogen ) . RD cells ( 0 . 2×106 per 60-mm dish ) were transfected with DNA ( 0 . 2 µg/ml ) by electroporation in GenePulser Xcell ( Bio-Rad ) instrument ( settings: Exponential Wave , 190 V; 975 microfarads [µF] ) in 400 µl OptiMEM medium ( Invitrogen ) . RNA ( 2 µg/ml ) and DNA ( 10 µg/ml ) into MEF cells ( 0 . 25×106 per 60-mm dish ) were transfected by electroporation ( Exponential Wave , 240 V; 975 µF ) in 400 µl OptiMEM medium . All electroporations were performed in 4-mm cuvettes ( Thermo Fisher Scientific ) . SFV4-Rluc and SFV4-Rluc-RDR were constructed , rescued and propagated as previously described [44] . The viral stocks were titrated using plaque titration on baby hamster kidney ( BHK ) -21 cells . For the infection of MEFs , which are considerably less susceptible to SFV4 infection than BHK-21 cells , the relative titers and MEF-specific MOIs were re-calculated based on the infectivity of the recombinant viruses , as measured by immunostaining of infected cells . The viral infection of primary MEFs was performed in OptiMEM supplemented with 0 . 1% FBS for 1 hr . The virus was subsequently aspirated , and fresh medium was added . An infectious center assay was performed essentially as previously described with minor modifications [87] . Briefly , 1 µg of RNA fractions extracted from infected MEF cells were electroporated ( two pulses 850 V , 25 µF , in 800 µl ) into BHK-21 ( baby hamster kidney ) cells . Tenfold dilutions of electroporated cells were seeded into six-well plates containing monolayers of naïve BHK-21 cells . After a 2 hr incubation at 37°C , the cell culture medium was aspirated , and the wells were overlaid with a medium containing carboxymethyl cellulose . After 2–3 days , the plaques were visualized by crystal violet staining and counted . The amount of IFN-β secreted into the cell culture medium was measured using a commercial Verikine Mouse Interferon-Beta ELISA kit ( PBL InterferonSource ) , according to the manufacturer's instructions . Cells were harvested ( scraped with a rubber policeman ) at different time points in a phosphate-buffered saline ( PBS ) on ice . Subsequently , cells were lysed with Renilla Luciferase Assay Lysis Buffer according to the manufacturer's instructions ( Promega ) . Lysate was mixed with Renilla Luciferase Assay Substrate diluted 100-fold in Renilla Luciferase Assay Buffer , and luminescence was measured on the GloMAX 20/20 Luminometer ( Promega ) . We used the algorithm developed in-house to design 21-nt siRNA oligos , which had 19-bp perfect match duplex and 2-nt 3′-overhangs . The sequences of siRNA oligonucleotides used in the study are as follows ( siRNA duplex name , guide strand [5′→3′] , passenger strand [5′→3′] ) : dhx58_mus_2304 , UUCUUAGAACAUCAUGGCAUA , UGCCAUGAUGUUCUAAGAACU; ifih1_mus_3004 , AUUGACAUGAUGCAUCUUCUC , GAAGAUGCAUCAUGUCAAUAU; ddx58_mus_2678 , AUAUCUUCCACGACGAAACUU , GUUUCGUCGUGGAAGAUAUUG . siRNA duplexes were synthesized and annealed by Proligo ( Sigma-Genosys ) , whereas negative control non-targeting siRNA #4611 and #4635 was purchased from Ambion . siRNA oligonucleotides at a final concentration of 20 nM were reverse-transfected into COP-5 cells ( 0 . 25–1 . 0×106 cells per 60-mm dish ) using 5 µl Lipofectamine RNAiMAX ( Invitrogen ) . For primary MEFs ( 0 . 25×106 cells per 60-mm dish ) , siRNA duplexes at a final concentration of 100 nM were transfected by electroporation in 4-mm cuvettes ( Thermo Fisher Scientific ) using a GenePulser Xcell ( Bio-Rad ) instrument ( settings: Square Wave , 1000 V; 2 pulses , 0 . 5 ms ) in 100 µl OptiMEM medium . Unless otherwise indicated , on the third day of culture , cells were transfected with 0 . 2 µg/ml of plasmid DNA or RNA using 5 µl Lipofectamine 2000 ( Invitrogen ) or infected by SFV4-Rluc-RDR . The amount of IFN-β in the cell culture medium was measured , and cells were harvested for the immunoblotting analysis on either the fourth or fifth day of culture . Proteins in cell extracts were resolved on 10% polyacrylamide/SDS gels in Mini PROTEAN Tetra Cell systems ( Bio-Rad ) . Subsequently , proteins were transferred to Immobilon-P ( Millipore ) polyvinylidene fluoride microporous 0 . 45 µm membranes using Trans-Blot Semi-Dry Transfer Cell apparatus ( Bio-Rad ) . Blots were incubated with various primary antibodies . Secondary goat anti-rabbit and anti-mouse antibodies conjugated with horseradish peroxidase were from LabAs Ltd . Immunoreactive bands were detected by enhanced chemiluminescence ( ECL ) ( GE Healthcare ) and subsequent exposure to X-ray film ( SuperRX , Fuji ) . Total RNAs , large RNAs ( >200 nt ) , and small RNAs ( <200 nt ) were extracted from cells and purified using the mirVana miRNA Isolation kit ( Ambion ) or TRIzol reagent ( Invitrogen ) , according to the manufacturer's instructions . Oligo ( dT ) -Cellulose Type 7 ( GE Healthcare ) was used for affinity-chromatography fractionation of total RNA into polyadenylated ( polyA+ ) and non-polyadenylated ( polyA− ) RNA species . For RNA extraction from native low melting agarose gel , β-Agarase I enzyme was used accordingly to manufacturer's protocol ( New England Biolabs ) . When required , PD-10 desalting columns ( GE Healthcare ) containing Sephadex G25 were used for buffer exchange . The resulting OD260/OD280 and OD260/OD230 ratios for all RNA preparations exceeded 2 . 1 , as determined by measurements obtained using a ND-1000 spectrophotometer ( NanoDrop Technologies , Inc . ) . The integrity of the RNA was confirmed by denaturing formaldehyde agarose gel electrophoresis . Alternatively , RNA was resolved on non-denaturing agarose gel electrophoresis , stained with ethidium bromide , its image was recorded and analyzed by NIH ImageJ 1 . 46 software ( http://rsb . info . nih . gov/ij/download . html ) . Two micrograms of nucleic acid were treated with DNase I ( 0 . 1 U/µl ) and alkaline phosphatase ( 0 . 1 U/µl ) at 37°C and 50°C , respectively , for 1 hr in a volume of 20 µl . For RNase digestion experiments , 2 µg of RNA was digested with RNase A , RNase III , or RNase T1 at the specified concentrations at 37°C for 1 hr in a volume of 20 µl . The undiluted ( 1× ) RNase concentrations used in the reactions were 1 µg/ml ( RNase A ) , 1 U/µl ( RNase III ) , and 1 U/µl ( RNase T1 ) . Enzyme-treated RNAs were precipitated with ethanol in the presence of sodium acetate and glycogen prior to transfection . Sub-cellular fractionation was performed as previously described [88] . In brief , pRep-RDR or pRep-RDR/GAA transfected COP-5 cells ( 4–6×107 ) were harvested , washed , and resuspended in 800 µl of HB buffer ( 8 . 6% sucrose , 3 mM Imidazole , pH 7 . 4 ) supplemented with protease inhibitors ( Roche ) . Subsequently , cells were homogenized by passing homogenate through 22G1 ¼ needle mounted onto 1-ml syringe until the ratio of unbroken cells to free nuclei was 10% to 90% , as examined under microscope . Unbroken cells and nuclei were pelleted by centrifugation and post-nuclear supernatant ( PNS ) collected . Concentration of sucrose in the PNS was adjusted to 40 . 6% using 62% sucrose solution and refractometer . PNS was loaded in the bottom of an SW41 centrifuge tube and overlaid with 4 . 5 ml of 35% sucrose , 3 ml of 25% sucrose , and 3 ml of 8 . 6% sucrose cushions . All sucrose cushions also contained 3 mM Imidazole pH 7 . 4 and 1 mM EDTA . Tubes were centrifuged for 1 . 5 hr at 35000 rpm in a Beckman Optima L-90 K ultracentrifuge at 4°C . After centrifugation the pellet , containing cytosolic ribonucleoprotein complexes and whitish bands of membrane particles at every interphase between sucrose cushions were collected . Subsequently , RNA was extracted from each fraction with TRIzol Reagent ( Invitrogen ) . Before isopropanol precipitation 40 µg of RNA-grade glycogen was added to each sample for maximal recovery of RNA . COP-5 or RD cells transfected with pRep , pRep-RDR , or pRep-RDR/GAA were washed twice with phosphate-buffered saline ( PBS ) , fixed with 4% paraformaldehyde in PBS for 10 min at 22°C , and permeabilized with 0 . 5% Triton X-100 in PBS for 5 min at 22°C . Blocking and antibody binding was performed in two different ways . First , samples were treated with block buffer ( 10% goat serum and 1% BSA in PBS ) for 1 hr at 22°C , and then incubated for 1 hr at 22°C with antibodies against LAMP2 ( H4B4 , Abcam ) and nsP1 diluted in antibody-binding buffer ( 3% BSA and 0 . 05% Tween 20 in PBS ) . Second , samples were incubated in block buffer ( 10% goat serum , 1% BSA , and 0 . 2% Triton X-100 in PBS ) , and consequently with antibodies against dsRNA ( J2 ) and nsP1 in 3% BSA , 0 . 2% Triton X-100 , and 10 mM MgCl2 antibody-binding buffer . Antibody binding was detected using appropriate antibodies conjugated with Alexa fluor 488 and 568 ( Invitrogen ) . Specifically , for H4B4 and J2 binding detection , secondary antibodies purchased from Life Technologies and reacting with the Fc portion of the heavy chain of mouse IgG1 ( A-21121 ) and IgG2a ( A-21134 ) were used respectively . SlowFade Gold antifade reagent with DAPI ( Invitrogen ) was used for counterstaining of cells nuclei . Samples were imaged on a Nikon ECLIPSE TE2000-U inverted microscope and recorded with Nikon DXM1200C Digital Camera . Images were collected using 60× immersion objective and processed with Nikon Capture NX2 and ACT-1C software . Oligo ( dT ) -Cellulose type 7 powder ( GE Healthcare ) was suspended in sterile water and resulting gel was used for the preparation of gravity-flow chromatography columns . Subsequently , oligo ( dT ) columns were washed with 10 volumes of water and 5 volumes of 100 mM sodium hydroxide ( pH∼10 ) . The pH of the oligo ( dT ) -columns was brought to 7 . 5 by equilibrating it with 10 volumes of TEN0 buffer ( 10 mM TrisHCl , 1 mM EDTA , pH 7 . 5 ) and subsequently with 10 volumes of TEN500 ( 10 mM TrisHCl , 500 mM sodium chloride , 1 mM EDTA , pH 7 . 5 ) . Total RNA samples were heat-denatured ( 70°C , 10 min ) in water , chilled on ice and loaded onto columns in TEN500 buffer . Then , unbound nonpolyadenylated RNAs were collected . Oligo ( dT ) columns with bound polyadenylated RNAs were extensively washed with 30 volumes of TEN500 buffer . Finally , bound RNA was eluted with 10 volumes of TEN0 and RNA-containing fractions were pooled . RNA samples were denatured in loading buffer ( 1×MOPS , 50% formamide , and 6% formaldehyde ) for 5 min at 100°C , chilled on ice and separated on 1% agarose 6% formaldehyde-containing denaturing gel using 1×MOPS buffer system ( 5 V/cm , 5 hr , 4°C ) . Consequently , samples were transferred to nylon membranes ( Hybond N+ , GE Healthcare ) in 10X SSC using capillary blotting technique for 16 hr . After UV cross-linking at 0 . 12 J/cm2 the membrane was blocked in hybridization solution ( water-reconstituted DIG Easy Hyb Granules [Roche] ) at 65°C for 30 min in the hybridization vessels . The RNA probes were generated with phage T7- and T3-RNA polymerases run-off in vitro transcription using linearized DNA as templates and labeled with digoxigenin-UTP-containing RNA labeling mix ( Roche ) . Consequently , RNA probes were treated with DNase I to remove template DNA , purified on Illustra S-300HR Columns ( GE Healthcare ) , denatured and added to the hybridization vessels at 100 ng/ml for hybridization at 65°C for 18 hr . The membranes were washed twice in 2X SSC , 0 . 1% SDS for 10 min at 25°C and then twice in 0 . 1X SSC , 0 . 1% SDS for 15 min at 65°C . Consequently , membranes were washed , blocked , and incubated with Fab fragments against digoxigenin , conjugated to alkaline phosphatase for detection of hybridized signals using CDP-Star ( DIG Luminescent Detection Kit , Roche Applied Science ) according to manufacturer's instructions . Finally , membranes were exposed to SuperRX X-ray films ( Fuji ) or for longer exposure times in ImageQuant RT ECL system ( GE Healthcare ) . Model dsRNA for RNase digestion experiment was generated by annealing of two single-stranded RNAs generated by T7 and SP6 RNA polymerases via run-off transcription from the plasmid DNA containing hepatitis C virus subgenomic replicon sequence fragment ( ∼4000 bp ) flanked by corresponding promoters . RNAs were dissolved in RNA annealing buffer ( 10 mM TrisHCl , 20 mM NaCl , pH 7 . 5 ) , denatured for 1 min at 98°C , then incubated at 75°C for 10 min , and finally cooled to room temperature during 1 hr . DIG-labeled full length SFV4-Rluc RNA was incubated in 1× alkaline hydrolysis buffer ( 50 mM NaHCO3/Na2CO3 , 1 mM EDTA , pH 9 . 2 ) at 95°C for 6 min and rapidly cooled to 4°C . Subsequently , fragmented RNA was purified using RNeasy Mini Kit ( QIAGEN ) . Small RNA species ( ∼20–23 nt ) were depleted from polyA− RNA samples by three rounds of purification using RNeasy Mini Kit ( QIAGEN ) . Subsequently RNAs were incubated in the reaction buffer either in the absence ( negative control ) or presence of the RNA 5′ polyphosphatase ( Epicentre , Illumina ) . After purification using RNeasy Mini Kit ( QIAGEN ) , the RNAs were subjected to sequential ligation with pre-adenylated and blocked at its 3′-terminus full-DNA 3′Linker ( IDT Linker-1 , 5′-rApp-CTG TAG GCA CCA TCA AT-ddC-3′ , Integrated DNA Technologies ) and full-RNA 5′Linker ( 5′-GCC ACC UCG AGU CAC ACC GUA AGU UUC-3′ [89] ) essentially as described previously with minor modifications [61] . First , we used T4 RNA Ligase 1 ( New England Biolabs ) for both steps . Second , both ligation reactions were performed in a single tube ( “one-pot synthesis” ) . Third , after second denaturation step additional ligase was added . Ligation mixtures were used directly for reverse transcription and PCR . RNA ( 100 ng ) was reverse-transcribed using the SuperScript III First-strand Synthesis System for RT-PCR ( Life Technologies ) in a final volume of 20 µl according to the manufacturer's instructions with minor procedure modifications . Briefly , the RNA , the primer , and dNTPs were incubated for 2 minutes at 95°C and cooled quickly on ice . The remaining components ( reverse transcriptase buffer , reverse transcriptase , MgCl2 , dithiothreitol , RNase inhibitor , and water ) were mixed and added on ice . The reactions were initiated by shifting the temperature to 50°C for 2 hr and stopped by heating at 85°C for 5 minutes . Subsequently , RNAs were removed by RNase H treatment . Two microliters of the RT reaction mixture was used for subsequent PCR analysis , which was performed either with the Phusion or with the Dynazyme II polymerases ( Thermo Scientific ) according to the manufacturer's instructions . For strand-specific RT , we used the HPLC-purified primers ( Microsynth ) 5′SFV ( 5′-ATG GCG GAT GTG TGA CAT ACA CGA C-3′ ) and 3′SFV ( 5′-GGA AAT ATT AAA AAC CAA TTG CAA AAT AAA ATA-3′ ) as previously described to efficiently amplify SFV DI-RNA [59] for negative and positive strand detection , respectively . Both primers were used for PCR amplification of the RT products . For the amplification of cDNA , corresponding to tagged SFV replicase generated products , we used HPLC-purified primers Y-adaptor-a ( 5′-GCC ACC TCG AGT CAC ACC GTA-3′ ) [89] and AF-JIG-37 ( 5′-CAA GCA GAA GAC GGC ATA CGA ATT GAT GGT GCC TAC AG-3′ ) [90] , the latter primer was also used for RT . | Type I interferons ( IFN ) are critical for mounting effective antiviral responses by the host cells . For RNA viruses , it is believed that IFN is triggered exclusively by viral double-stranded RNA ( dsRNA ) or RNA containing a 5′-triphosphate ( 5′-ppp ) that is produced during viral genome replication or transcription driven by viral replicases . Here , we provide strong evidence suggesting that the viral replicase also generates 5′-ppp dsRNA using cellular RNA templates , which trigger IFN . This finding indicates that viral replicase is capable of activating the host innate immune response , deviating from the paradigm that viral nucleic acid replication or transcription must be initiated in the host cell to trigger IFN production . Using Semliki Forest virus ( SFV ) as a model , we show that the magnitude of innate immune response activation by the viral replicase plays a decisive role in establishing viral infection . We demonstrate that in contrast to the wild-type SFV replicase , a non-pathogenic mutant replicase triggers increased IFN production , which leads to a shutdown of virus replication . Consequently , excessive IFN induction by the viral replicase can be dangerous for an RNA virus . Thus , we delineate a novel mechanism by which an RNA virus triggers the host cell immune response leading to RNA virus replication shutdown . | [
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| 2013 | RIG-I and MDA-5 Detection of Viral RNA-dependent RNA Polymerase Activity Restricts Positive-Strand RNA Virus Replication |
Lujo virus ( LUJV ) is a novel member of the Arenaviridae family that was first identified in 2008 after an outbreak of severe hemorrhagic fever ( HF ) . In what was a small but rapidly progressing outbreak , this previously unknown virus was transmitted from the critically ill index patient to 4 attending healthcare workers . Four persons died during this outbreak , for a total case fatality of 80% ( 4/5 ) . The suspected rodent source of the initial exposure to LUJV remains a mystery . Because of the ease of transmission , high case fatality , and novel nature of LUJV , we sought to establish an animal model of LUJV HF . Initial attempts in mice failed , but infection of inbred strain 13/N guinea pigs resulted in lethal disease . A total of 41 adult strain 13/N guinea pigs were infected with either wild-type LUJV or a full-length recombinant LUJV . Results demonstrated that strain 13/N guinea pigs provide an excellent model of severe and lethal LUJV HF that closely resembles what is known of the human disease . All infected animals experienced consistent weight loss ( 3–5% per day ) and clinical illness characterized by ocular discharge , ruffled fur , hunched posture , and lethargy . Uniform lethality occurred by 11–16 days post-infection . All animals developed disseminated LUJV infection in various organs ( liver , spleen , lung , and kidney ) , and leukopenia , lymphopenia , thrombocytopenia , coagulopathy , and elevated transaminase levels . Serial euthanasia studies revealed a temporal pattern of virus dissemination and increasing severity of disease , primarily targeting the liver , spleen , lungs , and lower gastrointestinal tract . Establishing an animal LUJV model is an important first step towards understanding the high pathogenicity of LUJV and developing vaccines and antiviral therapeutic drugs for this highly transmissible and lethal emerging pathogen .
Beginning in the 1930s , novel pathogenic arenaviruses have been increasingly recognized as emerging threats to human health [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . During the 1960s and 1970s , several previously unknown arenaviruses emerged as a significant public health threats and causes of a severe and often fatal human hemorrhagic fever ( HF ) syndrome . In 2008 , Lujo virus ( LUJV ) , a novel member of the family Arenaviridae , was first identified after an outbreak of severe HF in southern Africa [11] . During this outbreak , the index patient was transported by air from Lusaka , Zambia , to a private hospital in Johannesburg , South Africa , thus giving the virus its name , Lu-Jo . The index patient died from the infection approximately 12 days after the onset of the presumed first symptoms , and 2 days after hospitalization in Johannesburg . During transport and hospitalization of the index patient , a total of 4 health care workers ( 3 nurses and 1 janitor ) were infected with LUJV . After a period of 10–13 days of progressively severe illness , 3 of these individuals died , resulting in a total case fatality of 80% ( 4/5 ) . Limited data available from these patients indicated that LUJV HF was characterized by thrombocytopenia , elevated liver transaminases , coagulopathy , viral antigen in multiple tissues , neurological symptoms in some cases , and eventual death . While the outbreak was small , the ease with which LUJV spread among the primary , secondary , and tertiary contacts with the index patient , and the lack of a defined etiology , caused significant alarm . The viral cause of the outbreak was identified as a novel arenavirus only after the last case fatality [12] . The suspected source of exposure of the index patient to LUJV ( presumably a rodent ) remains unknown . The arenaviruses are a large and genetically diverse group of over 30 viruses broadly divided into New World and Old World serogroups . They are exclusively rodent-borne , except Tacaribe virus , which was isolated from a bat [13] , [14] . Phylogenetically , LUJV is distinct from both the Old World and New World arenavirus lineages , and is the sole member of a distinct branch more closely related to the known Old World arenaviruses [12] . Although many arenaviruses are not pathogenic , a large number can cause a spectrum of human disease ranging from neurological symptoms in pediatric or immunocompromised patients ( e . g . , Lymphocytic choriomeningitis virus , LCMV ) [15] , [16] , [17] , [18] , [19] , [20] , to hemorrhagic syndromes with high case fatalities ( e . g . , Lassa virus ( LASV ) , Junin virus ( JUNV ) , and Machupo , Guanarito , Chapare , and Sabia viruses ) [21] , [22] , [23] . All arenaviruses are enveloped particles containing bi-segmented , single-stranded , ambi-sense RNA genomes encoding a total of 4 genes [13] . The large ( L ) genome segment ( ∼7 . 2 kb ) contains the viral RNA-dependent RNA polymerase and the multi-functional Z-protein , an important matrix protein that is also responsible for virus budding . The small ( S ) genome segment ( 3 . 4 kb ) encodes the viral nucleoprotein ( NP ) and the glycoprotein precursor ( GPC ) , which is post-translationally processed into the G1 and G2 structural proteins . Both NP and Z proteins function as viral virulence factors antagonizing host cell interferon responses by a variety of mechanisms [21] , [24] , [25] , [26] . Although many experimental vaccine candidates and antiviral drugs are under development [27] , [28] , [29] , [30] , currently the only available vaccine for any pathogenic arenavirus is the live-attenuated Candid1 vaccine for JUNV , which is restricted for use only in high-risk individuals such as laboratorians and those living in endemic areas [31] , [32] . The antiviral drug ribavirin has shown some efficacy in treating LASV and some other arenaviruses , but its side effects limit use to high-risk exposures and severe cases [33] , [34] . Due to the unique characteristics of the LUJV outbreak and the highly novel genomic nature of LUJV , we sought to establish an animal model capable of developing HF similar to that observed in the 4 fatal human cases . Establishing a robust animal model is necessary for further investigating LUJV pathogenesis , and to provide a system to test potential vaccine candidates and antiviral therapeutic drugs . Initial experiments with LUJV infection in 2-day-old newborn and 14-day-old weanling mice failed to provide a lethal model . This was highly surprising given the near uniform lethality of pathogenic New World or Old World arenaviruses in newborn or weanling mice , respectively [12] , and highlights another unique feature of LUJV . We next attempted to develop a LUJV HF model in guinea pigs ( Cavia porcellus ) , which have been used since the 1960s as reliable models for a variety of pathogenic New World and Old World arenaviruses [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] . The inbred strain 13/N guinea pig is highly susceptible to LASV infection; the animals develop severe , progressive , and ultimately fatal disease 15–21 days post-infection ( PI ) , showing many pathological changes that closely mimic Lassa fever in humans [37] . Given their susceptibility to Old World arenaviruses , we began experiments in strain 13/N guinea pigs to study LUJV virulence and pathogenesis . Here , we report successfully establishing a lethal model of LUJV HF in strain 13/N guinea pigs infected with either authentic wild-type or a recombinant full-length reverse genetics-derived LUJV . This robust and highly uniform animal model will permit further detailed investigations into the molecular determinants of LUJV pathogenesis , and provide an in vivo system for testing novel anti-viral therapeutics and vaccines against this highly pathogenic and unique arenavirus .
All work with infectious virus or infected animals was conducted at the Centers for Disease Control and Prevention ( CDC , Atlanta , Georgia , USA ) , in a biosafety level 4 laboratory . All laboratorians and animal handlers adhered to international biosafety practices appropriate for biosafety level 4 , strictly following infection control practices to prevent cross-contamination between individual animals . All animals were individually housed in an isolator-caging system ( Thoren Caging , Inc . , Hazleton , PA , USA ) with a HEPA-filtered inlet and exhaust air supply . All procedures and experiments described herein were approved by the CDC Institutional Animal Care and Use Committee ( IACUC ) and conducted in strict accordance with the Guide for the Care and Use of Laboratory Animals [45] . All animals were housed in a climate-controlled laboratory with a 12 h day/12 h night cycle . The CDC is an Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) fully accredited research facility . No human patient derived clinical materials were used in the completion of these studies . A total of 8 litters of pregnant outbred mice were obtained from a commercial vendor ( Charles River Laboratories , Wilmington , MA , USA ) . All mice were housed as individual family units , and supplied a commercially available mouse chow and water ad libitum . The cage environment was enriched with large amounts of soft bedding , shredded paper , and cotton nestlets . After infection , each animal was observed at least once per day , and its health assessed and scored by experienced CDC veterinarians or animal health technicians . Animals were humanely euthanized with isoflurane vapors once clinical illness scores ( including , but not limited to , neurological signs , piloerection , ocular discharge , weight loss , changes in mentation , ataxia , dehydration , or dyspnea ) indicated that the animal was in distress or in the terminal stages of disease . A total of 47 strain 13/N guinea pigs ( healthy adult males and females aged 1 . 0–1 . 5 years ) were obtained from an established breeding colony located at the University of Iowa ( Ames , IA , USA ) . All animals were housed individually on deep soft bedding and given food ( commercial guinea pig chow , alfalfa cubes , and fresh green parsley ) , and water supplemented with guinea pig appropriate vitamins ad libitum , following standard laboratory animal husbandry protocols for guinea pigs . After infection , each animal was observed at least twice per day , and its health assessed and scored by experienced CDC veterinarians or animal health technicians . Animals were humanely euthanized with isoflurane vapors and sodium pentobarbital ( Schering-Plough , Kenilworth , NJ , USA ) at either predetermined times PI , or once clinical illness scores ( including , but not limited to , piloerection , ocular discharge , weight loss , changes in mentation , ataxia , dehydration , dyspnea , or hypothermia ) indicated that the animal was in the terminal stages of disease . Wild-type LUJV ( wtLUJV ) from the Centers for Disease Control and Prevention Viral Special Pathogens Branch reference collection was passaged in VERO-E6 cells five times before use . A full-length recombinant LUJV ( recLUJV ) was derived from cDNA plasmids using T7-driven reverse genetics as reported in Bergeron et al . ( manuscript in review ) . To differentiate recLUJV from wtLUJV , a few silent ( non-coding ) nucleotide changes were introduced into the full-length L segment clones of recLUJV ( also described in Bergeron et al . , manuscript in review ) . For mouse experiments , wtLASV-Josiah and wtJUNV-XJ13 were prepared and utilized as described in [46] , [47] . Prior to use , all virus stocks were titrated and full-length genomic sequences were verified using standard techniques as described in [46] , [47] . Specimens of liver , lung , spleen , kidney , whole blood , urine , and/or pleural effusion or abdominal fluid ( if present ) were collected sterilely at 2 , 5 , 7 , 9 , 12 , and 14 days PI , and from moribund animals that reached experimental end-points . For RNA extraction , approximately 100 mg specimens of tissues were stored in RNA extraction buffer ( Tripure , Roche Diagnostics , Indianapolis , IN , USA ) at −80°C until homogenization in a high-throughput tissue grinder ( Genogrinder2000 , BT&C Inc . , Lebanon , NJ , USA ) . An equal volume of molecular grade chloroform was added to each specimen homogenate and vortexed . After a 10 minute spin at >10 , 000 rpm in a microcentrifuge , the supernatant was collected and an equal volume of 70% ethanol was added . The supernatant and ethanol was used for total RNA extraction ( RNAeasy 96 platform , Qiagen , Valencia , CA , USA ) following the manufacturer's recommended protocols . Briefly , LUJV RNA was detected using qRT-PCR with primers and probe with internal ( Zen , Integrated DNA Technologies ) and 3′ Iowa Black-FQ quencher moieties specific for the NP gene ( forward primer: 5′-CTCACACCCACAGGAAAT-3′; reverse primer: 5′-GGCCATACATCTCTTCCAGA-3′; probe: 5′-6FAM-ACCCTACAC/Zen/CTCCACAGAACGAAAG-IowaBlackQuencherFQ-3′ ) . For each viral genome detection reaction , 1 uL of total RNA was added to a one-step qRT-PCR ( Invitrogen ) , where the first stand was synthesized using Superscript III at 50°C for 15 min , denatured at 94°C for 2 min , and amplified for 40 cycles of 94°C for 15 s and 60°C for 1 min ( ABI 7500 , Life Sciences , Grand Island , NY , USA ) . LUJV RNA genome equivalents in infected blood , fluid , or tissue specimens were quantitated using a standard curve generated by serial dilutions of a known-titer stock virus spiked into normal whole guinea pig blood . The results of all qRT-PCR tests were normalized to endogenous rodent-specific controls ( glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) , Invitrogen ) following the manufacturer's recommended protocols to account for sample-to-sample variation in RNA extraction efficiency . Guinea pig whole blood was collected by intracardiac techniques into either EDTA-coated or heparin-coated vacutainer tubes . Complete blood counts ( CBC ) were obtained using the Hematrue blood analyzer ( HESKA , Loveland , CO , USA ) . Blood chemistry profiles were obtained from heparinized samples using either the Piccolo point of care chemistry analyzer ( Abaxis , Union City , CA , USA ) or the Hitachi P-module analyzer ( Hitachi Hi-Tech , Tokyo , Japan ) . Liver , spleen , lung , and kidney tissues were collected 2 , 5 , 7 , 9 , 12 , and 14 days PI ( serial euthanasia groups ) , and from moribund animals that reached experimental end-points of terminal disease . Specimen RNA was treated with DNase I ( Qiagen ) followed by RNA cleanup utilizing the RNeasy Mini columns and wash buffers ( Qiagen ) per manufacturer's recommendations . Total RNA was quantified after DNase I treatment and cleanup using a NanoDrop spectrophotometer ( Thermo Scientific , Wilmington , DE , USA ) . Previously reported gene specific primers were used to detect interleukin ( IL ) -1b , IL-2 , IL-8 , IL-12p40 , tumor necrosis factor alpha ( TNFa ) , transforming growth factor beta ( TGFb ) , regulated upon activation normal T-cell expressed and secreted ( RANTES ) , interferon gamma ( IFNg ) , monocyte chemotactic protein ( MCP ) -1 , inducible nitric oxide synthetase ( iNOS ) , and GAPDH [48] , [49] . Generally , 50 ng of RNA was used for each individual qRT-PCR; however , for samples with low RNA concentrations , a minimum of 5 ng was used . Invitrogen's SuperScript III Platinum SYBR green one-step qRT-PCR kit was used for 25 uL total volume reactions , with final reaction concentrations of 1× reaction mix , 0 . 2 M primers , 0 . 5 uL enzyme mix , and 50 ng RNA . Identical thermocycling profiles were utilized for all assays; 55°C for 10 min; 95°C for 5 min; amplification for 40 cycles of 95°C for 15 s and 58°C for 30 s; 40°C for 1 min; and a dissociation curve ( CFX96 Touch , Bio-Rad , Hercules , CA , USA ) . A gene-specific real-time assay was developed for IL-10 ( GenBank accession JN020146 ) using the GenScript real-time PCR primer design tool , IL10 F 5′-CACAGGATCAGCTGGACAAC–3′ , IL10 R 5′-GGGCATCACCTCCACTAGAT-3′ , and IL10 Probe 5′ ( FAM ) -CCTGGGTTGCCAAGCCTTGTC- ( BHQ1 ) 3′ . The Invitrogen SuperScript III Platinum one-step qRT-PCR kit was used for 25 µL total volume reactions following the manufacturer's protocol and the following thermocycling profile; 55°C for 10 min , 95°C for 2 min , amplification for 40 cycles of 95°C for 15 s and 60°C for 30 s . Guinea pig GAPDH was used as the internal control calibrator . Bio-Rad CFX manager software v2 . 1 was used to analyze the cycling threshold ( CT ) values and melt curves for each reaction , and results were analyzed using the comparative CT method as described by Schmittgen and Livak [50] . The fold change of each serial euthanasia group was compared to mean fold change of the sham-infected control guinea pigs ( N = 6 ) , and the standard error of the mean was calculated for each experimental group . Due to technical issues during RNA extraction , the day 5 PI spleen data was generated from only 1 infected animal . At the time of collection , tissue specimens were fixed in 10% neutral buffered formalin and gamma-irradiated ( 2 . 0×106 RAD ) prior to sectioning into 4 um-thick slices and staining with hematoxylin and eosin following routine histology protocols . All analyses were completed using the PRISM v5 . 0 program ( Graphpad , LaJolla , CA , USA ) . Potentially significant differences between wtLUJV and recLUJV groups were evaluated using a student's t-test . In subsequent analyses , wtLUJV and recLUJV data were combined for all day 9 and terminal group analyses . For the complete blood counts , clinical chemistry , and gene regulation data , significant differences between LUJV-infected and sham-infected animals at each time point were analyzed using a one-way analysis of variance ( ANOVA ) with Dunnett's adjustment for multiple comparisons ( *p<0 . 05; **p<0 . 01 , ***p<0 . 001 ) .
Mice were monitored for 28 days PI with 500 FFU of wtLUJV , wtLASV-Josiah , wtJUNV-XJ13 , or inoculation with DMEM as a negative control . As expected , wtJUNV-XJ13 caused uniform neurological signs and lethality by 15 days PI in 2-day-old , but not 14-day-old mice . In contrast and as expected , wtLASV infection resulted in near uniform ( 90% ) lethality in 14-day-old weanling mice , but was non-lethal in 2-day-old newborn mice . Infection was confirmed by the detection of anti-Lujo virus specific antibodies at 28 days post-infection in 3 surviving animals . Surprisingly , wtLUJV did not cause any signs of clinical illness or lethality in either 2-day-old or 14-day-old mice regardless of the dose ( up to 2 . 0×103 FFU ) or inoculation route ( intracranial , subcutaneous , or intraperitoneal ) ( Fig . S1 and data not shown ) .
The pathogenic arenaviruses are genetically diverse , globally distributed , and capable of causing human illness ranging from encephalitis to severe and often lethal HF . Divided into New World and Old World lineages , these rodent-borne viruses are most often transmitted to humans by direct contact or aerosol exposure to infectious rodent excreta , or , in some cases , via a chain of human-to-human transmissions within hospital settings . The overall public health impact can range from only a few cases ( e . g . , Sabia or Chapare viruses ) to over 100 , 000 cases per year ( e . g . , LASV ) [22] , [51] . LUJV is the most recently discovered pathogenic arenavirus , identified in 2008 after a high fatality ( 80% ) cluster of cases among primary , secondary , and tertiary contacts with the index patient [52] . A number of factors mark LUJV as a unique arenavirus . Although the data from the 2008 outbreak are limited , the high case fatality was striking compared with most other arenavirus outbreaks that typically are associated with case fatalities of 10–40% [53] , [54] . Phylogenetically , LUJV is distinct from all previously identified arenaviruses , forming a unique lineage more closely related to the Old World than New World arenaviruses , but with over 40% divergence from LASV at the nucleotide level [12] . The unique genomic sequence and high antigenic diversity compared to other Old World and New World arenaviruses greatly complicated its initial diagnosis by molecular ( RT-PCR ) or serology ( IgM/IgG ) techniques [11] . Unfortunately , due to these and other factors , the virus later named LUJV was only identified as the specific cause of the 2008 outbreak several weeks after the last fatal case of Lujo HF . To assess the in vivo characteristics of LUJV , we began with the traditional newborn and weanling outbred mouse models of arenavirus infection [12] . These experiments further demonstrated the unique virulence properties of LUJV compared to New World ( JUNV-XJ13 ) and Old World ( LASV-Josiah ) prototype arenaviruses . Both JUNV and LASV are highly virulent in newborn and weanling mice , respectively , causing lethality in an age-dependent pattern [12] , [46] , [47] , [55] . In previous experiments , JUNV dosages as low as 1 . 0×101 FFU caused uniform lethality after intracranial inoculation into 2-day-old mice ( data not shown ) . Similar dosages of LASV were lethal in 14-day-old mice ( data not shown ) . However , LUJV was non-lethal in mice regardless of the route of inoculation ( intracranial , subcutaneous , or intra-peritoneal ) , mouse age at inoculation ( 2 or 14 days old ) , or viral dose ( ranging up to the maximum dose tested , 2 . 0×103 FFU; Fig . S1 , and data not shown ) . In marked contrast with the mouse results , LUJV caused severe , rapidly progressive , and uniformly lethal and hemorrhagic disease in strain 13/N guinea pigs . In this model , we observed an apparent incubation period of 5 to 6 days from the time of inoculation to the first clinical signs of illness ( fever and weight loss ) . Over the next 24 to 48 h , the animals began to display signs of progressive illness ( bilateral ocular discharge , continued fever , weight loss , and dehydration ) until they were found dead or humanely euthanized when moribund . By day 5 PI , significant hematological changes began to occur , including hypoproteinemia , thrombocytopenia , and lymphopenia ( Fig . 3 ) . Interestingly , although we saw consistent elevation in key serum transaminase enzyme ( aspartate transferase , alkaline phosphatase , and alanine transferase ) activities , these were neither dramatic nor statistically significant and are typical of the generally poor release of tissue transaminases in guinea pigs in the face of tissue damage or necrosis . Subjective comparisons of clinical illness severity , and gross and histologic pathology between guinea pigs infected with LUJV or LASV suggests that LUJV caused a more profound illness ( greater and more rapid weight loss , and frank hemorrhage and congestion in gastrointestinal organs , bladder , lymph nodes , and abdominal cavity ) , tissue damage ( hepatic and myocardial necrosis ) , and hallmarks that may be consistent with disseminated intravascular coagulation ( DIC ) than LASV in preliminary experiments completed in our laboratory and as reported in [37] ( Figs . 5 and 6 , and data not shown ) . Previous studies of human pathogenic New World ( JUNV , Guanarito ( GTOV ) , Machupo ( MACV ) ) and Old World ( LASV ) arenaviruses using rodents and other small animal models failed to demonstrate clear consistent signs of HF ( reviewed in [56] ) . LUJV infection of guinea pigs , however , resulted in severe infarction , fibrin deposition , and hemorrhage in multiple organs , suggesting DIC . The progressive reductions in platelet numbers ( 9 , 12 , and 14 days PI ) are consistent with the consumption of platelets at sites of localized virus inflammation and tissue destruction , or , in the most extreme cases , may be consistent with DIC at later time points PI . Anecdotally , the animal with severe frank hemorrhage into the abdominal cavity developed profound thrombocytopenia ( 99×103 uL−1 ) . Unfortunately , no assays to determine coagulation factor parameters ( i . e . , activated partial thromboplastin time ( APTT ) or prothrombin time ( PT ) ) were completed to directly assess the influence of these coagulation pathways on the observed coagulopathy . It is clear that further work is essential to definitively characterize the underlying mechanisms of these observations before firm conclusions can be drawn . For these types of studies this guinea pig model may provide a potentially useful alternative to non-human primate models for studying basic pathogenesis of a bona fide human pathogenic arenavirus causing severe coagulopathy and HF . The rapid rise and magnitude of LUJV-specific antigen deposition , histologic pathology , and RNA titers in tissues , blood , urine , and abdominal fluid were surprising . Within 48 h of infection , the virus already disseminated to the liver , spleen , kidneys , and lungs , and was rapidly replicating up to 1 . 8×105 TCID50 eq/g of liver . Interestingly , this high-titer replication continued for another 48–72 h before the onset of illness , indicated by increased body temperature and weight loss 5–6 days PI . Tissue viral loads remained extremely high throughout the course of the disease , with death occurring 11–16 days PI . Like other arenaviruses , LUJV broadly modulates host immune responses during infection . The molecular basis of the seemingly high LUJV virulence in humans has not been characterized . However , recent work using reverse genetics-derived recombinant viruses indicates that LUJV has unique promoter elements that influence the expression of the viral NP and glycoprotein , and an unusually long intergenic region sequence on the viral L segment , which influences expression of the Z protein ( Bergeron et al . , in-review ) . Both LASV and lymphocytic choriomeningitis virus NP and Z proteins have immunomodulatory properties and function as potent antagonists of host cell antiviral responses ( [21] , [24] , [25] , [26] and reviewed in [57] ) . These and other yet unrecognized molecular motifs may augment the apparently enhanced virulence of LUJV by increasing viral replication or interfering with immunoregulatory mechanisms within the host . The ability of LUJV to influence the immune response is illustrated in guinea pigs by the finding that , despite the very high viral loads by day 5 PI , pro-inflammatory cytokine/chemokine genes , such as IL-1b and RANTES , were downregulated 2–4-fold early during infection ( Fig . 7 ) . In contrast , as early as day 5 PI , potent mediators of macrophage and neutrophil activation and inflammation ( IL-8 , MCP-1 ) and pro-inflammatory molecules ( IL-12p40 , and IFNg ) were transcriptionally upregulated ( 10 to >100-fold ) compared to mock inoculated animals , especially in liver and kidney tissues ( Fig . 7 , and Table S1 ) . Potentially important and robust induction of the broad immunomodulatory mediator IL-10 was also detected in lungs by day 9 PI , and may signal attempts to limit and control pro-inflammatory activity , allowing for further virus replication . Overall , the pattern of gene induction and histological evidence from our study allow for the speculation that , in early stages of infection , the animals mounted a pro-inflammatory and innate immune response ( presumably involving macrophages , neutrophils , NK-cells ( i . e . , Kurloff cells in the guinea pig ) , and/or NKT-cells ) in multiple tissues . This was followed by a predominantly Th1 response dominated by IFNg , MCP-1 , and IL-12p40 , which likely stimulated activation and enhanced function presumably of NK cells , CD4+ TH1 cells , and/or CD8+ cytotoxic lymphocytes , resulting in a strong bias towards cell mediated immunity . Since LUJV is not highly cytopathic in cell culture , these responses may have been more deleterious than helpful to the host , due to immune cell-mediated destruction of vital organs . This hypothesis is consistent with recent work describing enhanced LASV pathogenesis due to deleterious T-cell mediated activation and stimulation of monocytes/macrophages , leading to tissue destruction in humanized HHD mice [58] . Regardless of the immune mechanisms stimulated by LUJV infection , these responses were insufficient to control virus replication and dissemination throughout the host , and to prevent eventual lethality . Similarly to LASV infection in humans and non-human primate animal models , LUJV does not appear , at least at the mRNA transcriptional level , to elucidate an end-stage cytokine storm as seen in fatal hemorrhagic cases of infection with Ebola , Marburg , or Rift Valley fever viruses [59] , [60] , [61] , [62] . Although IFNu-producing cells ( presumably natural killer cells , NKT-cells , and/or macrophages ) are clearly activated , the increase in mRNA encoding the TNFa , iNOS , and IL-1b genes was not significant even in terminal cases . Among the limited genes analyzed in this study , we speculate that complete dysregulation of the host immune response is not responsible for the dramatic vascular permeability changes and coagulopathy observed in the guinea pig model of LUJV HF . Although our results are suggestive , more definitive studies of the guinea pig immune response are necessary before drawing distinct conclusions regarding the roles of inflammatory mediators , immune effector cells , and viral virulence factors in the pathogenesis of LUJV HF in this animal model . The dramatic severity of the clinical illness , short survival times , high tissue viral loads , and the mRNA gene expression patterns in visceral organs further highlight the unique pathogenic characteristics of LUJV compared with other studies of LASV or JUNV [37] , [44] . Our in vivo data , taken together with recent insights into the unique genomic elements regulating viral replication in cell culture , stress the importance of further work to elucidate the possibly novel pathogenic mechanisms employed by LUJV to cause HF in both humans and guinea pigs . Direct comparisons of the precise pathogenic mechanisms utilized by LUJV and other pathogenic arenaviruses are underway , and may reveal insights into the apparent enhanced virulence of LUJV in humans and guinea pigs , and provide broader understanding of arenavirus HF . Regardless of the exact mechanisms , LUJV is clearly highly pathogenic , easily transmitted in health care settings , and a potential health threat in southern Africa . Establishing a robust and reliable animal model of severe and lethal LUJV HF is a critical first step for further investigations to increase our understanding of LUJV and for developing anti-viral therapeutics or experimental vaccines for this new and unique threat to human health . | The pathogenic arenaviruses are a diverse group of human pathogens capable of causing a wide range of human illness ranging from encephalitis to severe hemorrhagic fever throughout the New and Old World . In 2008 , a previously unknown virus ( now named Lujo virus ) caused a high case fatality outbreak ( 80% ) in southern Africa . Limited data available from these patients indicated that LUJV HF was characterized by thrombocytopenia , elevated liver transaminases , coagulopathy , viral antigen in multiple tissues , neurological symptoms in some cases , and eventual death . The source of exposure of the index patient remains unknown . Due to the unusually high lethality and rapid human to human spread , we sought to develop an animal model of Lujo hemorrhagic fever . We report here that after infection with Lujo virus , Strain 13/N guinea pigs develop a hemorrhagic fever syndrome similar to the disease observed in human patients . This animal model of severe Lujo hemorrhagic fever is a critical first step to increase our understanding of this highly pathogenic virus , and to develop anti-viral therapeutics or experimental vaccines for this new and unique threat to human health . | [
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| 2012 | Severe Hemorrhagic Fever in Strain 13/N Guinea Pigs Infected with Lujo Virus |
Parasitic flatworms of the genus Schistosoma cause schistosomiasis , a neglected tropical disease that affects hundreds of millions . Treatment of schistosomiasis depends almost entirely on the drug praziquantel ( PZQ ) . Though essential to treating and controlling schistosomiasis , a major limitation of PZQ is that it is not active against immature mammalian-stage schistosomes . Furthermore , there are reports of field isolates with heritable reductions in PZQ susceptibility , and researchers have selected for PZQ-resistant schistosomes in the laboratory . P-glycoprotein ( Pgp; ABCB1 ) and other ATP binding cassette ( ABC ) transporters remove a wide variety of toxins and xenobiotics from cells , and have been implicated in multidrug resistance ( MDR ) . Changes in ABC transporter structure or expression levels are also associated with reduced drug susceptibility in parasitic helminths , including schistosomes . Here , we show that the activity of PZQ against schistosome adults and juveniles ex vivo is potentiated by co-administration of either the highly potent Pgp inhibitor tariquidar or combinations of inhibitors targeting multiple ABC multidrug transporters . Adult worms exposed to sublethal PZQ concentrations remain active , but co-administration of ABC transporter inhibitors results in complete loss of motility and disruption of the tegument . Notably , juvenile schistosomes ( 3–4 weeks post infection ) , normally refractory to 2 µM PZQ , become paralyzed when transporter inhibitors are added in combination with the PZQ . Experiments using the fluorescent PZQ derivative ( R ) -PZQ-BODIPY are consistent with the transporter inhibitors increasing effective intraworm concentrations of PZQ . Adult worms in which expression of ABC transporters has been suppressed by RNA interference show increased responsiveness to PZQ and increased retention of ( R ) -PZQ-BODIPY consistent with an important role for these proteins in setting levels of PZQ susceptibility . These results indicate that parasite ABC multidrug transporters might serve as important targets for enhancing the action of PZQ . They also suggest a potentially novel and readily-available strategy for overcoming reduced PZQ susceptibility of schistosomes .
Schistosomiasis is a highly prevalent tropical disease caused by parasitic flatworms of the genus Schistosoma . It is widespread , affecting hundreds of millions throughout the tropics and sub-tropics , with devastating effects on human health and economic development . Estimates suggest that schistosomiasis is responsible for almost 300 , 000 deaths annually in sub-Saharan Africa alone [1] , [2] , [3] . In the absence of an effective vaccine , chemotherapeutic intervention remains the main means of treating and controlling schistosomiasis . The current drug of choice is praziquantel ( PZQ ) . PZQ is effective against all human schistosome species and has relatively mild side effects [4] , [5] , [6] . It has proved successful in large-scale control efforts targeting schistosomiasis in several countries [7] , [8] and , as a result of its advantages and reduced costs , has become the only available antischistosomal treatment in most parts of the world [5] , [9] . Unfortunately , reliance on a single drug for a disease of this magnitude is dangerous [6] , and is particularly so for PZQ since the mode of action is not rigorously defined [10] , [11] , [12] . Additionally , reported failure rates for PZQ in the field are typically in the range of 30–50% [13] , [14] , [15] , and several field isolates [4] , [16] and experimentally-induced , drug-selected schistosomes [17] , [18] exhibit reduced susceptibility to the drug , perhaps a harbinger for the emergence and ultimate spread of PZQ resistance [reviewed in 12] , [19] , [20] . Schistosomes also show major stage-specific differences in PZQ susceptibility; immature worms are refractory to PZQ , making treatment largely ineffective until approximately 5–6 weeks post-infection [21] , [22] , [23] , [24] . One strategy proposed to enhance drug efficacy and overcome drug resistance is to augment current anthelmintics with agents targeting different , but potentially interacting sites of action , including cellular components that regulate rates of drug uptake , metabolism , or efflux . Efflux transporters that mediate multidrug resistance have been advanced as particularly appealing targets of this type [25] , [26] . P-glycoprotein ( Pgp; ABCB1 ) and related efflux transporters such as multidrug resistance associated protein 1 ( MRP1; ABCC1 ) and breast cancer resistance protein ( BCRP; ABCG2 ) are members of the ATP binding cassette ( ABC ) superfamily of proteins . ABC transporters use energy generated from ATP hydrolysis to translocate compounds across the membrane . They typically exhibit broad substrate specificity , with their most obvious physiological role being to remove or exclude metabolic toxins and xenobiotics , including therapeutic agents , from cells and tissues . However , in addition to this detoxification activity , ABC transporters can transport a variety of biologically significant signaling molecules with high affinity [27] , [28] , [29] , [30] , and have been implicated in a wide array of physiological functions [31] , [32] , [33] , including modulation of cell death pathways [34] , [35] and immune function [36] . Most notably , ABC multidrug transporters underlie multidrug resistance , a phenomenon in which cells that develop resistance to a particular drug show simultaneous cross-resistance to several structurally unrelated compounds [37] . ABC transporters have additionally been implicated in insusceptibility to anthelmintics , as changes in expression levels and allele frequencies of Pgp and other ABC transporters are found in parasitic helminths exhibiting reduced drug sensitivity [reviewed in 38] , [39] , [40] , [41] . Like mammals , schistosomes have genes for a wide variety of ABC transporters , including Pgp , MRP1 , and BCRP [41] , [42] . Previous work has focused on particular S . mansoni orthologs of Pgp ( SMDR2 ) and MRP1 ( SmMRP1 ) , and the role they may play in the parasite's physiology and susceptibility to PZQ . For example , S . mansoni upregulate expression of SMDR2 , SmMRP1 , and other drug transporter RNAs and anti-Pgp and anti-MRP1 immunoreactivity in response to sub-lethal concentrations of PZQ [43] , [44] , [45] . Furthermore , some adult worms with reduced susceptibility to PZQ exhibit higher basal levels of these transporters [43] , [44] , and PZQ interacts directly with expressed recombinant SMDR2 , as both an inhibitor and a likely substrate [46] . Our work has also implicated these transporters in schistosome reproduction [47] , while others have demonstrated likely involvement of these transporters in parasite excretory activity [48] , [49] . Here , we show that disruption of schistosome ABC transporter function ( by pharmacological inhibition ) or expression ( by RNA interference ) can potentiate the antischistosomal activity of PZQ against adult worms in culture , appearing to increase the effective intraworm concentration of PZQ . Remarkably , co-administration of MDR inhibitors with PZQ also renders PZQ-insusceptible juvenile schistosomes susceptible to PZQ . Based on these findings , as well as those discussed above , we hypothesize that schistosome ABC transporters modulate the responsiveness of schistosomes to PZQ . These results also suggest that augmentation of standard PZQ therapy with readily-available inhibitors of Pgp or other multidrug transporters has the potential to enhance drug efficacy and possibly prevent emergence or spread of PZQ resistance .
In these experiments , we tested whether inhibitors of ABC multidrug transporters could potentiate the activity of sub-lethal concentrations of PZQ against adult schistosomes ex vivo . Worms were incubated in PZQ alone ( 500 nM ) or in PZQ combined with the ABC transporter inhibitors for 12–24 hours , removed to drug-free medium , and then tested for recovery of motility following 48 hours in drug-free medium . S . mansoni adults exposed to various ABC multidrug transporter inhibitors in combination with 500 nM PZQ exhibit significant loss of motility compared to those exposed to PZQ alone . Tariquidar ( XR9576 ) , a third-generation , highly potent Pgp inhibitor [50] , [51] , [52] , [53] , is particularly effective ( Fig . 1 ) ; inclusion of 10 µM tariquidar with 500 nM PZQ results in essentially complete loss of detectable schistosome motility . In contrast , worms in PZQ alone remained highly active . Other inhibitors were effective at potentiating PZQ activity in combinations that block different classes of ABC transporters ( combinations A , B , C; see Materials and Methods ) . Thus , Combination A includes three compounds and Combination B includes two compounds that inhibit three classes of mammalian transporters ( Pgp , MRP1 , and BCRP ) ; Combination C contains inhibitors of two classes of mammalian transporters ( Pgp and MRP1 ) . All of these inhibitor combinations have significant effects on adult schistosome motility when combined with 500 nM PZQ . Interestingly , Combination A ( zosuquidar , Ko143 , MK 571 ) also significantly suppresses worm motility on its own ( Fig . 1 ) . In addition to dramatic effects on adult worm motility , PZQ also causes disruption of the tegument [54] . ABC multidrug inhibitors also potentiate these effects of PZQ on tegumental integrity . Thus , as shown in Fig . 2 , adult schistosomes incubated in the relatively low PZQ concentration of 500 nM show little if any indication of the type of tegumental disruption seen with higher levels of PZQ . In contrast , those schistosomes co-incubated in PZQ plus the ABC transporter inhibitors display significant tegumental damage , with the normal tubercular surface showing multiple blebs and lesions . Interestingly , 10 µM tariquidar appears also to produce some damage to the tegument in the absence of PZQ ( Fig . S1 ) , perhaps signifying potential effectiveness as an antischistosomal on its own . Though we have not quantified the level of damage , visual inspection suggests that PZQ plus tariquidar produces a more severe effect than tariquidar alone . In contrast , no obvious effects on the tegument are apparent when worms are incubated without PZQ in Combinations A , B , or C . We have used ( R ) -PZQ-BODIPY [21] , [46] , a fluorescent derivative of the active enantiomer [55] , [56] , [57] , [58] of PZQ , to explore the mechanism by which ABC transporter inhibitors might be enhancing the effective potency of PZQ on adult schistosomes . To investigate whether transporter inhibition increases retention of BODIPY-PZQ-linked fluorescence , we exposed adult schistosomes to 1 µM ( R ) -PZQ-BODIPY in the presence or absence of ABC transporter inhibitors . Following an overnight incubation , the medium was replaced with medium containing the same ABC transporter inhibitors ( if any ) , but not the fluorescent PZQ . As controls , we also examined the effects of the transporter inhibitors on their own; some of these inhibitors ( eg , tariquidar ) increased worm fluorescence in the absence of ( R ) -PZQ-BODIPY , and were therefore excluded from further analysis . Adult schistosomes exhibit some green autofluorescence , but incubation in ( R ) -PZQ-BODIPY produces a dramatic increase in green fluorescence within worms ( Fig . 3 ) . The intensity of this diffuse fluorescence within males appears to increase dramatically when either the Pgp inhibitor dexverapamil or the MRP1 inhibitor MK 571 is included with the ( R ) -PZQ-BODIPY ( Fig . 3 ) . Male worms exposed to both ( R ) -PZQ-BODIPY and either of these ABC transporter inhibitors exhibit a significant 1 . 7–1 . 8-fold increase in fluorescence intensity compared to worms exposed to ( R ) -PZQ-BODIPY alone ( Fig . 4 ) . It is possible that inhibition of the schistosome transporters blocks efflux of the BODIPY moiety of BODIPY-PZQ rather than ( or in addition to ) the PZQ moiety . However , worms incubated in CellTracker BODIPY , a membrane-permeable form of BODIPY that is modified intracellularly to a membrane-impermeable form , showed no significant difference in fluorescence intensity between worms incubated with or without dexverapamil or MK 571 ( Fig . S2 ) . Juvenile ( 3–4 week post infection ) S . mansoni are refractory to PZQ , both ex vivo and in vivo [21] , [22] , [23] , [24] . In these experiments , we examined whether inhibitors of Pgp and other ABC transporters could potentiate the activity of PZQ against juvenile S . mansoni , rendering those worms susceptible to the drug . Schistosomes were incubated overnight in 2 µM PZQ . As reported by others [24] , while in PZQ they exhibit limited motility and show signs of contractile paralysis . However , following subsequent incubation for up to 72 h in the absence of PZQ , these worms recover completely , and exhibit high levels of motility ( Fig . 5 ) . In contrast , schistosomes incubated in both 2 µM PZQ and ABC transporter inhibitors do not recover , and instead continue to remain paralyzed after recovery in the absence of PZQ and inhibitors ( Fig . 5 ) . Thus , juvenile worms exposed during the initial incubation period to 10 µM tariquidar or transporter inhibitor combinations A , B , or C in addition to PZQ show significant reductions in worm motility . Drug Combination A ( zosuquidar , Ko143 , MK 571 ) , which disrupted adult motility on its own , also appeared to paralyze juvenile schistosomes , while tariquidar and Combinations B and C had no significant effects on juvenile schistosome motility in the absence of PZQ . We used siRNAs designed against different S . mansoni ABC transporters to knock down expression of these genes . Initial experiments in which we suppressed expression of individual transporters failed to enhance susceptibility of adult schistosomes to PZQ consistently . We subsequently tested simultaneous knockdown of multiple transporters . In these experiments , expression of the different target RNAs ( SMDR2 , SmMRP1 , ABCA4 , ABCB6 , and ABCC10/MRP7; see Materials and Methods for further details ) was reduced by 25–75% ( Fig . S3 ) . Adults in which expression of these genes is suppressed by RNAi are significantly less motile in 800 nM PZQ than control worms ( Fig . 6 ) . In contrast , there are no significant effects of knockdown on worm motility in the absence of PZQ , indicating that the suppression of transporter expression is not in itself reducing motility of these worms , but is rather decreasing their ability to counter the effects of PZQ . We have observed similar results upon knockdown of the four transporters , SMDR2 , SmMRP1 , ABCA4 , and ABCB6 . Similar to the effects of ABC transporter inhibitors described above , knockdown of expression of these transporters also increases retention of ( R ) -PZQ-BODIPY within the worm ( Fig . 7 ) . Thus , worms in which four ABC transporters ( SMDR2 , SmMRP1 , ABCA4 , ABCB6 ) have been targeted by siRNAs show significantly higher intraworm BODIPY-PZQ fluorescence than control worms exposed to luciferase siRNA . These results indicate that reduced transporter expression , like transporter inhibition , can increase effective concentrations of PZQ ( or at least BODIPY-PZQ ) within the parasite .
In the absence of effective vaccines , chemotherapy continues to be the major strategy for treatment and control of infections by schistosomes . Currently , PZQ is effectively the only drug available for treatment of a disease affecting hundreds of millions , a situation with the potential for disastrous consequences . Furthermore , although PZQ is effective overall , it does have limitations . Only certain schistosome stages are sensitive to PZQ , and reductions in parasite prevalence and transmission following drug treatment are often less than optimal . Typical 30% treatment failure rates are seen in the field , with up to 50% failure reported [13] , [14] , [15] . Even these numbers may be optimistic , as the standardly-used Kato-Katz technique for measuring schistosome egg counts is not reliable and tends to underestimate infection levels [59] , [60] , [61] . The threat of emerging drug resistance also represents an ever-present concern , particularly in mass drug administration programs . Here , we show that inhibition or reduced expression of S . mansoni ABC multidrug transporters increases the susceptibility of both adult and immature schistosomes to PZQ . ABC multidrug transporters underlie multidrug resistance in mammalian cells , and have been linked to drug resistance in parasites , including helminths . We have hypothesized [41] that the efflux activity of Pgp and other parasite ABC multidrug transporters may serve to protect the parasite against PZQ . Previously , we showed that higher expression levels of schistosome ABC transporters are associated with reduced susceptibility of worms to PZQ [43] , [44] . Furthermore , PZQ inhibits and is likely transported by S . mansoni Pgp ( SMDR2 ) [46] . These two observations suggest a mechanism by which reduced ABC transporter expression or disrupted function might enhance schistosome susceptibility to PZQ . Thus , part of the parasite's defense against PZQ may include efflux of the drug via these transporters . Indeed , if some variant of the “hydrophobic vacuum” model for Pgp [62] , [63] is correct , parasite Pgp and similar transporters may in fact be preventing PZQ from crossing the cell membrane , translocating it to the extracellular space from the lipid bilayer . Reducing transporter activity or levels may thereby increase effective dose and effectiveness . On the other hand , increased expression ( or activity ) of schistosome ABC transporters may serve to reduce susceptibility , and could perhaps represent a mechanism underlying development or maintenance of PZQ resistance . Our results showing that transporter inhibitors increase both PZQ susceptibility and BODIPY-PZQ fluorescence in worms are consistent with this hypothesis . On its own , the third-generation , highly potent Pgp inhibitor tariquidar [50] , [51] , [52] , [53] consistently and significantly increased susceptibility to PZQ in both juvenile and adult worms . In contrast , all the other inhibitors we tested increased PZQ susceptibility only when combined with other compounds that act on ABC transporters of other classes , possibly indicating functional redundancy in the ability of these transporters to protect the parasite against PZQ . Interestingly , tariquidar also appears to be a substrate of BCRP at low concentrations and a BCRP inhibitor at higher ( >100 nM ) concentrations [64] . On the other hand , elacridar , a compound which inhibits both Pgp and BCRP in mammals with sub-micromolar affinity [65] , [66] , did not increase PZQ susceptibility in a consistent manner unless used in combination with a mammalian MRP1 inhibitor ( Reversan ) . This apparent requirement that multiple schistosome ABC transporter types must be blocked to enhance PZQ potency ( with the exception of tariquidar ) was surprising , as a single Pgp reversing agent ( eg , racemic- , or R ( + ) -verapamil ) has been reported to enhance activity of anthelmintics against other trematodes [67] , as well as nematodes [40] . Furthermore , a single Pgp or MRP1 inhibitor can disrupt egg production in schistosomes and other flatworms [47] , [68] . However , it is clear from the S . mansoni and S . japonicum genomes that there is a high level of redundancy in ABC transporter genes [41] , and it is possible that the pharmacological sensitivities of some schistosome ABC transporters differ from those of their mammalian counterparts . Notably , PZQ appears to be a substrate for one S . mansoni transporter , the Pgp homolog SMDR2 [46]; in contrast , PZQ is not a substrate for mammalian Pgp , though it is an inhibitor [69] . Interestingly , single inhibitors ( eg , dexverapamil , MK 571 ) increase intra-worm fluorescence following exposure of adult schistosomes to ( R ) -PZQ-BODIPY , indicating that blocking a single class of transporters can raise effective PZQ concentrations within the worm . The precise mechanism by which these intraworm PZQ concentrations might be enhanced , and the specific sites of PZQ retention within the parasite , are worthy of further investigation . It is of course possible that the effects of the ABC transporter inhibitors on PZQ susceptibility are non-specific . In order to test that possibility , we performed experiments to determine whether knocking down levels of transporter gene expression also increases parasite susceptibility to PZQ . We found that simultaneous knockdown of schistosome ABC transporters from different classes could indeed render worms more susceptible to PZQ . The requirement for knockdown of multiple transporters to enhance PZQ susceptibility may reflect the redundancy in these genes discussed previously , as well as the limited efficiency ( 25–75% ) of knockdown . Reduced expression of ABC transporters appears to have no measurable effect on motility of adult worms unless those worms are also exposed to PZQ , suggesting that the knockdown is not simply compromising the health of the parasite , but is instead having a more specific effect on how the parasite responds to PZQ , an interpretation supported by the increased retention of ( R ) -PZQ-BODIPY in these worms . It is also possible that disrupting expression or function of these transporters could interfere with the worm's ability to respond to other drugs and stressors , and might therefore represent a general strategy for potentiating activity of these agents against the parasite . One interpretation of our results might be that combination of a single ABC transporter inhibitor ( other than tariquidar ) with PZQ might not augment PZQ antischistosomal action in real-world situations , and tariquidar , an experimental and expensive compound , is clearly not a realistic option . However , it will be important to determine whether the results we have obtained in vitro are predictive for schistosomes in vivo , where host factors that could increase parasite vulnerability might come into play . Furthermore , we and others have shown that single ABC transporter inhibitors disrupt schistosome egg production in vitro and similarly decrease parasite egg burden and resultant pathology in vivo [47] , [68] . Should PZQ resistance arise , these inhibitors - many of which are approved , safe , and inexpensive - might be combined with PZQ in a strategy to reduce spread of those resistant worms by reducing egg production and transmission of resistant parasites . Such a strategy could be exploited as a multifaceted approach to reduce morbidity , disease transmission , and spread of resistance [70] .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health . Animal handling and experimental procedures were undertaken in compliance with the University of Pennsylvania's Institutional Animal Care and Use Committee ( IACUC ) guidelines ( Animal Welfare Assurance Number: A3079-01 ) . PZQ , R ( + ) -verapamil HCl ( dexverapamil ) , elacridar ( GF120918 , GG918 ) , and MK 571 were from Sigma-Aldrich ( St . Louis , MO ) . Tariquidar was from MedKoo Biosciences ( Chapel Hill , NC ) . Ko143 was from Enzo Life Sciences ( Farmingdale , NY ) , zosuquidar ( LY335979 ) was from Toronto Research Chemicals ( Toronto , ON ) , and Reversan was from Santa Cruz Biotechnology ( Dallas , TX ) . Drugs were dissolved in dimethyl sulfoxide or ethanol for stock solutions , and were diluted to an appropriate concentration in culture media . Female Swiss Webster mice infected with S . mansoni ( NMRI strain ) were provided by the Schistosomiasis Resource Center for distribution by BEI Resources , NIAID , NIH ( S . mansoni , Strain NMRI - exposed Swiss Webster mice , NR-21963 ) . Worms were perfused as described [71] at 6–7 weeks post-infection to obtain adult worms , or at 3–4 weeks post-infection to obtain juvenile worms . Perfused worms were maintained in schistosome medium [RPMI ( Life Technologies , Grand Island , NY ) plus 10% FBS ( Sigma-Aldrich ) and 1% penicillin/streptomycin ( Sigma-Aldrich ) ] at 37°C and 5% CO2 . For the drug treatments , 2–3 worm pairs per well were incubated overnight ( up to 24 h ) in a 12-well plate in schistosome medium with either 500 nM ( adult ) or 2 µM ( juvenile ) PZQ , in the presence or absence of inhibitors of ABC transporters . Following the drug treatments , worms were washed once with culture media and maintained and monitored for an additional 48 to 72 h in medium alone for the phenotypic analysis . In addition to testing ABC transporter inhibitors individually , we also assessed the effects of three combinations of inhibitors that would target multiple classes of the transporters . Combination A contained the Pgp ( ABCB1 ) inhibitor [72] zosuqidar ( 10 µM ) , the BCRP ( ABCG2 ) inhibitor [73] Ko143 ( 10 µM ) , and the MRP1 ( ABCC1 ) inhibitor [30] MK 571 ( 25 µM ) . Combination B contained the Pgp/BCRP inhibitor [65] , [66] elacridar ( 10 µM ) and the MRP1 inhibitor [74] Reversan ( 20 µM ) . Combination C contained MK 571 ( 25 µM ) and the Pgp inhibitor [75] , [76] dexverapamil ( 20 µM ) . Dexverapamil is an enantiomer of verapamil with significantly less activity than the S ( − ) enantiomer against calcium channels , but which retains potent and selective competitive inhibitory activity against Pgp [77] . Control experiments contained carrier without drugs . There was some variation between different worm batches in their sensitivity to PZQ; in our experiments , we analyzed only those sets of schistosomes in which the large majority of worms remained motile following exposure to 500 nM PZQ . Fluorescent ( R ) -PZQ-BODIPY was synthesized and purified as described previously [21] , [78] , and dissolved in DMSO . Though ( R ) -PZQ-BODIPY is active against schistosomes , it is far less potent ( ∼100-fold ) than ( R ) -PZQ [78] . This reduced activity allowed us to use the probe without confounding effects from drug-induced contraction or paralysis . For testing of ABC transporter inhibitors , adult schistosomes were incubated overnight in schistosome medium plus 1 µM ( R ) -PZQ-BODIPY , in the presence or absence of the Pgp inhibitor dexverapamil ( 20 µM ) or the MRP-1 inhibitor MK 571 ( 20 µM ) . Controls included worms incubated in schistosome medium alone ( plus DMSO carrier ) , and worms incubated in transporter inhibitors alone . Indeed , this second control showed that incubation of schistosomes in tariquidar produced changes in worm fluorescence on its own , precluding the use of that compound in further experiments . Following the initial incubation , the medium was removed , and , following three washes , was replaced with schistosome medium without the ( R ) -PZQ-BODIPY , but still containing the transporter inhibitors that were included in the initial incubation . Fluorescence of live worms was observed at different time points following removal of the BODIPY-PZQ . Following 3 h of incubation , worms were fixed in 10% NBF ( Sigma , St . Louis , MO ) , and photographed at identical exposures with a QImaging Qicam Fast 1394 digital camera on a Leica DMI3000B fluorescent microscope . For testing the effects of ABC transporter knockdown , siRNA-treated worms were incubated in schistosome medium for 3 days following electroporation with siRNAs . They were then incubated overnight in ( R ) -PZQ-BODIPY and processed in the same manner as the worms exposed to ABC transporter inhibitors . For quantitation of fluorescence in the inhibitor-exposed worms , two regions of the worm were defined , and fluorescence intensity within corresponding regions of each worm was measured using Image J [79] . Data from both regions were essentially identical; the data presented in Fig . 4 are from one of these regions , slightly posterior to the oral sucker . We also examined fluorescence in worms incubated with 1 µM CellTracker Green BODIPY ( Life Technologies , Inc . ) , a membrane-permeable form of BODIPY that is modified intracellularly to a membrane-impermeable form . For quantitation of fluorescence in worms subjected to RNAi ( see below ) , the integrated intensity corresponding to the entire worm was measured using Image J and background intensity subtracted [80] . Plotted values are normalized to the mean control values . We also attempted to test the effects on retention of rhodamine-123 , a fluorescent Pgp substrate , but control worms showed inconsistent results , and these experiments were not pursued further . Knockdown of RNAs encoding SMDR2 ( NCBI Acc . #L26287 ) , SmMRP1 ( NCBI Acc . #GU967672 ) , ABCA4 ( Smp_056290 ) , ABCB6 ( Smp_134890 ) , and MRP7/ABCC10 ( Smp_147250 ) was as described [47] , [81] . Briefly , following an overnight incubation in schistosome medium , adult worms ( 5 males plus 5 females ) were placed in a 0 . 4 cm electroporation cuvette ( USA Scientific , Ocala , FL ) containing 50 µl siPORT ( Life Technologies , Grand Island , NY ) plus 3 µg of each of the siRNAs ( IDT , Coralville , IA ) , either singly or in combination , targeting SMDR2 , SmMRP1 , MRP7 , ABCA4 , and ABCB6 , or up to 15 µg luciferase siRNA ( Life Technologies , Grand Island , NY; 3 µg per experimental siRNA used ) . The luciferase siRNA used for our control shows no significant similarity to any sequences from the S . mansoni gene database . siRNAs against the S . mansoni transporters were designed using the IDT SciTools RNAi Design server; sequences and targets are listed in Table 1 . Worms were electroporated in this solution with a 20 ms square-wave pulse of 125 volts ( BioRad Pulser XCell ) . Following electroporation , worms were incubated in schistosome medium for 2 days . They were then sorted into 2–3 males/female pairs per well in a 12-well plate , in which they were subsequently incubated in schistosome medium with carrier alone , or in medium plus PZQ ( 800 nM ) , as described above , and subsequently analyzed for motility . Schistosomes were observed using a Leica stereomicroscope and digitally recorded ( QImaging Qicam Fast 1394 ) . These video recordings were then used to analyze motility of individual worms over 5- or 10-second time spans using MaxTraq-Lite+ motion analysis software ( Innovision Systems , Columbiaville , MO ) . We used the change in distance between the head and tail of individual worms as an estimate of motility ( as the worm moves , this distance will change ) . Head-tail distance was measured every 0 . 3 s by calculating the distance between digitized markers placed manually at the same points on the worm in each frame analyzed . The amount by which the measured distance changed from its previous value was calculated for each measurement , and the average change in head-tail distance was then averaged for each worm and normalized to control values . To correct for digitizing errors , the same process was used for dead worms , and the measured average motility ( which theoretically should be 0 ) was subtracted as “noise” from the experimental values . Other measures of motility ( changes in angle between anterior and posterior ends of the worm or edge-based tracking by adapting algorithms in the program Cell Track [82] ) produced similar results ( not shown ) . Based on controls using 20 µM PZQ ( to completely paralyze the worms; Fig . S4 ) or dead worms , it is clear that this method is valid for measuring inhibition of activity . On the other hand , it does not appear to be as effective in registering hyperactivity ( see Fig . S4 ) , in which evaluating changes in head-tail distance may not account for increases in spasticity , twitching , or peristalsis , none of which would be likely to change head-tail distance . In the experiments here , we measured only decreases in motility . We found no significant differences in motility between male and female worms under any of the test conditions; therefore all data presented combine results for both sexes . For all experiments , several independent wells ( ≥3 ) were tested for each condition . All experiments were also repeated with worms perfused from at least two independent batches of infected mice at different times . Motility of individual worms was assessed ( numbers of worms tested are provided in the figure legends ) , and analyzed as described . Total RNA was extracted as described [44] , using RNAqueous-4-PCR ( Life Technologies ) and subsequently treated with Turbo-DNAase ( Life Technologies ) according to the manufacturer's instructions . Two step qRT-PCR was employed to measure RNAi knockdown . First-Strand cDNA was synthesized using the SMARTer cDNA synthesis kit ( Clontech , Mt . View , CA ) and amplified using the Brilliant II SYBR green qRT-PCR Master kit ( Agilent Technologies , Santa Clara , CA ) on an Applied Biosystems 3500 Genetic Analyzer according to the manufacturer's recommendations ( omitting the cDNA synthesis step ) . Primers used for the amplification of SMDR2 , SmMRP1 and 18S ribosomal RNA have been described previously [43] , [44] . Primers used for amplification of ABCA4 ( Smp_056290 ) were: ABCA4-Sy1 ( 5′- GGGTGGTATGACAACAGCAA -3′ ) and ABCA4-Sy2 ( 5′- GAGCTGAAATTGGCCCTCTA -3′ ) . Primers used for amplification of ABCB6 ( Smp_134890 ) were: ABCB6-Sy1 ( 5′- TGCTATTGCCGCTGACATAC -3′ ) and ABCB6-Sy2 ( 5′- CCAATGCTGATGTAGCTTCG -3′ ) . Primers used for amplification of MRP7 ( Smp_147250 ) were: MRP7-Sy1 ( 5′- AGCTGGTGGGAGCAGTCTTA -3′ ) and MRP7-Sy2 ( 5′- ATCCAACTGGTGTGTGACGA -3′ ) . Data were analyzed using the 2−ΔΔCt method [83] to determine the relative expression ratio between target ( transporters ) and reference genes ( 18S RNA ) . Adult male schistosomes were fixed overnight with 10% NBF , and morphological changes were examined on a Leica bright field microscope using a 20× objective . Images were acquired on a QICAM ( QImaging , Surrey , BC ) digital camera in an equivalent area near the oral sucker region , to establish consistency of observation for all the samples . Images were analyzed with QCapture Pro 7 software ( QImaging ) or commercial image processing software ( eg , Canvas , ACD Systems ) . Data were analyzed with GraphPad Prism or Excel , expressed as arithmetic means ± SEM , and tested for statistical significance using statistical tests noted in the figure legends . | Schistosomes are parasitic flatworms that cause schistosomiasis , a tropical disease affecting hundreds of millions worldwide . Praziquantel ( PZQ ) is the current drug of choice against schistosomiasis , and , indeed , is the only approved antischistosomal treatment available in most parts of the world . Though effective overall , PZQ has limitations , including its lack of activity against immature schistosomes . Furthermore , reported cure rates in the field are often below optimal levels , and there is increasing evidence that schistosomes can become resistant to the drug . ABC transporters such as P-glycoprotein are efflux transporters that mediate detoxification of cells via removal of toxins and xenobiotics , including drugs . They underlie multidrug resistance in mammalian cells , and are also associated with drug resistance in parasitic worms , including schistosomes . Here , we show that compounds that inhibit these efflux transporters potentiate the activity of PZQ against schistosomes , including normally PZQ-insensitive juvenile worms . Similarly , suppressing expression of these transporters also increases adult worm responsiveness to PZQ . Our experiments may provide insights into the role of these drug transporters in PZQ action , and could also translate into new therapeutic strategies for augmenting treatment of schistosome infections and overcoming drug resistance . | [
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| 2014 | Inhibition or Knockdown of ABC Transporters Enhances Susceptibility of Adult and Juvenile Schistosomes to Praziquantel |
A century after the discovery of Trypanosoma cruzi in a child living in Lassance , Minas Gerais , Brazil in 1909 , many uncertainties remain with respect to factors determining the pathogenesis of Chagas disease ( CD ) . Herein , we simultaneously investigate the contribution of both host and parasite factors during acute phase of infection in BALB/c mice infected with the JG and/or CL Brener T . cruzi strains . JG single infected mice presented reduced parasitemia and heart parasitism , no mortality , levels of pro-inflammatory mediators ( TNF-α , CCL2 , IL-6 and IFN-γ ) similar to those found among naïve animals and no clinical manifestations of disease . On the other hand , CL Brener single infected mice presented higher parasitemia and heart parasitism , as well as an increased systemic release of pro-inflammatory mediators and higher mortality probably due to a toxic shock-like systemic inflammatory response . Interestingly , coinfection with JG and CL Brener strains resulted in intermediate parasitemia , heart parasitism and mortality . This was accompanied by an increase in the systemic release of IL-10 with a parallel increase in the number of MAC-3+ and CD4+ T spleen cells expressing IL-10 . Therefore , the endogenous production of IL-10 elicited by coinfection seems to be crucial to counterregulate the potentially lethal effects triggered by systemic release of pro-inflammatory mediators induced by CL Brener single infection . In conclusion , our results suggest that the composition of the infecting parasite population plays a role in the host response to T . cruzi in determining the severity of the disease in experimentally infected BALB/c mice . The combination of JG and CL Brener was able to trigger both protective inflammatory immunity and regulatory immune mechanisms that attenuate damage caused by inflammation and disease severity in BALB/c mice .
Chagas disease ( CD ) , a life-long complex illness caused by the protozoan parasite Trypanosoma cruzi , was firstly described by Carlos Chagas in 1909 , but it is still acknowledged by the World Health Organization ( WHO ) as one of the most important neglected tropical diseases and as a significant public health problem in Central and South America [1] . T . cruzi is transmitted to humans and other susceptible hosts mainly through contact with the feces of infected blood-feeding triatomines , but alternative routes such as blood transfusion , organ transplant , vertical transmission ( congenital ) or ingestion of contaminated food ( oral transmission ) are presently more important in the current context of CD . Despite one century of research , the most intriguing challenge to understanding the physiopathology of CD still lies in the complex host-parasite interrelationship . From the clinical point of view , T . cruzi infections progress in two phases . Patent parasitemia and parasitism in a wide variety of host cells characterize the acute phase of disease . This phase normally passes unnoticed because the signs and symptoms are similar to those of most common infections: fever , swollen lymph nodes , hepato- and/or splenomegaly . Sterile immunity is rarely achieved after T . cruzi infection , and most of the patients that survive the acute phase remain in a life-long asymptomatic state ( indeterminate form ) during the chronic phase of infection . However , a significant percentage of these patients ( about 40% ) develop deadly clinical forms of the disease up to 20 years after the first contact with the parasite , as a result of progressive tissue damage mainly involving the esophagus , colon and/or heart . On average , 5–10% of the T . cruzi infected individuals develop the digestive form of the disease and 30–40% develop cardiomyopathy ( cardiac form ) , the most severe clinical manifestation of CD . The associated cardio-digestive form is observed in 2–3% of the patients [2] . The severity and prevalence of the different clinical forms of CD vary among different regions [2] , but the cause of this clinical and epidemiological heterogeneity is a puzzling and yet unresolved question . Despite many uncertainties , it is more and more clear that the pathogenesis of CD is very complex and is a multifactorial trait influenced by several factors related to the parasite , the host and maybe also the environment [3]–[10] . Concerning to parasite related factors , there is extensive and well-characterized intraspecific genetic diversity in T . cruzi , which has been demonstrated by different biological , biochemical and molecular approaches [11] . The coexistence of mixed infections in vertebrate and invertebrate hosts has also been demonstrated in natural situations [12]–[14] and this certainly plays an important role in the context of the pathogenesis of CD . For instance , distinct parasite populations have been found in different tissues ( blood , esophagus and heart ) of the same chronically infected patients [14] , [15] , suggesting that specific tissue tropism of the parasite is one of the major factors determining the pathology of this illness . Similarly , T . cruzi genetic variability was shown to be an important factor influencing tissue tropism and pathogenesis in BALB/c mice double-infected with an artificial mixture of JG ( T . cruzi II ) and Col1 . 7G2 ( T . cruzi I ) monoclonal populations [3] . A clear difference in tissue tropism was observed after three months post-infection: the Co1 . 7G2 clone predominated over the JG strain in the rectum , diaphragm , esophagus , and blood , while a striking amount of the JG strain was observed in the heart muscles of coinfected mice . Intriguing results were also observed by Franco et al . ( 2003 ) in studying the effects of coinfection with two T . cruzi populations exhibiting opposing virulence and pathogenicity in Holtzman rats: the CL Brener ( T . cruzi VI ) clone , which induces severe and diffuse myocarditis with high mortality , and the JG strain , which causes moderate acute myocarditis with no mortality . Although less virulent when compared to CL Brener in single infections , the JG strain was the only parasite detected in the rat tissues at the end of the acute phase of the double infection , in contrast to the results observed in the single infection protocols [8] . Concerning to host related factors , it is also well accepted that genetic polymorphisms associated with the host's immune response have an essential role in determining the course of T . cruzi infection . In fact , there is evidence that changes in cytokine expression patterns during the course of infection play an important role in the disease outcome [7] . For instance , in vitro exposure to T . cruzi trypomastigotes induces higher expression of IL-10 in monocytes isolated from indeterminate patients relative to cardiac patients , suggesting an immunological imbalance among patients with the cardiac clinical form of CD [16] . The lower expression of IL-10 among cardiac patients was associated with occurrence of a polymorphism in the promoter region of the IL-10 gene [5] . Furthermore , associations of polymorphisms in the genes for BAT-1 and NFκB with the development of cardiomiopathy were also described for CD in the Brazilian population [10] , [17] . Host genetic factors are also involved in determining parasite tissue tropism in experimental CD . Andrade et al . ( 2002 ) clearly demonstrated that the genetic background of mouse strains ( BALB/c , DBA-2 , C57BL/6 , and Swiss ) influences the differential tissue distribution of JG and Col1 . 7G2 populations in double-infected animals [4] . Subsequently , using congenic mice , Freitas et al . ( 2009 ) identified MHC-associated genes as those mainly involved in determining the differential tissue tropism of these two parasite populations [9] . In conclusion , there are many studies alternatively demonstrating the importance of parasite or host immune response factors influencing the pathogenesis of the CD , but the present work is probably the first one that simultaneously investigates the mechanism and the contribution of both parts . Herein , we assess the parasitemia , body weight evolution , survival rate , different hematological parameters , heart parasitism and histopathology , and heart differential tissue tropism . We also perform quantitative analyses of serum cytokines and nitric oxide as well as flow cytometry analyses of spleen cells during the acute phase of infection with the JG and/or CL Brener in BALB/c mice . We clearly demonstrate that coinfection with JG and CL Brener is able to trigger both protective inflammatory immunity and regulatory immune mechanisms that are capable of both attenuate damage caused by inflammation and disease severity induced by single infection with CL Brener in BALB/c mice .
Six to eight-week-old inbred male BALB/c mice , bred and maintained in the animal breeding units at the Instituto de Ciências Biológicas ( ICB/UFMG ) , or Centro de Pesquisas René Rachou/Fundação Oswaldo Cruz ( CPqRR/FIOCRUZ ) , both in BH/MG , Brazil , were used . We used two different T . cruzi populations: the JG strain ( T . cruzi II ) , which was isolated from a chagasic patient with mega esophagus , and the CL Brener clone ( T . cruzi VI ) , which was obtained from CL strain isolated from a Triatoma infestans specimen . Parasite major lineages were identified as recently recommended by an expert committee [18] . Both T . cruzi populations were maintained by intraperitoneal ( i . p . ) inoculation of infective blood trypomastigotes in Swiss mice . Prior genetic characterization of T . cruzi populations used in this work ( Table 1 ) was done by typing seven polymorphic microsatellite loci [19] , [20] , and the genes for 24Sα rDNA [21] and cytochrome oxidase subunit II ( COII ) [22] . For BALB/c mice infections , infective blood trypomastigotes were obtained from retroorbital plexus of JG or CL Brener infected Swiss mice . Both trypomastigote populations were counted and diluted in LIT medium . BALB/c mice were i . p . inoculated with 0 . 10 ml of a suspension containing 100 trypomastigotes of JG or CL Brener ( single infection ) , or a mixture of 50 trypomastigotes of each ( double infection ) . Age- and sex-matched non-infected BALB/c mice were used as controls . Experimental groups consisted of three , six or twelve mice . The experiments were repeated at least twice . Parasitemia was assessed by counting the bloodstream form of parasites in 5 . 0 µl of tail vein blood of JG and/or CL Brener infected mice , on alternate days from the 5th day p . i . until the time point that the parasites became undetectable [23] . Data were expressed as number of trypomastigotes per milliliter of blood . Survival was determined by daily inspection post-infection ( p . i . ) , and mice were weighed on alternate days to monitor the systemic repercussions during the course of infection . At 7 , 14 and 21 days post-infection , mice were bled from the axillary plexus under xylazine/ketamine anesthesia and peripheral blood ( PB ) was collected with anticoagulant for hematological analyses or without anticoagulant for serum cytokine and nitric oxide ( NO ) assays . The hematological parameters ( leukocytes , red blood cells , platelets , hematocrit and hemoglobin ) were determined using the ABX Micros ABC Vet automatic system ( Horiba ABX diagnostics , Montpellier , France ) . Differential leukocyte counts were determined under the oil immersion objective ( 100× ) , using standard morphological criteria , in peripheral blood smears stained with May-Grünwald-Giemsa , and the absolute number of each leukocyte subtype per ml of PB was determined . To compare the effects of JG and/or CL Brener infection on influx of inflammatory cells in the heart , we analyzed the intensity of myocarditis morphometrically . For this experiment , animals were euthanized by cervical displacement , and the hearts were removed and sliced transversally at 7 , 14 and 21 days p . i . The apical half of each heart was washed in phosphate-buffered saline ( PBS ) and stored in absolute ethanol at 4°C for PCR assays; the heart bases were fixed in 4% phosphate-buffered formaldehyde and used for histopathology . After 24 hours of fixation , the tissues were paraffin-embedded , and three 5-µm thick , semi-consecutive sections were obtained and stained by hematoxylin-eosin ( H&E ) . Heart inflammation was assessed in the left ventricle free wall . For quantitative analyses , ten fields from each of the three semi-consecutive sections were randomly captured with the 40× objective , corresponding to a total myocardium area of 234 . 376 µm2 . Images were captured at a resolution of 1392×1040 pixels with a Cool SNAP-Pro cf Collor microcamera ( Media Cybernetics , Bethesda , MD , EUA ) and transferred to a computer using Image-Pro Express version 4 . 0 software for Windows ( Media Cybernetics ) . After proper calibration , captured images were analyzed with KS300 software ( Zeiss , Jena , Germany ) . The nucleus area from each cell presented in the analyzed fields was digitalized and automatically measured in µm2 . The results were expressed by nucleus area/total area ratio . Heart parasitism was evaluated by counting the number of parasite nests in three semi-consecutive sections as visualized by light microscopy with a 40× objective . PCR was additionally performed in parallel samples . Detection of parasites in heart tissue samples was performed by specific PCR amplification of a fragment of about 330 bp from variable regions of minicircle kinetoplast DNA ( kDNA ) molecules of T . cruzi , as previously described [3] with some modifications . Tissue samples , stored in absolute ethanol , were fragmented and submitted to alkaline lysis , as follows: the fragmented samples were boiled in the presence of 50 mM NaOH for 10 min , and after neutralization with 130 mM Tris-HCl pH 7 , samples were 10-fold diluted in sterile Milli-Q water and used as the DNA template for PCR . Samples from uninfected BALB/c mice were used as a negative control . PCR was carried out in a final volume of 20 µl containing 1 . 5 mM MgCl2 , Green GoTaq Reaction Buffer pH 8 . 5 ( Promega , Madison , Wisconsin , USA ) , dNTPs at 250 µM , primers ( S35: 5′-AAATAATGTACGGGKGAGATGCATGA-3′ and S36: 5′-GGTTCGATTGGGGTTGGTGTAATATA-3′ ) at 1 . 0 µM , 1 . 0 U of GoTaq DNA Polymerase ( Promega ) and 1 . 0 µl of 10-fold diluted alkaline lysis products . Amplification was performed in a PT100 thermocycler ( MJ Research ) using an initial denaturation step at 94°C for 5 min followed by 35 amplification cycles including an annealing step at 60°C , extension at 72°C and denaturation at 94°C , each for 1 min . At the end , the extension step was extended to 10 min . The PCR products were visualized on a 6% polyacrylamide gel using silver staining , as previously described ( Santos et al . , 1993 ) . Differential tissue tropism of both T . cruzi populations was assessed by analyzing the LSSP-PCR profiles and one of the previously typed polymorphic microsatellite loci ( TcAAAT6 ) in positive tissue samples from double-infected mice . The relative proportions of JG and CL Brener in the positive heart tissue samples obtained from double-infected mice were estimated using the LSSP-PCR assay , as previously described [3] with some modifications . For this , kDNA amplicons were subjected to electrophoresis on an ethidium bromide stained 1 . 5% agarose gel ( 1 . 0% agarose , 0 . 5% low melting point agarose ) at 100 V for 1 h 30 min . The DNA bands corresponding to the 330-bp amplicons from variable regions of T . cruzi kDNA minicircles were purified from the gel , diluted 10-fold in sterile Milli-Q water , and subjected to a second PCR assay at low stringency , using a single fluorescent primer . The PCR was carried out in a final volume of 10 µl containing 1 . 5 mM MgCl2 , Colorless GoTaq Reaction Buffer pH 8 . 5 ( Promega ) , dNTPs at 50 µM , fluorescent primer ( S35G*: 5′-Fluorescein ATGTACGGGGGAGATGCATGA-3′ ) at 4 . 5 µM , 1 . 6 U of GoTaq DNA Polymerase ( Promega ) and 1 . 0 µl of a solution containing the ∼330-bp DNA fragments prepared as described above . Amplification was performed in a PT100 thermocycler ( MJ Research ) as follows: an initial denaturation step at 94°C for 5 min , followed by 40 amplification cycles with the annealing step at 30°C , extension at 72°C , and denaturation at 94°C , each for 1 min . The final extension step was extended to 10 min . To determine the DNA fragment sizes , the LSSP-PCR products were analyzed by 6% polyacrylamide gel electrophoresis under denaturing conditions ( 8 M urea ) in an Automatic Laser Fluorescent ( ALF ) sequencer ( GE Healthcare , Milwaukee , Wisconsin , USA ) followed by data analysis using the Allelelocator software ( GE Healthcare ) . Areas under specific peaks from JG and CL Brener curves were used to estimate the relative proportions of each population in reference to a standard curve , as previously described [3] . Briefly , genomic DNA samples from JG and CL Brener were mixed in different proportions ( JG/CL Brener: 9/1 ( lane 2 ) , 3/1 ( lane 3 ) , 1/1 ( lane 4 ) , 1/3 ( lane 5 ) and 1/9 ( lane 6 ) ) , and subjected to PCR assays . Fluorescent products of PCR were loaded into a 6% polyacrylamide gel under denaturing conditions in an automated DNA sequencer . The proportions of the sum of areas under specific peaks ( Figure 1A ) of each population were used to construct a standard curve . The standard curve was obtained using GraphPad Prism 5 . 00 software ( GraphPad Software , San Diego , California , USA ) by point-to-point analysis without the choice of any specific model . We used 96 points calculated with the x values ( relative proportion of JG/CL Brener ) ranging from 0 . 0 to 0 . 9 for building the standard curve ( data not shown ) . The relative proportions of JG and CL Brener in the hearts of the double infected mice were also assayed by genotyping the TcAAAT6 microsatellite locus . To achieve that , a full nested PCR protocol was used , as previously described [20] with some modifications . Briefly , PCR was performed in a final volume of 15 µl containing 10 mM Tris-HCl pH 9 . 0 , 50 mM KCl , 0 . 1% Triton X-100 ( Buffer B , Promega ) , 2 . 5 mM MgCl2 ( Promega ) , 0 . 5 U of Taq DNA Polymerase ( Promega ) , dNTPs at 250 µM , primers ( TcAAAT6ex-forward 5′-ACGCACTCTCTTTGTTAACAG-3′ and TcAAAT6ex-reverse 5′-CCGACAACGATGACAGCAAT-3′ ) at 0 . 3 µM and 1 . 0 µl of DNA template ( 10-fold diluted alkaline lysis products ) . Amplification was performed in a PT100 thermocycler ( MJ Research ) using the step-down protocol modified for amplification of T . cruzi DNA as follows: an initial denaturation step at 94°C for 5 min; annealing at 58°C for 30 s; extension at 72°C for 1 min and a denaturation step at 94°C for 30 s . After every five cycles , the annealing temperature was decreased by two degrees to 55 , 53 , 51 and finally 48°C . At this last temperature , the number of cycles was increased to 15 , followed by a final extension step at 72°C for 10 min . A second round of amplification was performed in same conditions described above but with inner primers ( TcAAAT6-forward 5′-FluoresceinGCCGTGTCCTAAAGAGCAAG-3′ and TcAAAT6-reverse 5′-GGTTTTAGGGCCTTTAGGTG-3′ ) . For the second PCR round , 10% of the amplified products obtained in the first PCR round were used as the DNA template . The determination of allele sizes was performed as described above . Areas under specific peaks from JG and CL Brener were used to estimate the relative proportions of each population by reference to a standard curve ( Figure 1B ) , as described above . For cytokine analysis , serum samples were collected as previously described and stored at −20°C until used . Cytokines ( IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-12p70 , IFN-γ , CCL2 and TNF-α ) were measured with BD CBA Mouse Cytokine assay kits according to the manufacturer's specifications ( BD Biosciences , CA , USA ) . Serum nitric oxide ( NO ) , an oxidation product of arginine by NO synthase , was measured as nitrite ( NO2− ) , the stable product of reactive nitrogen intermediates , at 7 , 14 and 21 days p . i . in samples collected as described above . Serum nitrite levels were assessed using the Griess reaction , after deproteination of samples with 1 M ZnCl2 . Nitrite concentrations were determined by extrapolation from a standard curve constructed using various concentrations of sodium nitrite ( NaNO2− ) , and the results were expressed in µM . Spleen samples were collected at 7 , 14 and 21 days p . i . in RPMI-1640 ( GIBCO , Grand Island , NY , USA ) . Spleen cell suspensions were prepared as previously described [24] and kept on ice . Cells were counted and incubated for 12 h at 37°C in a 5% CO2 humidified incubator and re-incubated again for more 4 h in the presence of 10 µg/ml brefeldin A ( BFA ) ( Sigma , St . Louis , MO , USA ) , in the same conditions . Cell samples were then treated with 2 . 0 mM ethylenediaminetetraacetic acid ( EDTA ) ( Sigma , St . Louis , MO , USA ) for 10 min at room temperature and washed once with FACS buffer ( PBS with 0 . 5% of bovine serum albumin ( BSA ) pH 7 . 4 ( Sigma , St . Louis , MO , USA ) . After washing , cells were incubated with undiluted rat anti-mouse ( anti-CD4 , anti-CD8 , anti-CD49b or anti-MAC-3 ) or hamster anti-mouse ( anti-CD69 ) monoclonal antibodies ( mAbs ) specific for different cell surface markers and labeled with fluorescein isothiocyanate ( FITC ) , phycoerythrin ( PE ) or peridinin chlorophyll-alpha protein ( PerCP ) , all purchased from BD Biosciences Pharmingen ( San Diego , CA , USA ) . Cell suspensions were homogenized and incubated for 30 min at room temperature in the dark . Cell surface-labeled samples were treated with FACS Lysing/fix Solution ( BD Pharmingen ) , immediately vortexed and incubated at room temperature for 3 min in the dark . After the lysis/fixation procedure , membrane-labeled spleen cells ( except for the samples incubated with anti-CD69-PE ) were permeabilized for 10 min with FACS permbuffer ( FACS buffer with 0 . 5% saponin , Sigma , St . Louis , MO , USA ) , washed and resuspended in FACS buffer containing the following antibodies: anti-IL-10 , anti-IL-12p70 , anti-IFN-γ or anti-TNF-α ( BD Biosciences Pharmingen , San Diego , CA , USA ) . After intracytoplasmic staining , cells were washed with FACS buffer and were fixed with FACS FIX Solution ( 10 g/L paraformaldehyde , 1% sodium cacodylate , 6 . 65 g/l sodium chloride , 0 . 01% sodium azide ) . Data acquisition was performed in a Becton-Dickinson FACScalibur flow cytometer ( BD Pharmingen , San Diego , CA , USA ) with CELLQuest software provided by the manufacturer . A total of 30 , 000 ( for only surface labeling ) or 50 , 000 ( for intracellular cytokines ) events per tube were acquired . Flow cytometry analyses were performed using CELLQuest software , and the absolute number of each spleen cell subtype per spleen was determined . All statistical analyses were performed using GraphPad Prism 5 . 00 ( GraphPad Software , San Diego , California , USA ) . The parameters studied , except survival analysis , were analyzed by One Way Analysis of Variance , and when differences between groups were verified , multiple comparisons were performed by the Student-Newman-Keuls' post-test . Survival analysis was carried out using the Kaplan-Meier method , and the significance of differences between groups was assessed using the logrank test . P-values of 0 . 05 or less were considered significant . The results were expressed as mean ± SEM .
BALB/c mice were infected with 100 trypomastigotes of JG or CL Brener ( single infection ) or coinfected with 100 trypomastigotes derived from a recently prepared mixture of both T . cruzi populations in a 1∶1 proportion via intraperitoneal route . The parasitemia levels , assessed from the 5th day p . i . to the time point that the parasites became undetectable , revealed that JG single infected mice presented lower parasitemia in relation to all other infected animal groups in spite of the day p . i . evaluated . In contrast , animals single-infected with CL Brener presented higher parasitemia , while mice coinfected with JG and CL Brener presented intermediate levels of parasitemia ( Figure 2A ) . This behavior cannot be explained by the simple effect of the relative reduction in the CL Brener inoculum from 100 trypomastigotes ( used in the single infections ) to 50 trypomastigotes ( used in the double infection ) , since mice single-infected with 50 trypomastigotes of CL Brener present similar parasitemia , symptoms and survival curve to those observed among mice single-infected with 100 trypomastigotes forms of CL Brener ( data not shown ) . Body weight loss was more significant among mice infected with CL Brener alone or in the presence or absence of JG in relation to other groups . This was especially noteworthy on the 21st day p . i . , when body weights among CL Brener single infected ( 22 . 8±1 . 4 g ) or coinfected ( 24 . 0±0 . 9 g ) mice were significantly lower in relation to naïve ( 27 . 0±0 . 4 g ) or JG single infected ( 29 . 0±0 . 5 g ) animals . However , the body weights of all infected mice that overcame the acute phase of disease returned close to those of naïve mice during the course of infection ( Figure 2B ) . Similar to the parasitemia and body weight loss levels , the mortality rate was null among naïve or single JG infected mice , higher among CL Brener single infected mice ( 75% ) and intermediary among the coinfected mice ( 55% ) . However , despite slight differences observed in the mean survival time among CL Brener single infected ( 24±3 days p . i . ) and coinfected ( 29±3 days p . i . ) mice , the survival curves of animals infected with CL Brener alone or in the presence of JG were not significantly different ( Figure 2C ) . Peripheral blood samples were analyzed to assess the effects of JG and/or CL Brener infection on hematological parameters . There was a significant reduction in global leukocyte numbers on days 7 and 14 p . i . among CL Brener infected mice , in the presence or absence of JG . Infection with JG alone led to reduction only on day 7 p . i . ( Figure 3A ) . Differential leukocyte counts also revealed variation among the groups of infected mice ( Figure 3B–F ) . A significant reduction in lymphocyte counts was observed for all groups , but the magnitude of lymphopenia was more intense among CL Brener single infected mice ( Figure 3B ) . Neutrophilia ( Figure 3C ) and bastonet neutrophilia ( Figure 3D ) were observed at day 21 p . i . among CL Brener infected animals . JG infected mice presented almost normal neutrophil and bastonet neutrophil counts , while coinfected animals presented intermediary counts ( Figure 3C and 3D ) . Significant eosinopenia was observed in all infected mice on 7th and 14th days p . i . , but returned to basal levels by 21 days p . i . ( Figure 3E ) . Regarding monocyte counts , significant reduction was only observed on the 14th day p . i among animals infected with CL Brener in the presence or absence of JG ( Figure 3F ) . Besides leukocyte amounts , the hemoglobin level , hematocrit and red blood cell concentration were also determined . A significant reduction in these parameters was only observed on the 21st day p . i . and only among animals infected with CL Brener in the presence or absence of JG ( Table 2 ) . Platelet counts were not significantly different among experimental groups during the course of infection ( data not shown ) . Parasite-induced cell destruction followed by focal inflammation usually correlates to tissue damage and heart malfunction . We evaluated heart inflammatory infiltrates and parasitism to assess the differential effects of infection with JG and/or CL Brener in heart tissue lesions at 7 , 14 and 21 days p . i . As expected , JG and/or CL Brener infected mice presented typical heart histopathological alterations of the acute phase of infection , such as inflammatory infiltrates predominantly constituted by mononuclear cells , edema and some degree of degenerative changes of the myocardium ( Figure 4 ) . At 7 days p . i . , the heart inflammatory response was more intense among JG infected mice in relation to animals infected with CL Brener or coinfected , and the inflammatory foci , when present , were small ( data not shown ) . At the 14th day p . i . , in all heart samples we noticed moderate inflammatory foci but we did not observe significant differences among infected mice ( data not shown ) . Parasite nests were not visible yet . At the 21st day p . i . , however , hearts from CL Brener infected mice presented more intense and diffuse inflammation in the ventricular and atrial walls when compared to JG infected mice , as well as when compared to coinfected animals ( Figure 4A–D and 4F ) . The acute myocarditis induced by JG was predominantly focal and more restricted to the epicardial face of the myocardium ( Figure 4B ) . Coinfected animals presented acute myocarditis of intermediary intensity ( Figure 4D ) when compared to JG or CL Brener single infected mice ( Figure 4B , 4C and 4F ) . Furthermore , heart tissue parasitism , evaluated by counting the number of parasite nests in three HE-stained semi-conservative sections , was significantly higher among CL Brener single infected mice in comparison to animals infected with JG only or coinfected . This last group presented an intermediary level of heart parasitism at 21 days p . i . ( Figure 4E ) . It is important to notice that the pattern of myocarditis induced by single infection with JG was rarely associated with tissue damage ( see detail in Figure 4B ) . Meanwhile , the pattern induced by CL Brener was more often associated with cardiomyocyte degeneration associated to disrupted nests of parasites ( see detail in Figure 4C ) than that observed in hearts from coinfected mice , ( see detail in Figure 4D ) . As expected in the acute phase of experimental T . cruzi infection , parasite kDNA was detected by PCR in heart tissue samples from all infected mice on the 14th and 21st days p . i . However , only scarce amounts of parasite kDNA were detected in a small number of animals on the 7th day p . i . ( data not shown ) . The differential tissue tropism of JG and CL Brener in heart tissue samples from coinfected animals was evaluated by analyzing both LSSP-PCR and polymorphic microsatellite locus profiles . LSSP-PCR profiles of heart tissue samples obtained from coinfected mice revealed the presence of JG-specific amplicons ( 229 , 233 and 258 bp ) in 66% and 100% of samples collected on the 14th and 21st days p . i . , respectively . The CL Brener-specific amplicon of 248 bp , in turn , was detected in 100% of samples collected on both the 14th and 21st days p . i . The relative amount of CL Brener/JG kDNA detected in the hearts analyzed varied from 76±9 to 77±8% in samples collected on the 14th and 21st days p . i . , respectively . Similar results were observed using TcAAAT6 microsatellite locus analysis . Heart tissue samples obtained from coinfected mice revealed the presence of JG-specific alleles ( 271 and 275 bp ) in 16% and 50% of samples collected on the 14th and 21st days p . i . , respectively . The CL Brener-specific allele ( 263 bp ) was detected in 100% of samples collected on both the 14th and 21st days p . i . The relative amount of CL Brener/JG kDNA detected in the heart samples analyzed varied from 97±3 to 93±3% in samples collected on the 14th and 21st days p . i . , respectively . To assess whether differences in the outcome of infection with the JG and/or CL Brener were associated with particular patterns of cytokine response , we determined the levels of cytokines ( IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-12p70 , IFN-γ , CCL2 and TNF-α ) and nitrite , a more stable NO-derived metabolite , in serum samples collected at 7 , 14 and 21 days p . i . A slight increase in serum levels of IL-2 ( naïve mice: 1 . 70±0 . 13 and CL Brener infected: 2 . 18*±0 . 11 , mean ± SEM , *P<0 . 05 ) and IL-5 ( naïve mice: 2 . 70±0 . 45 and CL Brener infected mice: 5 . 08*±0 . 38 , mean ± SEM , *P<0 . 05 ) was detected only among CL Brener infected mice in relation to naïve mice and only on the 14th day p . i . The biological significance of these small variations is unclear . Although measurable amounts of IL-4 and IL-12p70 were detected in serum samples from all experimental groups at all timepoints analyzed , no difference among groups was found ( data not shown ) . Different patterns were observed , however , for all other measured cytokines . There were significant differences in serum levels of pro-inflammatory cytokines such as TNF-α , CCL2 , IL-6 and IFN-γ at 14 and 21 days p . i . among CL Brener infected or coinfected mice in relation to naïve animals or JG single infected mice . It is noteworthy that IL-10 levels were maintained close to the basal level among infected animals on the 7th and 21st days p . i . , while pro-inflammatory cytokine levels ( TNF-α , CCL2 , IL-6 and IFN-γ ) presented a great variation among different experimental groups during the acute phase of infection ( Figure 5A–E ) . Since cytokines act in a network of mutual interactions in vivo , ratios of pro-inflammatory cytokines ( TNF-α , CCL2 and IFN-γ ) to the immunoregulatory cytokine IL-10 were analyzed . The ratios of serum TNF-α , CCL2 or IFN-γ to serum IL-10 on the 7th , 14th and 21st days p . i . showed that animals infected with CL Brener or coinfected presented an increase in all TNF-α/ , CCL2/ or IFN-γ/IL-10 ratios in at least one of three points analyzed in relation to both naïve mice and JG infected animals . However , coinfected animals had significant reductions in the ratio of TNF-α/IL-10 at 14 days , and of CCL2/IL-10 at 21 days p . i . , suggesting a modulating role in the coinfection with JG and CL Brener in BALB/c mice ( Figure 6A–C ) . Regarding serum NO-derived metabolite levels , only NO2− was measured in the present work and no significant difference between experimental groups at 7 , 14 and 21 days p . i . was found ( data not shown ) . Since the cytokines measured can be produced by more than one cell type , we evaluated next which cell type could be the source of the pro-inflammatory cytokine TNF-α and the immunoregulatory cytokine IL-10 in the spleen . These two cytokines were chosen because they represent opposite points on the inflammatory spectrum of immune response; the ratio between them revealed a significant difference between single infection with CL Brener and coinfection with JG and CL Brener . Analysis of TNF-α-producing cells showed that MAC-3+ , NK , CD4+ and CD8+ T cells were important sources of this cytokine at days 14 and 21 p . i . for all infected mice ( Figure 7A and 7B; 8A and 8B; 9 ) . At day 21 p . i . , the number of TNF-α-producing MAC-3+ cells was reduced in mice infected with CL Brener or coinfected ( Figure 7A ) . However , the number of TNF-α-producing CD4+ and CD8+ T cells was augmented in coinfected and CL Brener infected mice , respectively ( Figure 8A and 8B ) . Interestingly , the number of IL-10-producing macrophages and CD4+ T cells showed a significant increase in coinfected mice at day 14 p . i . ( Figure 7C and 8C ) . In addition , no decrease in the number of these cell subpopulations was observed at any time point analyzed in coinfected mice .
The acute phase of CD is characterized by both high parasitemia and tissue parasitism , but most of the patients present few or no clinical symptoms in this phase of disease . Therefore , studies related to the early activation phase induced by natural T . cruzi infection in humans are scarce , and most information concerning parasite-associated features and host immunity related to T . cruzi infection is derived from studies using experimental models , in particular the murine model . Currently , we have a large amount of scientific information concerning the immune response during the early activation phase in animals acutely infected with T . cruzi , especially in BALB/c and C57BL/6 mice , which present different susceptibility to various intracellular pathogens , among them T . cruzi [25] , [26] . However , most of these studies are either restricted to single-infected mice or are focused on analyses of few parasitological or immunological parameters . Co-existence of natural mixed infections among humans certainly plays an important role in the context of CD , and the complex interrelationships between host- and parasite-related factors might ultimately influence the outcome of infection with T . cruzi . Herein , we investigated the effects of the association of JG and CL Brener during the acute phase of infection in BALB/c mice by simultaneously analyzing different parasitological , histopathological and immunological parameters . To better simulate natural infection conditions , inocula of 100 trypomastigotes of each parasite population ( JG or CL Brener ) or of a mixture of them ( JG and CL Brener ) were used . This inoculum is much smaller than those commonly used in the literature , which frequently reach 104 trypomastigotes or more per animal . Assessment of parasitemia and heart parasitism revealed great differences in parasite burden between JG and/or CL Brener infected mice . JG single infected mice presented lower parasitemia and heart parasitism compared to the CL Brener infection , which induced high parasitemia and heart parasitism , at least in the acute phase of infection . Animals coinfected with JG and CL Brener presented levels of parasitemia and parasitism at an intermediate level compared to those from JG or CL Brener single infected mice . The significant reduction in heart parasite nests observed in coinfected animals when compared to CL Brener infected ones correlates with a decrease in the number of inflammatory cells ( measured by nucleus area ) , suggesting that coinfected animals had a less intense inflammatory reaction in the heart . It is plausible that the reduction of these two important parameters contributes to the trend observed in the mortality curve showing improved survival of coinfected mice . Direct identification of parasite populations in heart tissue samples from double-infected mice revealed a relative predominance of CL Brener , varying from 50 to 100% in all analyzed tissues depending on the technique and on the time elapsed since infection considered . Although the percentage of JG detected was always lower than CL Brener , we observed a progressive increase in the presence of JG in the heart samples from double-infected animals throughout the acute phase of infection . The relative predominance of one of the parasite population in the hearts of the animals seems to correlate to the genetic aspects of the parasites and the hosts , rather than to the initial inoculum used , since similar results were observed using a mixture containing 50 or 100 parasites of each parasite population . In addition , previous studies have demonstrated that variation of one component of the parasite mixture or of the mouse genetic background , especially MHC-associated genes , can interfere in the relative predominance of a parasite population in different tissues , as well as in disease evolution [3] , [4] , [9] , [27] , [28] . The severity of disease induced by T . cruzi infection in BALB/c mice was measured by the assessment of body weight loss , heart tissue damage and mortality rate , and it corresponded to the parasite population involved . Absence of evident symptoms of disease , moderate acute myocarditis and null mortality were observed among JG single infected mice . On the other hand , CL Brener single infected mice presented gradual and progressive disease , characterized by anorexia , lethargy and cachexia , as well as severe acute myocarditis and a high mortality rate . Interestingly , coinfected animals presented symptoms similar to those presented by CL Brener infected mice , yet with lower magnitude . In addition , these animals presented less heart tissue damage , a reduced mortality rate and longer mean survival time compared to CL Brener single infected mice . It is interesting that Franco et al . ( 2003 ) , working with the same T . cruzi populations but a different host , observed similar results [8] . Regardless of the experimental model studied , the inflammatory response triggered by infection or tissue damage involves the coordinated recruitment of blood components ( plasma and leukocytes ) to the site of infection or injury . The relative and absolute numbers of peripheral blood cells are critically regulated in physiological conditions , and disruptions in this physiological balance can be clinically detected in several disease states . In accordance with this , we found considerable variations in blood leukocyte counts among T . cruzi infected animals . The leukopenia observed among infected mice during the early phase of infection is probably caused by an intense recruitment of leukocytes to the inflammatory sites , and the return of total leukocyte counts close to basal levels at 21 days p . i . was mainly related to a significant increase in neutrophil counts . More importantly , we observed a severe and persistent lymphopenia among CL Brener single infected or coinfected animals at 14 and 21 days p . i . , a condition that can be associated with both an immunosuppressive state and the high mortality rates observed among these animals during the course of infection . Marcondes et al . ( 2000 ) reported severe hematological alterations , characterized by pancytopenia and a low number of bone marrow blood cell precursors , in particular erythroblasts and megakaryoblasts , in mice infected with T . cruzi . Infection was accompanied by anemia , decrease in hematocrit and hemoglobin levels , as well as an exponential growth of parasites , and high mortality [29] . In the present study , we showed a significant anemia , with decreases in both number of red blood cells and hematocrit , as well as in hemoglobin levels among animals single infected with the CL Brener or coinfected at 21 days p . i . , which may contribute to high mortality rates among these mice . The lifespan of murine red blood cells is from 1 to 2 months; anemia was detected earlier than this . Therefore , reduced lifespan of red blood cells should be considered as an additional factor that contributes to anemia , which can influence survival of T . cruzi infected hosts . T . cruzi is capable of infecting a wide variety of host cells , but the persistence of this parasite in cardiac , skeletal and smooth muscle cells is , at least in part , a key aspect of both the chronic phase of the infection , as well as the outcome of disease . The first step to ensure T . cruzi survival and successful infection is to enter host cells . Several molecules present on host cells and on the parasite surface are essential for the process of cell invasion [30] and are capable of stimulating an innate immune response upon the first encounter [31]–[33] . These early interactions are critical for immediate control of parasitemia and parasitism , as well as for establishment of a cytokine-rich microenvironment that influences the generation and direction of the downstream adaptive immune response . With this in mind , we scrutinize the host response to T . cruzi infection through analysis of immunological parameters , such as serum cytokine and NO levels , as well as analysis of the expression profile of cytokines by several spleen cell subpopulations . High levels of serum TNF-α during the course of disease are usually associated with toxemia symptoms ( anorexia , lethargy and cachexia ) and high mortality rates . TNF-α , also known as cachectin , is produced primarily by mononuclear phagocytes ( monocytes and macrophages ) and acts as a multipotent modulator of immune responses; it is also a potent endogenous pyrogen , a well-known mediator of cachexia , and a marker of sepsis . Due to its multiple functions in immunological activity , TNF-α plays a critical role in several conditions that involve systemic inflammatory responses , such as sepsis and toxic shock [34] , [35] . In accordance with this , animals single-infected with JG presented serum TNF-α levels similar to those found in naïve mice , and no clear symptoms of disease . Hölscher et al . ( 2000 ) showed that the TNF-α neutralization not only attenuated disease progression , but also prolonged the survival of IL-10−/− mice infected with T . cruzi . Taking these findings together , it is reasonable to assume that TNF-α can be the direct mediator of mortality due to a toxic shock-like systemic inflammatory response observed among animals infected with CL Brener and , to a lesser extent , with both strains ( coinfected ) . At the same time , it is widely recognized that inflammatory responses have a critical role in protection against infection , though they may contribute to the pathology of it . Therefore , to avoid pathological side effects , the inflammatory reaction induced during immune responses must be tightly regulated . In tune with this , wild-type C57BL/6 mice infected with T . cruzi survived , but IL-10−/− mice with the same genetic background presented a high mortality rate , despite presenting low parasitemia levels and high systemic production of pro-inflammatory cytokines ( IFN-γ , IL-12 , and TNF-α ) during the acute phase of infection [34] , [36] . These findings show that IL-10 , an anti-inflammatory cytokine , has a critical role in control of the immune response during experimental T . cruzi infection . In this study , we observed that although coinfected animals presented high levels of serum TNF-α during the acute phase of infection , the potential toxic effects of TNF-α were counterbalanced by the production of significant serum levels of IL-10 . This resulted in a low and significant TNF-α/IL-10 ratio that may have contributed to the lower mortality rate and to the higher survival time observed among coinfected animals In fact , there are several reports on the immuno-modulatory role of IL-10 in infectious diseases including Chagas disease . In canine infection by T . cruzi , the development of chronic cardiomyopathy correlates with high levels of IFN-γ and TNF-α and low levels of IL-10 [37] . In human Chagas disease as well the presence of a polymorphic allele of IL-10 gene , which results in lower expression of this cytokine is associated with cardiomiopathy and a severe form of the disease [5] . Moreover , a study on cerebral malaria showed recently that coinfection of mice with non-lethal Plasmodium berghei XAT suppressed experimental cerebral malaria caused by infection with Plasmodium berghei ANKA . The modulatory effect of the coinfection was abolished in IL-10-deficient mice clearly showing the central role of IL-10 in inhibiting the inflammatory cytokines IFN-γ and TNF-α involved in brain damage [38] . In addition to the reduction in the ratio of TNF-α to IL-10 , we also found a significant decrease in the CCL2/IL-10 ratio in serum samples from animals infected with CL Brener in the presence of JG . CCL2 ( MCP-1 ) is another important pro-inflammatory mediator characterized as a monocyte-specific chemoattractant that also attracts NK cells and T lymphocytes . It is mainly produced by macrophages in response to a wide range of cytokines such as IL-6 , TNF-α and IL-1β , but can upon stimulation also be produced by a variety of cells , such as fibroblasts and endothelial cells . CCL2 is secreted in the course of T . cruzi infection and participates in T . cruzi uptake and activation of trypanocidal activity in macrophages . Paiva et al . ( 2009 ) showed that mononuclear cells from T . cruzi-infected CCL2−/− mice ( in contrast to WT mice ) do not form heart focal infiltrates . In this case , the parasite burden is greater , and tissue infiltrates are composed of less-activated CD8 lymphocytes and macrophages , which are essential to control parasite growth [39] . In the present study , we found high levels of CCL2 among mice single-infected with CL Brener , which can explain , at least in part , the intense myocarditis characterized by inflammatory infiltrate ( predominantly constituted of mononuclear cells ) observed among these animals . Reduction in the CCL2/IL-10 ratio in the mice may have also contributed to controlling the inflammatory reaction in the heart and the improved survival of the coinfected mice . Therefore , our results suggest that production of IL-10 , a key element in the control of tissue damage triggered by exacerbated inflammatory response during the course of infection , elicited by coinfection with JG and CL Brener may have an important role in modulation of heart inflammation and survival . Flow cytometry analysis of spleen cell subpopulations producing IL-10 revealed that frequency of IL-10-producing MAC-3+ and CD4+ T cells were both elevated in coinfected mice when compared to single-infected ones . In conclusion , our work reinforces that differential outcomes of T . cruzi infection can be influenced by the complexity of the infecting T . cruzi population and parasite load , as well as by factors related to regulation of acute inflammatory response that are essential for protection against infection , but may also contribute to pathology . | Chagas disease , a life-long parasitic disease caused by the flagellate protozoan Trypanosoma cruzi , was discovered a century ago by the Brazilian physician Carlos Chagas , and remains one of the most neglected tropical diseases , affecting 13 million people in Latin America . Disease is characterized by distinct clinical courses , varying from asymptomatic to severe forms with damage to heart and/or gastrointestinal tract . The causes of the different clinical manifestations are not completely understood , but they certainly involve both parasite and host features . In this study , the authors analyzed immune response of BALB/c mice to infection with two different T . cruzi populations . One of them ( JG ) caused low parasitism and low levels of pro-inflammatory mediators associated with no clinical manifestation of the disease . The other ( CL Brener ) caused severe disease , high mortality and high levels of pro-inflammatory mediators . The coinfection , however , triggered singular regulatory immune mechanism ( s ) that attenuated damage caused by inflammation and disease severity that are typical of the single infection with CL Brener . As mixed infection is naturally found in patients in endemic areas , these results can explain , at least in part , the complexity of the immune responses and consequently the various clinical manifestations of the disease . | [
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| 2010 | Coinfection with Different Trypanosoma cruzi Strains Interferes with the Host Immune Response to Infection |
The natural history and potential impact of mosquito-specific flaviviruses on the transmission efficiency of West Nile virus ( WNV ) is unknown . The objective of this study was to determine whether or not prior infection with Culex flavivirus ( CxFV ) Izabal altered the vector competence of Cx . quinquefasciatus Say for transmission of a co-circulating strain of West Nile virus ( WNV ) from Guatemala . CxFV-negative Culex quinquefasciatus and those infected with CxFV Izabal by intrathoracic inoculation were administered WNV-infectious blood meals . Infection , dissemination , and transmission of WNV were measured by plaque titration on Vero cells of individual mosquito bodies , legs , or saliva , respectively , two weeks following WNV exposure . Additional groups of Cx . quinquefasciatus were intrathoracically inoculated with WNV alone or WNV+CxFV Izabal simultaneously , and saliva collected nine days post inoculation . Growth of WNV in Aedes albopictus C6/36 cells or Cx . quinquefasciatus was not inhibited by prior infection with CxFV Izabal . There was no significant difference in the vector competence of Cx . quinquefasciatus for WNV between mosquitoes uninfected or infected with CxFV Izabal across multiple WNV blood meal titers and two colonies of Cx . quinquefasciatus ( p>0 . 05 ) . However , significantly more Cx . quinquefasciatus from Honduras that were co-inoculated simultaneously with both viruses transmitted WNV than those inoculated with WNV alone ( p = 0 . 0014 ) . Co-inoculated mosquitoes that transmitted WNV also contained CxFV in their saliva , whereas mosquitoes inoculated with CxFV alone did not contain virus in their saliva . In the sequential infection experiments , prior infection with CxFV Izabal had no significant impact on WNV replication , infection , dissemination , or transmission by Cx . quinquefasciatus , however WNV transmission was enhanced in the Honduras colony when mosquitoes were inoculated simultaneously with both viruses .
The majority of the >70 recognized flaviviruses ( family Flaviviridae , genus Flavivirus ) are arthropod-borne , and include some of the world's most historically- and medically-important viruses including Yellow fever ( YFV ) and the Dengue ( DENV ) viruses . Gaunt et al . [1] described four distinct evolutionary clades within the genus Flavivirus that correlated with geography , vector , and associated disease: tick-borne , Culex-borne , Aedes-borne , and no known vector . Basal to all of these groups was Cell fusing agent virus ( CFAV ) , an insect-only flavivirus discovered in an Aedes aegypti cell line more than 30 years ago [2] . Recently , a number of novel flaviviruses which cluster phylogenetically with CFAV have been isolated and identified from a diversity of field-collected mosquitoes and ticks around the world , including known arbovirus vectors . These arthropod-specific viruses collectively represent a unique clade of flaviviruses and include Ngoye virus from Rhipicephalus ticks in Senegal [3] , Kamiti River virus ( KRV ) , isolated from Aedes mcintoshi Huang in Kenya [4] , [5] , CFAV isolated from Ae . aegypti in Thailand and Puerto Rico [6] , [7] , Quang Binh virus from Culex tritaeniorhynchus Giles in Vietnam [8] , and Nounané virus from Uranotaenia mashonaensis Theobald in Côte d'Ivoire [9] . Additionally , many strains of Culex flavivirus ( CxFV ) have been isolated from Culex pipiens L . in Japan [10] , and North America [11] , Culex tarsalis Coquillett throughout the western United States and Canada [11]–[12] ( Bolling et al . , unpublished data ) , Cx . restuans Theobald from Texas [13] , and Cx . quinquefasciatus Say from Guatemala [14] , the Yucatan Peninsula [15] , Texas and Trinidad [13] . While there has been extensive genetic characterization of these viruses , the natural history and potential impact of mosquito-specific flaviviruses on the transmission efficiency of arboviruses of public health importance such as West Nile virus ( WNV ) remains unclear . Arbovirus superinfection in mosquitoes and mosquito cell culture has been previously studied [16]–[25] . Infection with one flavivirus has been shown to suppress infection and prevent transmission of a second , antigenically-similar flavivirus . This phenomenon was demonstrated for Japanese encephalitis virus and Murray Valley encephalitis ( MVE ) virus superinfections in Culex tritaeniorhynchus Giles [16] , two different strains of WNV in Culex pipiens form molestus Forskal [17] , and WNV and St . Louis encephalitis virus in Cx . quinquefasciatus [18] . Sabin [19] demonstrated that high doses of YFV administered to Ae . aegypti previously infected with DENV still resulted in transmission of YFV , although mosquitoes were less susceptible to secondary infection with YFV . Similar findings have been reported in cell culture , where homologous superinfections were inhibited but secondary infection with a heterologous virus was permitted [22]–[25] . Therefore , based on previous observations , a primary infection of mosquitoes with a mosquito-specific flavivirus has the potential to interfere with infection or transmission of WNV acquired secondarily . West Nile virus activity has been documented in Guatemala since 2003 , beginning with the detection of WNV seroconversions in horses [26] . Serological evidence of WNV transmission has since been found in wild birds and chickens ( Morales-Betoulle et al . , unpublished data ) and WNV has been isolated from several species of Culex ( Culex ) mosquitoes including Cx . quinquefasciatus ( Morales-Betoulle et al . , unpublished data ) . Culex quinquefasciatus is abundant in the urban WNV transmission focus comprising the city of Puerto Barrios , Guatemala , however , there has been little evidence of WNV-associated human disease in Guatemala or elsewhere in Latin America [27] . CxFV Izabal strain has also been found co-circulating with WNV in Cx . quinquefasciatus in Guatemala [14] . Minimum infection rates of CxFV in Cx . quinquefasciatus in Latin America were 20 . 8 per 1000 in Mexico [15] and 4 . 7 per 1000 in Guatemala [14] . Prevalence of CxFV Izabal in Cx . quinquefasciatus and the potential for this mosquito-specific flavivirus to disrupt WNV transmission is one of several hypotheses that have been proposed to explain the lack of human disease attributable to WNV in Latin America [15] . The objective of this study was to determine if prior infection with CxFV Izabal altered the vector competence of Cx . quinquefasciatus for transmission of WNV . The specific aims of this work were to: 1 ) compare replication kinetics of a Guatemalan isolate of WNV in CxFV Izabal – infected ( CxFV Izabal ( + ) ) and CxFV Izabal-uninfected ( CxFV Izabal ( − ) ) C6/36 cells and female Culex quinquefasciatus , 2 ) compare infection , dissemination and transmission rates of WNV in Cx . quinquefasciatus either infected or uninfected with CxFV Izabal , 3 ) determine whether WNV transmission by CxFV Izabal ( + ) Cx . quinquefasciatus is influenced by WNV blood meal titer , mosquito colony , simultaneous inoculation with CxFV , or inoculation with heat-inactivated CxFV . These data test the null hypothesis that there is no difference between the replication or transmission of WNV in CxFV Izabal ( + ) and CxFV Izabal ( − ) cells or mosquitoes
All CxFV experiments utilized CxFV Izabal isolate GU-06-2692 , passage 1 , isolated from a pool of Cx . quinquefasciatus in Puerto Barrios , Guatemala , 2006 [14] . West Nile virus isolate GU-06-2256 , passage 3 , also isolated from Cx . quinquefasciatus in Puerto Barrios , Guatemala , was used for flavivirus co-infection and vector competence studies . The growth of CxFV Izabal in cell culture was compared to that of KRV , strain SR-75 [4] , [5] . Aedes albopictus C6/36-ATCC cells ( American Type Culture Collection , Manassas , VA ) maintained at 28°C in were used for growth and plaque titration of CxFV Izabal , and Vero ( African green monkey kidney ) cells maintained at 37°C were used for WNV plaque titrations . Two strains of Cx . quinquefasciatus were used in this study . The Sebring colony was originally established from Florida in 1988 and has been in colony at the CDC in Fort Collins since 2004 . In an attempt to utilize viruses and vectors from the same geographic region , Cx . quinquefasciatus from Tegucigalpa , Honduras were colonized in September 2008 . Generations F5/6 and F12 of the Honduras colony were used in this study . Prior to use , both colonies were confirmed CxFV-negative by RT-PCR . CxFV Izabal was quantified from cell culture supernatant and homogenized mosquitoes by plaque titration on C6/36 cells [28] . Plaque assays were performed on C6/36 cell monolayers in 6-well plates using a double overlay method in nutrient media ( 5× Earle's BSS , 6 . 6% yeast extract-lactalbumin hydrolysate , 6% sodium bicarbonate , 4% FBS , and 0 . 4% gentamycin ) mixed 1∶1 with 2 . 6% low-melt Sea Plaque agarose . Second overlay containing neutral red was added at seven DPI . WNV was quantified by Vero cell plaque assay using the double overlay method [28] . Second overlay containing neutral red was added 2 DPI . CxFV Izabal viral RNA was also quantified by qRT-PCR . RNA extractions were performed using the QIAamp Viral RNA Mini Kit according to manufacturer's instructions ( Qiagen Inc . , Valencia , CA ) with an elution volume of 100 µl . Quantification of viral RNA from 10-fold dilutions of RNA extracted from 100 µl stock virus of known concentration was used for the qRT-PCR standard curve . Four qRT-PCR primer and probe sets were designed from NS5 and E gene regions of CxFV RNA using Primer Express software ( Applied Biosystems Inc , Foster City , CA ) ( Table 1 ) . The complete genome sequence of CxFV ( GenBank Accession number NC_008604 ) [10] and available RNA sequence from CxFV Izabal ( EU805805 , EU805806 ) [14] were used to select primers . CxFV Izabal primer and probe sensitivity and specificity were evaluated by sequence comparison to CFAV and CxFV ( Table 1 ) , and by testing each primer and probe set on a dilution series of available isolates of CxFV Izabal , WNV and KRV [4] , [5] . qRT-PCR assays were correlated to plaque titration on C6/36 cells . Ten-fold serial dilutions of CxFV Izabal were split such that RNA was extracted from 100 µl of each virus dilution for quantification by qRT-PCR , and the remaining sample was subjected to plaque titration on C6/36 cells as described above . RNA copies/mL determined for each virus concentration were plotted against the corresponding pfu/mL determined by C6/36 cell plaque titration . All animals were handled in strict accordance with the standards and policies of the Department of Health and Human Services' Office of Laboratory Animal Welfare ( OLAW ) and the US Department of Agriculture's Animal Welfare Act . All animal work was approved by the Centers for Disease control Institutional Animal Care and Use Committee , Protocol # 06-011 . Hyperimmune polyclonal antisera against CxFV Izabal was generated in Swiss-Webster mice . Twenty-five adult female mice were housed in groups of five animals per cage . Each of four groups of five mice was immunized intraperitoneally with 0 . 1 mL CxFV Izabal virus seed ( infected C6/36 cell , passage 1 , tissue culture supernatant ) diluted either 1∶10 or 1∶100 in Dulbecco's phosphate buffered saline ( PBS ) . Mice were administered boosters of the same virus stock and concentration 3 wks and 6 wks following the initial vaccination . The fifth group was sham-inoculated with 0 . 1 mL PBS . Hyperimmunized mice were bled out by cardiac puncture three weeks following the third immunization . Blood was collected directly into microtainer tubes and centrifuged for serum separation . Pooled and individual aliquots of hyperimmune sera were stored at −80°C . Because some antibodies in the sera were found to bind to C6/36 cells during immunofluorescence assay ( IFA ) , sera were 4× cross-adsorbed against sonicated C6/36 cells to remove C6/36-specific antibodies . Uninfected C6/36 cells in DMEM maintenance medium containing 2% FBS were harvested from a T25 flask and washed once with PBS . Washed cells were pelleted by centrifugation at 5000 rpm for 5 min at 4°C , and resuspended in 1 mL PBS . Aliquots of 50 µl cell suspension were transferred to 0 . 2 mL PCR tubes and sonicated for 5 min . Five hundred microliters of pooled sera was cross-adsorbed with 200 µl sonicated C6/36 cell suspension at room temperature for 1 . 5 hrs with continuous mixing . Cell debris was removed from adsorbed sera by centrifugation at 5000 rpm for 5 min . The supernatant was adsorbed three additional times as above using fresh sonicated cells . Clarified antiserum was stored at −20°C . Immunofluorescence assay using polyclonal mouse anti-CxFV serum was optimized using slides spotted with CxFV Izabal ( + ) and CxFV Izabal ( − ) C6/36 cells . To generate infected cells for spot slides , a T25 flask was inoculated with CxFV Izabal at a multiplicity of infection ( MOI ) of 0 . 1 . Virus was allowed to adsorb for 1 hour at 28°C in 1 mL DMEM with 2% FBS , rocking every 15 min . After one hour , the volume of medium was increased to 5 mL . Cells were harvested at 4 DPI , washed twice with cold PBS , and acetone-fixed to 12-well multispot slides for 10 min ( Thermo Electron Corp . , Pittsburgh , PA ) . Uninfected C6/36 cells were harvested and fixed to slides as negative control . Spot slides were incubated with serial 2-fold dilutions of polyclonal mouse anti-CxFV for 30 minutes at 37°C in a humid box . Slides were washed twice for 10 min in PBS and air dried . Slides were then incubated for 30 min at 37°C in a humid box with secondary antibody conjugate AlexaFluor 488 goat anti-mouse IgG H+L ( Invitrogen , Molecular Probes , Eugene , OR ) , diluted 1∶1000 in PBS with 0 . 08% trypan blue . Again , slides were washed twice with PBS , rinsed briefly with distilled water , and air dried . Cover slips were mounted using SlowFade Gold mounting medium ( Invitrogen , Molecular Probes , Eugene , OR ) and visualized with a Zeiss AxioImager Z1 ( Carl Zeiss Microimaging , Inc . , Thornwood , NY ) . For IFA on mosquito tissues , mosquitoes were dipped briefly in 70% EtOH to destroy hydrophobicity . Midguts and ovaries were dissected in PBS using fine forceps on a microscope slide . Dissected tissues were placed on poly-L-lysine-coated slides ( Polysciences , Inc . Warrington , PA ) and allowed to dry . Wells were drawn around each tissue using a TexPen plastic pen ( Mark-Tex Corp . , Englewood , NJ ) , and tissues were fixed in ice cold acetone for 10 min . Headsquashes were performed by squashing dissected heads directly onto clean spot slides with a coverslip and manually removing pieces of cuticle , followed by acetone fixation . Immunostaining of mosquito tissues was performed as described above . For staining of CxFV+WNV co-infected tissues , human anti-WNV IgG obtained from the CDC reference collection ( CDC , DVBID , Fort Collins , CO ) was used as a primary antibody in addition to the anti-CxFV serum . Both CxFV and WNV antisera were used at 1∶320 dilution . Secondary staining of co-infected tissues utilized AlexaFluor 594 goat anti-mouse IgG H+L and AlexaFluor 488 goat anti-human IgG H+L each diluted 1∶1000 in PBS/trypan blue . CxFV Izabal growth was measured in C6/36 cells and in Cx . quinquefasciatus . To measure virus growth in cell culture , C6/36 cells were inoculated with CxFV Izabal at an MOI of 0 . 03 or 0 . 1 . Virus was allowed to adsorb for one hour at 28°C in 1 mL DMEM containing 2% FBS . After one hour , cells were washed three times with PBS , and 5 mL cell culture maintenance medium was added . One flask of no-virus control was maintained simultaneously . Supernatant aliquots were harvested from each flask at 0 , 1 , 2 , 4 , 6 , 8 , 10 , 12 , and 14 DPI and stored at −80°C . Samples were clarified by centrifugation and titrated by C6/36 cell plaque assay as described above . For growth in vivo , groups of Cx . quinquefasciatus Sebring strain mosquitoes were infected with CxFV Izabal either by intrathoracic inoculation [29] or per os . For inoculations , approximately one week-old female Cx . quinquefasciatus were inoculated with 1 . 9 log10 pfu±1 . 6 log10 pfu CxFV Izabal . Mosquitoes were housed in screened paperboard pint containers held at 28°C and 95% relative humidity . Three to five mosquitoes were harvested on Days 0 , 2 , 4 , 8 , 12 , and 14 post inoculation . For virus infection per os , C6/36 cells were inoculated with CxFV Izabal at an MOI of 0 . 1 , as above . Virus-infected cell culture supernatant was harvested 4 DPI and clarified by centrifugation at 8000 rpm for 10 min at 4°C . The artificial blood meal contained two parts freshly-harvested , clarified CxFV Izabal in cell culture supernatant , two parts defibrinated chicken blood ( washed 3× in PBS ) , and one part FBS+10% sucrose , warmed to 37°C . Culex quinquefasciatus Sebring mosquitoes were allowed to feed for 30 min from a Hemotek feeder ( Discovery Workshops , Accrington , Lancashire , UK ) . All unfed and partially fed mosquitoes were removed and discarded . An aliquot of the infected blood meal was reserved and held at 37°C during the length of the feed , then stored at −80°C for titration . Three to five mosquitoes were harvested at 0 , 2 , 4 , 8 , 12 , and 14 DPI and processed as described above . The effect of CxFV Izabal infection on WNV growth in cell culture and in mosquitoes was also determined . In cell culture , C6/36 cells were inoculated as above with CxFV Izabal at an MOI of 0 . 1 . At 2 DPI , the supernatant was removed , cells were washed 3× with PBS and infected with WNV at an MOI of 0 . 1 . WNV was adsorbed for 1 hour at 28°C . Cells were washed with PBS and 5 mL DMEM maintenance medium replaced . Concurrently , a control flask uninfected with CxFV Izabal was inoculated with WNV in the same manner . An aliquot of supernatant was harvested from each flask on Days 0 , 2 , 4 , 6 , 8 , 10 , and 14 following WNV infection , and WNV titer determined by Vero cell plaque assay . For WNV growth in mosquitoes , Cx . quinquefasciatus Sebring mosquitoes were divided into three groups . The first group was inoculated intrathoracically with 3 . 3 log10 pfu CxFV Izabal , the second group was mock-inoculated with an empty glass capillary needle , and the third group was not inoculated . Seven days post inoculation , each group was administered a WNV-infectious blood meal of 6 . 3 log10 pfu/mL . Three to five mosquitoes were harvested on Days 0 , 2 , 4 , 8 , and 10 days post infection with WNV and processed as described above . For each time point , the average WNV titers in CxFV Izabal ( + ) , CxFV Izabal ( − ) and mock-inoculated groups were compared by 2-tailed pairwise Student's t-tests at the 5% significance level , assuming unequal variances . For each growth curve , mosquitoes were homogenized individually in 2 mL conical microcentrifuge tubes containing a single copper bb and 1 mL DMEM with 10% FBS . Mosquitoes were ground for 4 min at 20 cy/s on a mixer mill MM300 ( Retsch , Haan , Germany ) . Homogenates were clarified by centrifugation at 8 , 000 rpm for 10 minutes at 4°C . Supernatants were stored at −80°C until virus quantification . The ability of WNV to infect , disseminate , and be transmitted by Cx . quinquefasciatus infected with CxFV Izabal was evaluated across multiple WNV blood meal titers , routes of exposure to WNV , strains of Cx . quinquefasciatus mosquito , and prior infection with viable or inactivated CxFV Izabal . Artificial , infectious WNV blood meals were prepared as described above . In each experiment , one week-old Cx . quinquefasciatus were exposed to CxFV Izabal by intrathoracic inoculation with 2 . 8–3 . 3 log10 pfu seven days prior to receiving an artificial , WNV-infectious blood meal . Each CxFV Izabal ( − ) group was held , uninoculated , for one week and given the same WNV-infectious blood meal as the CxFV ( + ) group . In the first experiment , groups of Sebring strain Cx . quinquefasciatus , infected and uninfected with CxFV , received a WNV-infectious blood meal of 7 . 8 log10 pfu WNV per mL . In the second experiment , CxFV ( + ) and CxFV ( − ) Sebring and Honduras strain Cx . quinquefasciatus received WNV infectious blood meals of 8 . 9 log pfu per mL . In the third experiment , CxFV-positive and –negative Sebring and Honduras strain Cx . quinquefasciatus received a high titer ( 7 . 4–7 . 5 log pfu/mL ) or low titer ( 5 . 4–5 . 6 log pfu/mL ) WNV-infectious blood meal . Additional groups of mosquitoes were also inoculated with heat-inactivated ( 56°C for 45 min ) CxFV Izabal to determine whether or not actively-replicating virus was necessary for any observed interference with WNV transmission . For each WNV-infectious blood meal , an aliquot was reserved for plaque titration on Vero cells . Fully engorged mosquitoes were double-caged and held at 28°C at 95% relative humidity , and provided either 5% sucrose solution or raisins . At 14 days following the WNV-infectious blood meal , bodies , legs , and saliva were harvested from each live remaining specimen in each group and assayed for WNV by Vero cell plaque assay . Bodies and legs were each homogenized separately as described above . For saliva collections , specimens were knocked down by freezing at −20°C for 1 min , then , inside a glove box , wings were clipped off and the proboscis of each specimen was inserted into a capillary tube containing 5 µl Spectrosol immersion oil . After 20 min of salivation , specimens were removed from the capillary tube , and legs and bodies were separated into individual tubes . The tip of each capillary tube containing salivary expectorate was clipped off into a 1 . 7 mL microcentrifuge tube containing 450 µl DMEM with 10% FBS . Salivas were centrifuged for 5 minutes at 5000 rpm at 4°C to draw the oil out of the capillary tube , and stored at −80°C . The percentage of CxFV ( + ) and CxFV ( − ) mosquitoes that became infected , disseminated , and transmitted WNV were compared by Fisher exact test . The mean WNV titers in mosquito bodies and saliva 14 DPI between CxFV ( + ) and CxFV ( − ) experimental groups were compared by 2-tailed Student's t-tests assuming unequal variances . To further evaluate WNV transmission in Cx . quinquefasciatus with a known WNV-disseminated infection , groups of Cx . quinquefasciatus Sebring and Honduras strain were inoculated with either WNV only or inoculated simultaneously with CxFV Izabal and WNV . Sebring specimens were inoculated with either 4 . 0 log10 pfu WNV ( n = 66 ) or with 4 . 0 log10 pfu WNV+3 . 6 log10 pfu CxFV ( n = 27 ) per mosquito . Honduras specimens were inoculated with either 3 . 9 log10 pfu WNV ( n = 36 ) or 3 . 9 log10 pfu WNV+3 . 3 log10 pfu CxFV ( n = 53 ) per mosquito . Nine days post inoculation , saliva was collected from each specimen as described above , and bodies were stored whole at −80°C . Salivary expectorates were analyzed as above .
Four novel quantitative RT-PCR ( qRT-PCR ) primer and probe sets were designed to amplify CxFV Izabal ( Table 1 ) . No amplification was obtained from WNV or KRV with any of the primer/probe sets . Primer and probe sequences were aligned to available genome sequences for CxFV [10] and CFAV to further examine their specificity for CxFV Izabal ( Table 1 ) . Correlation between CxFV Izabal qRT-PCR and C6/36 plaque assays was >99% ( r = . 9992 ) . The equation for the trendline fit to the data was y = 2 . 47x , with the y-intercept fixed at zero ( Fig . 1 ) . Replication kinetics of CxFV Izabal were determined in C6/36 cells and in Cx . quinquefasciatus Sebring strain mosquitoes ( Figs . 2 , 3 ) . Replication of WNV was also monitored in CxFV Izabal ( + ) and ( − ) C6/36 cells , and in CxFV Izabal ( + ) and ( − ) Cx . quinquefasciatus Sebring ( Figs . 4 , 5 ) . In C6/36 cells , CxFV Izabal ( passage 1 ) reached a maximum titer of approximately 7 . 0 log10 plaque forming units ( pfu ) /mL six days following infection at either multiplicity of infection ( MOI ) of 0 . 03 or MOI = 0 . 1 , approximately one log less than that observed for KRV ( Fig . 2 ) . CxFV Izabal caused evident cytopathic effects ( CPE ) in C6/36 cells , completely destroying the cell monolayer by 8 days post infection ( DPI ) following inoculation at an MOI of 0 . 1 . In Cx . quinquefasciatus Sebring mosquitoes exposed to CxFV Izabal by intrathoracic inoculation , CxFV Izabal reached a peak titer of 4 . 3 log10 pfu/mosquito approximately 8 DPI . Mosquitoes were not susceptible to CxFV Izabal infection following oral exposure ( Fig . 3 ) . Growth of WNV was not inhibited by CxFV Izabal in either C6/36 cells or Cx . quinquefasciatus Sebring mosquitoes ( Figs 4 , 5 ) . WNV titers were not significantly different between CxFV Izabal ( + ) , CxFV Izabal ( − ) or mock-inoculated Cx . quinquefasciatus at 1 , 2 , 8 , or 10 days following per os infection with WNV ( p>0 . 05 ) ( Fig . 5 ) . At 0 DPI the average WNV titer in CxFV Izabal ( + ) mosquitoes was significantly less than in the mock-infected group ( p = 0 . 016 ) . It is unclear why this might be since mosquitoes were harvested immediately post-feeding and all three treatment groups imbibed the same WNV-infectious blood meal . At 4 DPI the average WNV titer in the CxFV Izabal ( + ) group was significantly higher than in the CxFV ( − ) mosquitoes ( p = 0 . 0048 ) ( Fig . 5 ) . The biological significance of this observation is unclear , as this difference disappeared at 8 and 10 DPI ( Fig 5 ) . It is probable that this difference between groups is an artifact of small sample sizes ( 3–5 mosquitoes per time point ) . The percentage of CxFV ( + ) and CxFV ( − ) Cx . quinquefasciatus that became infected , developed a disseminated infection , and transmitted WNV were not significantly different for either mosquito strain or any WNV blood meal titer examined ( Fisher Exact test , p>0 . 05 , Table 2 ) . Furthermore , WNV infection , dissemination , and transmission rates in mosquitoes inoculated with heat-inactivated CxFV Izabal did not differ significantly from those inoculated with live CxFV Izabal or uninfected with CxFV Izabal ( Table 2 ) . There was extensive variation in WNV body and saliva titers in each of these groups ( Table 3 ) . West Nile virus titers in mosquito bodies and salivary expectorates were not significantly different between CxFV Izabal ( + ) and CxFV Izabal ( − ) Cx . quinquefasciatus when mosquitoes were exposed orally to WNV ( Table 3 , Student's t-test , p>0 . 05 ) . One group of Sebring Cx . quinquefasciatus and one group of Honduras F12 Cx . quinquefasciatus failed to become infected ( Table 2 ) . We speculate that these results were not due to experimental treatment , but rather to small sample sizes and the relatively low WNV titer in those particular blood meals , potentially approaching a threshold of infection of approximately 5 log10 pfu WNV/mL [30] , [31] . A significantly higher percentage of Honduras Cx . quinquefasciatus transmitted WNV when co-inoculated simultaneously with CxFV Izabal ( 98% , n = 53 ) than when inoculated with WNV alone ( 69% , n = 36 ) ( p = 0 . 0014 , Fisher Exact test ) ( Fig . 6 ) . The percentage of Sebring Cx . quinquefasciatus that transmitted WNV when co-inoculated simultaneously with CxFV Izabal ( 93% , n = 27 ) was not significantly different from those inoculated with WNV alone ( 88% , n = 66 ) ( p>0 . 05 , Fisher exact test ) ( Fig . 6 ) . The percentage of intrathoracically-inoculated specimens that transmitted WNV alone was also significantly less in the Honduras colony as compared with the Sebring colony , suggesting a more effective salivary gland barrier to WNV in the Honduras colony ( p = 0 . 033 , Fisher exact test ) ; 87% of Sebring specimens ( n = 66 ) transmitted WNV compared with only 69% ( n = 36 ) of the Honduras specimens . For the Sebring colony , the average WNV titer in salivary expectorates for specimens inoculated with WNV only was 4 . 4 log10 pfu ( n = 58 ) , and not significantly different from an average titer of 4 . 7 log10 pfu in the expectorates of WNV+CxFV Izabal group ( n = 25 ) ( Student's two-tailed t-test , p = 0 . 11 ) . For the Honduras colony , the average WNV titer in salivary expectorates for specimens inoculated with WNV only was 4 . 6 log10 pfu ( n = 25 ) compared with 4 . 8 log10 pfu in the WNV+CxFV Izabal group ( n = 52 ) ( Student's two-tailed t-test , p = 0 . 38 ) . For these groups , co-inoculated mosquitoes that transmitted WNV also contained CxFV Izabal in their saliva and mosquitoes that did not transmit WNV also did not transmit CxFV ( n = 12 ) . Mosquitoes infected with CxFV Izabal only ( n = 5 ) did not have CxFV Izabal in their saliva . Midgut ( Fig . 7 ) and head tissues ( Fig . 8 ) of mosquitoes inoculated simultaneously with CxFV Izabal and WNV were observed to be infected with both viruses by IFA .
In this study we demonstrated that sequential infection of C6/36 cells or Cx . quinquefasiatus mosquitoes with CxFV Izabal and WNV did not interfere with either growth or transmission of WNV . This finding is not surprising given that Culex flaviviruses are being discovered in mosquito populations around the world in locations where WNV and other flaviviruses circulate sympatrically . Therefore , the prevalence of CxFV Izabal in Cx . quinquefasciatus in Guatemala does not explain the lack of human disease attributable to WNV in this region . Growth of WNV in C6/36 cells was not inhibited by prior infection of CxFV Izabal . The WNV titer in CxFV Izabal ( + ) C6/36 cells did not reach the maximum titer observed in CxFV Izabal ( − ) cells due to death of cells caused by CxFV Izabal ( Fig . 4 ) . As suggested by Hoshino et al . [10] , CPE observed in C6/36 cells may be the result of an unnatural association between this Culex-derived virus and Aedes-derived cell line since CxFV apparently replicates avirulently in its mosquito host . Therefore , future studies should include utilization of Culex-derived cell lines . The natural host range of CxFV Izabal across mosquito species and genera is not known . Data regarding the establishment of superinfection by homologous viruses in cell culture have been variable . C6/36 cells persistently infected with Aedes aegypti densonucleosis virus remained permissive to infection with Haemagogus equinus densovirus ( HeDNV ) , arguing against the induction of an anti-viral or immune state in the cells that would otherwise inhibit superinfection by this a similar virus [32] . However , interference between superinfecting alphaviruses in mosquito cell culture has been documented multiple times [22]–[24] . The cellular and molecular mechanisms that support replication of WNV in CxFV ( + ) cells are not known and require further study . Overall , neither growth nor transmission of WNV in Cx . quinquefasciatus was significantly affected by CxFV Izabal when viruses were administed sequentially . These findings are in contrast to what has been found previously for flavivirus - flavivirus superinfections involving WNV in Culex mosquitoes , however insect-only flaviviruses are fairly divergent from other vector-borne flavivirues such as WNV [5] , [10] , [33] . Previous studies have demonstrated that transmission of a superinfecting flavivirus was blocked if the secondary flavivirus was antigenically-similar to the primary infecting flavivirus [17] , [18] . Interference to arbovirus superinfection in mosquitoes or mosquito cells by homologous viruses could be the result of RNA interference ( RNAi ) . RNAi is a mechanism by which invertebrates respond to viral infection through the specific recognition and degradation of viral mRNA sequences by virus-derived small interfering RNAs ( viRNA ) [34]–[38] . However , our data demonstrate that prior infection with CxFV Izabal does not interfere with WNV replication when the viruses are inoculated simultaneously , or when mosquitoes are exposed to WNV one week following inoculation with CxFV Izabal . If an RNAi pathway was induced in Cx . quinquefasciatus by CxFV Izabal it would most likely still be effective seven days post-inoculation when mosquitoes were exposed to WNV as it has been previously reported that viRNAs targeting Sindbis virus in C6/36 cells were first detected 48h following infection and were still abundant 7 DPI [38] , and viRNAs targeting WNV in Cx . quinquefasciatus midguts were detected 7 and 14 days post exposure to WNV [37] . There are numerous potential reasons why mosquitoes would be permissive to co-infecting flaviviruses . First , if CxFV Izabal induced an RNAi response in Cx . quinquefasciatus , the viRNAs generated might not be sufficiently homologous to WNV to interfere with the establishment of WNV infection . RNAi is a highly sequence-specific mechanism with little tolerance to mismatches between the viRNA trigger and mRNA target sequences [39] , and the nucleotide sequence identity between CxFV and other vertebrate-infecting flaviviruses is relatively low . Kim et al . [13] reported between 25 and 52% nucleotide sequence homology between CxFV ( TX24518 ) and WNV among structural and non-structural genes . Similarly , Hoshino et al . [10] reported 17–25% sequence identity for structural proteins and 17–40% identity among non-structural proteins between their Japanese isolate of CxFV and other flaviviruses . Therefore , any interference of CxFV Izabal viRNAs with WNV was probably minimal . Secondly , it is possible that over a history of co-evolution between insect-only flaviviruses and their mosquito hosts , these viruses have evolved a way to either evade or suppress an immune mechanism that would otherwise interfere with their own replication , or replication of a subsequently-infecting virus . Flock house virus encodes an RNAi suppressor protein , B2 , that is necessary for establishment of viral infection in Drosophila S2 cells [40] . Virus-encoded suppressors of RNAi have also been found in plant viruses such as tobacco etch potyvirus [41] . The molecular mechanisms that permit co-existence of both WNV and CxFV Izabal , potentially even within the same tissues and cells , requires further study . Thirdly , CxFV replication in mosquito cells is presumably similar to that of other flaviviruses due to similar genome organization [10] , [13] . Zebovitz and Brown [22] determined that interference of superinfecting alphaviruses in cell culture was due to competition for replication sites or metabolites , and that viral RNA synthesis was necessary for inhibition of alphavirus superinfection . The non-structural proteins encoded by flaviviruses play important roles in virus replication and maturation [42] , and the 5′ and 3′ untranslated regions contain conserved nucleotide sequences and RNA secondary structures involved in virus replication and translation [43] . Camissa-Parks et al . [44] discovered that the 3′ stem loop structure of CFAV differed from that of other vertebrate-infecting flavivirus RNAs , and the 3′ pentanucleotide sequence , which is completely conserved among mosquito- and tick-borne flaviviruses contained a point mutation in cell fusing agent virus . The function of this pentanucleotide sequence element is thought to be involved in the binding of cellular or viral proteins to the 3′ stem loop structure during RNA replication [43] . The 3′ UTR of CxFV also was found to contain four tandem repeats , hypothesized to be specially adapted for replication in the mosquito host [10] since deletion of conserved tandem repeat sequences alters virus growth properties [43] . Finally , CxFV may target and replicate in different mosquito tissues than WNV or other flaviviruses . In Culex quinquefasciatus inoculated simultaneously with CxFV Izabal and WNV , midgut and head tissues became infected with both viruses , demonstrating a potential for physical interaction between CxFV Izabal and WNV ( Figs . 7 , 8 ) . However it is unclear if these tissue tropisms would be the same for mosquitoes naturally-infected with CxFV or if infection of these tissues is an artifact of inoculation , or inoculation simultaneously with WNV . More work is needed on characterizing the tissue tropisms of CxFV in naturally-infected mosquitoes and the mechanism by which this virus propagates and is transmitted . One limitation of this study is that the natural mechanism by which mosquitoes become infected with CxFV has not yet been elucidated , so mosquitoes in this study were infected with CxFV Izabal by intrathoracic inoculation . It is unclear how or if the results of this study would be different using naturally-infected mosquitoes . Route of infection has been shown to affect the outcome of arbovirus superinfection studies . Most notably , Aedes triseriatus mosquitoes that were infected transovarially with LaCrosse virus remained permissive to secondary infection with a homologous or heterologous bunyavirus [45] , whereas mosquitoes exposed to the primary infection by intrathoracic inoculation became refractory to superinfection after seven days [20] . Ideally , these studies should be repeated using mosquitoes naturally-infected with CxFV to fully understand the dynamics of interaction , or lack thereof , between these two flaviviruses within the mosquito vector . However the advantage of inoculations is that experiments can be standardized by infecting mosquitoes of the same age with approximately the same amount of virus , and 100% infection rates are assured . Interestingly , both CxFV Izabal and WNV were found in saliva of co-infected specimens when mosquitoes were exposed to both viruses simultaneously by intrathoracic inoculation , but no CxFV Izabal was found in the saliva of singly-infected specimens . This observation suggests that CxFV Izabal may be infecting the salivary glands by “piggybacking”on WNV . This phenomenon has been suggested for expansion of cellular tropism by human immunodeficiency virus ( HIV ) , whereby Epstein-Barr virus , cytomegalovirus , human t-lymphotrophic virus , and sperm proteins share large regions of similarity with the CD4 protein of T-helper lymphocytes , a cellular receptor used by HIV [46] . Because HIV binds to CD4 , binding of HIV to CD4 homologues on other co-infecting viruses or sperm may allow HIV to “piggyback” into additional cell types which it normally would not infect [46] . The molecular basis for this interaction between CxFV Izabal and WNV and how these results compare to natural infection is unknown . Our intrathoracic inoculation data suggest that CxFV Izabal may have the potential to enhance WNV transmission in some mosquito populations; however WNV transmission was not enhanced in Honduras colony when mosquitoes were exposed per os ( Table 2 ) . In summary , this is the first study to address the potential effect of an insect-specific flavivirus on transmission of WNV . We have demonstrated that CxFV Izabal does not interfere with growth of WNV in C6/36 cells or in Cx . quinquefasciatus , nor does it inhibit infection , dissemination , or transmission of WNV . These findings are in contrast to what would be expected based on previous studies following flavivirus – flavivirus superinfections . We hypothesize that both CxFV Izabal and WNV have evolved mechanisms for persistence and transmission by a common mosquito vector , Cx . quinquefasciatus , despite the presence of mosquito immune defenses and the prevalence of co-circulating flaviviruses . Future studies should address the effect of CxFV and WNV co-infection in mosquitoes naturally infected with CxFV , as well as the tissue tropisms and molecular mechanisms of CxFV replication and transmission in mosquitoes . | Unlike most known flaviviruses ( Family , Flaviviridae: Genus , Flavivirus ) , insect-only flaviviruses are a unique group of flaviviruses that only infect invertebrates . The study of insect-only flaviviruses has increased in recent years due to the discovery and characterization of numerous novel flaviviruses from a diversity of mosquito species around the world . The widespread discovery of these viruses has prompted questions regarding flavivirus evolution and the potential impact of these viruses on the transmission of flaviviruses of public health importance such as WNV . Therefore , we tested the effect of Culex flavivirus Izabal ( CxFV Izabal ) , an insect-only flavivirus isolated from Culex quinquefasciatus mosquitoes in Guatemala , on the growth and transmission of a strain of WNV isolated concurrently from the same mosquito species and location . Prior infection of C6/36 ( Aedes albopictus mosquito ) cells or Cx . quinquefasciatus with CxFV Izabal did not alter the replication kinetics of WNV , nor did it significantly affect WNV infection , dissemination , or transmission rates in two different colonies of mosquitoes that were fed blood meals containing varying concentrations of WNV . These data demonstrate that CxFV probably does not have a significant effect on WNV transmission efficiency in nature . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
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| [
"virology"
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| 2010 | Transmission of West Nile Virus by Culex quinquefasciatus Say Infected with Culex Flavivirus Izabal |
Replicative DNA polymerases are frequently stalled by DNA lesions . The resulting replication blockage is released by homologous recombination ( HR ) and translesion DNA synthesis ( TLS ) . TLS employs specialized TLS polymerases to bypass DNA lesions . We provide striking in vivo evidence of the cooperation between DNA polymerase η , which is mutated in the variant form of the cancer predisposition disorder xeroderma pigmentosum ( XP-V ) , and DNA polymerase ζ by generating POLη−/−/POLζ−/− cells from the chicken DT40 cell line . POLζ−/− cells are hypersensitive to a very wide range of DNA damaging agents , whereas XP-V cells exhibit moderate sensitivity to ultraviolet light ( UV ) only in the presence of caffeine treatment and exhibit no significant sensitivity to any other damaging agents . It is therefore widely believed that Polη plays a very specific role in cellular tolerance to UV-induced DNA damage . The evidence we present challenges this assumption . The phenotypic analysis of POLη−/−/POLζ−/− cells shows that , unexpectedly , the loss of Polη significantly rescued all mutant phenotypes of POLζ−/− cells and results in the restoration of the DNA damage tolerance by a backup pathway including HR . Taken together , Polη contributes to a much wide range of TLS events than had been predicted by the phenotype of XP-V cells .
DNA replication involves a rapid but fragile enzymatic mechanism that is frequently stalled by damage in the DNA template . To complete DNA replication , DNA lesions are bypassed by specialized DNA polymerases , a process called translesion synthesis ( TLS ) ( reviewed in [1] , [2] ) . A number of TLS polymerases , including Polη and Polζ , that are conserved throughout eukaryotic evolution , have been identified in yeast and mammals . Polη deficiency is responsible for a variant form of xeroderma pigmentosum ( XP-V ) [3] , [4] that is characterized by UV photosensitivity and a predisposition to skin cancer ( reviewed in [5] ) . Deficiency in Rev3 , the catalytic subunit of Polζ , results in a considerably more severe phenotype , compared with Polη . In fact , Rev3 disruption is lethal to mouse embryogenesis [6] . Chicken DT40 cells deficient in Rev3 exhibit significant chromosome instability and hypersensitivity to a wide variety of DNA-damaging agents [7]–[10] . In addition to their role in TLS , both Polη and Polζ can contribute to homologous DNA recombination ( HR ) in DT40 cells [9] , [11] , [12] . Exposure to UV induces cyclobutane pyrimidine dimers ( CPDs ) and 6-4 UV photoproducts in DNA . While Polη can efficiently and accurately bypass CPDs [4] , [13]–[15] , no single DNA polymerase has been shown to be capable of effectively bypassing 6-4 UV photoproducts in vitro . This suggests that the coordinate use of more than one polymerase is required to bypass such damage in vivo . In support of this notion , several biochemical studies have suggested that lesion bypass can be effected by two sequential nucleotide incorporation events [16] , [17] . For example , to bypass the 6-4 UV photoproduct , Polη inserts a nucleotide opposite the damage as a first step , followed by extension from the inserted nucleotide as a second step . This extension process has been shown to be catalyzed by yeast Polζ and by human Polκ [1] , [2] , [18] . The contribution of mammalian Polζ to the extension step remains elusive , because functional Polζ has not been purified [19] , [20] . Recently , replication of episomal plasmid DNA carrying various lesions was analyzed in mammalian cell lines to define the role of each DNA polymerase in TLS past individual DNA lesions [15] , [21] , [22] . Shachar et al . suggest the sequential usage of Polη and Polζ in TLS past the cisPt-GG lesion [21] , while results of others do not support this two-step TLS model [15] , [23] . Indeed , evidence for the two-step model in the replication of chromosomal DNA has so far been lacking . By contrast , both human and yeast Polη can bypass CPDs effectively in vitro without extension polymerases [4] . Combining genetic tractability with a number of sensitive phenotypic assays , the chicken DT40 B lymphocyte cell line provides a unique opportunity to precisely analyze the role of individual DNA polymerases in TLS as well as in HR . The immunoglobulin loci of DT40 cells undergo constitutive diversification in culture by a combination of gene conversion ( which depends on HR ) and point mutation ( which depends on TLS [24] ) . This diversification is driven by activation-induced deaminase ( AID ) [25] , [26] , which catalyzes the deamination of cytosine to generate uracil in the immunoglobulin loci . The uracil is then eliminated by uracil glycosylase to form abasic sites , which are thought to be the lesions that trigger bypass , either by gene conversion or by mutagenic translesion synthesis ( reviewed in [27] ) . To study TLS in a different context , an episomal plasmid-based system was recently developed to examine the replication of a plasmid carrying site-specific lesions , in this case 6-4 UV photoproducts , in DT40 cells [28] . To investigate the functional interaction between Polη and Polζ in the DT40 cell line , we created POLη−/−/POLζ−/− DT40 cells ( hereafter called polη/polζ cells ) . Unexpectedly , depletion of Polη in the polζ cells suppressed virtually all mutant phenotypes associated with the loss of Polζ , including genome instability and hypersensitivity to DNA-damaging agents . Furthermore , the reconstitution of POLη−/−/POLζ−/− cells with intact human Polη , but not the polymerase-deficient mutant carrying D115A/E116A substitutions , increased their hypersensitivity to DNA-damaging agents to the level of the POLζ−/− cells , indicating that Polη-dependent DNA synthesis is toxic in the absence of Polζ . Remarkably , this alleviation of the polζ phenotype was associated with the restoration of effective translesion synthesis . These data provide in vivo support for the two-step model of lesion bypass , with Polζ playing a critical role in the extension step following nucleotide incorporation by Polη .
We generated polη/polζ cells by inactivating both REV3 alleles of the polη DT40 cells using a previously published gene-targeting strategy ( Figure 1A ) [9] , [12] . The growth properties of the mutant cells were examined by measuring their growth rate and cell-cycle profile . As reported previously , the polη cells had a normal growth rate , whereas the polζ cells proliferated more slowly , exhibiting an increase in the sub-G1 fraction , indicative of spontaneous cell death during the cell cycle ( Figure 1C ) . The loss of Polζ caused a significant increase in the number of spontaneous arising γH2AX foci , which represent replication collapse ( Figure S1 ) . Interestingly , deletion of POLη in the polζ cells reversed their growth retardation and reduced the rate of cell death ( Figure 1B and 1C ) . Similarly , the number of spontaneous chromosomal aberrations was significantly reduced in polη/polζ cells , compared with polζ cells ( Table 1 ) . Ectopic expression of human Polη in polη/polζ cells diminished their growth rate to the level of polζ cells . This observation does not reflect general toxicity of the overexpressed human Polη , since its ectopic expression caused pronounced growth retardation only in the polη/polζ double mutant but not in wild-type cells ( Figure S2 ) . These observations indicate that the growth defect of polζ cells is dependent on the presence of Polη . The sensitivity of polη/polζ cells to genotoxic stresses was evaluated using a colony formation assay . polη cells showed a mild sensitivity to UV but not to the other genotoxic stresses , in agreement with the phenotype of mammalian XP-V cells [29] , [30] . In contrast , polζ cells showed a marked sensitivity to UV , ionizing radiation , cis-diaminedichloroplatinum-II ( cisplatin ) , and methylmethane sulfonate ( MMS ) ( Figure 2 ) , as previously described [9] . The polη/polζ mutant cells were less sensitive to UV than were the polζ cells . Furthermore , this double mutant showed significantly increased tolerance to ionizing radiation , cisplatin , and MMS , compared with the polζ cells . This increased tolerance of the polη/polζ cells was reversed by ectopic expression of human Polη . To investigate whether this reversion depended on the polymerase activity of human Polη , we expressed human POLη cDNA carrying D115A/E116A mutations in the polη/polζ cells . These mutations in the catalytic site abolish polymerase activity ( data not shown ) , as previously reported in yeast [31] . The expression of the mutant Polη had no effect on the sensitivity of the polη/polζ cells to the DNA-damaging agents ( Figure 2 ) , indicating that Polη-dependent DNA synthesis sensitizes polζ cells to these DNA-damaging agents . We wished to investigate if Polη and Polζ could collaborate in TLS past specific types of DNA damage . To this end , we performed two sets of experiments: analysis of immunoglobulin hypermutation , which in DT40 provides a readout of the bypass of abasic sites [24] , and analysis of the replication of a T-T ( 6-4 ) photoproduct on an episomal plasmid [28] . To induce Ig hypermutation , we overproduced AID in DT40 cells using a retrovirus vector [32] , [33] . This vector drives the monocistronic expression of AID and green fluorescent protein ( GFP ) , allowing a comparative assessment of the level of ectopic AID expression . At 24 hours after infection with the AID retrovirus , virtually all cells from each line displayed a strong GFP signal , indicating that the deficiency of Polη and Polζ did not affect the expression of AID ( Figure 3A ) . However , a substantial fraction of the polζ cells died at day 3 , and with the surviving polζ cells at day 10 showed a decrease level of GFP signals ( Figure 3A and 3B ) . Furthermore , polζ cells , but not polη or polη/polζ cells , displayed prominent chromosomal breaks at day 3 post-infection ( Figure 3C ) . Since the break sites on the chromosome were randomly distributed , the overexpressed AID protein may be targeting a number of different loci in addition to the Ig locus , in DT40 cells . These observations suggest that TLS past abasic sites created by the combined action of AID and uracil glycosylase may be performed less effectively in polζ cells , compared with polη/polζ or polη cells , resulting in chromosome breakage and cell death . To verify that Polη-dependent DNA synthesis was toxic to the AID-overproducing polζ cells , we reconstituted polη/polζ cells with either wild-type POLη or the catalytically inactive mutant polηD115A/E116A ) . Wild-type POLη expression sensitized the polη/polζ cells to the overexpression of AID , whereas the mutant POLη had no impact on cell survival ( Figure 3A and 3B ) . We thus conclude that , in polζ cells , TLS past abasic sites may be less effective because neither Polη nor any other polymerase can extend DNA synthesis following the Polη-mediated insertion of nucleotides opposite the abasic site . We have shown that AID overexpression results in increased TLS-mediated hypermutation at G/C base pairs in Ig V segments [32] . To define the role of Polη and Polζ in this TLS process , we determined the Ig Vλ nucleotide sequences of AID-overexpressing wild-type , polη , and polη/polζ cells . polζ cells were not analyzed because of the difficulty of ectopically expressing AID to the same extent as in the other lines . The number of non-templated point mutations ( PM , Figure 4A ) was somewhat lower in polη cells than in wild-type cells ( This slight reduction is not significant ( p = 0 . 15 , t-test ) [32] ) . Surprisingly , the level of Ig V mutations in polη/polζ cells was comparable to that of wild-type cells . This observation is in marked contrast with the fact that Rev1 , an essential factor for the function of Polζ [20] , plays a critical role in non-templated point mutations at the abasic site [34] . Moreover , Polζ played the critical role in cellular tolerance to AID-mediated abasic sites ( Figure 3 ) . These observations indicate that in the absence of both Polη and Polζ , other unidentified DNA polymerase ( s ) can participate in TLS past the abasic site . As the polη/polζ cells displayed a significant increase in the proportion of G/C to A/T transitions in the non-templated Ig V mutations ( 12/25; 48% ) , compared with wild-type cells ( 2/18; 11% ) ( Figure 4B ) , this unidentified DNA polymerase ( s ) may preferentially incorporate adenine opposite the abasic site in the absence of both Polη and Polζ . T-T ( 6-4 ) UV photoproducts represent the most formidable challenge to DNA replication , as they potently arrest replicative polymerases [35] . To analyze TLS past a T-T ( 6-4 ) photoproduct , we transfected two plasmids , pQTs and pQTo , [28] ( Figure 5 ) carrying T-T ( 6-4 ) UV photoproducts into DT40 cells . At two days after transfection we recovered only replicated copies , and were thus able to analyze the replication of site-specific T-T ( 6-4 ) UV photoproducts in vivo . This photoproduct can be arranged in one of two ways . In the staggered conformation ( pQTs ) ( Figure 5A ) , the lesions are separated by 28 intervening nucleotides and placed opposite a GpC mismatch . Replicated copies can thus result from TLS on the top or bottom strand . Error-free template switching should result in GpC at the site of the photoproduct , while TLS past this photoproduct may insert ApA ( accurate TLS ) or other nucleotides ( inaccurate TLS ) at this site . Note that our experiment was done in a nucleotide-excision repair-deficient ( xpa-deficient ) background , and thus excluded the recovery of replicated copies generated by error-free nucleotide-excision repair . In the second , unphysiological , replication template , the lesions are placed opposite to each other ( pQTo ) ( Figure 5E ) . Using this conformation , replicated DNA copies can be recovered as a consequence of TLS , but not by template switching . To assess the mode of bypass used when generating replicated copies of pQTs , we analyzed the nucleotide sequences of replicated copies of plasmids recovered from DT40 cells ( Figure 5B ) and determined the proportion of TLS relative to error-free template switching ( Figure 5C ) . Overall replication efficiency was comparable among cells carrying the various genotypes used in the previous study and this study . Previous study found that 45% and 55% of the recovered plasmid copies resulted from TLS in xpa and xpa/polη cells , respectively [28] . In comparison , the efficiency of TLS in xpa/polζ cells was significantly reduced , with less than 10% of the recovered plasmid generated as a consequence of TLS . All TLS events observed in the xpa/polζ cells were associated with deletion at damaged sites ( Figure 5D ) . Thus , as found previously [28] ( Figure 5C ) , we conclude that Polζ is required for the successful bypass of T-T ( 6-4 ) UV photoproducts by TLS . Remarkably , the xpa/polη/polζ cells displayed a normal TLS efficiency , indicating that the failure of TLS in polζ cells is dependent on the presence of Polη . We also classified the replication products obtained from the pQTo plasmid , where bypass can be effected only by TLS or deletion . As found in the previous study [28] , the loss of Polζ was frequently associated with the deletion of two or more nucleotides covering the site of the T-T ( 6-4 ) UV photoproduct ( Figure 5E and 5F ) . The loss of Polζ did not impair TLS in the absence of Polη , but severely compromised it in the presence of Polη . A possible explanation , discussed below , is that the Polη-dependent insertion of nucleotides opposite the T-T ( 6-4 ) UV photoproduct inhibits the completion of TLS in the absence of Polζ ( presumably due to defective extension from inserted nucleotides ) , while other unidentified DNA polymerases can perform the complete TLS reaction in polη/polζ cells . The milder phenotype of the polη/polζ cells , compared with polζ single mutants , led us to investigate the contribution of other TLS polymerases to TLS in polη/polζ cells . We previously showed that polκ/polζ cells show a higher sensitivity to mono-alkylating agents , compared with polζ cells , though polκ cells show normal sensitivity [36] , and thereby suggested that Polκ can partially substitute for the loss of Polζ . We therefore sought to determine whether Polκ contributes to damage tolerance in polη/polζ cells . To this end , we deleted the Polκ gene in polη/polζ cells and analyzed the phenotype of the resulting triple knockout polη/polκ/polζ clones ( Figure 6A ) . Deletion of the Polκ gene tended to reduce growth kinetics . However , the polη/polκ/polζ clones exhibited only limited increase in DNA-damage sensitivity , compared with polη/polζ cells ( Figure 6B ) . Likewise , overexpression of chicken Polκ did not increase cisplatin tolerance in polζ or polη/polζ cells ( data not shown ) . These observations imply that Polκ does not play an important role in TLS in polη/polζ cells . Replication arrest can be released by two major mechanisms: HR and TLS [37] . HR-dependent release is initiated by homologous pairing between the 3′ end of the arrested strand and the sister chromatid , followed by strand invasion and DNA synthesis to extend the invading 3′ strand ( Figure 7D ) . To analyze the efficiency of HR-mediated release from replication blockage , we analyzed sister chromatid exchange ( SCE ) ( Figure 6C and 6D ) [38] , [39] . The level of SCE is likely to be determined by two factors: the number of DNA lesions that cause a replication block and the efficiency of HR-dependent release from replication blockage . As previously reported [9] , [39] , the level of spontaneous SCE was slightly increased ( 1 . 5 to 2-fold ) in all TLS mutants compared with wild-type cells , presumably because lesions are more frequently channeled to HR . The polη cells displayed a markedly greater increase in the level of UV-induced SCE ( the number of spontaneous SCE subtracted from the number of SCE following UV irradiation shown in Figure 6D ) , which phenotype is attributable to defective TLS over UV-induced damage . In contrast , in the polζ cells , the UV-induced SCE level was similar to that of wild-type cells , suggesting that the defective TLS may not be adequately compensated by HR [9] . SCE was induced more efficiently in the polη/polζ cells than in the polζ cells , which is consistent with the increased UV tolerance of polη/polζ cells , compared with polζ cells . Reconstitution of the polη/polζ cells with wild-type POLη , but not with the catalytically inactive POLη , significantly reduced the number of UV-induced SCE events . Thus , the degree of UV tolerance correlated with the number of UV-induced SCE events at least in polζ and polη/polζ cells . This observation suggests that , in addition to TLS , HR-mediated release from replication blockages contributes to a significantly higher UV tolerance in polη/polζ cells than in polζ cells .
The improved damage tolerance of polη/polζ cells , compared with polζ cells , suggests following several possibilities . One possibility is that Polζ somehow inhibits Polη action and that Polη does not actually have a significant role when Polζ is present . However this possibility may be unlikely , since physical interaction between Polη and Rev1 , and Polη dependent recruitment of Rev1 to the DNA damage site support the sequential actions of Polη followed by Polζ rather than inhibitory action of Polζ on Polη [44]–[47] . Thus , more likely possibility is that , Polη generates a replicative intermediate in an attempt to bypass the lesion , but cannot complete an effective bypass reaction without Polζ ( Figure 7A ) . We suggest that the abortive intermediates generated by Polη in the absence of Polζ lead to a difficult-to-rescue replication collapse , thereby accounting for the hypersensitivity of the single polζ mutant ( Figure 7B ) . The modest phenotype of XP-V cells indicates that Polζ may efficiently mediate TLS past a variety of DNA lesions , either in collaboration with other polymerases or possibly on its own . This situation can be explored further in the light of current TLS models . In the canonical model for TLS replication , arrest by agents such as UV , MMS , and cisplatin , leads to PCNA becoming mono-ubiquitinated [40] , [48]–[50] . This increases the affinity of PCNA for Polη and other Y-family TLS polymerases by virtue of their UBM and UBZ ubiquitin-binding motif [44] , [48] , [50]–[52] and likely contributes to the accumulation of Y-family polymerases at the sites of blocked replication forks . It has been suggested that these polymerases can compete with each other by mass action to attempt to carry out TLS . In the case of CPDs , if Polη wins this competition; bypass can occur without the need for a second polymerase . However , with other lesions such as a T-T ( 6-4 ) photoproduct , there is currently no evidence to show that any single polymerase can complete bypass . Polη may be able to start the bypass by incorporating opposite at least the 5′ base of the lesion , but it cannot extend from the resulting mismatch ( Figure 7A ) . This would explain the abortive intermediate referred to above . To complete TLS , Polζ is required to extend from the inserted nucleotides ( Figure 7A ) , an explanation that is consistent with the sequential action of Polη and Polζ demonstrated in in vitro studies [2] . A further implication of this model is that no other polymerase can effectively substitute for Polζ in this extension step ( Figure 7B ) . The successive action of Polη and Polζ might be mediated by the association of the two polymerases with Rev1 [45] , [53] . The idea of a Rev1-mediated switch from Polη to Polζ is supported by the fact that Polη tightly interacts with Rev1 [1] , [46] , [47] , [53] , [54] . Moreover , DNA-damage-induced Rev1 focus formation appears to be dependent on Polη [46] , [47] . Adding to these findings , the present work establishes a role for Polη in the bypass of a wider range of DNA damage than previously thought and demonstrates the in vivo importance of the two-step bypass of many lesions . The effect on TLS of depleting both Polη and Polζ is distinctly different in DT40 cells than in human cells . Ziv et al . showed that depletion of Rev3 sensitized cells to UV more severely at two days after irradiation in Polη-deficient XPV fibroblasts than in Polη-proficient control cells [22] , although its impact on Polη-proficient DT40 cells was considerably stronger than on Polη-deficient DT40 cells . There are several explanations for this apparent difference . First , we favor the idea that DT40 may be a more reliable cell line than others to evaluate TLS by measuring cellular survival due to following reasons . The cell cycle distribution is distinctly different between DT40 and other mammalian cell lines . In DT40 cells , ∼70% of the cells are in the S phase , and the G1/S checkpoint does not function at all [55] . In most of the mammalian cell lines , on the other hand , more than 50% of the cells are in the G1 phase , and G1/S checkpoint works at least partially . Therefore , environmental DNA damage interferes with DNA replication more significantly in DT40 cells than in mammalian cell lines . Accordingly , TLS contributes to the cellular survival of the colony formation assay to a considerably higher extent in DT40 cells than in mammalian cell lines . Ziv et al . , on the other hand , evaluated TLS by measuring the number of living cells at 48 hours after UV irradiation [22] . Since a majority of UV irradiated cells may have stayed outside the S phase at 48 hours , it is unclear whether this survival reflects the efficiency of TLS . The same laboratory also analyzed TLS past a cisplatin G-G intrastrand crosslink located in a gapped episomal plasmid [21] . Depletion of Rev3 with or without codepletion of Polη in U2OS cells resulted in an 80% reduction in TLS past the lesion , irrespective of the presence or absence of Polη . The relevance of this finding to TLS during the replication of chromosomal DNA remains elusive . Yoon et al . investigated TLS in a double-stranded plasmid containing a single 6-4 photoproduct as well as replication origins derived from the SV40 virus [23] . Depletion of Rev3 or Rev7 in NER-deficient XP-A fibroblasts reduced the efficiency of TLS in the episomal plasmid by approximately 50% , with a similar reduction obtained in XP-V fibroblasts . This result using human fibroblasts is clearly different from the data we obtained using DT40 cells . Given the close sequence similarity between the two polymerases in human and chicken cells , we consider it unlikely that the mechanisms of lesion bypass are fundamentally different between the two organisms . The apparent difference between mammalian cells and DT40 may be caused by the incomplete si-RNA mediated inhibition in human cells versus the null mutation we have used in DT40 . Another possible reason to explain this difference is the active HR system in DT40 cells , and the different usage order of TLS DNA polymerases because the DT40 B lymphocyte line undergoes Ig V hypermutation through TLS [32] . The usefulness of the three episomal plasmid systems to the analysis of TLS occurring during replication of chromosomal DNAs should be further investigated [15] , [21] , [22] , [28] . Irrespective of the reason for this apparent difference , our data clearly indicate that , as discussed above , under some conditions Polη can hinder the efficient progress of the replication fork past lesions mediated by Polζ . It is remarkable that deletion of the three major TLS polymerase genes , POLη , POLκ , and POLζ , results in only a mild reduction in the growth kinetics of DT40 cells ( Figure 6A ) . This is in marked contrast with the immediate cell death associated with the massive chromosomal breaks generated upon deletion of Rad51 [56] . These observations imply that during DNA replication , if replication blocks are encountered , HR can at least partially compensate for defective TLS . The significant functional redundancy between TLS and HR is supported by our previous report , which concludes that the deletion of both RAD18 and RAD54 , a gene involved in HR , as well as the deletion of both REV3 and RAD54 , are synthetically lethal to cells [9] , [39] . We show here that depletion of Polη irrespective of the status of Polζ markedly increases the level of UV-induced SCE ( Figure 6C ) , suggesting that DNA damage that cannot be resolved by TLS because of the absence of Polη may be resolved by HR , leading to increased SCE levels ( Figure 7D ) . However , SCE is not induced to the same level in polζ cells ( Figure 6C ) . Thus , nucleotide incorporation by Polη appears to represent a point of commitment in the TLS reaction beyond which rescue by HR is problematic . This is likely to be explained by the creation of an intermediate , possibly the mismatched primer terminus , which can be efficiently extended by Polζ , but which cannot readily initiate HR . In summary , the significant increase in the cellular tolerance of polη/polζ cells to DNA-damaging agents , compared with polζ cells , can be partially attributable to more efficient HR in polη/polζ cells than in polζ cells . However , the normal level of TLS-dependent Ig V mutation and restoration of TLS during 6-4 photoproduct bypass in polη/polζ cells ( Figure 4B ) suggests that one or more other unidentified TLS polymerases can act as a substitute and carry out TLS when both Polη and Polζ are missing ( Figure 7C ) .
Generation of polζ ( rev3 ) - and polη-deficient DT40 cells was described previously [9] , [12] . To generate polη/polζ cells , we sequentially introduced two rev3 gene-disruption constructs ( rev3-hygro and rev3-His ) [9] into polη ( PuroR/BlsR ) cells . A puromycin-resistant XPA disruption construct was used to disrupt the single XPA allele and recreate the xpa/rev3 cell line . After removal of the BlsR marker gene from the polη/polζ cells by the transient expression of CRE recombinase , a blasticidin-resistant XPA disruption construct was used to generate the xpa/polζ/polη cell line . A puromycin-resistant POLκ disruption construct was used to generate polη/polζ/polκ cells . To make a POLη expression plasmid , we inserted human POLη cDNA into the multi-cloning sites ( MCS ) of an expression vector , pCR3-loxP-MCS-IRES-GFP-loxP [57] . A mutant Polη that lacks polymerase activity ( Mutant POLη ) was generated by inserting D115A/E116A mutations into human Polη cDNA . The conditions for cell culture , selection , and DNA transfection were described previously[58] . The growth properties of cells were analyzed as described previously [58] . For the retrovirus infection , the pMSCV-IRES-GFP recombinant plasmid was constructed by ligating the 5 . 2 kb BamHI-NotI fragment from pMSCVhyg ( Clontech ) with the 1 . 2 kb BamHI-Not1 fragment from pIRES2-EGFP ( Clontech ) . Mouse AID cDNA [33] was inserted between the BglII and EcoRI sites of pMSCV-IRES-GFP . The preparation and infection of the retrovirus were carried out as previously described [33] . Expression of the GFP was confirmed by flow cytometry . The efficiency of infection was more than 90% as assayed by GFP expression . pQTs and pQTo plasmids containing a T-T ( 6-4 ) photoproduct were generated and transfected into DT40 cells as previously described [28] . Karyotype analysis was performed as described previously [56] . Cells were treated with colcemid for 3 hours to enrich mitotic cells . Measurement of SCE level was performed as described previously [38] . For UV-induced SCE , cells were suspended in PBS and irradiated with 0 . 25 J/m2 UV followed by BrdU labeling . Genomic DNA was extracted at 14 days after subcloning . The rearranged Vλ segments were PCR amplified using 5′-CAGGAGCTCGCGGGGCCGTCACTGATTGCCG-3′ as the forward primer in the leader-Vλ intron and 5′-GCGCAAGCTTCCCCAGCCTGCCGCCAAGTCCAAG-3′ as the reverse primer in the 3′ of the JCλ intron . To minimize PCR-introduced mutations , the high-fidelity polymerase , Phusion ( Fynnzymes ) was used for amplification ( 30 cycles at 94° C for 30 s; 60° C for 1 min; 72° C for 1 min ) . PCR products were cloned using a Zero Blunt TOPO PCR Cloning Kit ( Invitrogen ) and subjected to sequence analysis with the M13 forward ( -20 ) or reverse primer . Sequence alignment using GENETYX-MAC ( Software Development , Tokyo ) allowed the identification of changes from the parental sequences in each clone . As described previously [59] , all sequence changes were assigned to one of three categories: point mutation , gene conversion , or ambiguous . This discrimination is based on the published sequences of Vλ pseudogenes that can act as donors for gene conversion . For each mutation , the database of Vλ pseudogenes was searched for a potential donor . If no pseudogene donor containing a string >9 bp could be found , the mutation was categorized as a non-templated point mutation . If such a string was identified and there were further mutations that could be explained by the same donor , then all these mutations were categorized as a single long-tract gene conversion event . If there were no further mutations , indicating that the isolated mutation could have arisen through a conversion mechanism or could have been non-templated , it was categorized as ambiguous . | DNA replication is a fragile biochemical reaction , as the replicative DNA polymerases are readily stalled by DNA lesions . The resulting replication blockage is released by translesion DNA synthesis ( TLS ) , which employs specialized TLS polymerases to bypass DNA lesions . There are at least seven TLS polymerases known in vertebrates . However , how they cooperate in vivo remains one of central questions in the field . We analyzed this functional interaction by genetically disrupting two of major TLS polymerases , Polη and Polζ , in the unique genetic model organism , chicken DT40 cells . Currently , it is widely believed that Polη plays a very specific role in cellular tolerance to ultraviolet light–induced DNA damage . Polζ , on the other hand , plays a key role in cellular tolerance to a very wide range of DNA–damaging agents , as POLζ−/− cells are hypersensitivity to a number of DNA damaging agents . Our phenotypic analysis of POLη−/−/POLζ−/− cells shows that , unexpectedly , the loss of Polη significantly rescued all mutant phenotypes of POLζ−/− cells . The genetic interaction shown here reveals a previously unappreciated role of human Polη in cellular response to a wide variety of DNA lesions and two-step collaborative action of Polymerase η and ζ . | [
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| 2010 | Simultaneous Disruption of Two DNA Polymerases, Polη and Polζ, in Avian DT40 Cells Unmasks the Role of Polη in Cellular Response to Various DNA Lesions |
Lipids are main fuels for cellular energy and mitochondria their major oxidation site . Yet unknown is to what extent the fuel role of lipids is influenced by their uncoupling effects , and how this affects mitochondrial energetics , redox balance and the emission of reactive oxygen species ( ROS ) . Employing a combined experimental-computational approach , we comparatively analyze β-oxidation of palmitoyl CoA ( PCoA ) in isolated heart mitochondria from Sham and streptozotocin ( STZ ) -induced type 1 diabetic ( T1DM ) guinea pigs ( GPs ) . Parallel high throughput measurements of the rates of oxygen consumption ( VO2 ) and hydrogen peroxide ( H2O2 ) emission as a function of PCoA concentration , in the presence of L-carnitine and malate , were performed . We found that PCoA concentration < 200 nmol/mg mito protein resulted in low H2O2 emission flux , increasing thereafter in Sham and T1DM GPs under both states 4 and 3 respiration with diabetic mitochondria releasing higher amounts of ROS . Respiratory uncoupling and ROS excess occurred at PCoA > 600 nmol/mg mito prot , in both control and diabetic animals . Also , for the first time , we show that an integrated two compartment mitochondrial model of β-oxidation of long-chain fatty acids and main energy-redox processes is able to simulate the relationship between VO2 and H2O2 emission as a function of lipid concentration . Model and experimental results indicate that PCoA oxidation and its concentration-dependent uncoupling effect , together with a partial lipid-dependent decrease in the rate of superoxide generation , modulate H2O2 emission as a function of VO2 . Results indicate that keeping low levels of intracellular lipid is crucial for mitochondria and cells to maintain ROS within physiological levels compatible with signaling and reliable energy supply .
Fatty Acids ( FAs ) are main sources of cellular energy affecting mitochondrial energetics and redox balance . The lipid energy content becomes available from β-oxidation as reducing equivalents and acetyl CoA ( AcCoA ) of which the latter , after further processing in the tricarboxylic acid cycle , also supplies most of the energy as NADH and FADH2 , which , in turn , fuel the buildup of the proton motive force for oxidative phosphorylation ( OxPhos ) . Under physiological conditions , the non-esterified forms of FAs represent an important fuel supply in many tissues . However , persistent excess of FAs and accumulation of triacylglycerols in non-adipose tissues are associated with metabolic disorders like diabetes , hyperlipidemia and lipodystrophies [1 , 2] . Preserving the intracellular redox environment is crucial for vital functions such as division , differentiation , contractile work and survival , amongst many others [3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11] . Mitochondria are main drivers of intracellular redox [12 , 13 , 14 , 15 , 16] , playing a central role in the development of diabetes and obesity complications [17 , 18 , 19 , 20 , 21] . Hearts from diabetic subjects are particularly prone to excess ROS because sympathetic hyper-activation and -glycemia are present in a large cohort of these patients [22 , 23] . These two conditions may alter cardiac and skeletal muscle redox conditions [5 , 6] endangering mitochondrial function [7 , 8] . Perturbations of cardiac mitochondrial energetics and increased mitochondrial ROS emission can account for tissue redox imbalance [8 , 11 , 12 , 13] and abnormal cardiac contractility leading to systolic and diastolic dysfunction in diabetic patients [17 , 18 , 19 , 20 , 21] . These abnormalities are common features in T1DM and type 2 diabetes mellitus ( T2DM ) patients [1 , 9 , 10] and they underlie diabetic cardiomyopathy , a major life-threatening complication that limits life quality and expectancy [3 , 19] . Although available evidence indicates the participation of oxidative stress in the etiology of T1DM , obesity-induced insulin resistance and T2DM [10 , 17 , 24 , 25 , 26] , the role of dysfunctional β-oxidation per se as an underlying cause of metabolic disorder remains a topic of active research and debate [10] . Prevailing wisdom indicates that the myocardial shift from glucose to FA utilization occurring in diabetes may aggravate mitochondrial dysfunction , fueling contractile deficit [25 , 27] . Dysfunctional lipid metabolism in diabetes has been implicated in the development of cardiac impairment [28] and lipotoxicity resulting from accumulation of triacylglycerols and free FAs in the cytoplasm , which lead to the generation of apoptosis inducers such as diacylglycerol and ceramide [29] . In contrast , other studies have reported that FAs may actually benefit cardiac function in the course of metabolic syndrome [17 , 30 , 31] . In T1DM [32] and T2DM animal models [18 , 21] exhibiting impaired heart function when subjected to metabolic stress caused by hyperglycemia and elevated energy demand , it was shown that , unlike insulin , palmitate was able to rescue contractile function from the detrimental action of hyperglycemia . The beneficial effect of palmitate was concomitant with a higher content of reduced glutathione ( GSH ) and augmented mitochondrial ROS-scavenging capacity [18 , 21] . Together with peroxisomes , mitochondria represent the main subcellular compartments where lipid degradation occurs . Yet , the impact of dietary lipids on mitochondrial redox status and ROS emission , and their downstream effects on energetics are not fully elucidated . Thus , we investigated the basic mechanisms underlying the impact of lipid-precursor availability for β-oxidation on the energetic and redox responses from heart mitochondria of a previously described animal model of T1DM in STZ-treated GP that harbor glucose levels similar to those found in human T1DM [32 , 33] . More specifically , we analyzed how the relationship between mitochondrial respiration and ROS emission is altered as a function of PCoA in T1DM GPs and Sham controls . The experimental results are interpreted with the help of a two-compartment mitochondrial energetic-redox computational model [15] that includes β-oxidation [34] functionally linked to main redox couples and scavenging systems distributed in mitochondrial matrix and extra-matrix compartments , and transport between compartments of ROS species and GSH ( Fig 1 ) .
We quantified VO2 and H2O2 emission in isolated heart mitochondria from Sham and diabetic GPs under β-oxidation conditions with PCoA , in the presence of 0 . 5mM malate and 0 . 5mM L-carnitine , and in the absence ( state 4 ) or presence ( state 3 ) of 1mM ADP . As a caveat , Mal is needed to feed the TCA cycle to enable the efficient regeneration of Coenzyme A from acetyl CoA and cycling of β-oxidation [34] . Fig 2 depicts the results obtained in VO2 ( Fig 2A and 2B ) and H2O2 emission ( Fig 2C and 2D ) under states 4 and 3 respiration and as a function of PCoA concentration ( 0 to 800nmol PCoA/mg mito prot equivalent to 0 to 40μM PCoA: see Fig 3 ) in control ( Sham ) and diabetic ( STZ ) groups . State 3 respiration increased , attaining an apparent plateau level of ~125nmol O2/min/mg mito prot at 600nmol PCoA/mg mito prot in mitochondria from both Sham and diabetic GPs ( Fig 2B ) . At amounts > 600nmol PCoA/mg mito prot VO2 further augmented an additional ~25% suggesting uncoupling of respiration ( Fig 2B ) . State 4 respiration was stable at ~15nmol O2/min/mg mito prot up to 400nmol PCoA/mg mito prot , subsequently increasing with PCoA concentration ( Fig 2A ) . Mitochondrial ROS release remained approximately constant at ~ 150-200pmol H2O2/min/mg mito protein up to 200nmol PCoA/mg mito protein , in Sham and diabetic groups , and for both states 4 and 3 respiration ( Fig 2C and 2D ) . Thereafter , a PCoA concentration-dependent increase in H2O2 emission occurs at PCoA > 200nmol/mg protein that in states 4 and 3 respiration plateaus in Sham at ~ 600pmol H2O2/min/mg mito protein ( Fig 2C and 2D ) , In contrast , H2O2 emission from diabetic mitochondria under state 3 respiration increased almost 2-fold higher compared to Sham controls ( Fig 2D ) , whereas in state 4 lower values than Sham were attained ( Fig 2C ) . The relationship between the rates of respiration and H2O2 emission in heart mitochondria from Sham and diabetic GPs is also shown in Fig 2 . In state 3 respiration , the ROS efflux stays approximately constant in the VO2 range from 50 to 100 nmol O2/min/mg mito prot but ~2-fold higher in diabetic as compared to Sham ( Fig 2F ) . At higher state 3 respiratory fluxes , ROS emission increases steadily as a function of VO2 up to ~ 130nmol O2/min/mg mito prot , and plateauing at VO2 > 150nmol O2/min/mg mito prot , although higher in diabetic than in Sham GPs ( Fig 2F ) . At both ends of low and high respiration exhibited by mitochondria exposed to different PCoA concentrations ( Fig 2A–2D ) , the H2O2 emission expressed as a percentage of the total O2 consumption flux [13] were for Sham/diabetic GP , respectively , 0 . 45%/0 . 89% and 0 . 38%/0 . 63% in state 3 ( Fig 2F ) and 0 . 82%/0 . 82% and 2 . 2%/1 . 96% in state 4 respiration ( Fig 2E ) . Unlike in state 4 mitochondria from diabetic GPs , in which H2O2 emission augmented steadily as a function of VO2 , in Sham ROS release remained independent from VO2 to increase only after a certain threshold of respiratory flux was overcome ( Fig 2E ) . Together , the experimental results obtained so far show that at PCoA < 200nmol/ mg prot , VO2 and H2O2 emission remained constant , while both increased as a function of the lipid precursor within the range 200–600nmol PCoA/mg mito prot , with overt ( non-compensated ) respiratory uncoupling and excess ROS emission happening at > 600nmol/ mg mito prot . To help interpret the mechanisms underlying the observed increase in VO2 and ROS efflux from mitochondria , our computational model was utilized to simulate the experimental data under conditions mimicking those employed with isolated mitochondria . The simulations shown in Fig 3 reproduce the shape of the increase in VO2 as a function of PCoA concentration ( 0 to 40μM PCoA equivalent to 0 to 800nmol PCoA/mg mito prot: see Fig 2 ) observed in the experiments corresponding to states 4 ( compare Fig 2A with Fig 3A ) and 3 respiration ( compare Fig 2B with Fig 3B ) . In state 4 respiration the model results reproduce the rise of VH2O2 at PCoA concentration above 20 μM while further showing that the extent of the increase in VH2O2 can be modulated by the scavenging capacity of mitochondria , i . e . , achieving a higher VH2O2 at lower scavenging levels ( simulated with different concentrations of glutathione reductase , GR; compare Fig 3C with Fig 2C ) . In state 3 respiration , model simulations are able to reproduce the rise and saturation of VH2O2 and , additionally , that the response can be modulated by the antioxidant capacity of mitochondria ( compare Fig 3D with Fig 2D ) . Consequently , as suggested by the model simulations , the difference between Sham and diabetic mitochondrial H2O2 emission may be due to the lower scavenging capacity of the STZ-treated GPs ( Fig 3 ) . Mechanistically speaking , our model simulations attribute a direct role to uncoupling of the mitochondrial inner membrane triggered by PCoA > 20μM ( or > 400nmol PCoA/mg mito prot ) as a main determinant of the modulation of the overall shape of the relationship of VO2 and VH2O2 vs . PCoA ( Fig 2A–2D ) , along with the apparent sigmoidal behavior exhibited by these two fluxes under state 3 respiration when plotted together ( Fig 2F ) . According to the model , the plateau of VH2O2 at high VO2 , corresponding to PCoA > 30μM ( or > 600nmol PCoA/mg mito prot; compare panels F from Figs 2 and 3 ) can be explained from a PCoA concentration-dependent uncoupling of mitochondria at high lipid concentration , reducing H2O2 emission through decreased ROS generation by the respiratory chain . On the other hand , overwhelming and/or causing impairment of the ROS scavenging systems can also modulate ( up or down ) the relationship between both respiratory and ROS fluxes ( Figs 2 and 3 ) . To investigate the impact of lipid uncoupling on mitochondrial energetics , we performed experiments with isolated mitochondria consuming PCoA while monitoring NAD ( P ) H [35] , and the results are depicted in Fig 4 . As a measure of mitochondrial energetics , we monitored NAD ( P ) H levels during β-oxidation with PCoA 0–20μM ( equivalent up to 400nmol PCoA/mg mito prot ) . Within the PCoA concentration range evaluated , mitochondria conserve the state 4→3 transition triggered by 5mM G/M followed by 1mM ADP , as a lipid-independent way to assess the energetic response [21 , 36] , but start to show some impairment at 20μM PCoA , as can be judged from the response to ADP ( Fig 4A ) . Computational simulations mimicking the experimental protocol show , firstly , that the experimentally determined initial NAD ( P ) H response to PCoA addition corresponds to the expected redox rise from β-oxidation while its subsequent decrease could be explained by PCoA consumption via β-oxidation ( Fig 4B ) , as indicated by the extent of the NAD ( P ) H peak vs . PCoA ( Fig 4A ) ; secondly , that at PCoA > 20μM an uncoupling effect by the lipid becomes noticeable , leading to a diminished response to ADP during the state 4→3 transition ( Fig 4A and 4B ) . The differences between experimental and model simulations data after PCoA , but before G/M , addition , can be explained by the fact that , unlike in the experiment , PCoA is “clamped” in the model at the indicated concentration . This explains that the more pronounced NADH oxidation observed in the model at 25μM compared to 20μM PCoA corresponds , to a certain extent , to the uncoupling effect of the lipid whereas in the experiments PCoA is consumed faster via β-oxidation thus the uncoupling effect is less prominent . Besides it is worth mentioning that the model could simulate the respiratory coupling ratio ( RCR ) observed experimentally to an acceptable approximation ( ~4 vs . 6 , theoretical vs . experimental , respectively ) . As a caveat , while experimentally the RCR decreased from 6 to ~5 at 40μM PCoA ( = 800nmol/mg mito prot ) ( Fig 2A and 2B ) , in the model it dropped from ~4 to ~2 at 10μM and >30μM PCoA , respectively . Thus , in the model the effect of uncoupling is higher than in the experiments . Mechanistically , the PCoA uncoupling effect was taken into account by the model through an increase of the mitochondrial leak via the PCoA-dependent increase in proton conductance ( Fig 5A and 5B ) . Under parametric conditions in which the model was able to reproduce the overall shape of H2O2 emission as a function of mitochondrial respiration ( compare panel F from Figs 2 and 3 ) , Fig 5 depicts changes in protein conductance and redox components of the glutathione and thioredoxin antioxidant systems as a function of PCoA , within the experimentally assayed concentration range , in both states 4 ( Fig 5A and 5B ) and 3 respiration ( Fig 5C and 5D ) . At PCoA > 20μM , a significant increase in the mitochondrial H2O2 emission flux ( VH2O2 ) happens in state 4 respiration ( Fig 5A and 5B ) , together with a parallel decrease in mitochondrial glutathione ( GSHm ) , accompanied by a slight decrease in the reduced pool of thioredoxin ( Trx[SH]2 ) , followed by NAD ( P ) H oxidation at higher PCoA concentration ( Fig 5B ) . In state 3 respiration , VH2O2 describes a biphasic curve of increase as a function of proton conductance ( Fig 5C ) , in which the initial phase occurs associated with an abrupt decrease in GSHm whereas the second , smoother phase appears to be determined by oxidation of Trx[SH]2 ) and NAD ( P ) H at relatively higher PCoA concentrations ( Fig 5D ) . Together , the qualitative behavior of the experimental and model results converge in indicating the combined involvement of uncoupling and the ROS scavenging systems in the mitochondrial energetic-redox response to PCoA during β-oxidation . The explanation offered by the model , i . e . , that the concomitant increase in respiratory and H2O2 emission fluxes elicited by PCoA concentration ( > 20μM ) under state 3 respiration is associated with interdependent actions of lipid-elicited uncoupling and overwhelming/impairment of the matrix GSH and thioredoxin ( Trx ) antioxidant systems ( Fig 5 ) , was further tested experimentally . We asked whether mitochondria from diabetic GPs possessed a different protein expression profile of the antioxidant systems as compared to Sham controls , or the lipid was eliciting enzymatic activity impairment leading to loss of antioxidant capacity . The protein expression level of main mitochondrial ROS scavenging systems by Western blot revealed no significant differences between seven components of the antioxidant systems in mitochondria from Sham and diabetic GPs ( Fig 6A and 6B ) . A similar outcome was found between wild types and two different animals models of type 2 diabetes , db/db mice [21] and Zucker diabetic fatty rats [18] . Since these results pointed out differences in activity as responsible for the results observed , we analyzed two of the main branches of the antioxidant defenses , GSH and Trx systems . Their antioxidant capacity was estimated by quantitating H2O2 emission in the absence or in the presence of 1-chloro-2 , 4 dinitrobenzene ( DNCB ) and auranofin ( AF ) , two specific inhibitors of GSH/Trx , respectively [13 , 16] , when mitochondria from Sham or diabetic GPs were consuming PCoA/malate or glutamate and malate ( G/M ) . Judging from the specific mitochondrial H2O2 emission when GSH/Trx are inactive ( presence of AF+DNCB ) or active ( absence of inhibitors ) , with substrates PCoA/malate , the amount of ROS generated under state 4 respiration that was scavenged was 87% and 79% in Sham and diabetic , respectively , and 83% and 73% in state 3 respiration ( Fig 6C ) . With G/M in state 4 respiration , the amount of ROS generated that was scavenged was 97% and 95% in Sham and STZ , respectively , whereas it represented 98% and 98% in state 3 respiration ( Fig 6D ) . The results obtained indicate that in the presence of PCoA the mitochondrial GSH/Trx scavenging capacity was lower than in G/M , and more so in diabetic than in Sham mitochondria , suggesting that the lipid oxidation or intermediates from the β-oxidation pathway reduced the activity of the antioxidant systems evaluated , resulting in their being overwhelmed . Comparatively , and with the exception of state 3 respiration in the presence of AF+DNCB , mitochondria from diabetic GPs released significantly more ROS than controls , both with PCoA ( Fig 6C ) or G/M ( Fig 6D ) . The lower ROS emission from diabetic as compared to Sham mitochondria , when both GSH/Trx systems were inhibited with AF+DNCB , unveils previously described deficits in the GP animal model [32] in both the electron flow through the respiratory chain ( Complex II and IV ) and the phosphorylation system , which may ultimately constrain ROS generation . Model simulations of the increase in mitochondrial H2O2 emission in response to inhibition of the glutathione reductase from the GSH/Trx system , were able to reproduce semi-quantitatively the experimental results in the presence of PCoA as substrate ( compare panels C-E in Fig 6 ) . Irrespective of the changes in ROS efflux , VO2 did not change at low or high antioxidant capacity ( Fig 6F ) . Taken together , experimental and modeling results enable us to conclude that , compared to Shams , the larger ROS emission exhibited by heart mitochondria from diabetic GPs is due to a lipid-dependent decrease in antioxidant activity ( namely GSH and Trx ) , and an associated mitochondrial uncoupling . These results also indicate that PCoA modulates the relationship between respiration and ROS emission from mitochondria within the concentration range of 200-600nmol/mg mito prot , with respiratory uncoupling and energetic-redox impairment occurring at PCoA > 400nmol/mg mito prot .
Through β-oxidation , FAs are main metabolic fuels for heart and skeletal muscle function [38] . In the heart , two thirds of the cellular ATP is generated from FAs which provide reducing equivalents ( NADH and FADH2 ) via mitochondrial β-oxidation . The higher energy delivered by the saturated FA palmitate in the form of reducing power ( i . e . , three times higher than from glucose in ATP/mol substrate ) , provides electrons to antioxidant systems and mitochondrial respiration [17 , 39] . It has been shown that energization of mitochondria by substrate oxidation increases the antioxidant potential of the thioredoxin system in the mitochondrial matrix where Trx ( SH ) 2 rose in parallel with NAD ( P ) H and GSH as well as mitochondrial membrane potential ( ΔΨm ) after glutamate/malate addition and remained high both in states 3 and 4 respiration [16 , 21] . The rate of β-oxidation is led by demand , implying that increased work rate and ATP demand drives faster OxPhos and tricarboxylic acid cycle activity [38] . Although existing evidence favors the idea that during T1DM the myocardial shift from glucose to FA utilization may aggravate mitochondrial and contractile dysfunctions [27 , 40] , recent studies show that FAs may actually benefit cardiac function , at least acutely , in the course of metabolic syndrome . Cardiac myocytes from T1DM GPs exposed to high glucose and adrenergic stimulation with isoproterenol were not able to fully contract and relax , an effect that was found associated with mitochondrial oxidized redox status leading to impaired ATP synthesis along with altered Ca2+ handling and myocyte mechanical function [32] . In this T1DM GP animal model [32] , as well as in db/db mice exposed to high glucose and β-adrenergic stimulation [21] , and in the Zucker Diabetic Fatty rat , where hyperglycemia had a significant negative impact on contractility of heart trabeculae [18] , palmitate was able to rescue contractile performance via higher antioxidant capacity of the GSH/Trx systems . The pathogenesis of diabetes involves alterations in lipid oxidation by mitochondria . Inherited or acquired mitochondrial dysfunction may cause slow FA degradation driving the accumulation of intramyocellular lipids [41 , 42] . Also , mismatch between excess lipid supply with respect to demand may generate excess ROS [10] . Although acceleration of the β-oxidation flux could improve insulin sensitivity , disease may also ensue from inappropriately elevated β-oxidation flux in the absence of demand . Central to any of these possible situations is to determine the mechanisms through which mitochondria control ROS release as a function of lipid availability , and how this affects their energetic function . In this regard , the present work shows that , in the heart , mitochondria can increase their ROS release as a function of the rate of β-oxidation dependent respiration , but also that impairment of mitochondrial energetics-redox function would only start to happen after a certain threshold of PCoA concentration ( > 400 nmol/min/mg prot ) is crossed , triggering , for example , progressive uncoupled respiration and ROS emission in both states 4 and 3 respiration in Sham and diabetic mitochondria ( Figs 2 and 3 ) and impairment of the state 4→3 transition ( Fig 4 ) . Enhanced H2O2 emission was caused by concomitant OxPhos uncoupling with decreased activity of matrix GSH/Trx ROS scavenging systems according to experimental ( Fig 6 ) and modeling evidence ( Fig 5 ) . In agreement with previous reports [17 , 18 , 21 , 32 , 43 , 44 , 45] , these results support the notion that in the diabetic heart the antioxidant capacity is lower , thus explaining , at least in part , the increased levels of oxidative stress observed . Besides their metabolic role in energy provision , long-chain FAs affect cellular membranes and enzyme catalysis [46] . Non-esterified and esterified FAs interfere with mitochondrial OxPhos in vitro [47] acting as weak uncouplers [48] by increasing state 4 respiration [49 , 50] . Under reverse electron transport , FAs dramatically decrease mitochondrial ROS generation by an as yet unknown mechanism [51] . In contrast , the relatively low ROS emission by mitochondria under forward electron transport is significantly increased in the presence of FA [48 , 51] . In the present work , we show that the latter is likely true at relatively high concentrations of lipid precursor whereas at relatively lower concentrations ( ≤ 400nmol PCoA/mg mito prot ) H2O2 emission stays constant and low , although higher in diabetic as compared to control mitochondria ( Fig 2 ) . Mitochondria modulate both the release as well as scavenging of H2O2 from the cytoplasm thus playing a key role in cellular redox conditions and redox-dependent signaling , vital for normal cell function [17 , 52 , 53] . Using targeted viral gene transfer vectors expressing redox-sensitive GFP fused to sensor domains to measure H2O2 or oxidized glutathione in H9c2 cells , and selective knockdown ( by 50%-90% ) or overexpression of antioxidant enzymes , Dey and colleagues [14] showed that ROS scavenging by mitochondria significantly contributes to cytoplasmic ROS handling . Knockdown of the cytosolic antioxidant enzymes had no statistically significant effect on mitochondrial matrix H2O2 , in agreement with the idea that the mitochondrial scavenger reserve capacity was high enough to buffer H2O2 diffusing into the matrix even when the cytoplasmic system was impaired [14] . Keeping a proper cellular/mitochondrial redox environment is vital for optimal excitation-contraction ( EC ) coupling as well as energy supply in the heart [53 , 54 , 55] . Intracellular redox balance affects Ca2+ handling by functionally stabilizing a wide range of proteins implicated in EC coupling [30] including the sarcoplasmic reticulum ( SR ) Ca2+ release channels , the SR Ca2+ pumps , and the sarcolemmal Na+/Ca2+ exchanger [56] . Consistent with the concept of a prominent role of lipids on governing the intracellular redox status , it was shown that palmitate determines a transition from oxidized-to-reduced redox state coupled to a marked GSH rise that abated ROS levels drastically in cardiomyocytes from T1DM and T2DM hearts . As a consequence of its favorable effect on cellular redox balance , palmitate significantly improved contractile performance in cardiomyocytes from STZ-treated GPs [32] , db/db mice [21] and heart trabeculae from Zucker rats [18] . The findings described herein suggest that keeping the intracellular levels of FAs low is critical to avoid detrimental oxidative stress . Under lipid surplus , development of tissue lipotoxicity and dysfunction are linked to alterations in LD biogenesis and regulation of hydrolysis of triacylglycerols [57] . In pathologic states lipotoxicity may occur over time [29] , despite triacylglycerol accumulation , when either the cellular capacity for triacylglycerol ( TAG ) storage is exceeded or when triglyceride pools are hydrolyzed , resulting in increased cellular free FA levels . Thus , the duration and extent of lipid overload may determine if a cell is protected or damaged . Lipid storage and utilization appears to be a tightly regulated cellular process ( reviewed in [39] ) . Perilipins are involved in modulation of LD storage-utilization dynamics [57] . Reduced expression of perilipins may promote both lipolysis and fat oxidation , resulting in reduced intracellular TAG and adipose mass whereas excessive lypolysis and defective lipid storage may promote insulin resistance and impaired cardiac function through chronic mitochondrial FA overload . As a matter of fact , excessive triacylglycerol catabolism by perilipin5-deficient hearts is paralleled by increased FA oxidation and enhanced ROS levels leading to age-dependent decline in heart function . Consequently , uncontrolled lipolysis and defective lipid storage may impair cardiac function through chronic mitochondrial FA overload [58 , 59] . Proper mitochondrial function is needed to sustain energy supply reliably while releasing ROS levels compatible with signaling . However , lipids in excess can derail both of these critical functions . In keeping with the results reported herein , cytoplasmic mechanisms for “sequestering” FAs ( and those from lipotoxic intermediates ) to keep their concentration low become relevant . Metabolic channeling of lipid transport and β-oxidation , involving direct delivery into mitochondria , may represent a reliable and efficient way to ensure energy supply and redox control . Such a mechanism could avoid exceeding the limit of lipid storage capacity and help in hindering lipotoxicity , which is relevant under heavy influx of FAs as happens in skeletal muscle or heart in matching energy supply with demand when subjected to high workload .
GPs were rendered diabetic by Hilltop Lab Animals , Inc . ( Scottsdale , PA ) , following the procedure that we described previously [32] . Briefly , male GPs ( 200-250g ) were made diabetic by a single intra-peritoneal injection of buffered streptozotocin ( STZ group , 80 mg/kg in citrate buffer pH 4 . 5 ) . Littermate animals received an equivalent volume of vehicle ( citrate buffer pH 4 . 5 ) ( Sham ) . According to our protocol , elsewhere described [32] , Sham and STZ animals were utilized after 4 weeks of STZ administration , the minimum time needed to observe the T1DM phenotype . Diabetic GPs had 36% higher levels of glucose in blood ( in mg/dl±S . E . M: Sham 153±3 . 2 vs . STZ 208±6 . 5; p<0 . 001 , i . e . , from ~8 to ~12mM glucose , n = 26 and n = 24 , respectively ) . No significant differences in body weight between the two groups of animals were detected [32] . Procedures for the isolation and handling of mitochondria from guinea pig hearts were performed as previously described [12 , 35] . High-throughput–automated 96-well microplate reader analyses of respiration ( XF96 extracellular flux analyzer; Seahorse Bioscience ) [13] , and ROS ( H2O2 ) emission ( Flex Station 3 , Molecular Devices ) , were performed in parallel in freshly isolated mitochondria from GP heart . The rate of O2 consumption , VO2 , was evaluated in mitochondria ( using the equivalent of 10μg of mitochondrial protein ) under β-oxidation fueled conditions in the presence of PCoA in a dose-response manner ( 5–40μM , corresponding to 100–800 nmol PCoA/mg . mito prot ) , 0 . 5mM malate and 0 . 5mM L-carnitine , in a medium ( buffer B , 200μl final assay volume ) containing ( in mM ) : 137 KCl , 2 KH2PO4 , 0 . 5 EGTA , 2 . 5 MgCl2 , and 20 HEPES at pH 7 . 2 and 37°C in presence of 0 . 2% fatty acid–free BSA [13] . The VO2 corresponding to states 4 and 3 respiration was determined before and after addition of 1mM ADP , respectively . Respiratory Control Ratios ( state3/state4 ) of 5 or higher were obtained . The experimental rates are expressed in pmol min-1 mg-1 mitochondrial protein to enable comparison with data in the literature . However , modeling results are expressed in mM s-1 for VO2 and μM s-1 for VH2O2 . A conversion factor of 1 μmol min-1 mg-1 protein = 16 . 67 mM s-1 relates both flux units based on a mitochondrial volume of 1 μl per mg of mitochondrial protein . The day before the experiment , 120μl polyethylenimine ( 1:15000 dilution in buffer B of a 50% solution of polyethylenimine ) were added to the wells of the XF96 plate and incubated overnight at 37°C . For comparison purpose , internal controls were run in the absence of β-oxidation with NADH-linked substrates ( G/M , 5/5 mM each ) . Before the experiment , the solution of polyethylenimine was removed . After transfer of appropriate amounts of mitochondrial suspension into each well ( 10μg of mitochondrial protein ) , the microplate was centrifuged at 3 , 000 x g for 7 min at 4°C using a swinging bucket rotor ( S5700; Beckman Coulter ) . To avoid temperature inhomogeneity effects , the plate was incubated at 37°C for 20 min before starting the assay in the Seahorse Bioscience equipment . Using mitochondria from the same preparation ( 10μg mitochondrial protein ) , parallel fluorescence measurements of H2O2 emission with the Amplex Red kit ( Invitrogen ) ( λexc = 530 nm and λem = 590 nm ) were performed with a Flex station , under the same aforementioned substrate and buffer conditions , with the exception that BSA was not added and the plate wells were not coated with polyethylenimine . The H2O2 emissions corresponding to states 4 and 3 respiration were quantified before and after addition of 1mM ADP , respectively . The specific rate of H2O2 emission was determined in each well using an internal standard H2O2 solution , and calculated from the slopes of the normalized Amplex Red signal with respect to initial fluorescence ( F0 ) , before the addition of substrate . This procedure automatically discounts any drift of the baseline due to effects not specifically produced by the corresponding treatment [12 , 13] . Both VO2 and ROS emission measurements were performed in the linear range of detection with respect to the amount of mitochondrial protein utilized . Examples of raw data from O2 consumption rate and H2O2 emission measurements are displayed in Fig C in S1 Text . Mitochondrial protein was determined using the bicinchoninic acid method protein assay kit ( Thermo Fisher Scientific ) . Since the experiments reported herein are performed in aqueous media to avoid introducing additional hydrophobic chemicals ( i . e . , diluent ) other than the lipid itself and the natural substrates of mitochondrial respiration , we chose PCoA because its critical micellar concentration ( CMC ) is in the range of 40–60μM at 23°C , which also depends upon ionic composition and pH [60 , 61] . In our mitochondrial assay buffered medium , at 37°C and in the absence of mitochondria , PCoA starts to form micelles at 40μM concentration , as checked by fluorometry using 90o light scattering . To rule out unspecific surfactant effects elicited by PCoA within the concentration range utilized ( up to 40μM ) , we measured mitochondrial respiration in the absence of malate ( needed for feeding the tricarboxylic acid [TCA] cycle to enable β-oxidation to proceed ) . Under these conditions , VO2 with PCoA was very low , up to 3 and 7nmol O2 . min-1 . mg-1 prot in states 4 and 3 , respectively . Comparatively , under the same conditions but in the presence of malate , respiration with PCoA was up to 22 and 125nmol O2 . min-1 . mg-1 prot in states 4 and 3 , respectively . Importantly , as direct evidence that the mitochondria are coupled , at all PCoA concentrations there are clear differences between states 3 and 4 respiration ( Fig 2A and 2B and Fig C in S1 Text ) , i . e . , in uncoupled mitochondria states 4 and 3 respiration would be similar . Together , these controls rule out uncoupling due to unspecific permeabilization of mitochondrial membranes by PCoA . With the aim of both controlling for putative binding effects of PCoA to albumin and its impact on mitochondrial H2O2 emission , and the reproducibility of the PCoA dose-response , we performed experiments in two different experimental setups ( high throughput Flex station plate reader and low throughput fluorometric monitoring ) , and with the same freshly prepared mitochondria , in the absence or presence of 0 . 2% free FA BSA . These controls show that the results are reproducible , independently from the experimental set up , and that the results are not significantly different in the absence or presence of BSA in states 4 and 3 respiration ( Fig D in S1 Text ) . We also present controls of mitochondrial respiration with 5mM G/M , in the absence or presence of 0 . 2% free FA bovine serum albumin , conducted in Seahorse XF96 equipment , showing that VO2 is not significantly affected by BSA . NAD ( P ) H , and H2O2 emission with Amplex Red , were determined as previously described [12 , 13 , 35] , and monitored simultaneously with a wavelength scanning fluorimeter ( QuantaMaster; Photon Technology International , Inc . ) using the same above mentioned medium for measuring respiration and a multidye program for simultaneous online monitoring of different fluorescent probes . Heart tissue from Sham or STZ-treated GPs were homogenized using a polytron homogenizer into 5 volumes/weight of extraction buffer ( 50 mM Bis-Tris ( pH 6 . 4 ) , 2% SDS with protease inhibitor cocktail ( EDTA Free , Roche ) . Protein concentration was determined using bicinchoninic acid [BCA] method protein assay kit ( Thermo Fisher Scientific ) . Equal protein loads of extract were run on 4–12% Acrylamide Bis-Tris Gels ( Life Technologies ) . Gels were transferred ( using Biorad wet transfer apparatus ) to nitrocellulose membranes with Tris/Glycine buffer and membranes blocked with Odyssey blocking reagent ( Li-Cor Biosci . ) in TBS buffer . Membranes were then probed with primary antibodies raised to the following antioxidant proteins: Thioredoxin 2 ( Trx2 , Rabbit Polyclonal , Abfrontiers ) , Thioredoxin Reductase 2 ( TrxR2 , Rabbit polyclonal , Abfrontiers ) , Glutatione Reductase ( GR , Rabbit polyclonal , Ab Frontiers ) ; Superoxide Dismutase 2 ( SOD2 , Rabbit Polyclonal , Santa Cruz Biotechnol . ) , Nicotinamide nucleotide transhydrogenase ( NNT , Rabbit polyclonal , Aviva Systems Biology ) , Glutathione Peroxidase 4 ( Gpx4 , Rabbit polyclonal , Abcam ) , Peroxiredoxin 3 ( Prx3 , Rabbit polyclonal , Abfrontiers ) . Fluorescent secondary antibodies labeled with either IRDye 800CW or IRDye 680RD was used to visualize protein bands utilizing an Odyssey Infrared Scanner ( Li-Cor Biosci . ) and bands quantitated using Odyssey software . L-carnitine and palmitate were from Sigma , and palmitoyl Coenzyme A ammonium salt from Avanti Polar Lipids , Inc . Data were analyzed with the software GraphPad Prism [Ver . 6; San Diego , CA] or MicroCal Origin . Significance of the difference between treatments was evaluated with one-way ANOVA using Tukey's multiple comparison test , or with a t test [small samples , paired t test with two tail p values] and the results presented as mean±SEM [95% confidence interval] .
The present model ( Fig 1 ) accounts for the β-oxidation pathway from PCoA , which was modeled based on van Eunen et al . [34] . The model formulation considers the transport of PCoA from the cytoplasmic to the mitochondrial matrix via carnitine palmitoyltransferase I ( CPT1 ) , carnitine acylcarnitine translocase ( CACT ) and carnitine palmitoyltransferase II ( CPT2 ) ( Eqs . S1-S3 in S1 Text ) . As a caveat , our formulation differs from that of van Eunen and colleagues , since the only substrates of CPT2 considered are PCoA and palmitoyl carnitine . Thus , the competition of CPT2 and CPT1 through the various acyl-carnitine and acyl-CoA species was not taken into consideration in our β-oxidation model ( section 1 in S1 Text ) . The β-oxidation model describes the catabolism of PCoA through the recursive action of four enzymes: very long- , long- , middle- and short-chain fatty acyl-CoA dehydrogenases , catalyzing consecutive steps in cycles , where in each of seven cycles 2-carbon units ( i . e . , AcCoA ) are released . β-oxidation reactions occur coupled to the reduction of either flavin adenine dinucleotide ( FAD ) in the steps catalyzed by the fatty acyl-CoA dehydrogenases , or NAD+ by β-hydroxy-acyl CoA dehydrogenases ( equations S4-S22 and S31-38 , respectively , in S1 Text ) . A more detailed description of the β-oxidation model equations and parameters can be found in S1 Text . The coupling between β-oxidation , the TCA cycle and mitochondrial electron transport chain is accomplished through NADH , AcCoA and FADH2; the latter reduces the electron transferring protein FAD that , in turn , donates electrons to ubiquinone in the respiratory chain [62] . The role of PCoA in the present model can be both as a substrate—providing AcCoA and reduction equivalents to feed the TCA cycle and the respiratory chain through the electron carriers NADH and FADH2—and as an uncoupler at high concentration ( above 200nmol/mg mito prot ) . The PCoA-mediated uncoupling is modeled as an increase in the proton conductance , gH ( Eq . S137 in S1 Text ) as a function of the cytoplasmic PCoA concentration to the fourth power . The need for a fourth power dependence can be attributed to the system approaching the critical micellar concentration ( CMC ) and more molecules of PCoA being incorporated into the mitochondrial membrane , altering its permeability . A more detailed description of the β-oxidation model equations and parameters can be found in S1 Text . Model simulations were run with a code written in MATLAB ( The Mathworks , Natick , MA ) using the ODEs15 integrator . In S1 Text , the system of ordinary differential equations ( ODEs ) ( section 2 Appendix ) and the Matlab code for the full computational model as well as parameters ( Tables A-M ) , and initial conditions ( Table N ) are listed . Results reported correspond to steady state behavior , when the relative time derivative of each variable is ˂ 1 . 10−10 sec-1 . | Lipids are main sources of energy for liver and cardiac and skeletal muscle . Mitochondria are the main site of lipid oxidation which , in the heart , supplies most of the energy required for its blood pumping function . Paradoxically , however , lipids over supply impair mitochondrial function leading to metabolic syndrome , insulin resistance and diabetes . In this context , scientific debate centers on the impact of lipids and mitochondrial function on diverse aspects of human health , nutrition and disease . To elucidate the underlying mechanisms of this issue , while accounting for both the fundamental role of lipids as energy source as well as their potential detrimental effects , we utilized a combined experimental and computational approach . Our mitochondrial computational model includes β-oxidation , the main route of lipid degradation , among other pathways that include oxygen radical generation and consumption . Studies were performed in heart mitochondria from type 1 diabetic and control guinea pigs . Model and experimental results show that , below a concentration threshold , lipids fueling proceeds without disrupting mitochondrial function; above threshold , lipids uncouple mitochondrial respiration triggering excess emission of oxidants while impairing antioxidant systems and the mitochondrial energy supply-demand response . These contributions are of direct use for interpreting and predicting functional impairments in metabolic disorders associated with increased circulating levels of lipids and metabolic alterations in their utilization , storage and intracellular signaling . | [
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| 2017 | Mitochondrial respiration and ROS emission during β-oxidation in the heart: An experimental-computational study |
Transcription elongation by RNA polymerase II was often considered an invariant non-regulated process . However , genome-wide studies have shown that transcriptional pausing during elongation is a frequent phenomenon in tightly-regulated metazoan genes . Using a combination of ChIP-on-chip and genomic run-on approaches , we found that the proportion of transcriptionally active RNA polymerase II ( active versus total ) present throughout the yeast genome is characteristic of some functional gene classes , like those related to ribosomes and mitochondria . This proportion also responds to regulatory stimuli mediated by protein kinase A and , in relation to cytosolic ribosomal-protein genes , it is mediated by the silencing domain of Rap1 . We found that this inactive form of RNA polymerase II , which accumulates along the full length of ribosomal protein genes , is phosphorylated in the Ser5 residue of the CTD , but is hypophosphorylated in Ser2 . Using the same experimental approach , we show that the in vivo–depletion of FACT , a chromatin-related elongation factor , also produces a regulon-specific effect on the expression of the yeast genome . This work demonstrates that the regulation of transcription elongation is a widespread , gene class–dependent phenomenon that also affects housekeeping genes .
It is well known that RNA pol II accumulates under repressive conditions on some tightly regulated genes of higher eukaryotes , like human c-Myc [1] and Drosophila hsp70 [2] . RNA pol II pausing is in fact a frequent situation since a significant proportion of metazoan genes exhibits paused polymerases at promoter-proximal sites [3]–[8] . Although pauses and arrests during transcription elongation seem to be also common phenomena further downstream [9] . In yeast , almost 2500 repressed genes show poised RNA pol II in the stationary phase [10] but only a few , like CYC1 and those encoding NTP-biosynthetic enzymes , display an accumulation of RNA pol II at their 5′ region under repressive conditions in exponential growing cells [11] , [12] . For NTP genes , transcription regulation works at the level of initiation through an attenuation mechanism [12] , [13] . It is not clear whether the accumulation of RNA pol II at the 5′ end in the other cases responds to a pausing phenomenon . In any case , RNA pol II pausing at promoter-proximal sites is not a frequent phenomenon in exponentially growing yeast [14] which has been proposed to reflect the different chromatin organization of the transcription start sites in yeast compared to metazoa [15] . In the last 20 years , biochemical and genetic analyses have revealed a numerous set of factors playing auxiliary roles in RNA Polymerase II ( RNA pol II ) -dependent transcription elongation [16] . The textbook view of transcriptional machinery is a uniform set of players that all genes require equally . However , it is already well known that the diversity in core promoter elements throughout the genome reflects certain gene-specific roles of the general transcription factors involved in the pre-initiation complex ( PIC ) assembly . For instance , yeast TATA box-containing genes are highly regulated and preferentially utilize SAGA rather than TFIID if compared to TATA-less promoters [17] . According to such differences , a TBP regulatory network to explain gene-specific differences in the PIC assembly has been proposed [18] . Similarly , several examples of gene-specific roles of elongation factors have been described . Mutations affecting the integrity of the yeast THO complex , involved in transcription elongation and mRNP biogenesis , decrease the expression levels of long transcription units , but do not significantly influence the mRNA levels of the shorter ones driven by the same promoter [19]–[21] . TFIIS , an elongation factor that is dispensable for the expression of most yeast genes , is absolutely required for the activation of IMD2 in response to NTP depletion [22] . Mammalian splicing factor SC35 also plays a gene-specific role in transcription elongation since its depletion produces an accumulation of inactive RNA pol II on several , but not all , active transcription units [23] . The transcription of the p53-dependent gene p21 does not require the phosphorylation of the carboxy-terminal domain of RNA pol II ( CTD ) in the serine residue situated at position 2 ( Ser2 ) . This indicates that the requirement of P-TEFb for transcription elongation is also gene-specific [24] . The chromatin factor FACT , involved in chromatin remodeling and reassembly during transcription elongation [25] , [26] , is also dispensable for the expression of p21 [24] . Likewise , the expression of the yeast CUP1 gene , which can be transcribed by a mutant version of RNA Pol II lacking the CTD [27] , is not affected by FACT depletion [28] . Furthermore by comparing five genes under the control of the same promoter , we have previously shown that FACT is not equally required by all the genes during transcription , and that this differential requirement is related to the chromatin configuration of the transcribed region [28] . In this work , we investigated the distribution of actively elongating and total RNA pol II by means of a new methodological approach that combines genomic run-on ( GRO ) and ChIP-on-chip . We detected significant gene-specific differences in the proportion of active RNA pol II present in the transcribed regions . The effect of FACT depletion was also differential for some gene functional categories such us those encoding mitochondrial proteins , or housekeeping genes encoding cytosolic ribosomal proteins and factors involved in ribosome biogenesis . We found that the transcription elongation of ribosome-related genes responds to regulatory stimuli mediated by the protein kinase A pathway , and by the Rap1 transcription factor for those genes that encode structural ribosomal proteins . We also found that an inactive form of RNA polymerase II , which is phosphorylated in the Ser5 residue of the CTD but is hypophosphorylated in Ser2 , accumulates along the full length of these genes , during standard growing conditions .
We measured the association of RNA pol II with yeast genes in exponentially grown cells in YPD by performing RNA pol II ChIP-on-chip experiments ( RPCC , Pelechano et al . , to be published elsewhere ) . All normalized and processed genomic data are included in Table S1 . We compared the Rpb1-binding data obtained by RPCC with the transcription rate ( TR ) data previously measured by GRO [29] . We found that the ribosomal protein genes ( RP regulon ) were relatively enriched in Rpb1 ( using a Myc-tagged version of it , see Figure S1 ) . Even clearer results were obtained when the RPCC experiments were performed with the 8WG16 antibody , which recognizes RNA pol II CTD ( Figure 1A ) . Gene classes relating to cytosolic ribosome and translation presented significantly high ChIP/TR ratios ( Table 1 ) . A prominent RNA pol II enrichment was also detected in RP genes with an antibody that recognizes the CTD repeats when these are phosphorylated in the serine residue situated at position 5 ( Ser5 ) ( Figure 1B and Table 1 ) . All statistically significant GO categories found in all the genomic experiments are included in Table S2 . We reason that the difference between the GRO and the RPCC data , reflected in the ChIP/TR ratios , could be due to the different degree of accumulation of non-actively elongating RNA pol II either in a step prior to initiation or arrested during elongation ( likely back-tracked ) . Our data indicate that the inactive form of RNA pol II ( not producing a run-on signal ) which accumulated in RP genes was phosphorylated in the Ser5 residue of the CTD . Therefore , we conclude that it should have passed the initiation step of transcription . The detected imbalance between the amounts of RNA pol II bound to RP genes and their TR may either be an intrinsic feature of these genes or reflect the occurrence of a novel mechanism that regulates their expression . In order to test these two possibilities , we calculated the ChIP/TR ratios in three different culture conditions: i ) exponential growth in glucose medium , ii ) 2 h after transferring glucose-grown cells to galactose-containing medium ( non growing cells due to the metabolic shift ) , and iii ) exponential growth in galactose medium ( 14 . 5 h in galactose ) . Then we did a clustering analysis to group genes in accordance with their ChIP/TR patterns . As shown in Figure 2A , two clusters were detected ( numbers 0 and 3 ) in which the ChIP/TR ratio clearly decreased during the shift from glucose to galactose ( 2 h ) , and continued to decrease when cells grew exponentially in galactose ( 14 . 5 h ) . The difference between these two clusters was the kinetics of the ChIP/TR decrease , that is , more intense in the first step for cluster number 0 and deeper in the second step for cluster number 3 ( Figure 2A ) . The genes belonging to the RP and RiBi regulons were significantly enriched in cluster 0 , although RiBi genes were also located in cluster 3 ( Figure 2A ) . The RiBi regulon comprises all the genes encoding the non RP proteins involved in rDNA transcription , tRNA synthesis , ribosome biogenesis and translation ( see [30] for a more precise definition of RiBi ) . The opposite scenario ( higher ChIP/TR ratios in galactose than in glucose ) was detected for clusters 6 and 8 , which were statistically enriched in mitochondria-related genes . We detected a general genome-wide correlation between the ChIP/TR ratios of cells exponentially growing in glucose and those of cells exponentially growing in galactose , indicating that the lower ChIP/TR ratios shown by the RP and RiBi regulons in galactose and the higher ChIP/TR ratios displayed by mitochondria-related genes indeed reflect a specific regulatory phenomenon ( Figure 2B and Table 1 ) . In the first step of the experiment ( 2 h ) , the TR and the amounts of RNA pol II binding to most genes , including the RP regulon , sharply decreased ( Figure 2C ) . This reduction in the genome expression , particularly of the RP genes , is consistent with lack of growth after shifting the culture from glucose to galactose media . Transcription increased in step two ( 14 . 5 h ) once cells recovered their exponential growth rate , as did the TR of the RP genes and the amount of RNA pol II bound to them ( Figure 2C ) . During these successive down- and up-regulation steps however , the ChIP/TR ratios of RP and RiBi genes continuously decreased in relation to the genome average ( Figure 2D; Figure S2A , S2B ) . This was mainly due to a decrease in the relative amount of bound RNA pol II ( Figure S2C ) rather than to a relative change in their TR ( Figure S2D ) . Mitochondria-related genes also underwent a similar down- and up-regulation cycle in both their TR and the levels of bound RNA pol II ( Figure 2C ) , although the average ChIP/TR ratios increased in this case ( Figure 2D; Figure S2A , S2B ) . This increase in the ChIP/TR ratio of mitochondria-related genes was also due to a change in the relative amount of bound RNA pol II ( Figure S2C ) rather than to a variation in the relative TR of respiration genes ( Figure S2D ) . We conclude that the shift from glucose to galactose , and not the growth rate , was the stimulus responsible for the ChIP/TR regulation of RP , RiBi and mitochondria-related genes . The transcriptional response of RP genes to glucose levels is mediated by the TOR and Ras-PKA pathways [31] . To further confirm that glucose signaling regulates the ChIP/TR ratios of ribosomal related genes , we analyzed a Δtpk1 mutant . Tpk1 is one of the three PKAs present in yeast and it is physically located on those genes which are highly transcribed [32] . As shown in Figure 2E , the ChIP/TR ratio of RP and RiBi genes lowered , while the mitochondrial genes increased in the Δtpk1 when compared to an isogenic wild type grown in YPD . The results of Δtpk1 resembled those of the wild type in galactose ( compare Figure 2B and 2E ) . Tpk2 is a second PKA catalytic subunit which is physically located on the promoter regions of RP genes [32] . The results of analyzing a Δtpk2 mutant showed similar patterns to those observed in Δtpk1 . We did not observe any significant variation in the ChIP/TR ratios of RP genes between Δtpk1 and Δtpk2 . We conclude that PKA mediates the signal which regulates the ChIP/TR ratios of RP , RiBi and mitochondrial genes in response to the glucose-galactose shift , but its participation in this regulation does not depend on a particular catalytic subunit . The chromatin elongation factor FACT is formed in yeast by Spt16 , Pob3 and Nhp6 [33] . It has been shown that FACT is physically located on active yeast genes along the whole length of their transcription units [34] . In order to test whether the accumulation of inactive RNA pol II on RP genes in glucose is extensive to transcription elongation factors , we measured the amount of Spt16 bound to yeast genes by performing ChIP and by hybridizing the same kind of arrays as we used for GRO and RPCC experiments . As Figure 3A depicts , there is a general positive correlation between the amount of Spt16 bound to a gene and its TR ( see also Figure S3A ) , and similarly to RNA pol II , the RP genes show higher levels of Spt16 than those expected for their TR ( Figure 3A and Figure S3B ) . In contrast , the RP genes showed no Spt16 enrichment in relation to the amount of RNA pol II bound to them ( Figure 3B ) . Irrespectively of the TR , we found a close correlation between RNA Pol II and Spt16 levels of occupancy , as well as a constant Spt16/Rpb1 ratio ( Figure S3C ) , which suggests that the presence of RNA pol II on a gene , rather than its transcriptional activity , causes FACT recruitment . By using a Tet-off::SPT16 strain , we have previously shown that there is some gene-specificity in the effect of Spt16 depletion on yeast transcription [28] . In order to test whether the excess FACT present in RP genes was dispensable for their actual transcription rates in glucose , we analyzed the transcriptional effect of Spt16-depletion on a genome-wide scale . Following the GRO procedure , we were able to calculate the effect of Spt16 depletion on TR of 5257 genes , which represents 91% of the genes present in the yeast genome . We took these measurements at depletion times at which neither the growth rate nor the viability of the cells was affected . Five hours after adding doxycycline the overall mRNA levels in the cell were not affected ( Figure S4A ) but the TR of most genes had decreased ( Figure 3C and Figure S4B ) , which is in agreement with the general positive roles played by FACT in transcription [35] , [36] . By carrying out a gene-ontology analysis of the TR decrease , we detected functional classes of genes that were particularly sensitive or insensitive to Spt16 depletion . We found that the RP and RiBi regulons were especially resistant to Spt16 depletion ( Figure 3C and Table 1 ) , whereas those genes related to the mitochondria were ranked as hypersensitive ( Table 1 ) . Since ribosomal proteins genes are generally short and contain introns , we analyzed the influence of several structural gene features on sensitivity to Spt16 depletion . No correlation with gene length , G+C content or intron presence was found ( Figure S5A , S5B , S5C ) . Since RP genes are highly transcribed , we also checked the influence of TR itself on the response to Spt16 depletion . We found a linear correlation between TR under depletion conditions and control conditions , thus ruling out that highly expressed genes were proportionally less sensitive to Spt16 depletion ( Figure S5D ) . We performed additional GRO experiments with doxycycline-treated cells over a longer time to achieve a more severe depletion of Spt16 . As shown in Figure 3C , the RP regulon showed a similar distribution of TR to the rest of the genome after 7 h of treatment with doxycycline . At this depletion time almost no overrepresentation of gene ontology classes was observed ( Table 1 ) . These results confirm that the slight accumulation of FACT on the RP and RiBi regulons , in relation to the levels of actively elongating RNA pol II present , makes these genes transiently resistant to Spt16 depletion . Collectively , these results suggest that not only RNA pol II , but additional elements of the transcription elongation machinery , are enriched on the ribosome-related genes in glucose , if compared to their TR . The exceeding signal of RPCC over GRO in glucose for RP genes suggests that an accumulation of non-transcribing RNA polymerases takes place . The accumulation of inactive RNA pol II on ribosome-related genes is compatible with a post-recruitment mechanism of transcription regulation . With paused metazoan genes , the intragenic distribution of RNA pol II is biased toward the 5′ end . In order to know whether the RNA pol II enrichment of RP genes also involves a biased distribution of the enzyme , we analyzed in detail the distribution of RNA pol II on a representative RP gene ( RPS3 ) by ChIP ( Figure 4A–4D ) . As expected , we found higher levels of total RNA pol II in glucose than in galactose ( Figure 4B ) , but we observed lower levels of Ser5- , and much lower levels of Ser2-phosphorylated RNA pol II in glucose on the 3′ end of the transcribed region ( Figure 4B ) . These different intragenic distributions of RNA pol II are fully compatible with a lower elongation efficiency of RNA pol II in glucose in relation to galactose . The representation of the data following the normalization procedure described by [37] supports this conclusion ( Figure 4C ) . Likewise , the representation of the levels of phosphorylated RNA pol II , normalized by the total levels of the enzyme , reveals a clear difference between the two conditions . Whereas phosphorylation in galactose followed the standard pattern , with a moderate decrease of Ser5-phosphorylation along the gene and a sharp increase of Ser2-phosphorylation towards the 3′ end , the increase of Ser2-phosphorylation in glucose along the gene was considerably less evident ( Figure 4D ) . Very similar results were obtained with the detailed analysis of the gene encoding ribosomal protein L25 ( Figure S6A , S6B , S6C , S6D ) . We also investigated the intragenic distribution of active RNA pol II by performing a detailed run-on analysis of the RPS3 gene . In this case , we found similar patterns in both glucose and galactose with comparable levels on the 3′ and medium regions of the transcribed region , and with higher levels at the 3′ end of the gene in galactose than in glucose ( Figure 4E ) . These results support the hypothesis that the accumulation of RNA pol II on RP genes in glucose took place during elongation and was due to a transcriptionally inactive form of RNA pol II that lacked normal levels of Ser2 phosphorylation . In order to confirm the variation in the intragenic distribution of RNA pol II in RP genes from glucose to galactose , we repeated the RPCC experiments described before with a new type of DNA macroarrays containing 300 bp-long probes covering separately the 5′ and the 3′ ends of the transcribed regions of a set of randomly chosen genes ( Rodríguez-Gil et al , submitted ) . We found that most of the RP genes present in this array presented a higher RPCC 5′/3′ ratio in glucose than in galactose ( Figure 4F and 4G ) . This seems to be specific for RP genes since the RiBi genes represented in the array showed similar RPCC 5′/3′ ratios in the two media , as most non ribosomal genes did ( Figure 4F and 4G ) . We also discovered that neither RP nor RiBi genes showed significantly higher GRO 5′/3′ ratios in glucose than in galactose , thus confirming that the enrichment of RNA pol II located toward the 5′ end of RP genes in glucose consisted of transcriptionally inactive molecules ( Figure S6E ) . So far we have described a novel regulated phenomenon affecting the RP genes expression . It is expected that the mechanism underlying it would be operated by the transcription factors that specifically regulate these genes . A transcription factor playing a mayor role in RP genes transcription is Rap1 , a multifunctional protein that also acts as the main duplex DNA binding protein at telomeres , which not only contributes to silencing in both the subtelomeric regions and the mating type loci , but also activates the transcription of glycolytic genes ( reviewed by [38] , [39] ) . Rap1 is essential for the RP expression as it organizes chromatin configuration at the RP genes promoters and allows the binding of Fhl1-Ifh1 , that is , the other two main transcription factors regulating the transcription of RP genes [40] . An important domain of Rap1 is its silencing domain , which is involved in the subtelomeric recruitment of factors that regulate telomere length and gene silencing [41] . Since mutants lacking the silencing domain of Rap1 are viable and do not show reduced levels of RP gene expression [42] , we decided to measure the influence of this mutation on the level of RNA pol II bound to RP genes and on their TR . As shown in Figure 5A , RP were the most enriched genes in RNA pol II in both the wild-type and the rap1Δsil mutant . However , and importantly , they were more transcribed in rap1Δsil than in the wild type ( Figure 5B ) . Consequently , RP genes displayed a significantly low ChIP/TR ratio in the rap1Δsil mutant ( Figure 5C ) . As expected , mitochondria-related genes were unaffected by rap1Δsil mutation ( Figure S7 ) . The ChIP/TR ratios of RiBi genes , most of which are not directly regulated by Rap1 , did not undergo mayor change either ( Figure S7C ) . In this case , they displayed slightly higher levels of both RNA pol II binding and transcriptional activity ( Figure S7A , S7B ) , which probably reflect their upregulation in response to the overexpression of RP genes caused by rap1Δsil . As expected , rap1Δsil mutation also led to an increase in the TR of subtelomeric genes , but not in RNA pol II binding ( Figure S8 ) . We also investigated the distribution of RNA pol II along RPS3 in the rap1Δsil mutant . We found no clear difference in the intragenic distribution of bound RNA pol II when compared to the wild type ( Figure 5D ) . However , we detected higher levels of active RNA pol II in the mutant measured by run-on , throughout the transcribed region ( Figure 5E ) . Similar results were found for RPL25 ( Figure S9A , S9B ) . We conclude that the silencing domain of Rap1 participates in the mechanism which controls the proportion of RNA Pol II that is effectively active on RP genes during transcription elongation .
In this work , we show that the transcriptionally active proportion of RNA pol II bound across the genome is gene-specific and can be regulated in response to physiological stimuli . The presence of glucose causes an accumulation of inactive RNA pol II on RP genes . FACT , a general chromatin factor that is recruited to transcribed genes , also presents an uneven distribution , similar to that shown by RNA pol II . This indicates that not only RNA pol II accumulates on some genes , but other components of the transcriptional machinery that follow this enzyme during elongation also do . Conversely , the presence of galactose , or more likely , the absence of glucose , leads to a decrease in the proportion of inactive RNA pol II on RP and RiBi regulons , and increases it on mitochondria-related genes . Briefly , a set of at least 1000 genes ( more than 15% of the yeast genome ) coordinately changes the fraction of RNA pol II that is effectively active during their transcription . Genome-wide analysis has shown that TOR and PKA pathways co-regulate several gene regulons in yeast , including RiBi , RP and respiration-related genes [31] . Whereas TOR acts as an activator of all three regulons , the PKA pathway acts by repressing respiratory genes and by activating the RP and RiBi genes . Here we show that the absence of either Tpk1 or Tpk2 , two of the yeast PKA variants , produces the same kind of changes in the ChIP/TR ratios on RP , RiBi and mitochondria-related genes as the changing growth of the wild type from glucose to galactose . This indicates that the overabundance of inactive RNA polymerases is characteristic of some specific groups of genes , under particular growth conditions , and that it is regulated by the PKA pathway . The fact that there is no difference between the lack of Tpk2 , a PKA subunit shown to be bound to RP genes promoters [32] and Tpk1 , a subunit bound to the body of most genes suggests that the effect is quantitative: a reduction in PKA activity caused either by the lack of the alternative subunits or the growth in galactose reduces the accumulation of inactive RNA pol II molecules on several kinds of yeast genes . The importance of gene-specific regulation during elongation across metazoan genomes can be deduced from the occurrence of RNA pol II pausing , which is a frequent phenomenon mainly affecting tightly regulated genes [3]–[7] . Our work indicates that the control of RNA pol II elongation is also a common regulatory mechanism in yeast . Unlike metazoan genes however , whose paused RNA pol II concentrate at specific promoter-proximal sites , elongation-regulated yeast genes , at least RPS3 and RPL25 , accumulate inactive RNA pol II along the length of their bodies with only some bias toward their 5′ moiety . This accumulation correlates with a decrease in Ser2-phosphorylated RNA pol II along these genes . The experimental evidence described in this work reveals that an excess of RNA pol II , phosphorylated in Ser5 , accumulates on the yeast RP genes in glucose . This situation is only compatible with a post-initiation form of RNA pol II . However , the absence of a comparably high run-on signal indicates that this extra amount of RNA pol II , which accumulates in glucose media , should be arrested after backtracking . A similar situation occurs in the Drosophila hsp70 gene upon depleting the TFIIS cleavage factor [43] . Regulation of ribosome synthesis is a key element in controlling cell homeostasis , cell size and proliferation [30] . A coordinated and balanced expression of all the ribosomal protein genes is also needed to ensure efficient ribosome assembly [44] , and to avoid the potential toxicity of free ribosomal proteins [45] . Regulation at the transcription elongation level may provide a gear box-like mechanism which enables a fine-tuning of RP and RiBi transcription by rapidly adjusting the proportion of recruited machinery that is effectively active in response to the specific translational requirements of each physiological state . According to a recently proposed model , a certain level of backtracking during elongation , in combination with a fast initiation step , provides a steadier mRNA population level than that which would be produced by an initiation model alone [46] . Accordingly , the regulation of RP transcription elongation would allow the expression of balanced amounts of translational machinery components . It would also contribute to avoid transcription bursts , which would be incompatible with the low transcriptional noise that characterizes yeast constitutive genes [47] and , more specifically , the RP expression [48] . In addition , and as suggested for Drosophila genes [5] , regulating the transcription at the elongation level enables a continuously open promoter configuration , an essential situation for genes like RP which are being permanently transcribed . A feedback regulation mechanism operating at the intron splicing level has been demonstrated for certain RP genes [49] . Since exon definition takes place during transcription elongation , an attractive hypothesis would be the existence of coordination between transcription elongation and RNA splicing in RP genes [50] . However , our data do not support such a hypothesis since RNA pol II enrichment , compared to TR , was detected in both intron-containing and intron-less RP genes ( data not shown ) . RP and RiBi regulons show different RNA pol II- and FACT-ChIP/TR ratios , which suggest that their regulation mechanisms are not identical . We have also detected this kind of regulation in the group of mitochondria-related genes , thus confirming the previously described control of CYC1 transcription after RNA pol II recruitment [11] , [51] . In this case , the ChIP/TR ratios were reciprocal to those of RP genes . If we consider this diversity , it is likely that at least one subset of the molecular elements regulating the proportion of active RNA pol II during elongation is gene-specific . We provide evidence for the specific involvement of the silencing domain of Rap1 in the mechanism required to maintain significant levels of inactive RNA Pol II on RP genes . We also show that the absence of either Tpk1 or Tpk2 produces the same phenotype on RP transcription . These results indicate that the proportion of inactive RNA pol II on RP genes is controlled by the factors that specifically regulate the transcription of RP genes . In such a scenario , Rap1 would regulate the transcription of RP genes at both the RNA pol II recruitment and transcription elongation levels ( Figure 6 ) . Tethering experiments using lexA-Rap1 chimeras have shown that the Rap1 DNA-binding domain itself is required for the activating function of Rap1 in RP transcription [40] . This observation , together with the ability of Rap1 to clear nucleosomes from the vicinity of its binding sites [52] , suggests that the positive contribution of Rap1 to RP transcription is exerted in a pre-initiation step . This is likely to be done by arranging a chromatin configuration of the promoter to allow the hosting of other RP transactivators like Fhl1-Ifh1 [40] and , eventually , the pre-initiation complex itself . The persistent occupancy of Rap1 on RP promoters , even under transcriptionally inactive conditions ( stress ) , suggests that this factor may also play a repressive role [53] . We provide evidence of a negative role of Rap1 on RP transcription elongation which is mediated by its silencing domain . This domain , located in the C-terminal part of the protein , has been previously shown to be important for the downregulation of RP transcription in response to certain defects in the secretory pathway [54] , [55] . Graham et al . [42] showed that it also affects the mRNA steady-state levels of RP genes . They attributed this effect to the secondary post-transcriptional consequences of the rap1Δsil deletions . Our results indicate that it is in fact a transcriptional effect since the silencing domain has a negative influence on the TR of both subtelomeric ( Figure S8 ) and RP genes ( Figure 5 ) without affecting RNA pol II recruitment . In Drosophila cells , hundreds of genes show that RNA polymerase II molecules paused after initiation ( about 20–50 bp from the TSS ) , which has been argued to deal with the presence of an H2AZ-containing positioned nucleosome [56] . In yeast , the advanced position of the first nucleosome , overlapping the TSS [15] , makes such a mechanism unlikely . However and as we show herein , the regulation of the chromatin configuration by DNA-binding proteins like Rap1 may also have an effect on elongation . The Rap1-dependent control that we propose for RP genes should not be the only one acting at the elongation level across the yeast genome as we have shown that at least two other functional groups of genes , RiBi and mitochondria-related genes , display a regulated variation in the proportion of active RNA polymerases . This variation is controlled by PKA , but does not depend on the silencing domain of Rap1 . It is tentative to hypothesize that the PKA pathway regulates a plethora of genes during the elongation step of transcription by using different chromatin-related factors .
The yeast strains used in this work are described in Table S3 . Cells were grown in YPD ( yeast extract 1% , peptone 2% , glucose 2% ) with agitation at 28°C , at OD600 = 0 . 5 . In the experiment done in the galactose medium , cells were harvested and changed to YPGal ( yeast extract 1% , peptone 2% , galactose 2% ) and grown for 2 h ( lag phase ) and 14 . 5 h ( exponential growth ) . For the SPT16 shut-off experiments , 5 µg/ml doxycycline was added to exponentially growing SJY6 cells ( OD600 = 0 . 1 ) . Since the experiments in this work were performed in rich media ( YPD ) , shorter times of incubation with doxycycline were required to reach the same level of Spt16 depletion described in [28] . Control cells were harvested after 5 hours of mock treatment . Genomic run-on ( GRO ) was done essentially as described in [29] . See supplementary materials and methods in Text S1 . To determine the intragenic distribution of elongating RNA pol II molecules , we used macroarrays containing 300 bp probes from the 5′ and 3′ ends of the coding regions of 377 yeast genes . These 5′-3′ macroarrays were manufactured by printing PCR products onto a nylon Hybond N+ membrane , similarly to that described for whole genome ORF macroarrays [57] . PCR products were obtained by using either yeast genomic DNA as a template and the primer pairs listed in Table S4 , or a plasmid containing the ORF , a common primer corresponding to the plasmid ( YGUF for 5′ probes or YGUR for 3′ probes ) , and a specific primer corresponding to the ORF , listed in Table S5 , following the procedure described in [57] . The run-on analysis of RPS3 and RPL25 was done as in GRO experiments , but using miniarrays on nylon Hybond N+ membranes . These miniarrays were done by printing the PCR products as described above using the amplicons listed in Table S6 . Probes of PRI2 , a gene whose run-on levels were not influenced by any of the elements tested in the GRO experiments , were included for normalization . A detailed protocol of RPCC will be published elsewhere ( Pelechano and Pérez-Ortín , submitted ) . A preliminary description of it is included in the supplementary materials and methods ( Text S1 ) . ChIP experiments of selected genes were performed as previously described [28] with minor modifications . Shortly , 50 ml of yeast culture were collected at O . D . 0 . 5 . Crosslinking was performed by adding 1% Formaldehyde to the culture and incubating at room temperature for 15 min . 2 . 5 ml of glycine was then added and culture was incubated 5 min . Cells were then harvested and washed four times with 25 ml Tris-HCl Buffer Saline at 4°C . The cell breakage was performed in 300 µl of lysis buffer ( see the above reference ) with glass beads , and the cell extracts were sonicated in a Bioruptor sonicator ( Diagenode ) for 30 min in 30 sec on/30 sec off cycles ( chromatin was sheared into an average size of 300 bp ) . Immunoprecipitation was performed with magnetic beads , which were coated with protein A ( Dynal ) and incubated with 8WG16 monoclonal antibody ( Bavco Covance ) , anti Ser2-P-CTD or anti-Ser5-P-CTD ( kindly provided by David Bentley ) beforehand . qPCR were performed to quantify immunoprecitation , using a 1∶1500 dilution for the input samples or a 1∶10 dilution for the immunoprecipitated samples . Immunoprecipitation was defined as the ratio of each probe specific product in relation to that of a non-transcribed region ( chromosome V , intergenic region V ) . Primers used are listed in Table S6 . All the experiments were done in triplicate except for the tpk2 mutant that was analyzed in duplicate . All the group functional enrichment analyses were done using the Fatiscan application from Babelomics [58] . The clustering of Figure 2A was done using a k-means algorithm and the STEM program [59] . Genomic data are stored in the Valencia Yeast ( VYdBase; http://vydbase . uv . es/ ) and GEO databases . The GEO accession number for the set of hybridizations is GSE14084 . | Transcription of DNA–encoded information into RNA is the first step in gene regulation . RNA polymerases initiate transcription at the promoter region and elongate the transcripts traveling throughout the gene until reaching the termination sequences . Classical models of transcriptional regulation were focused on the initiation step , but there is increasing evidence for gene regulation after initiation . We have investigated the importance of elongation in gene regulation using the yeast Saccharomyces cerevisiae , one of the main experimental systems in modern biology . By comparing the genomic distribution of RNA polymerase molecules with the actual transcriptional signal across the genome , we have detected that many genes are regulated at the elongation level . We show that yeast cells use this step to modulate the expression of several groups of genes , which have to be simultaneously regulated in a very coordinated manner . Genes encoding essential functions , like those related to protein synthesis and respiration , change their transcriptional activities in response to environmental stimuli , without changing in the same extension the amount of RNA polymerase that is physically associated to them . We also show that this kind of regulation , in spite of taking place during the elongation step , can be mediated by promoter-binding transcription factors . | [
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| 2009 | Regulon-Specific Control of Transcription Elongation across the Yeast Genome |
Isothermal microcalorimetry is an established tool to measure heat flow of physical , chemical or biological processes . The metabolism of viable cells produces heat , and if sufficient cells are present , their heat production can be assessed by this method . In this study , we investigated the heat flow of two medically important protozoans , Trypanosoma brucei rhodesiense and Plasmodium falciparum . Heat flow signals obtained for these pathogens allowed us to monitor parasite growth on a real-time basis as the signals correlated with the number of viable cells . To showcase the potential of microcalorimetry for measuring drug action on pathogenic organisms , we tested the method with three antitrypanosomal drugs , melarsoprol , suramin and pentamidine and three antiplasmodial drugs , chloroquine , artemether and dihydroartemisinin , each at two concentrations on the respective parasite . With the real time measurement , inhibition was observed immediately by a reduced heat flow compared to that in untreated control samples . The onset of drug action , the degree of inhibition and the time to death of the parasite culture could conveniently be monitored over several days . Microcalorimetry is a valuable element to be added to the toolbox for drug discovery for protozoal diseases such as human African trypanosomiasis and malaria . The method could probably be adapted to other protozoan parasites , especially those growing extracellularly .
Human African trypanosomiasis ( HAT ) , also known as African sleeping sickness , and malaria are important tropical diseases caused by protozoan parasites . HAT threatens millions of people living in sub-Saharan Africa [1] . In recent years , the number of cases dropped due to improved control measures such as trapping of tsetse flies , active surveillance and appropriate treatment of patients , and is currently estimated at 30 , 000 cases annually [2] . However , the disease may reemerge , if control is neglected . African sleeping sickness is fatal without treatment , so the availability of effective drugs is vital . Malaria has a higher public health impact with 225 million infections and almost 800'000 deaths annually [3] . The most affected populations are children and pregnant women in Africa . Effective drugs are available for prophylaxis and treatment , but drug resistant parasites represent a major challenge . Therefore , new drugs for both diseases are needed on a continuous basis particularly since no effective vaccine is yet available for either of these diseases , so drug development is of crucial importance . Drug discovery and development requires rapid methods for screening large number of compounds . For both trypanosomes and malaria parasites , in vitro drug activity tests are available . These are routinely performed in 96-well microtiter plates with a drug exposure time of 72 hours . For Trypanosoma brucei spp . bloodstream forms are cultivated axenically . Parasite inhibition is determined in a simple and cost-effective way using the viability marker Alamar blue ( resazurin ) [4] . P . falciparum is cultured as asexual erythrocytic stages , and parasite growth inhibition is classically assessed by measuring the uptake of tritium-labelled hypoxanthine [5] . Using these assays , the antiprotozoal activity of added compounds , expressed as 50% inhibitory concentration ( IC50 ) can be determined . These methods can also be used to determine the time of onset of drug action and the time to kill , which are of great importance for subsequent in vivo studies . However , following changes over time using these currently available in vitro tests is not very accurate and is particularly labor intensive . An alternative method of estimating growth inhibition is isothermal microcalorimetry . This nonspecific technique allows direct measurement of heat generated by biological processes in living cells . Growth of microorganisms results in an increase of heat flow over time which is documented by a continuous real-time electronic signal . The method has already been used to study heat production of bacteria , mammalian cells and worms [6]–[10] . For example , bacteria produce on average 1–3 pW heat per viable cell [6] , [11] . The detection time depends on the sensitivity of the instrument as well as the initial number of living cells , their growth rate and the amount of heat produced per cell [12] , [7] . To our knowledge , this technique has not been applied yet to any pathogenic protozoa . In the present study , we established microcalorimetry as a new tool for a rapid determination of effects of drugs on Trypanosoma brucei rhodesiense and Plasmodium falciparum . We used the real-time measurements of metabolic heat flow produced by these protozoan parasites , to measure the time of the onset of action at different drug concentrations and also the time to death of the parasite population .
Bloodstream forms of the T . b . rhodesiense strain STIB900 were cultivated in Minimum Essential Medium with Earle's salts , supplemented according to Baltz et al . [13] with the following modifications: 0 . 2 mM 2-mercaptoethanol , 1 mM sodium pyruvate , 0 . 5 mM hypoxanthine and 15% heat-inactivated horse serum . For calorimetry , trypanosomes were washed and diluted with fresh culture medium to give the desired initial cell density then transferred to 4 ml sterile glass ampoules which were hermetically sealed with a rubber septum . For the determination of a suitable cell density to use to obtain growth curves , ampoules were filled with 3 ml trypanosome culture containing 104 , 105 and 106 cells/ml initial densities , each in triplicate . Culture medium without trypanosomes served as negative control . Continuous heat measurements ( 1/sec ) were conducted over a period of up to 6 days . For determination of parasite densities at different time points , small aliquots ( ≈50 µl ) were collected through the rubber septum of the hermetically closed ampoules using a 1 ml syringe . Cell counting of motile trypanosomes was performed microscopically using a Neubauer chamber . The influence of the sample volume was evaluated using an initial density of 105 cells/ml , and 1 ml , 2 ml and 3 ml of culture medium each in triplicate . The standard drugs suramin , pentamidine and melarsoprol were selected to monitor drug action . Eflornithine was excluded because of its weak in vitro activity against African trypanosomes . We used a multiple of the IC50 value of each drug since time to kill can not be determined with an IC50 ( determined over 72 hrs ) or lower concentrations . For the investigation of drug activity , trypanosome cultures were diluted with fresh culture medium to a density of 105 cells/ml . Each ampoule was filled with 3 ml cell suspension and supplemented with suramin , pentamidine or melarsoprol at concentrations corresponding to 5× IC50 or 25× IC50 ( for actual concentrations in ng/ml see table 1 ) . Each measurement was performed in triplicate . The IC50 values were determined prior the experiment as previously described [14] . The P . falciparum strain NF54 was cultivated as previously described [15] , [16] . An aliquot of 0 . 5 ml of unsynchronized P . falciparum culture with 10% parasitemia and 5% hematocrit was mixed with fresh human erythrocytes and culture medium to give the desired initial parasitemia and 5% hematocrit . Samples with an initial parasitemia of 1 . 0 , 0 . 5 , 0 . 25 and 0 . 125% were tested to find the optimal initial parasitemia for the evaluation of fast-acting drugs . In addition , the influence of the volume on the thermal profile was evaluated using 4 ml-ampoules filled with 0 . 5 , 1 . 0 , 2 . 0 or 3 . 0 ml of unsynchronized culture with an initial parasitemia of 0 . 5% and 5% hematocrit . This was done because erythrocytes settle rapidly , so different volumes of culture medium in a 4 ml ampoule might influence parasite development owing to differences in oxygen supply and availability of nutrients . Ampoules filled with non-infected erythrocytes ( 5% hematocrit ) were used in triplicate as negative controls . Continuous heat measurements were conducted over a period of 5 days . Parasitemia was assessed by microscopic counting of Giemsa-stained smears prepared from samples aspirated at defined time points with a syringe through the rubber septum of the closed ampoules . For the drug test , aliquots of stock solutions of the drugs were mixed with fresh P . falciparum culture to give the desired concentration and then distributed into sterile calorimetry ampoules . The antiplasmodial drugs chloroquine , artemether and dihydroartemisinin were tested at concentrations corresponding to 3× and 10× the published IC50 values [17] . Dilutions of the 10 mg/ml stock solutions were freshly prepared in culture medium , immediately before the start of the experiment . An isothermal calorimetry instrument ( Thermal Activity Monitor , Model 3102 TAM III , TA Instruments , New Castle , DE , USA ) equipped with 48 channels was used to measure heat flow continuously at 37°C . The temperature of the instrument was maintained within 0 . 00001°C . The calorimetric sensitivity according to the manufacturer is ±0 . 2 µW . The calorimeter continuously measured heat generated or absorbed by test or control samples in air-tight 4 ml glass ampoules sealed with a rubber septum . The gas phase was ambient air . For each series of measurements , ampoules were introduced into the calorimeter and remained at least 15 minutes in the thermal equilibration position at 37°C before they were lowered into the measurement position . Due to short-term thermal disturbance after introduction of the samples into the measuring position of the calorimeter the heat signal of the first 1 hour was considered unspecific . For the study of the relationship between heat flow events and the number of parasites in the sample , ampoules were removed at defined time points and aliquots of the samples were aspirated with a 1 ml syringe through the rubber septum , and the parasites counted . Thermal changes in each ampoule were recorded as a continuous electronic signal ( in Watts ) , which is proportional to the heat production rate . After measurement , data were reduced from 1 second to 1 minute intervals and exported . Data reduction is optional and can be set individually to any degree after each experiment . Data analysis was accomplished using the manufacturer's software ( TAM Assistant , TA Instruments , New Castle , DE , USA ) and Origin 7 . 5 ( Microcal , Northampton , MA , USA ) . For the analysis of time points one hour of incubation time was added to the time of the calorimetric measurement . This time was needed for preparation of the ampoules , their transfer from the bench to the calorimeter and for equilibration in the calorimeter prior the measurement .
We plotted heat flow ( in µW ) over time in all present experiments as the heat flow data can be used as a proxy for the number of viable trypanosome cells . Figure 1 shows the heat flow over time from 3 ml samples in closed 4 ml glass ampoules of T . b . rhodesiense containing three different initial trypanosome densities of 104 , 105 and 106 cells/ml . With an initial trypanosome density of 104 cells/ml , a lag phase was observed before the exponential growth phase started . A stationary phase was reached after 48 hours . After 72 hours , the culture overgrew , which led to a decline of the heat flow . With an initial trypanosome density of 105 cells/ml , the exponential phase started immediately , and the stationary phase as well as the dying off phase was observed one day earlier than with 104 cells/ml . The maximum trypanosome density in the ampoules containing the cells was reached at maximum heat flow of around 8 µW . At the lower initial trypanosome densities of 104/ml and 105/ml , the heat flow increased continuously and only minor amplitude oscillations occurred when the maximum heat flow was reached ( above 8 µW ) . With 106 cells/ml initial trypanosome density , unexpected heat flow oscillations were noted during the first 24 hours . A first peak of up to 26 µW was observed 2 . 5 hours after the start of the experiment ( Figure 1 ) . Then , the heat flow dropped and an oscillating heat flow started with continuously decreasing amplitudes during the first 24 hours . After 48 hours , the culture overgrew and the heat flow was close to the base level . Parallel determinations of cell counts showed that the increase in heat production was consistent with the increase in cell numbers and the heat flow curves were similar to the growth curves obtained by cell counting ( data not shown ) . The following decrease in heat production is due to decreasing numbers of viable cells combined with decreasing metabolic activity of the cells over time . The intra- and inter-experimental reproducibility was evaluated by running three independent measurements . Each was performed on different days , in triplicates and with trypanosome cultures freshly diluted to the desired densities . As expected , the reproducibility was higher within one experiment than between experiments . However , reproducibility was good also between different experiments , as illustrated in Figure 1 , which shows the mean heat flow curves of triplicates of each experiment . In the subsequent experiments for monitoring drug action , a trypanosome density of 105 cells/ml was chosen to avoid the lag phase observed with 104 cells/ml and to avoid the strong oscillations originating from samples with 106 cells/ml initial density . A further disadvantage of using 106 cells/ml is that only a very limited growth is possible , since the maximum trypanosome density under ideal culture conditions is ∼2×106 cells/ml . The time-courses for heat flow using 0 . 5 ml , 1 ml , 2 ml and 3 ml of cell culture at an initial density of 105 cells/ml were all in a similar range ( data not shown ) . As the largest volume contained the highest total number of cells and therefore produced the highest heat flow peak , a volume of 3 ml was chosen for the following experiments . The standard drugs suramin , pentamidine and melarsoprol were selected to monitor drug action . The heat flow of cultures containing melarsoprol ( Figure 2B ) , pentamidine ( Figure 2C ) and suramin ( Figure 2D ) in concentrations corresponding to 5× IC50 and 25× IC50 was measured in parallel to that of control cultures containing no drug . Whereas the heat signal of the control cultures increased continuously to a peak value of 8 µW after 24–36 hours , curves for all drug containing samples reached markedly lower peaks , at earlier time points ( Figure 2A and Table 1 ) . Inhibition depended on the drug used , and its concentration . During the first 3 hours of measurement , the heat production of all samples increased in a similar way . The onset of action of the drugs was marked by a divergence in the continuously increasing heat flow curves of the drug containing specimens from the curves for the control specimens ( blue curves ) . For all three drugs , the onset of action was within the first 6 hours of drug incubation ( Figure 2A and Table 1 ) . Then after the peak the heat flow continuously declined over a few hours until the heat production was reduced to the level of the sterile medium control ( base level ) . The time required to reach this point , when the parasite culture was completely inactivated , was the time to kill . The fastest antitrypanosomal effect was observed with melarsoprol at 25× IC50 , with a decline in heat production starting within the first 3 hours and reaching base line after 12 hours of incubation . The slowest acting drug among those tested was suramin . The heat production of suramin-treated trypanosomes started to decline after 9 hours of drug incubation . The heat flow was reduced to the base level after 32 hours at 25× IC50 and after 42 hours at 5× IC50 concentrations . Blood stages of Plasmodium falciparum were cultured using human erythrocytes in culture medium [15] , [16] . In our samples , erythrocytes settled and accumulated at the bottom of the vial within a few hours . This could tend to reduce the availability of both oxygen and nutrients , and produce an accumulation of metabolites . Both these effects might lead to decreased viability of the cells . The cultures used to evaluate the influence of different sample volumes in the 4 ml closed glass ampoules all had the same concentration of cells and the same initial parasitemia . The largest volume tested contained the highest overall number of cells and thus produced the highest amount of heat . The course of the heat flow curves was more or less similar for the different sample volumes tested ( data not shown ) . However , with 3 ml or 2 ml samples , there was an initial pre-peak at 3–4 hours followed by a sharp drop at 7 hours before the heat flow increased again to reach the main peak , whereas with the 1 ml samples the heat flow signal increased steadily from the start of measurement until the main peak at ∼10 µW , and the heat flow curves of samples containing infected erythrocytes could easily be distinguished from those for uninfected erythrocytes ( controls ) immediately after the start of the measurement . This was not the case for the samples with 0 . 5 ml , the lowest volume tested . We therefore chose a volume of 1 ml at 5% hematocrit for the following experiments . The heat production of 1 ml specimens containing erythrocytes ( 5% hematocrit ) infected with P . falciparum at initial levels of parasitemia varying between 0 . 125%–1 . 0% increased , reached a peak and declined afterwards at all densities . The average time to peak was dependent on the initial parasitemia with the shortest time to peak being measured in the specimens with the highest initial parasitemia ( 1 . 0%: 43 h , 0 . 5%: 57 h , 0 . 25%: 72 h , 0 . 125%: 81 h ) ( Figure 3 ) . Microscopic observation of the culture by Giemsa staining confirmed that the decreasing heat flow after the peak was due to dying of the parasites ( data not shown ) . For the subsequent experiments we chose an initial parasitemia of 0 . 5% . With this concentration the heat flow reached a maximum peak value ( 9 µW ) among the initial parasitemia levels tested . The time to reach the peak was approximately 57 hours . The influence of three standard drugs was measured using 1 ml of a non-synchronous P . falciparum culture with 0 . 5% initial parasitemia . The previously determined IC50 values of 5 . 1 ( ±0 . 8 ) ng/ml for chloroquine , 1 . 2 ( ±0 . 1 ) ng/ml for artemether [17] and 0 . 76 ( ±0 . 04 ) ng/ml for dihydroartemisinin were taken as a reference . Figure 4 shows the heat flow curves of a typical measurement in triplicate containing drugs at 3× and 10× the IC50 . At 10× IC50 all drugs reduced the heat flow compared to the increasing heat flow observed in the drug free control ( Figure 4A ) . The curves were highly reproducible . The most effective drug tested was dihydroartemisinin at 10× IC50 ( Figure 4B ) , where the heat flow curves blended with those of the negative controls , containing uninfected erythrocytes . Chloroquine at 10× IC50 started to repress the parasite-specific heat production around 6 hours after measurement started . Afterwards the heat signal decreased continuously and matched the signal from uninfected erythrocytes from 48 hours onwards ( Figure 4C ) . At 10× IC50 , artemether appeared to be the least effective antiplasmodial drug of the ones tested . During 30 hours of measurement the heat flow signal was comparable with that of the samples with chloroquine at 10× IC50 . However , plasmodial activity could be observed afterwards by an increasing heat flow leading to a low peak of 4 . 5 µW at 85 hours ( Figure 4D ) . At the lower concentrations ( 3× IC50 ) , dihydroartemisinin was again more effective than chloroquine and artemether , which both led to similar heat flow curves comparable to those for the drug-free control .
The calorimetric approach described here , offers the possibility of obtaining real-time measurements in up to 48 samples in parallel . This is not possible with conventional drug activity assays , where inhibition is measured at a single time point . Estimation of the time of drug action requires several assays with different drug exposure times , making such analysis highly labour-intensive . Isothermal microcalorimetry allows a more accurate determination of the onset of action and the time to kill based on the continuous measurement of the heat flow ( 1×/sec ) . Calorimetry is an unspecific tool , which cannot discriminate between metabolically inactive cells and dead cells , but it does allow the continuous monitoring of metabolic changes in a parasite population with little effort . It can be used to gain additional information when combined with established drug sensitivity assays . In our study , we have plotted heat flow over time as the heat flow is proportional to microbial activity [6] . We considered that the heat flow correlates also with the number of viable ( i . e . , metabolically active ) cells because metabolic activity of each trypanosome cell is expected to be more or less constant until it reaches the dying off phase with the decrease of metabolic activity per cell . A similar effect was observed with CHO 320 cells by Kemp et al [10] . An integration of the heat flow leads to a heat over time curve . Heat is proportional to total biomass produced or the quantity of a metabolic product released [6] . As we were interested in viable cells only , we used heat flow data ( and not heat data ) for our analysis . In our microcalorimetric experiments , we found that the time to maximum heat flow varied according to the initial parasite density and the sample volume used . Optimization of these parameters led us to use a volume of 3 ml for T . b . rhodesiense with an initial trypanosome density of 105 cells/ml and of 1 ml for P . falciparum with an initial hematocrit of 5% and a parasitemia of 0 . 5% . At these conditions , the heat flow signals were in the µW range which is well above the detection limit , which was specified by the manufacturer as 200 nW . A limitation of this new methodology is the airtight sealing of the ampoules which is required for a proper measurement but could potentially affect the action of drugs , since no exchange of the gas phase or the culture medium is possible . Metabolic production of CO2 can lead to an acidification of the culture medium , as oxygen supply is limited . After a prolonged time of growth the conditions are likely to become non-physiological , therefore the period of accurate measurement is limited . After this , thermal effects might be misinterpreted . The samples were filled into the ampoules at normal laboratory conditions . Therefore the gas phase in each ampoule was normal ambient air rather than the special gas mixtures used for parasite cultures . An improvement of the culture conditions in the ampoules could be achieved by replacing the air in the ampoules by the gas used for trypanosome culture ( 5% CO2 in ambient air ) or the special mixture used for malaria parasite cultures ( 4% CO2 , 3% O2 , 93% N2 ) . However , no considerable deleterious effect of ambient air could be observed in our experiments over the maximum measurement period of 5 days . If further optimization of gas exchange is necessary for other experiments , a modified cap for the ampoules with a porous silicon rubber membrane could be used to allow gas exchange but no water evaporation [18] . The parasites used in our study are cultured under different conditions . African trypanosomes , as extracellular parasites , offer the advantage of axenic cultivation . There was therefore no background due to other cells to interfere with the heat flow signal , which allowed direct interpretation . P . falciparum , on the other hand , is an intracellular parasite . Non-infected erythrocytes produced a background heat level . They gave a rather constant heat flow between 1 and 2 µW . However , this background did not interfere with the interpretation of the heat flow curves produced by the parasite-infected erythrocytes ( Figure 3 ) . Another difference between the two parasites is the multiplication rate . Trypanosomes are mobile and replicate by binary fission , while P . falciparum after infecting the erythrocyte undergoes multiple replications and destroys the host cell . Erythrocytes tended to settle rapidly at the bottom of the ampoule , which may explain why smaller volumes ( 1 ml ) of P . falciparum cultures in the calorimetric ampoules gave better signals . When dense cultures of T . b . rhodesiense ( 106 cells/ml ) were used , they exhibited synchronized oscillations in heat production with a period of about 4 hours ( Figure 1 ) . These oscillations were highly reproducible even though the cultures themselves had not been synchronized prior to the experiments . Oscillations were also induced in samples with low initial trypanosome density when aliquots were taken for parasite counting or when the cell suspensions were mixed once they had reached a high trypanosome density . The oscillations were not dependent on the cell cycle as the generation time of T . b . rhodesiense is 8 to 9 hours . The oscillations may reflect underlying metabolic changes; glycolytic oscillations have been observed before in microcalorimetric studies by Lamprecht [19] in systems far from equilibrium . The author detected correlations of heat flow oscillations with NAD/NADH+ absorption in S . cerevisiae [19] . Analysis of the cause of heat flow oscillations in trypanosome cultures would be of interest but is beyond the scope of the current study . To avoid interference with drug inhibition by these oscillations , lower trypanosome densities were used for all experiments studying drug action . Using the optimal conditions described , three antitrypanosomal and three antiplasmodial drugs were used to demonstrate that microcalorimetry is a helpful tool to monitor drug action against the two pathogenic protozoans on a real time basis . With only two concentrations per drug , we could observe the rate of action of each of the drugs tested and also the differences between them . Among the antitrypanosomal drugs , melarsoprol was found to be the fastest acting , followed by pentamidine and then by suramin . This ranking is in agreement with data obtained by the standard drug assays with different drug exposure times where the difference of IC50 values at 24 and 48 hours was smallest for melarsoprol ( unpublished data ) . Similar studies have been performed with the antiplasmodial drugs . For P . falciparum , the microcalorimetric results are in agreement with data employing the standard [3H]hypoxanthine incorporation assay [20] . In our studies chloroquine and artemether showed pronounced parasite growth inhibition ( ≥94% ) after incubations of 6 hours or longer at concentrations of 10× the IC50 . The method can be used with more than two different drug concentrations , which would enable the inhibition kinetics of a compound to be fully described , with an accuracy far greater than that of standard drug assays . Another method which allows the determination of the time for a drug to exert its activity is real-time high content imaging . With this method , a higher throughput and more information can be obtained than with microcalorimetry . However , this results in a huge amount of data as pictures are taken instead of measuring a single value at each time point . Transmission light microscopy might not be sufficient to measure viability of trypanosomes , which are small and highly motile , or of plasmodia , which are intracellular , without any markers . Isothermal microcalorimetry has the further advantage that it is a label-free technique , therefore no interventions with the samples such as fixation , staining , or insertion of reporter genes in the parasites are required . Heat flow measurements can give information beyond viability and density such as indications of metabolic state [6] , [19] . In spite of its many advantages , it is not likely that microcalorimetry will replace the routine screening assays which are currently used to determine antiprotozoal activities of new compounds . With conventional standard assays a higher throughput can be generated , using 96 well or even 384 well formats . However , microcalorimetry is a promising tool for gathering information about selected compounds beyond the IC50 , such as the time until onset of action . Measuring this can be a challenge for fast acting compounds that begin to affect growth within the first 3 to 4 hours . In our studies with trypanosomes we found that the time for the preparation of the specimens , their transfer , and the equilibration time in the calorimeter could overlap with the time of onset of action . This problem could be reduced by using an injection system which allows equilibration in the calorimeter prior to the injection of the compounds ( personal communication Matthias Rottmann ) . The new approach to studying the effect of drugs , as described for the two model organisms T . b . rhodesiense and P . falciparum , may be applicable also for other protozoan parasites . Resistance or sensitivity analysis of different parasite isolates or screening for resistance development may be an additional interesting field for the use of microcalorimetry . Real-time drug inhibition data can be used in combination with pharmacokinetic and pharmacodynamic data as a helpful tool to predict the outcome of in vivo experiments in the field of drug discovery . | Microcalorimetry is a technology developed to record minute changes in temperature as a result of physical , chemical or biological reactions over time . The method has been applied to bacterial and eukaryotic cells and it was found that the metabolic activity of living cells in a culture medium produces enough heat flow to be measured . Protozoan parasites , some of which cause tropical diseases such as African sleeping sickness or malaria , are larger cells than bacteria and are metabolically very active . We explored the applicability of heat flow measurement to follow the growth of a parasite population and to study the effect of drugs . We first established optimal parameters for obtaining heat flow curves of a growing parasite culture . Then we added antiparasitic drugs at two concentrations and followed the heat flow curves over several days . Thus we could determine the time of onset of drug action and the time until all parasites stopped producing heat ( time to kill ) . The microcalorimeter measurements once per second allowed a continuous monitoring of changes in the parasite population . This novel tool is accurate and simple to use , and will certainly prove to be of great value for the discovery and development of new drugs for protozoan parasites . | [
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| 2012 | Isothermal Microcalorimetry, a New Tool to Monitor Drug Action against Trypanosoma brucei and Plasmodium falciparum |
Efficient sampling of visual information requires a coordination of eye movements and ongoing brain oscillations . Using intracranial and magnetoencephalography ( MEG ) recordings , we show that saccades are locked to the phase of visual alpha oscillations and that this coordination is related to successful mnemonic encoding of visual scenes . Furthermore , parahippocampal and retrosplenial cortex involvement in this coordination reflects effective vision-to-memory mapping , highlighting the importance of neural oscillations for the interaction between visual and memory domains .
Sampling of visual information has been shown to be rhythmic rather than continuous [1–3] . In particular , brain rhythms clocked by oscillations in the alpha ( 7–14 Hz ) range [4] constrain visual sampling: electroencephalography ( EEG ) /magnetoencephalography ( MEG ) studies in humans have shown that the trial-by-trial fluctuations in near-threshold visual perception performance depend on the phase of alpha oscillations prior to stimulus presentation [5 , 6] . Saccadic eye movements overtly sample visual scenes . Here we ask how brain oscillations and saccades are coordinated in order to allow visual information to be encoded in memory areas . We addressed this question by tracking eye movements in separate memory experiments involving MEG in healthy adults and intracranial recordings in epileptic patients ( Fig 1 ) . Participants were asked to remember images of visual scenes , and we later probed their memory . The phase locking [7] between presaccadic brain oscillations in relation to saccade onset was contrasted between later-remembered and later-forgotten images . Building on prior evidence on the cortical origins of alpha activity underlying visual information sampling [8 , 9] , we hypothesized that higher phase locking in occipital lobe would be related to successful memory performance . MEG and intracranial data both showed that eye movements are locked to the phase of alpha oscillations prior to a saccade . Importantly , this coordination was related to successful memory encoding .
In order to investigate the temporal coordination of saccades and brain oscillations , the time-frequency representations of phase and power of the MEG and intracranial data were aligned to saccade onsets . Accordingly , high presaccadic phase locking would demonstrate an effective coordination of saccades in relation to brain oscillations . The intracranial data recorded from 3 patients with occipital depth electrodes ( Fig 2A ) revealed a significantly higher phase locking for later-remembered as compared to later-forgotten trials in the alpha band ( 12–14 Hz , cluster randomization: p < 0 . 005 , controlling for multiple comparisons over frequencies , 2-sided test , fixed-effects statistics ) . Fig 2B depicts a time-frequency representation of the difference in phase locking , indicating that the effect is centered around 250 ms prior to saccade onset at 12–14 Hz . When aligning the data to saccade offset ( i . e . , fixation onset ) , no significant differences in phase locking were found ( presaccade: p > 0 . 21 , S1A Fig; postsaccade: p > 0 . 25 , S1B Fig ) , in line with the idea that activity timed to saccade onset is important for visual processing [10] . The intracranial results then guided the analyses in the group-level study by confining the frequency of interest to 12–14 Hz , where MEG data here is presented from 22 healthy participants performing the memory task ( see “Materials and methods” for exclusion criterions ) . A cluster-based permutation test revealed a significant difference in presaccade phase locking between later-remembered and later-forgotten images in the alpha band ( 12–14 Hz; cluster randomization: p < 0 . 01 , controlling for multiple comparisons over sensors , 2-sided test , Fig 3A ) . In the posterior sensors forming a cluster , the difference was most pronounced approximately 250 ms prior to saccade onset ( Fig 3A ) . Unfiltered data from exemplar depth electrodes and MEG sensors depicting saccade-onset locked potentials are shown in S2 Fig . Analyzing the phase locking for later-remembered and later-forgotten pictures separately suggested the existence of a preferred alpha phase for later-remembered , but not later-forgotten , trials ( S3 Fig ) . Additional analyses of presaccadic spectral power indicated that the phase-locking results were not biased by spectral power ( S4 Fig ) . A control analysis , in which we related phase locking to stimulus onset ( irrespective of saccadic eye movements ) , revealed no significant differences between later-remembered and later-forgotten scenes ( S8 Fig ) . However , when analyzing power after stimulus onset ( irrespective of saccadic eye movements ) , significantly less alpha power was found for remembered as compared to forgotten scenes ( 12–14 Hz; cluster-randomization: p < 0 . 019 , controlling for multiple comparisons over sensors and time , 2-sided test , S8 Fig ) , highlighting the difference between stimulus-onset-related subsequent memory studies ( for an overview , see [11] ) and the present saccade-related phase-locking analyses during free viewing . No difference was found when analyzing phase locking or power after saccade onset ( S9 Fig ) . Since the average fixation duration between typical eye movements is less than 500 ms , we also analyzed saccades with a minimum fixation duration of 200 ms prior to saccade onset . In this analysis , significantly higher phase locking for later-remembered trials than for later-forgotten trials at 10 Hz was found ( cluster randomization: p < 0 . 05 , controlling for multiple comparisons over sensors , 2-sided test , S5 Fig ) . Note that the shorter time window of 200 ms is at the expense of frequency resolution now being approximately 5 Hz ) . Because this analysis produced higher trial numbers , data from all 36 participants could be analyzed . Again , significantly higher phase locking for later-remembered trials than for later-forgotten trials was found ( cluster randomization: p < 0 . 005 , controlling for multiple comparisons over sensors , 2-sided test , S5 Fig ) . We conclude that the memory encoding related to saccades phase locked to alpha oscillations is robust with respect to presaccadic epochs of different lengths . In order to identify the sources of the effects , we computed phase locking in the alpha band for virtual sensors , applying a dynamic imaging of coherent sources ( DICS ) beamformer [12] . Cluster-based permutation statistics at the source level yielded a significantly higher phase-locking index for later-remembered trials than for later-forgotten trials ( p < 0 . 01 , 2-sided test; Fig 3B ) . The cluster spanned from visual to parietal and temporal areas , extending into the cerebellum ( Fig 3B ) . The largest differences were found in the parahippocampal gyrus and the retrosplenial cortex , which have been shown to support the encoding of visual scenes [13–15] , and extended into the posterior hippocampus . The MEG source localization is supported by intracranial data from parahippocampal depth electrodes in the 3 patients , showing significantly higher phase locking for later-remembered trials versus later-forgotten trials in the alpha range ( 8–10 Hz , p cluster < 0 . 05; 2-sided test , fixed-effects statistics; S6 Fig ) The memory performance of the 22 participants included in the main MEG analyses ( d-prime = 2 . 13 , SE = 0 . 11 ) was considerably higher than the memory performance in patients ( d-prime = 0 . 79 ) . In total , 38 , 177 saccades were detected in the eye tracking data ( 22 participants , mean = 1 , 735 . 3 , SE = 83 . 6 ) , resulting in an average saccade rate of 2 . 30 Hz ( SE = 0 . 11 ) . The saccade rate is at the lower end of the typically reported range , which can be partly explained by the use of conservative saccade detection criterions to exclude ambiguous eye tracking data . The saccade rate was significantly higher ( t21 = 7 . 34 , p = 3 . 17 * 10−7 ) for later-remembered ( mean = 2 . 37 Hz , SE = 0 . 10 ) versus later-forgotten ( mean = 2 . 01 Hz , SE = 0 . 11 ) scenes , which has been reported previously [16 , 17] , but see [18] for conflicting evidence . The average saccade duration was 28 . 8 ms ( SE = 0 . 6 ) , and the average fixation duration was 342 . 5 ms ( SE = 13 . 8 ) . Saccade directions displayed a horizontal bias but were not different for later-remembered versus later-forgotten trials ( Kuiper 2-sample test for each participant , all p-values > 0 . 1; S7 Fig ) . When only events with a minimum fixation period of 500 ms prior to saccade onset were included ( as in the main analyses ) , 3 , 837 saccades remained ( mean = 174 . 4 , SE = 13 . 26 ) in the eye tracking data , resulting in an average saccade rate of 0 . 36 Hz ( SE = 0 . 01 ) . There was no significant difference between saccade rates for later-remembered ( 0 . 22 , SE = 0 . 02 ) and later-forgotten ( 0 . 25 , SE = 0 . 02 ) scenes ( t21 = −1 . 77 , p = 0 . 091 ) , indicating that the subsequent memory effect found in all saccades ( above ) cannot be generalized across all types of saccades . The average saccade duration was 28 . 2 ms ( SE = 0 . 8 ) , and the mean fixation duration was 802 . 9 ms ( SE = 20 . 4 ) . Saccade directions displayed a horizontal bias but were not different for later-remembered versus later-forgotten trials ( Kuiper 2-sample test for each participant , all p-values > 0 . 1; S7 Fig ) . In the intracranial data , a total of 1 , 415 saccades were detected ( 3 participants , mean = 471 . 7 ) , resulting in an average saccade rate of 1 . 25 Hz and a mean fixation duration of 474 ms . Note that this low saccade rate can partly be explained by the fact that electrooculography ( EOG ) signals were used to detect saccades in patients , which is less sensitive than eye tracking , and by conservative saccade detection criterions to exclude ambiguous EOG data . The mean saccade duration in patients was 33 . 7 ms . The saccade rate for later-remembered scenes ( mean = 1 . 1551 ) was lower than for later-forgotten scenes ( mean = 1 . 2906 ) for the patient data . When only events that were free of saccades and blinks in a 0 . 5-s interval prior to saccade onset were included ( as in the main analyses ) , 434 saccades remained ( mean = 144 . 7 ) in the EOG data , resulting in an average saccade rate of 0 . 42 Hz . The saccade rate for later-remembered scenes ( mean = 0 . 44 ) was similar to that of later-forgotten scenes ( mean = 0 . 40 ) for the patient data . The average saccade duration was 26 . 2 ms , and the mean fixation duration was 878 . 6 ms .
In 2 independent data sets , we provide novel evidence for a functionally relevant coordination of saccadic eye movements and brain activity . Both the intracranial and the MEG data show that retinal inputs are temporally aligned to a preferential alpha phase . Importantly , this coordination was related to successful memory encoding , suggesting a mechanistic role for alpha oscillations in coordinating the encoding of visual information . Furthermore , our results point to an active involvement of task-relevant brain areas in this coordination: MEG and intracranial data yielded the occipital cortex , the parahippocampal gyrus , and the retrosplenial cortex as sources of the coordination of saccades and alpha phase , which have been shown to support the encoding of visual scenes [13–15] . The engagement of scene-selective areas may reflect effective vision-to-memory mapping along visual , parietal , and posterior temporal cortices [19] . Our findings are in line with work from the 1960s [20] suggesting a relationship between alpha oscillations and saccades; however , this effect was not related to perception and memory . They also support the notion of a preferred alpha phase for the execution of eye movements [21] , by suggesting that during optimal information encoding , the execution of saccades is on hold until the end of an alpha duty cycle . We propose that effective coordination of saccades and brain oscillations allows for optimizing the speed of processing in the visual system [22] . The intracranial data in occipital and parahippocampal electrodes showed enhanced phase locking in the alpha band for later-remembered trials as compared to later-forgotten trials , albeit with the frequencies being slightly lower in the parahippocampal ( 8−10 Hz ) than in the occipital depth electrodes ( 12−14 Hz ) . This could be interpreted as a shift in the dominant frequency of brain areas along the hierarchy , from visual to memory areas . The main results presented here rely on events with a minimum fixation duration of 500 ms prior to saccade onset . The upside of this selection is the exclusion of other saccades or blinks that would contaminate the time window of interest while keeping a reasonable frequency resolution . On the downside , these events may not reflect stereotypical eye movement behavior , which display an average fixation duration of approximately 250 to 300 ms . However , analyzing events with a minimum fixation duration of 200 ms ( at the expense of frequency resolution ) showed very similar phase-locking effects , thus underscoring the robustness of our core findings . Although memory studies often treat eye movements as artifacts , their interaction with memory processes has gained recent interest in the field [23 , 24] . Importantly , investigating naturalistic behavior in free-viewing paradigms , as used in the present study , has been shown to provide crucial insight into the interaction of eye movement behavior and memory processes , as , for example , relationships between visual sampling and recognition memory performance [16 , 17] or hippocampal blood oxygen level-dependent ( BOLD ) activity [25] . Going beyond these prior findings , the present results indicate that eye movements already have an effect on memory performance at the stage of their initiation , depending on their coordination with brain rhythms implicated in the sampling of visual information . The increase in memory encoding with saccades locked to alpha phase might be supported by anticipatory attentional deployment [26] . The fact that the phase-locking difference was found prior to saccade onset might suggest planning of the upcoming to-be-attended location [27] , resulting in a stronger locking between saccades and the phase of the alpha oscillation and ultimately improved memory encoding . The present results highlight the necessity for a coordination of alpha oscillations and eye movements for optimal memory encoding . Efficiently sampled visual information could then be integrated by the hippocampal memory system . A recent nonhuman primate study demonstrated that saccades were aligned to hippocampal oscillations of approximately 10 Hz [28] . Future studies should explore interregional synchronization in relation to oculomotor behavior , visual information sampling , and memory .
All participants gave written informed consent before the start of experiment in accordance with the Declaration of Helsinki . The study was approved by the local ethics committee ( commission for human related research CMO-2014/288 region Arnhem/Nijmegen NL ) . The patients , who volunteered to participate in the study , had depth electrodes implanted for diagnostic reasons . The patients gave written informed consent . The study was approved by the ethics committee of the University of Munich . For the MEG part , 36 young healthy adults were included in the study . Initially , 48 participants were recruited; however , 12 were removed because of not completing the study ( 7 participants ) , excessive movement artifacts ( 2 participants ) , or technical problems during the recordings ( 3 participants ) . The 36 participants included in this study ( 24 females; mean age 23 . 1 y , range 18−30 y; 35 right handed ) reported no history of neurological and/or psychiatric disorders and had normal or corrected-to-normal vision . Additionally , 3 male patients ( age range 30−60 y ) with a history of drug-resistant epilepsy were recruited from the Epilepsy Center , Department of Neurology , University of Munich , Germany . The study design comprised an MEG and an fMRI ( not reported here ) session . Session order was counterbalanced across participants . For each session , 3 stimulus sets of 100 photographs each were constructed . Half of the pictures depicted indoor scenes , the other half outdoor scenes ( exemplary scenes are shown in Fig 1 ) . Pictures were presented in the MEG chamber on a 39 × 46 cm back-projection screen subtending a visual angle of approximately 27° × 32° . Out of the 3 sets , 2 sets ( 200 scenes ) were presented during encoding . During test , these 2 sets were presented again , plus the third set ( 100 scenes as foils ) . Assignment of a set to encoding or test was counterbalanced across participants . Nine additional scenes were presented during a short practice session before encoding and test in order to explain the task . Participants were made aware about the memory test before the start of the experiment . Fig 1 illustrates the experimental procedure . At study , the pictures were presented for 4 s in random order with the constraint that no more than 4 scenes of the same type ( indoor/outdoor ) were shown consecutively . The participants were instructed to judge whether the depicted scene was indoors or outdoors by button press during the fixation cross . This encoding task was chosen to ensure attention to each scene and promote encoding of the images . Participants freely viewed the scenes; i . e . , they were not expected to fixate . A fixation cross with variable duration ( 1–2 s ) followed each scene . The study phase was followed by a distracter phase during which the participants solved simple mathematical problems for approximately 1 min , underwent approximately 5 min of fixation to different locations on the screen used to evaluate eye tracker accuracy , and spent approximately 1 min with eyes open and approximately 1 min with eyes closed . The distracter phase prevented participants from covert rehearsing . The distracter period was followed by the memory test . At test , the 200 pictures from the study phase and 100 new pictures ( foils ) were presented for 4 s each . The presentation order was randomized , with the constraint that no more than 4 scenes of the same type ( old/new ) were shown consecutively . After each scene , participants were prompted to indicate their confidence on whether the scene was old or new using a 6-point response scale , ranging from “very sure old” ( 1 ) to “very sure new” ( 6 ) . This picture of the rating scale remained until the participants responded . Before the next scene , a fixation cross with variable duration ( 750–1 , 250 ms ) was presented . The procedure for the patients with intracranial electrodes deviated slightly ( see below ) . MEG was recorded using a 275 whole-brain axial gradiometer system ( VSM MedTech/CTF MEG , Coquitlam , Canada ) installed in a magnetically shielded room . The data were sampled at 1 , 200 Hz following a low-pass antialiasing filter with a cutoff at 300 Hz . Additionally , horizontal and vertical electro-oculograms were recorded from bipolar Ag/AgCl electrodes ( <10kΩ impedance ) placed below and above the left eye and at the bilateral outer canthi . To track the position of the head during MEG recording , we used 3 head coils placed at anatomical landmarks ( nasion and both ear canals ) . Using a real-time head localizer [29] , the position of the head relative to the MEG helmet was tracked . Each participant’s nasion , left and right ear canal , and head shape were digitized with a Polhemus 3Space Fasttrack . Preprocessing of the data was done using the Fieldtrip toolbox [30] . Data were divided into single epochs ranging from 0 to 4 s after picture onset . Epochs were corrected for cardiac artifacts using independent component analysis ( ICA ) and sorted according to the behavioral performance of each participant’s confidence judgments during the recognition test phase . Pictures that were confidently judged as old ( responses 1 , 2 , and 3 ) constituted later-remembered scenes , and the remaining pictures were classified as later-forgotten scenes . An Eyelink 1000 ( SR Research ) eyetracker was used to monitor the horizontal and vertical movements of the participant’s left eye . Before recording , the eye tracker was calibrated by collecting gaze fixation samples from known target points to map raw eye data on screen coordinates . Participants fixated on 9 dots sequentially on a 3-by-3 grid . After the calibration run , a validation run was performed during which the difference between current gaze fixations and fixations during the calibration was obtained . The calibration was only accepted if this difference was smaller than 1° of visual angle . Eye tracking and MEG data were simultaneously recorded and analyzed using the Fieldtrip toolbox . Vertical and horizontal eye movements were transformed into velocities . Velocities exceeding a certain threshold ( velocity > 6× the standard deviation of the velocity distribution , duration > 12 ms , see Engbert and Kliegl [31] ) were defined as saccades . Saccade onsets during stimulus presentations in the study phase defined the events of interest ( trials ) . To avoid potential artifacts from other eye movements and provide a reasonable frequency resolution of 2 Hz , only events that were free of saccades and blinks in a 0 . 5-s interval prior to saccade onset ( i . e . , a minimum fixation period of 500 ms ) were included . Saccades that occurred during the presentation of scenes that were subsequently judged as old ( responses 1 , 2 , and 3 ) constituted later-remembered trials . Saccades that occurred during scenes that were subsequently judged as new ( responses 4 , 5 , and 6 ) constituted later-remembered trials . After excluding all participants that had less than 30 remaining trials per condition ( later remembered or later forgotten ) , 22 participants were included in the further analyses ( 3 , 837 trials in total , mean = 174 . 4 , SE = 13 . 26; mean number of remembered trials = 109 . 7 , SE = 8 . 8; mean number of forgotten trials = 64 . 7 , SE = 9 . 7 ) . In order to display the temporal dynamics of the phase locking , the trials were zero-padded to a length of 1 . 5 s ( i . e . , adding 500 ms of zeros before and after the 500 ms of data ) . Since typical eye movements occur approximately every 250–300 ms , the events with a minimum fixation period of 500 ms may not be representative . Therefore , we conducted additional phase-locking analysis on events with a minimum fixation period of 200 ms prior to saccade onset , including approximately 66% of all detected saccades . In the 22 participants , a total of 25 , 077 saccades ( mean = 1 , 139 . 9 , SE = 49 . 6; mean number of remembered trials = 802 . 7 , SE = 57 . 5 , mean number of forgotten trials = 337 . 1 , SE = 26 . 5 ) were included . Since this analysis produced higher trial numbers , data from all 36 participants could be analyzed ( total number of saccades = 43 , 226; mean = 1 , 201 . 8 , SE = 41 . 3; mean number of remembered trials = 918 . 7 , SE = 49 . 6 , mean number of forgotten trials = 282 . 9 , SE = 23 . 1 ) . These trials were zero-padded to a length of 0 . 6 s ( i . e . , adding 200 ms of zeros before and after the 200 ms of data ) . The frequency spectra of the phase and the power of the data were computed by applying a Fourier transformation to the 500 ms of data prior to saccade onset in each event , after multiplication with a hanning taper . Phase and power were calculated for frequencies between 2 and 30 Hz in steps of 2 Hz . The frequency spectra of the phase and the power were used to statistically test differences between conditions ( see “Statistics” ) . Synthetic planar gradient representations were approximated by relating the field at each sensor with its neighbors’ [32] . On each of the resulting 2 orthogonal gradients , Fourier coefficients were normalized by their amplitude , and the phase-locking index ( PLI ) [7] was calculated , by extracting the length of the resulting vector after averaging the phase angles: PLItf=|n-1∑r=1neiktfr| , where n = number of trials , and eik equals the complex polar representation of phase angle k in trial r , for time-frequency point tf . This was done for later-forgotten and later-remembered trials . The PLI quantifies the consistency of phases across trials at each given time-frequency point . To control for a bias in PLI due to different trial numbers in conditions , a sample of trials from the condition with the larger number of trials was randomly drawn , with the number of trials in this sample being equal to the number of trials in the condition with less trials . The PLI for this sample was computed . After repeating this procedure 1 , 000 times , PLI values were averaged . This average reflects an unbiased estimate of the PLI for all trials in the respective condition . After this step , the 2 planar gradients were combined . In order to depict the temporal dynamics of phase and power in the data , time-frequency representations were computed by a sliding time window approach with a window length of 0 . 5 s in steps of 50 ms across the zero-padded data . After multiplying a hanning taper to each window , the Fourier transformation was calculated for frequencies between 2 and 30 Hz in steps of 2 Hz . To identify potential confounds due to differences in spectral power , power was calculated on synthetic planar gradients , using the same approach as outlined above . Instead of computing the PLI , power values were calculated from the Fourier coefficients ( amplitude squared ) . The PLI analysis on events with a minimum fixation period of 200 ms prior to saccade onset was performed as defined above , with the exception that the window length was 200 ms in the sliding time window approach . Due to the resulting frequency resolution of 5 Hz , phase information was extracted from 5 to 30 Hz in steps of 5 Hz . To identify PLI differences in source space , a virtual sensor approach applying frequency-domain adaptive spatial filtering ( DICS beamformer [12] ) was implemented . This algorithm constructs a spatial filter for each specified location ( each grid point; 10-mm3 grid ) . The cross spectral density for the construction of the spatial filter was calculated for the frequency of interest ( 12–14 Hz , using orthogonal Slepian tapers around a center frequency of 13 Hz with spectral smoothing of +/− 2 Hz ) , for all trials ( common filter approach ) . Individual structural MR images , acquired on a 3T Siemens Magnetom Prisma MRI system ( Siemens , Erlangen , Germany ) , were aligned to the MEG coordinate system , utilizing the fiducials ( nasion , left and right preauricular points ) and individual head shapes recorded after the experiment . A realistic single-shell brain model [33] was constructed for each participant , based on the structural MRIs . The forward model for each participant was created using a common dipole grid ( 10-mm3 grid ) of the grey matter ( derived from the anatomical automatic labeling atlas [34] ) volume in MNI space warped onto each participant’s anatomy . The Fourier data were projected into source space by multiplying them with the spatial accordant filters , allowing for the phase to be estimated . The PLI was computed on the 2 orientations of the source model , and later averaged , for later-remembered and later-forgotten trials , respectively . Statistics followed a 2-step approach: first , differences in the intracranial data’s phase locking ( later-remembered versus later-forgotten trials ) were evaluated in a fixed-effect manner , by concatenating all electrodes from all patients . Cluster-based nonparametric permutation statistics [35] identified continuous frequency clusters with significant differences between later-remembered and later-forgotten PLI while controlling for multiple comparisons over frequencies . Only the cluster with the largest summed value was considered and tested against the permutation distribution . The null hypothesis that later-forgotten and later-remembered trials showed no difference in PLI was rejected at an alpha level of 0 . 05 ( 2-tailed ) . Second , statistical quantification of the MEG sensor-level data was performed by a cluster-based nonparametric permutation approach [35] , identifying clusters of activity on the basis of rejecting the null hypothesis while controlling for multiple comparisons over sensors . The frequency range ( 12–14 Hz ) for the sensor-level statistics was restricted to the outcome of the intracranial data analyses . For each sensor , a test statistic was calculated , based on a paired samples t test comparing the PLI for later-remembered versus later-forgotten trials . Sensors showing a significant effect ( p < 0 . 05 , 2-sided t test ) were clustered based on spatial adjacency , with a minimum of 2 adjacent sensors required for forming a cluster . T-statistics were summed in each cluster . Again , only the cluster with the largest summed value was considered and tested against the permutation distribution . The null hypothesis that later-forgotten and later-remembered trials showed no difference in PLI was rejected at an alpha level of 0 . 05 ( 2-tailed ) . Statistical quantification of the source-level data was also performed by a cluster-based nonparametric permutation approach , now considering the clustering in voxel space . The frequency range for the source-level statistics was defined by the outcome of the sensor-level statistics , and the alpha level was set to 0 . 05 ( 2-tailed ) . Cluster-based nonparametric permutation statistics [35] identified continuous spatial clusters with significant differences between later-remembered and later-forgotten PLI while controlling for multiple comparisons over voxels . Only the cluster with the largest summed value was considered and tested against the permutation distribution . The null hypothesis that later-forgotten and later-remembered trials showed no difference in PLI was rejected at an alpha level of 0 . 05 ( 2-tailed ) . Condition-specific PLIs for later-remembered and later-forgotten trials , separately ( see S3 Fig ) , at the time and frequency of interest ( 12–14 Hz , −0 . 25 ms ) were statistically quantified by comparing them to a distribution of surrogate PLI values . The surrogate PLI distribution was constructed for each participant and condition by shifting the data points in each condition’s trial circularly along the time axis with a random lag , for each sensor . PLI values were computed as explained above , for 1 , 000 random shifts . Subsequently , 10 , 000 surrogate grand averages were constructed by randomly drawing 1 PLI value from each participant’s surrogate distribution for each surrogate grand average . Condition-specific PLI grand averages were compared to these 10 , 000 surrogate grand averages on each sensor and considered to be significant if they were larger ( or smaller ) than 97 . 5% ( or 2 . 5% ) of the values in the surrogate grand average values ( 2-sided test ) . Three male patients ( age range 30–60 y ) with occipital depth electrodes were included in the study . The patients had a history of drug-resistant focal epilepsy and were implanted for diagnostic reasons . Recordings were performed at the Epilepsy Center , Department of Neurology , University of Munich , Germany . The patients gave written informed consent . The procedure and design of the study was identical to the MEG procedure and design ( see above ) , with the exception that only 100 pictures were presented during study and 200 scenes ( 100 old and 100 new ) were presented during the memory test . This was done to compensate for inferior memory performance in a clinical setting . Patient 1 had 10 depth electrodes implanted , covering bilateral temporal , parietal , and frontal regions and left occipital regions . Patient 2 had 10 depth electrodes implanted , covering right temporal , parietal , and occipital regions . Patient 3 had 11 depth electrodes implanted , covering left frontal , temporal , parietal , and occipital regions . The locations of the electrodes were determined using coregistered preoperative MRIs and postoperative CTs . Electrode locations were converted to MNI coordinates . Intracranial EEG was recorded from Spencer depth electrodes ( Ad-Tech Medical Instrument , Racine , Wisconsin , United States ) with 4–12 contacts each , 5 mm apart . Data were recorded using XLTEK Neuroworks software ( Natus Medical , San Carlos , California , US ) and an XLTEK EMU128FS amplifier , with voltages referenced to a parietal electrode site ( 1 , 000 Hz sampling rate ) . All electrodes that either were identified as located in the seizure onset zone or showed interictal spiking activity were excluded from analyses . Data were rereferenced offline to each contact’s neighboring contact ( bipolar montage ) . All bipolar electrodes with both contacts in the occipital cortex were included in the analyses . Additionally , horizontal and vertical eye movements were recorded from bipolar Ag/AgCl electrodes ( <10kΩ impedance ) placed below and above the left eye and at the bilateral outer canthi . Study phase data were cut into single epochs , ranging from 0 to 4 s after picture onset . Saccade onsets were extracted from EOG recordings using the method described above ( see “Eye tracking acquisition , analyses , and trial definition” ) . Saccade onsets during stimulus presentations defined the events of interest ( trials ) . All trials were visually inspected for artifacts ( e . g . , epileptiform spikes ) . Contaminated trials were excluded from the analyses . The encoding trials were sorted according to each participant’s confidence judgments during the test phase . Pictures that were confidently judged as old ( responses 1 , 2 , and 3 ) constituted hits , and the remaining pictures were classified as misses . Time-frequency analyses , PLI , and statistics were computed as described above . | In everyday life , we constantly move our eyes to sample visual information . In order to make the sampling efficient , these eye movements need to be coordinated with the intrinsic brain dynamics that constrain visual computations . The present study provides novel evidence for how this coordination is achieved at the neuronal level , from 2 independent data sets: direct brain recordings in epileptic patients and noninvasive magnetoencephalography recordings in healthy participants . Both studies showed that eye movements are locked to the phase of alpha oscillations—synchronous and coherent neuronal electrical activity at 7–14 Hz—just prior to a saccade , i . e . , a rapid eye movement that abruptly changes the point of fixation . Importantly , this coordination is predictive of successful memory encoding . | [
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| 2017 | Saccades are phase-locked to alpha oscillations in the occipital and medial temporal lobe during successful memory encoding |
Postweaning multisystemic wasting disease ( PMWS ) in piglets caused by porcine circovirus type 2 ( PCV2 ) is one of the major threats to most pig farms worldwide . Among all the PCV types , PCV2 is the dominant genotype causing PMWS and associated diseases . Considerable efforts were made to study the virus-like-particle ( VLP ) assembly and the specific PCV2-associated epitope ( s ) in order to establish the solid foundation for engineered PCV2 vaccine development . Although the N-terminal fragment including Nuclear Localization Signal ( NLS ) sequence seems important for recombinant PCV2 capsid protein expression and VLP assembly , the detailed structural and functional information regarding this important fragment are largely unknown . In this study , we report crystal structure of PCV2 VLP assembled from N-terminal NLS truncated PCV2 capsid protein at 2 . 8 Å resolution and cryo-EM structure of PCV2 VLP assembled from full-length PCV2 capsid protein at 4 . 1Å resolution . Our in vitro PCV2 VLP assembly results show that NLS-truncated PCV2 capsid protein only forms instable VLPs which were easily disassembled in solution , whereas full-length PCV2 capsid protein forms stable VLPs due to interaction between 15PRSHLGQILRRRP27 ( α-helix ) and 33RHRYRWRRKN42 ( NLS-B ) in a repeated manner . In addition , our results also showed that N-terminal truncation of PCV2 capsid protein up to 27 residues still forms PCV2 particles in solution with similar size and immunogenicity , while N-terminal truncation of PCV2 capsid protein with more than 30 residues is not able to form stable PCV2 particles in solution , demonstrating the importance of interaction between the α-helix at N-terminal and NLS-B in PCV2 VLP formation . Moreover , we also report the cryo-EM structure of PCV2 VLP in complex with 3H11-Fab , a PCV2 type-specific neutralizing antibody , at 15 Å resolution . MAb-3H11 specifically recognizes one exposed epitope located on the VLP surface EF-loop ( residues 128–143 ) , which is further confirmed by PCV1-PCV2 epitope swapping assay . Hence , our results have revealed the structural roles of N-terminal fragment of PCV2 capsid protein in PCV2 particle assembly and pinpointed one PCV2 type-specific neutralizing epitope for the first time , which could provide clear clue for next generation PCV2 vaccine and diagnostic kits development .
In early 1990s , a postweaning multisystemic wasting syndrome ( PMWS ) outbreak in Western Canada [1] was reported due to the spreading of a small , non-enveloped virus containing a single-stranded , circular DNA genome , which was later diagnosed as the porcine circovirus ( PCV ) [2] . Subsequently , PMWS and PCV pathogens were identified in pigs in USA , China and other countries [3] . PCV2 belongs to the family of Circoviridae containing several types , such as PCV1 and PCV2 [4] . Currently , PCV2 is the dominant pathogen causing PMWS and a number of associated diseases in pigs [5–7] , severely affecting swine production worldwide [8–15] . Remarkably , PCV seems to continue evolving to yield more pathogenic genotypes probably due to selection pressure . Initially , only PCV1 and PCV2 ( PCV2a , PCV2b , PCV2c , PCV2d , PCV2e and PCV2f ) were identified , however a distantly related porcine circovirus ( PCV3 ) were recently diagnosed both in US and China [16 , 17] . Among them , PCV1 is characterized as a non-pathogenic virus , PCV2 is identified as a causative agent for severe economic losses in the swine industry , whereas PCV3 is found to be associated with porcine dermatitis nephropathy syndrome ( PDNS ) , reproductive failure , etc [18 , 19] . Among them , PCV2b genotype was the most widely spread PCV pathogen worldwide , whereas PCV2d has become a predominant PCV2 pathogen during the global PCV2 genotype shift recently [20–23] . Notably , PCV2 capsid protein ( 233/234 amino acids ) encoded by the open reading frame 2 ( ORF2 ) of PCV2 is capable of self-assembly into VLP and resembling the icosahedral morphology of the native PCV2 virions [24] . Serological results show that neutralization antibodies with high titers eliciting from the in vitro assembled PCV2 VLPs provide strong protection for piglets from homologous genotype PCV2 infection [25] . In the market , the engineered PCV2 vaccine , Ingelvac CircoFLEX , developed from the self-assembled VLP from PCV2 capsid protein shows superior prophylactic efficacy compared to the traditional vaccines created from inactivated PCV2 viruses [26–28] . In literature , the crystal structure of in vitro assembled PCV2 VLP derived from N-terminally truncated PCV2 capsid protein was determined at 2 . 3 Å resolution , whereas the cryo-EM structures of in vitro assembled full-length PCV2 VLP was determined at 9 . 6 Å resolution [29] . These structures show that PCV2 VLPs are assembled from 60 copies of PCV2 capsid proteins with the icosahedral symmetry and several exposed loops located on the viral surface , which may serve as the immunodominant epitopes eliciting neutralization antibodies [29 , 30] . Although these structures have provided a starting point to understand the structural principles of PCV2 VLP assembly and neutralization epitope identification , two critical questions remain largely unanswered: 1 ) the role of the N-terminal fragment of PCV2 capsid protein , including the nuclear localization signal ( NLS ) in PCV2 VLP assembly , and 2 ) the structural epitopes that differentiate PCV1 and PCV2 genotypes . In this study , we sought to explore the structural roles of N-terminal fragments of PCV2 capsid protein in PCV2 assembly and investigate the structural basis of a PCV2 type-specific epitope . We determined the cryo-EM structure of non-tagged full length PCV2 capsid protein and the crystal structure of N-terminal His-tagged 1–45 residues truncated PCV2 capsid protein ( PCV2-His-Δ45 ) . Through the analysis and comparison of these two structures , we speculated that the interaction between the two N-terminal fragments of the PCV2 capsid protein ( 15PRSHLGQILRRRP27/α-helix and 33RHRYRWRRKN42/NLS-B ) plays a significant role in stabilizing the assembled PCV2 VLPs in solution . The systematic truncation results validated our assumption . Additionally , we determined the cryo-EM structure of the full-length PCV2 VLP in complex with the Fab fragment of a PCV2 type-specific neutralizing monoclonal antibody , in which the structural docking model clearly shows that the CDR regions of mAb-3H11 Fab bind to a protruding EF-loop region ( 134KATALT139 ) located on the PCV2 VLP surface . This EF-loop region ( 134KATALT139 ) could serve as a PCV2 type-specific neutralizing epitope . Our results could provide insightful information for next generation PCV2 vaccine and diagnostic kit development .
The full-length PCV2 capsid protein was expressed in E . coli and purified following a standard protocol developed in our lab [31] . The purity of the capsid protein can reach at least 95% ( S1A Fig ) . The purified protein samples were used for PCV2 VLP assembly checked by transmission electron microscopy and dynamic light scattering assays . Under transmission electron microscopy , homogeneous PCV2 VLPs particles with an average diameter of ~ 17 nm were observed ( S1B Fig ) . Consistently , dynamic light scattering ( DLS ) results show that nearly 99% of the capsid proteins self-assemble into particles in solution with an average radius of 8 . 36 nm ( S1C Fig ) . Next , we investigated whether or not the NLS sequence is important for VLP assembly in addition to its accessory role in the replication of PCV . Instead of replacing the NLS sequence by a non-NLS sequence , we completely deleted the N-terminal 1–45 residues and replaced them with a fused N-terminal His-tag “MGSSHHHHHHSSGLVPRGSH” derived from pET-28b vector to make PCV2-His-ΔN45 . As expected , no stable PCV2 particles were observed in solution , as confirmed by TEM , DLS and analytical FPLC . To explore the structural roles of N-terminal fragments of PCV2 capsid protein in PCV2 assembly , multiple PCV2 constructs were used for crystallization screening and structure determination . Among all the constructs screened , PCV2-His-ΔN45 was able to be concentrated to ~10 mg/ml and was successfully crystallized and optimized . Single-wavelength high-resolution diffraction data was collected in Taiwan synchrotron radiation resource center ( NSRRC ) and scaled to a space group of P21 , with the cell parameters as a = 194 . 12Å , b = 201 . 88Å , c = 231 . 28Å and β = 90 . 72° at the resolutions of 2 . 8Å . The structure was determined by MORDA/CCP4 by using 3R0R as the search model [29] , and the model was refined by REFMAC/CCP4 and rebuilt using Coot . There was one icosahedral particle per asymmetric unit with crystallographic statistics listed in Table 1 . Surprisingly , although PCV2-His-ΔN45 does not self-assemble into particle simultaneously in solution at relatively low concentration ( no more than 2mg/ml ) , the crystal structure of PCV2-His-ΔN45 showed that PCV2-His-ΔN45 capsid proteins indeed self-assemble into VLP within the crystals . At such conditions , the crystal structure of PCV2-His-ΔN45 displayed an icosahedral VLP structure consisting of 60 capsid protein subunits ( Fig 1A ) with clearly observable 2-fold , 3-fold and 5-fold axes ( Fig 1B , S2A and S2B Fig ) . In our crystal structure , the interpretable densities for the PCV2-His-ΔN45 start at residue 43Ser ( Fig 1C ) , and the densities for most of the surface loops , such as loop-BC , loop-CD and loop-EF etc , are clearly visible and easily traced ( Fig 1A ) . Although the extra 20 non-authentic residues “MGSSHHHHHHSSGLVPRGSH” were introduced to the vector expressing PCV2-His-ΔN45 protein , the traceable non-authentic sequences are only two residues long 43SH44 , which are located within the interior region of the PCV2 VLP . The extremely flexible nature of N-terminal fragment of PCV2 capsid protein strongly suggests that neither the NLS sequence nor the particular positively charged residues are absolutely essential for PCV2 icosahedral symmetry packing and/or particle assembly under this particular crystallization condition . To further validate the structural features observed from the crystal structure determined and to investigate whether or not in vitro assembled PCV2-VLPs indeed resemble the intact PCV2 viruses , more than 150 cryo-EM micrographs of in vitro assembled full-length PCV2-VLPs were collected under low dose condition . Approximately 12 , 000 individual PCV2 particles were excised and boxed from the selected 92 best micrographs . Next , contrast transfer function ( CTF ) correction for each micrograph was determined individually and the Fourier transformation was applied to each image by EMAN2 ( blake . bcm . tmc . edu/EMAN2 ) ( Fig 2A ) . Thirty-two groups of particles with different reflection orientations were built and averaged . Among them , only the good average classes with an apparent icosahedral shape were used to make the initial model ( Fig 2B ) . Roughly 9 , 600 particles were used for 3D reconstruction , which reveals a typical PCV2 viral capsid structure at 4 . 12Å resolution ( Fig 2C ) with an icosahedral T = 1 symmetry comprising 60 capsid protein subunits ( Fig 2D , S2C and S2D Fig ) . The densities corresponding to PCV2 capsid proteins were extracted from the reconstructed density map and viewed by UCSF Chimera software ( www . cgl . ucsf . edu/chimera/ ) [32 , 33] . The local resolution distribution was assessed by Resmap/Chimera [34] ( S3A–S3C Fig ) , showing significant resolution variation ranging from 5~6 . 0Å on the outer surface and inner concave regions to 3 . 5~4 . 0Å at β-strand barrel regions ( S3B Fig ) . The relatively low resolutions on the outer surface and inner concave regions of the VLP capsid suggest the structural flexibility of PCV2 capsid at these regions ( S3B Fig ) . Most of the main chains and some of the side chains of PCV2 capsid protein were clearly traced and the initial model was further refined by twenty rounds of real-space group refinement using PHENIX [35] ( S3D Fig ) . In our cryo-EM structure , the density of each capsid subunit is clearly visible even at r . m . s . d = 4 . 1 Å , showing a typical circovirus capsid protein fold consisting of eight β-strands joined by several loops located on the surface of the particle . By contrast , some surface loops , such as BC-loop ( 58KRTTVKTPS66 ) and CD-loop ( 79FLPPGGGSNPRSVPEE94 ) , are relatively difficult to trace and build due to the weak densities , suggesting the structural flexibility of these exposed surface loops . The N-terminal fragments , such as fragments 15PRSHLGQILRRRP27 and 33RHRYRWRRKN42 surrounding the N-terminal NLS region , are clearly visible on our cryo-EM density map contoured at level of 3 . 6 e/Å3 ( r . m . s . d = 3 . 60 Å . ) , suggesting the relative structural rigidity of these N-terminal fragments ( Fig 2E and 2F ) . In our cryo-EM structure , Arginine-rich residues ( 15PRSHLGQILRRRP27 ) next to NLS-A reach into the adjacent capsid protein located within PCV2 VLP chamber , and interact with its NLS-B fragment ( 33RHRYRWRRKNG43 ) to stabilize the VLP formation ( Fig 2F and 2G ) . Moreover , the charges and types of these amino acids could also play certain roles in determining the rate and/or stability for VLP assembly , probably due to the interior interactions of the amino acids proximal to the 5-fold axis . There is no density map available for N-terminal fragment 1MTYPRRRYRRRRHR14 due to structural flexibility at this region , strongly suggesting that this N-terminal fragment may not be essential for icosahedral capsid formation and stability . Table 2 shows the statistics of the refined PCV2 VLP model derived from our cryo-EM density map . In literatures , the non-traceable N-terminal residues are presumed to be located proximal to the icosahedral 5-fold axes . However , no clear structure has been modeled due to the structural flexibility of N-terminal part of the determined cryo-EM structure [29 , 30] . Remarkably , in our crystal structure of PCV2-His-ΔN45 at 2 . 8Å resolution , the traceable fragment 43GIFNTRL49 ( the numerical is based on the authentic sequence NO . ) is located along the 1st β-strand toward the interior core of the VLP . By contrast , in the cryo-EM structure , the traceable fragment 43SHFNTRL49 ( PCV2-full-length ) forms a loop structure pointing to the exterior direction with a β-turn formed by 47Thr and 48Arg ( Fig 1C ) . This observation suggests that the N-terminal fragment might be exposed at the capsid surface to mediate the virus-host interaction by “breathing” , which is often observed by other virus [36–38] . Though the NLS was truncated , the icosahedral VLPs derived from PCV2-His-ΔN45 capsid protein were observed within the crystal structure , probably due to crystal packing force in crystallization buffer . By contrast , PCV2-His-ΔN45 capsid protein itself is not able to self-assemble into stable VLP in solution no matter in low or high concentration ( S4A–S4D Fig ) . We speculate that the N-terminal fragment , including the NLS motif , play an important role in VLP stabilization . Consistently , the cryo-EM structure of non-tagged full-length PCV2 VLP at 4 . 12 Å resolution clearly shows that the interpretable N-terminal α-helix fragment 15PRSHLGQILRRRP27 interacts with the adjacent NLS-B fragment 33RHRYRWRRKNG43 located in the interior concavity , which is involved in VLP stabilization ( Fig 2F and 2G ) . To further investigate the structural roles of N-terminus of PCV2 capsid protein in PCV2 VLP assembly , we have systematically expressed and purified a series of truncated non-tagged PCV2 capsid proteins by truncation of every 3 residues , up to 30 residues ( Fig 3A and 3B ) , from N-terminus to test VLP assembly ability by both dynamic light scattering ( DLS ) and transmission electron microscopy ( TEM ) assays . As expected , DLS results show that PCV2 capsid proteins ( from full-length to ΔN27 ) self-assemble into particles with an average radius of ~ 8 . 5 nm in solution ( S5 Fig ) . Consistently , these PCV2 particles imaged under transmission electron microscopy show that the diameter ( 2 × radius ) of the majority of the PCV2 VLP particles is ~17 nm in the solution ( Fig 3C ) . By contrast , PCV2 capsid protein with N-terminal 30 residues truncation ( PCV2-ΔN30 ) was not able to form icosahedral VLP , which is confirmed by DLS , size exclusion chromatography ( SEC ) and TEM methods ( S6 Fig , Table 3 ) . Since the in vitro assembled N-terminal truncated PCV2-VLPs ( no more than 27 residues ) and full-length PCV2 VLPs have similar icosahedral shape and diameters , immune assays were performed to check the immunogenicity for these VLPs . Purified and in vitro assembled VLPs supplemented with adjuvant were injected into mouse , and poly-antibody mouse serum were collected every week for 12 weeks after immunization . Then enzyme-linked-immune-sorbent assays ( ELISA ) were performed to measure the antibody titers . As expected , antibody titers increased rapidly after the second booster vaccination and most of the antibody titers reached 106−108 ( Fig 3D ) . As compared to the full-length PCV2-VLPs , most of the truncated PCV2-VLPs ( PCV2-ΔN3 to PCV2-ΔN27 ) triggered similar immunogenicity responses from mice ( Fig 3D ) . Porcine circovirus contains several genotypes , such as PCV1 and PCV2 . Notably , although the sequences of these two genotypes are quite similar , the phenotypes vary significantly . PCV2 is considered a threatening pathogen , causing PMWS in piglets , whereas PCV1 is not . However , more and more data shows that the PCV viruses keep evolving under selection pressure and more infectious strains and genotypes could evolve by genome recombination of spreading PCV viruses with some genotypes that may not cause immediate PMWS associated symptoms among the infected piglets . Hence , it is critical to develop novel diagnostic methods to monitor the co-spreading of different PCV strains and genotypes among farms . Sequence alignment of the capsid protein sequences of several currently spreading PCV1 and PCV2 strains showed that the majority of the deviated sequences are located within the surface loop regions ( S7 Fig ) . Hence , we speculate that PCV2 type-specific mAb could have the ability to distinguish PCV2 from PCV1 by recognizing one of the surface epitopes . Previous results showed that the capsid sequence changes among the analyzed circovirus isolates do not yield any major structural changes in the viral capsid assembly but instead to the antigenic regions [39] . Hence , we assume that loop replacement will only change the local structures at the epitope regions but not the overall β-strands core region and capsid viral assembly . To this end , we performed systematic affinity screening between a PCV2 type-specific neutralizing mAb , named 3H11 [40] , and chimeric PCV2 capsid by replacing the capsid protein surface loop sequence by corresponding PCV1 capsid protein sequence . We have made seven chimeric PCV2 capsid proteins by replacing the loop sequences at the following loop regions one by one: BC-loop ( residues 58–66 ) , CD-loop ( residues 79–94 ) , DE-loop ( residues 108–116 ) , EF-loop ( residues 124–146 ) , FG-loop ( residues 153–156 ) , GH-loop ( residues 162–193 ) and HI-loop ( residues 204–208 ) ( S1 Table ) . Bio-dot ELISA assays were used to test the binding specificities between the PCV2 type-specific 3H11 mAb and the purified wild-type/chimeric proteins . As expected , 3H11 mAb specifically recognizes PCV2 EF-loop ( residues 128–143 ) since the replacement of PCV2 EF-loop sequence by corresponding PCV1 EF-loop sequence prevented the binding of 3H11 to PCV2 ( Fig 4A and 4B ) . In contrast , replacement of other PCV2 surface loops had marginal impact on 3H11 binding with purified chimeric PCV2 capsid proteins . Sequence comparison also suggested that residues 134–139 of EF-loop could be responsible for 3H11 binding due to large sequence differences between PCV1 and PCV2 capsid proteins ( S7 Fig ) . Consistently , both high-resolution X-ray and cryo-EM structures showed that these residues are stretched out from the capsid surface and exposed for mAb binding ( Fig 4C ) . To further investigate the structural detail of the PCV2 type-specific neutralizing epitope , we have determined the cryo-EM structure of PCV2 VLP in complex with 3H11-Fab fragment . Roughly 21 , 280 particles were used for 2D classification , and only good classes were selected for model building and refinement . The cryo-EM structure of the complex revealed an icosahedral symmetry with two-layer architecture comprising 60 copies of PCV2-3H11-Fab ( Fig 5A ) . The densities of PCV2 VLP and Fab fragments were segmented and extracted from the density map and visualized using UCSF Chimera software package ( www . cgl . ucsf . edu/chimera/ ) , which revealed the clear densities assigned to the Fab fragment of 3H11 and PCV2 VLP particles . In this structure , determined to ~15 Å when the 0 . 143 Fourier correlation criterions were used ( Fig 5B ) , the inner shell is composed of 60 copies of PCV2 and the outer shell is composed of 60 copies of 3H11-Fab fragments . To understand the molecular details of PCV2 type-specific epitope recognition by 3H11 , we docked the crystal structure of our PCV2 VLP and the homology model structure of 3H11 Fab , derived from a Rosetta calculation , into our cryo-EM density map of the complex one by one ( Fig 5C and 5D ) . At ~15 Å resolution , by taking advantage of the PCV2 VLP structure determined at high resolution and the conserved Fab structure , we were able to dock both PCV2 and 3H11 Fab models into the cryo-EM density of PCV2 in complex with 3H11-Fab ( ~4% atoms are outside at current contour level ) . In our docked model , the CDR regions of 3H11 Fab clearly bind to the protruding EF-loop located on the PCV2 VLP surface , further confirming that PCV2 EF-loop ( residues 128–143 ) contains a PCV2 type-specific neutralizing epitope ( Fig 5E , S8 Fig ) . Taken together , these data demonstrated that 3H11 precisely recognizes the PCV2 type-specific neutralizing epitope located on the surface EF-loop region .
The PCV2 capsid protein is the only protein responsible for PCV2 capsid formation and the dominant immunogenic host response . Our structural prediction suggests that PCV2 N-terminal fragment up to 40 a . a . could be structurally flexible , which is partially confirmed by the N-terminal truncated PCV2 structures published in literature [29 , 30] ( S9A–S9D Fig ) . The full-length PCV2 capsid protein was considered very difficult to express in soluble form in numerous protein expression systems [41] , suggesting the N-terminal fragment of PCV2 capsid protein could play a role in protein folding and/or capsid formation [42] . Consistent with these findings , N-terminal fragment truncated PCV2 capsid protein showed compromised immunogenicity and disability in PCV2 capsid assembly [43] . Notably , N-terminal fragment of PCV2 capsid protein , including several batches of Arginine-rich positively charged residues and a predicted α-helix embedded within the NLS sequences , is highly conserved among PCV family members , suggesting N-terminal fragment of PCV2 capsid protein does have functional roles in PCV2 particle assembly and/or PCV2 replication ( S10 Fig ) . However , due to the difficulty for full-length PCV2 capsid protein expression , no solid structural information of this N-terminal fragment available to illuminate the roles of these basic residues in capsid assembly and/or PCV2 replication at both molecular and structural level . In this study , we have reported a robust E . coli expression system expressing non-tagged full-length PCV2 capsid proteins , a series of N-terminal truncated PCV2 capsid proteins and one His-tagged N-terminal truncated PCV2 capsid protein in soluble form: PCV2-FL , PCV2-ΔN3 , PCV2-ΔN6 , PCV2-ΔN9 , PCV2-ΔN12 , PCV2-ΔN15 , PCV2-ΔN18 , PCV2-ΔN21 , PCV2-ΔN24 , PCV2-ΔN27 , PCV2-ΔN30 and PCV2-His-ΔN45 . Notably , both non-tagged PCV2-FL and all non-tagged PCV2 capsid proteins that were truncated up to 27 residues at the N-terminal are able to form regular icosahedral VLP with almost identical morphology and immunogenicity . The His-tagged NLS deleted capsid protein , PCV2-His-ΔN45 , forms icosahedral VLP only with the assistance of crystal packing since PCV2-His-ΔN45 itself is not able to form stable VLP at both low and high concentrations . Remarkably , in our cryo-EM structure determined at 4 . 12Å , clear densities are observed for the authentic N-terminal fragment 15PRSHLGQILRRRP27 including an α-helix , which interacts with the enriched Arginine-rich NLS-B ( 33RHRYRWRRKNG43 ) fragment located proximal to the β-barrel to stabilize the VLP formation in solution . Hence , based on these structural results , we speculate that PCV2 capsid protein could self-assemble in solution into PCV2 VLP without the assistance of N-terminal sequence . However , such VLP derived from N-terminal truncated PCV2 capsid protein , lacking the interactions between the α-helix in NLS from one capsid protein and NLS-B fragment from an adjacent capsid protein is instable and easily disassembled . Therefore , PCV2 N-terminus of PCV2 capsid protein , including the NLS fragment , play pivotal roles in PCV2 VLP stabilization rather than PCV2 VLP formation . Viral invasion often involves interactions between virus surface proteins and host receptors and surface molecules , such as glycosaminoglycans ( GAGs ) , heparin sulfate and other carbohydrate molecules . Although detailed in vivo mechanisms could be significantly different from those observed in in-vitro assays , the heparin sulfate binding motif located at the surface of the PCV2 capsid has been proposed to play a critical role in viral invasion . In literature , biochemical analysis suggests that the heparin sulfate binding motif is located at PCV2 D-β strand , whereas structural analysis suggests that the binding motif is located at PCV2 DE-loop [44 , 45] . Our high resolution structures , determined by both X-ray crystallography ( crystal packing ) and cryo-EM ( in solution ) approaches , provide a unique chance for us to re-examine the potential heparin binding sites at the PCV2 capsid surface . As expected , several patches of basic residues ( XBBXBX or XBBBXXBX ) are observed at the PCV2 capsid surface . Among them , a putative heparin binding motif ( 98IRKVKV103 ) was revealed from sequence analysis . However , this binding motif is located in the interior of the capsid VLP and significant structural rearrangement should be triggered upon host receptor binding if this motif is indeed involved in receptor binding . However , this kind of structural flexibility was not observed in the study of our PCV2 structures , as determined by X-ray crystallography and cryo-electron microscopy . Another conserved positively charged patch ( 179KRNQLWLR186 ) was observed on GH-loop , which is located proximal to the icosahedral 3- and 5-fold axes . However , this residue patch is quite flexible and may not be the heparin binding site , as the dramatic structural re-arrangement at the 3- and 5-fold axes could disrupt the capsid formation . At the other hand , PCV2 contains a lengthy Arginine-rich motif at its N-terminus . The NLS sequence is divided into NLS-A ( 5RRRYRRRRHRPR16 ) and NLS-B ( 33RHRYRWRRK41 ) by one α-helix ( 15RRRYRRRRHRPR24 ) ( S10 Fig ) . Although the structure of NLS-A fragment was not revealed in our cryo-EM analysis , the structure of the predicted α-helix ( 15PRSHLGQILR24 ) adjacent to NLS-A and the structure of NLS-B ( 33RHRYRWRRK41 ) were clearly traced and built ( Fig 2F ) . We speculate that the flexible NLS fragments including the α-helix 15PRSHLGQILR24 could be the heparin binding sites because: 1 ) NLS residues are located at the virus’ surface with flexible basic-charged patches which could interact with host receptors and other host surface molecules; 2 ) NLS residues are located far away from the capsid assembly axes so that structural rearrangement of the VLP does not disrupt PCV2 capsid formation; 3 ) the well-refined NLS ( 15PRSHLGQILR24 ) fragment is indeed an α-helix , which meets the prediction that heparin binding sites should be located near a rigid α-helix or β-strand; 4 ) PCV2 NLS fragment is used as a cell-penetrating peptide to enhance intracellular delivery of plasmid DNA during viral infection [46 , 47] . Therefore , our research , partially supported by our cryo-EM structure , strongly suggests that the flexible PCV2 N-terminal NLS fragment located at the PCV2 capsid surface could serve as the heparin binding site involved in the viral infection on host cells . In literature , several PCV2 type-specific epitopes were predicted based on structural and biochemical analyses . High-resolution structures of PCV2 VLP suggested that some of the seven surface loops may serve as the epitopes eliciting PCV2 type-specific antibodies [29 , 48] . However , the biochemical and structural data differ in confirming these epitopes . To identify and validate the PCV2 type-specific neutralizing epitopes , which are crucial for PCV2 vaccine and diagnosis kits development , we have systematically screened for a PCV2 type-specific neutralizing monoclonal antibody and mapped the recognition sites back to the surface of PCV2 VLP . We were able to isolate one neutralizing mAb which specifically recognizes PCV2 instead of PCV1 . Follow-up PCV1/PCV2 loop swapping experiments , followed by bio-dot ELISA experiments , showed that this particular mAb recognizes PCV2 EF-loop ( 134KATALT139 ) , which is an exposed loop located at the PCV2 capsid surface revealed by both X-ray and cryo-EM structures . Notably , although PCV1 and PCV2 infection are widely spread in swine farms , postweaning multisystemic wasting syndrome is caused primarily by PCV2 . In practice , PCV2 capsid proteins have been widely used as a diagnostic antigen for serologic detection of PCV2 infection . However , PCV1 was frequently misdiagnosed as PCV2 due to the high sequence similarity between PCV1 and PCV2 capsid protein . In this study , based on the 15Å cryo-EM structure of PCV2 VLP in complex with 3H11-Fab , we were able to , for the first time , identify that EF-loop ( residues 134KATALT139 ) is indeed a PCV2 type-specific neutralizing epitope . The full-length of PCV2 EF-loop contains ~23 residues , in which residues 127–138 are entirely exposed to the surface and highly divergent between PCV1 and PCV2 . 134KATALT139 is the most divergent fragment within the EF-loop . Hence , the identification that EF-loop is the PCV2 type-specific neutralizing epitope should provide a new strategy for the next generation PCV2 vaccine design and generation of PCV2 type-specific antigens for PCV2 diagnosis . The global genotypic shift from PCV2a to PCV2b occurred around 2003 [49] . It was followed by another shift from the predominant PCV2b to PCV2d in more recent times [20–23] . The newly emerged PCV2d , identified in PCV2 vaccine failure investigations , might have the ability to replicate in pigs under vaccination pressure [21] . Due to the emergence of a new PCV2 genotype , concerns have been raised over the efficacy of current commercial PCV2 vaccines [50] . Although commercially available PCV2a-based vaccines have been shown to have cross-protection against PCV2d ( mPCV2b ) in a PRRSV-mPCV2b-challenge mode [50] , field trials suggest that the efficacy of these PCV2 vaccination may be PCV2 genotype-dependent [51] . Therefore , it is urgent to develop a broad spectrum PCV vaccine to prevent another wave of newly emerged PCV from spreading worldwide . In literature , commonly developed classes of broad-spectrum vaccines are: broad-spectrum vaccines , polyvalent vaccines and structure-based vaccines [52] . In broad-spectrum vaccines , antigens with broad spectrum neutralizing epitopes are capable of conferring protection against a range of viral genotypes . Polyvalent vaccines are synthesized through a mixture of antigens from several of a virus’ genotypes into a single vaccine and have been shown to be as effective as targeted monovalent vaccines [53]; for viruses with a limited number of genotypes , such as PCV2 ( PCV2a ~ PCV2d ) , it is practical to develop a conjugated polyvalent vaccine from existing prevalent genotypes . Structure-based vaccines , containing multiple epitopes ( both B- and T-cell epitopes ) , elicit neutralizing antibody responses to several genotypes of a virus [54] . Structure-based vaccines could be designed using the latest proteomic techniques and bioinformatics tools , which rely on type-specific neutralizing epitope identification . In this manuscript , our results detail these two points: 1 ) Structural roles of PCV2 capsid protein N-terminus in PCV2 particle assembly . 2 ) The identification of a PCV2 type-specific neutralizing epitope . We identified PCV2b-specific neutralizing epitope , located on the EF-loop ( 134KATALT139 ) . Based on the sequence homology , this EF-loop region is conserved in PCV2a and PCV2b as 134KATALT139 , and conserved in PCV2c and PCV2d as 134KANALT139 . Therefore , it is possible to design a broad-spectrum prophylactic vaccine that targets both PCV2a+2b and PCV2c+2d , effectively overcoming vaccine selection pressure . The PCV2 type-specific neutralizing epitope identified in this work is not only able to differentiate between non-pathogenic PCV1 and PCV2 –a necessity in PCV diagnostic kits— , but can also be used for PCV structure-based design of a chimera vaccine . In summary , by the combination of X-ray crystallography and cryo-EM approaches , we successfully observed the structural principles of PCV2 assembly , PCV2 type-specific neutralizing epitopes and PCV2 heparin binding sites . Our efforts not only revealed the structural details of E . coli expressed PCV2 VLPs , providing an efficient approach to manufacture low-cost high efficient VLP vaccines against PCV2 infection; but also identified the PCV2 type-specific neutralizing EF-loop epitope , providing a new strategy to design new diagnosis kits effectively discriminating threatening PCV2 from non-threatening PCV1 strains .
The protocol of animal study was approved by the Committee on the Ethics of Animal Experiments of the National Research Center for Veterinary Medicine ( Permit Number: 20160313088 ) . The study was conducted following the Guide for the Care and Use of Animals in Research of the People's Republic of China . Open reading frame 2 ( ORF2 ) of porcine circovirus type 2 ( PCV2 ) was amplified by DNA polymerase and sub-cloned into a modified pET vector to express the full-length capsid protein without tag . The constructed expression plasmid pET-ORF2 was transformed into Escherichia coli BL21 ( DE3 ) competent cell in presence of Kanamycin antibiotics . The cells were grown in LB media supplemented with Kanamycin antibiotics to an OD600 value reaching 0 . 6 , a final concentration of 0 . 4 mM IPTG was added to induce the recombinant protein expression . After cultured overnight , cells were harvested with a centrifuge and re-suspended in lysis buffer containing 20 mM Tris ( pH 7 . 4 ) , 500 mM NaCl , 1 mM DTT and 2 mM EDTA . The cells suspension was kept on ice and lysed using a homogenizer for 4 times . After ultra-centrifuge by 40 , 000 rpm for 1 hour , the supernatant was collected and purified by ion exchange chromatography and hydrophobic chromatography columns . The truncated ( PCV2-ΔN3 to PCV2-ΔN30 ) capsid proteins were expressed and purified using the similar strategies by PCR amplifying the corresponding DNA sequences from the full-length PCV2 capsid protein gene and sub-cloned into the similar pET vector without N-terminal His-tag . The N-terminal truncated and His-tagged PCV2 ( PCV2-His-ΔN45 ) gene was amplified from the corresponding DNA sequences from the full-length PCV2 capsid protein gene and sub-cloned into pET28a vector with N-terminal . The PCV2-His-ΔN45 protein was purified by Histidine affinity chromatography column , followed by ion exchange chromatography and hydrophobic chromatography columns . The loop swapped chimeric PCV2 clones were generated by swapping the corresponding PCV1 capsid sequences ( 58KGGYSQPS66 , 79FLPPSGGTNPLPLPEQ94 , 108RDPITSNER116 , 124ILDANFVTPSTNLAYDPYINYSS146 , 153PFTY156 , 162TPKPELDKTIDWFHPNNKRNQLWLHLNTHTNV193 , 204NAATA208 ) to the PCV2 capsid protein sequence , respectively , followed by the same protein expression and purification protocols described . All the clones were validated by sequencing . The capsid proteins ( full-length and truncated proteins ) were purified at 4°C by using an ÄKTA automatic protein purification system ( GE Healthcare ÄKTA explorer 10 system ) . Before sample loading , the prepackaged Supdex200 column was equilibrated with the buffer containing 20 mM sodium phosphate ( pH 6 . 5 ) and 500 mM NaCl . After centrifugation for 10 mins , the supernatant of the purified sample was loaded onto High Performance gel filtration columns ( GE Healthcare ) at the flow rate of 0 . 5 ml/min . After loading , the eluted sample was collected by a 96 wells fraction collector , and checked by 12% acrylamide SDS-PAGE gel . The retention volume of each run of the full-length or truncated capsid proteins was recorded , and all the curves are fitted together to compare the size of the samples . After mixed with Freund’s complete adjuvant , recombinant full-length and truncated capsid proteins were injected into BALB/c mice , respectively . Same amount of BSA alone mixed with Freund’s complete adjuvant was injected in mice as negative control . A booster immunization with the same dosage of PCV2 capsid protein mixed with Freund’s incomplete adjuvant was administered 2 weeks later . The serums of these immunization mice were collected every week , and the anti-PCV2 antibody titers were measured by indirect ELISA . Followed the routine hybridoma protocol , the spleen cells were collected from immunized mouse and fused with SP2/0 myeloma cells . Hybridomas were obtained by limiting dilution , and the antibody generated was checked by indirect ELISA . After selected , the cultured hybridoma cells were injected into pristane-treated BALB/c mice to gain ascetic fluids , in which the antibody was purified by Protein-A affinity chromatography . Purified truncated PCV2 capsid proteins ( PCV2-ΔN3 , PCV2-ΔN6 , PCV2-ΔN9 , PCV2-ΔN12 , PCV2-ΔN15 , PCV2-ΔN18 , PCV2-ΔN21 , PCV2-ΔN24 , PCV2-ΔN27 ) were coated on a 96-well ELISA plate with stepwise decrements amount in a buffer containing 20 mM phosphate buffer ( pH 6 . 5 ) , 500 mM NaCl . After blocked by blocking buffer , the serum was added and the mixture was incubated at 37°C for 30 mins . After 3 times washing , followed by the addition of secondary IgG-HRP ( Goat against mice ) , the titer measurement was performed according to the instructions of Light Shift Chemiluminescent EMSA kit ( Thermo Fisher Scientific Inc . , USA ) . The antibody titers in the serum were recorded and calculated by the reading score multiple by dilution times . Our cryo-EM structure of complex of PCV2 VLP and mAb-3H11 Fab fragments showed that the PCV2 type-specific epitope is located within the EF-loop of PCV2 capsid protein . To validate the structural work , bio-dot blotting assay were performed to detect binding between mAb-3H11 and PCV2/PCV1 loop swapping hybrid capsid proteins . Purified wide-type and PCV2/PCV1 hybrid capsid proteins ( PCV2-WT , PCV2-hybrid-BC , PCV2-hybrid-CD , PCV2-hybrid-DE , PCV2-hybrid-EF , PCV2-hybrid-FG , PCV2-hybrid-GH , and PCV2-hybrid-HI ) were coated on a Nitrocellulose membrane by 96-well Bio-dot device ( Bio-Rad ) with stepwise decrements amount in a buffer containing 20 mM phosphate buffer ( pH 6 . 5 ) , 500 mM NaCl . After blocked by blocking buffer , mAb-3H11 was added and the mixture was incubated at 37°C for 30 min . After washing for 3 times , secondary IgG-HRP ( Goat again mice ) was added and the detection was performed according to instruction of Light Shift Chemiluminescent EMSA kit ( Thermo Fisher Scientific Inc . , USA ) . The image was collected on the G-Box biomolecular imager ( GE Healthcare ) . The DLS measurements were performed at room temperature on a DynaPro ( protein solution ) DLS instrument . Before measuring , all protein samples and control buffers were firstly filtered through a 0 . 22μm filter to avoid any dust and unwanted aggregates , and degassed on a thermal vacuum , followed by ultracentrifuge for at least 15 mins . The measurement cuvette was rinsed with Milli-Q water , 100% methanol and filtered water again for several times to cleanse and remove dust . The sample buffer , served as a blank , was firstly measured and protein samples were measured thereafter . For each sample , at least 20 acquisitions were collected for data analysis . PCV2-His-ΔN45 capsid protein was concentrated to ~12mg/ml and screened for crystallization conditions by hanging drop method . More than 500 conditions were screened and the best quality crystals were obtained in the buffer containing 2 M ammonium sulfate , 0 . 1 M citrate acid ( pH4 . 6 ) . After optimization , single crystals were picked and flash frozen in a cryo-protection solution containing the crystallization buffer plus 15% glycerol . Single wavelength ( at 1 . 0 Å ) diffraction data was collected at Taiwan National Synchrotron Radiation Resource Center ( NSRRC ) and processed using HKL2000 software [55] . The structure was determined by MORDA/CCP4 ( www . ccp4 . ac . uk ) using 3R0R as the search model [29] . The model was built using the program Coot and refined to 2 . 8Å resolution using REFMAC/CCP4 . The crystallographic statistic detail of the structure is listed in Table 1 . The structural and electrostatics figures were prepared using PyMOL ( Delano Scientific ) . 5 μl purified N-terminal truncated PCV2 VLPs specimens ( PCV2-ΔN3 , PCV2-ΔN6 , PCV2-ΔN9 , PCV2-ΔN12 , PCV2-ΔN15 , PCV2-ΔN18 , PCV2-ΔN21 , PCV2-ΔN24 , PCV2-ΔN27 and PCV2-ΔN30 ) were applied to carbon coated copper grids , respectively . The grids were treated by phosphato-tungstic acid ( PTA ) and dried . Images were taken on a FEI Tecnai 12 electron microscope operated at 120 kV , with a magnification of 67 , 000× . 5 μl purified PCV2 VLPs specimen and PCV2 VLP in complex with 3H11-Fab fragment were loaded onto a carbon coated Quatifoil 2/1 grid , respectively , blotted with filter paper ( two times , each time per 2 seconds ) and rapidly plunged into liquid ethane , pre-cooled by liquid nitrogen . Cryo-EM images were taken from the frozen grids in a FEI Titan Krios TEM cryo electron microscope operated at 300 kV , with a magnification of 67 , 000× and a pixel size of 1 . 69Å/pixel . Measured defocus values of these images range from -1 μm to -4 μm . The details were shown in the cryo-EM data processing flowchart for the full-length PCV2 VLPs ( S11 Fig ) . Protein Data Bank: The structure factor and coordinate for PCV2-His-ΔN45 have been deposited with accession codes 5ZJU . EM data bank: The cryo-EM structures of full-length PCV2 VLP and PCV2 VLP in complex with 3H11-Fab have been deposited to EMDB with the accession codes EMD-6746 and EMD-6961 , respectively . The model coordinate of full-length PCV2 VLP has been deposited to EMDB with the accession codes 5ZBO . | Porcine circovirus type 2 ( PCV2 ) is considered as one of the most wide-spread pathogens threatening swine production by causing postweaning multisystemic wasting disease ( PMWS ) in piglets worldwide . Several VLP-based PCV2 vaccines are commercially available which significantly reduce the viral burden and virally induced lesions . However , prophylactic efficacy of VLP-based PCV2 vaccine largely relies on the correct VLP assembly from the individual PCV2 capsid protein . Notably , limited structural information of PCV2 N-terminal fragment containing arginine-rich patches significantly delays our understanding of PCV2 assembly at the molecular level , and the lack of solid evidence in identification of PCV2 type-specific epitope delays the development of PCV2 type-specific diagnosis kits . In this study , through the combination of structural and immunological approaches , we are able , for the first time , to disclose the structural details of the N-terminal Nuclear Localization Signal ( NLS ) region of PCV2 capsid protein . We show that the interaction between the α-helix from one capsid protein and the NLS-B from an adjacent capsid protein within the pentamer stabilizes the assembled PCV2 VLP in solution . Moreover , by the combination of structural determination and biochemical mapping , we have identified that a short linear sequence ( 134KATALT139 ) located within PCV2 EF-loop is a unique PCV2 type-specific neutralizing epitope . Therefore , our work has revealed the detailed structural information of PCV2 particle assembly and a PCV2 type-specific neutralizing epitope , which should provide insightful information for virus-host interaction studies and next-generation PCV2 vaccine and type-specific diagnostic kits development . | [
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| 2019 | Structural roles of PCV2 capsid protein N-terminus in PCV2 particle assembly and identification of PCV2 type-specific neutralizing epitope |
The pontine neurons ( PN ) represent a major source of mossy fiber projections to the cerebellum . During mouse hindbrain development , PN migrate tangentially and sequentially along both the anteroposterior ( AP ) and dorsoventral ( DV ) axes . Unlike DV migration , which is controlled by the Netrin-1/Dcc attractive pathway , little is known about the molecular mechanisms guiding PN migration along the AP axis . Here , we show that Hoxa2 and Hoxb2 are required both intrinsically and extrinsically to maintain normal AP migration of subsets of PN , by preventing their premature ventral attraction towards the midline . Moreover , the migration defects observed in Hoxa2 and Hoxb2 mutant mice were phenocopied in compound Robo1;Robo2 , Slit1;Slit2 , and Robo2;Slit2 knockout animals , indicating that these guidance molecules act downstream of Hox genes to control PN migration . Indeed , using chromatin immunoprecipitation assays , we further demonstrated that Robo2 is a direct target of Hoxa2 in vivo and that maintenance of high Robo and Slit expression levels was impaired in Hoxa2 mutant mice . Lastly , the analysis of Phox2b-deficient mice indicated that the facial motor nucleus is a major Slit signaling source required to prevent premature ventral migration of PN . These findings provide novel insights into the molecular control of neuronal migration from transcription factor to regulation of guidance receptor and ligand expression . Specifically , they address the question of how exposure to multiple guidance cues along the AP and DV axes is regulated at the transcriptional level and in turn translated into stereotyped migratory responses during tangential migration of neurons in the developing mammalian brain .
In the developing central nervous system ( CNS ) , neurons migrate sometimes over long distances from their birthplace to their final location , where they condense in specific nuclei . As neuronal function depends upon precise connectivity with their targets , the final positioning of migrating neurons is critical to the building of ordered connectivity between pre- and postsynaptic partners . In many brain regions , neurons migrate in a tangential direction , orthogonal to the radial axis and independently of radial glia , resulting in mixing of cells that originated from distinct ventricular regions [1] . Mounting evidence suggests that tangentially migrating neurons are guided during their journey by the same set of attractive and repulsive guidance cues that regulate axonal pathfinding and topographical mapping [2] . However , little is known about how exposure of migrating neurons to several simultaneous extrinsic inputs along the orthogonal axes of the brain may be integrated at the transcriptional level and in turn translated into directional migratory responses specific for each neuronal population . In the mouse hindbrain , the precerebellar system is a suitable model to study the molecular mechanisms controlling long-distance tangential migration . Precerebellar nuclei are essential for coordinated motor activity and provide the principal input to the cerebellum [3] . They convey information to the cerebellum through the climbing and mossy fiber projection systems . Although the only source of climbing fibers is the inferior olivary nucleus ( ION ) , mossy fibers have multiple origins , such as the lateral reticular ( LRN ) and external cuneate ( ECN ) nuclei in the posterior hindbrain , and the pontine gray ( PGN ) and reticulotegmental ( RTN ) nuclei , collectively referred thereafter as pontine neurons ( PN ) , in the anteroventral hindbrain [4] . The early patterning of the mouse hindbrain along the anteroposterior ( AP ) axis is characterized by a segmentation process into distinct morphological segments called rhombomeres , resulting in spatial segregation of the neuroepithelium contributing to each segment [5] . Distinct precerebellar neuronal populations are contributed by rhombomeric portions of the rhombic lip , a stripe of neuroepithelium that arises dorsally at the interface with the roof plate and runs throughout the rostrocaudal extent of the hindbrain , giving rise to spatiotemporally defined sequences of migratory populations [4 , 6–12] . Indeed , rhombomere ( r ) 1 rhombic lip derivatives specifically contribute to the external granular layer ( EGL ) of the cerebellum [12] , whereas a recent fate mapping study subdivided the mouse r2–r8 rhombic lip into two distinct domains: the auditory lip , extending from r2 to r5 and giving rise to the brainstem cochlear nuclear complex; and the precerebellar lip , which generates the precerebellar nuclei , running posteriorly to r5 [6] . Mossy fiber projection neurons migrate along two distinct subpial streams: the posterior extramural stream , whose neurons contribute to the LRN and the ECN , and the anterior extramural stream that is formed by the PN contributing to the PGN and the RTN [4] . The latter undergo a long-distance rostral migration through several rhombomeric domains before turning ventrally to reach a stereotypic anteroventral position in the mature brain stem . The ventral pathway of PN migration was shown to involve Deleted in colon cancer ( Dcc ) /Netrin-1–mediated chemoattraction [13–15] and the Slit receptor Robo3 [16] . Here , we studied the intrinsic and extrinsic molecular mechanisms regulating the directionality of migration of PN along the rostral pathway . During embryo development , the transcriptional readout of AP positional information is provided by the Hox gene family of transcription factors [5] . In the developing hindbrain , Hox genes have been involved in providing segmental identity to rhombomeres and rostrocaudal patterning information to developing neurons [5 , 17] . More recently , it has been shown that Hox gene expression is maintained up to late stages of development in specific neuronal subpopulations in the hindbrain and spinal cord , where they may be important for the establishment of topographically organized sensory and motor circuits [18–20] . However , the potential role of Hox genes in orienting directional neuronal migration through regulation of guidance molecules remain largely unknown . Herein , we found that the paralog group ( PG ) 2 Hox genes , Hoxa2 and Hoxb2 , are required to maintain normal migration of PN along the rostral pathway . In Hoxa2 and Hoxb2 mutants , subsets of PN prematurely migrated ventrally , settling at ectopic posterior locations . Interestingly , the PN migratory defects observed in PG2 Hox mutants phenocopied those found in Robo1;Robo2 , Slit1;Slit2 , and Robo2;Slit2 compound mutants . Furthermore , in PG2 Hox mutants the expression levels of Robo2 and Slit2 were decreased and chromatin immunoprecipitation assays demonstrated the direct binding of Hoxa2 on the Robo2 locus . In addition , we identified the facial motor nucleus ( FMN ) , which is located in ventrolateral r6 , as an important source of Slit ligands for Robo receptor–expressing PN . This is supported by the finding that in the Phox2b knockout mice , which completely lack the FMN , the PN undergo the same ectopic and premature ventral migratory defects as observed in Hox PG2 , Robo1;Robo2 , Slit1;Slit2 , or Robo2;Slit2 compound mutants . Altogether , our data provide important novel insights into the intrinsic and extrinsic molecular determinants involved in tangential migration of PN neurons along the AP axis .
Tangentially migrating PN originate in the r6–r8 rhombic lip between embryonic day ( E ) 13 . 5 and E17 . 5 [10] . They migrate first ventrally and then rostrally until they reach a final anteroventral position in the pontine primordium ( Figure 1A–1C; [6] ) . During their migration , PN navigate across distinct rhombomere-derived territories and express several molecular markers , including the homeobox-containing gene Barhl1 ( Figure 1A , 1B , 1E , and 1H; [21] ) . To map the migratory route of PN in relationship to rhombomere territories , we simultaneously performed lacZ staining and in situ hybridization with Barhl1 on whole-mount brains or tissue sections from Krox20::Cre;ROSA26R ( Figure 1A and 1B ) , Krox20::Cre;Z/AP ( Figure 1G–1I ) , or R4::Cre;Z/AP ( Figure 1D–1F ) mouse lines [20 , 22–24] in which lacZ or alkaline phosphatase expression is permanently activated in r3 and r5 or r4 progenies , respectively . The migratory pathway of Barhl1+ PN neurons can be divided into three distinct phases ( Figure 1A–1C ) . First , PN undertake a short ventral migration upon leaving the r6–r8 rhombic lip ( phase 1 ) . In the next step ( phase 2 ) , PN turn rostrally , parallel to the AP axis . They travel through r5 and r4 , where they pass between the vestibulocochlear ( VIIIth , dorsally ) and the facial ( VIIth , ventrally ) nerve roots ( Figure 1C–1F; [25] ) and continue migrating through r3 until they reach the trigeminal ( Vth ) nerve root located in the caudal aspect of r2 ( Figure 1A–1C ) . Interestingly , the PN stream never enters r2 but makes an abrupt change of direction and undertakes a final ventral migration within r3 ( phase 3 ) ( Figure 1A–1C and 1G–1I ) . In the latter aspect of their migration , PN leave r3 , reenter r4 , and fan out within the r4-derived domain until they finally settle near the floor plate , abutting the r3- and r5-derived territories ( Figure 1B and 1G–1I; [6] ) . Such a stereotyped migratory pathway suggested that , in addition to dorsoventral ( DV ) positional cues , PN may express and/or respond to molecular cues along the AP axis , such as the Hox gene products . Indeed , in situ hybridization analysis using specific Hox antisense probes revealed that migrating PN expressed a Hox program characteristic of an r6–r8 axial origin , namely Hox PG2–5 genes . Specifically , transcripts for Hoxa2 , Hoxb2 , Hoxa3 , Hoxb3 , Hoxb4 , Hoxd4 , and Hoxb5 were expressed in PN throughout their migration and in the settling pontine nuclei ( Figure 1J–1R; and unpublished data ) . Such expression patterns suggested that PN may be endowed with molecular information as to their origin and relative position along the AP axis throughout their migration . To address the potential involvement of Hox genes in PN rostral migration , we focused on the PG2 genes , Hoxa2 and Hoxb2 . These genes perform important roles in rostral hindbrain patterning [20 , 26–29] , and their expression is maintained throughout migration , nucleogenesis , and establishment of connectivity to cerebellum of PN ( both RTN and PGN ) up to postnatal stages ( Figure 1J–1K′ , 1Q , and 1R; and unpublished data ) . In Hoxa2 heterozygous mutants [30] , PN displayed a normal migratory behavior as assessed by whole-mount in situ hybridization on E14 . 5 and E15 . 5 hindbrains with antisense probes for known markers of migrating PN , such as Barhl1 ( Figure 2A–2C; [21] ) , Pax6 , and Tag1 ( [31 , 32]; unpublished data ) . In contrast , in Hoxa2−/− homozygous mutant mice , we observed defects of PN navigation along the rostral pathway ( Figure 2D–2I ) . Although migratory abnormalities could be observed in all Hoxa2−/− mutants ( n = 15 ) , they were variably penetrant . In some cases ( n = 10 ) , small cohorts of neurons left the stream prematurely and migrated ventrally towards the midline ( Figure 2D–2F ) . Ectopic neurons expressed Barhl1 , Pax6 , and Tag1 , and often condensed in small ectopic nuclear formations close to the midline , posterior to the normal location of pontine nuclei ( Figure 2D and 2E; and unpublished data ) . In these mutants , only a subset of PN migrated ectopically , whereas the bulk of the stream maintained a caudorostral migratory path . In order to investigate whether this subset was a random or specific subpopulation of PN , we analyzed the status of the RTN and PGN by in situ hybridization with Barhl1 and Pax6 antisense probes on Hoxa2−/− brains at E17 . 5 days postcoitum ( dpc ) ( Figure S3 , and unpublished data ) . A general slight reduction of both PGN and RTN was apparent ( Figure S3K and S3L ) , suggesting that the Hoxa2 inactivation randomly induced the ectopic migration of subsets of neurons contributing to both nuclei . In further support of this idea , in a number of mutant fetuses ( n = 5 ) , we observed more-severe defects in which almost the entire PN stream undertook a premature ventral migration ( Figure 2G and 2H ) . It is also noteworthy that the abnormal migratory phenotypes were often asymmetrically distributed among the two sides ( Figure 2H and 2I; and unpublished data ) . Finally , by in situ hybridization with Barhl1 , Pax6 , Robo3 , and Tag1 antisense probes on E13 . 5 , E14 . 0 , E14 . 5 , and E17 . 5 whole-mount brains , we found ectopic migrations throughout development of PN , arguing that the Hoxa2 inactivation did not selectively affect neurons migrating within a particular time window ( Figures 2D–2I and S3A–S3J ) . Altogether , these data indicated stochastic compensation for the loss of Hoxa2 along the AP migratory path of PN . This may result from partial functional redundancy with other Hox genes expressed in migrating PN ( Figure 1 ) . Indeed , premature ventral migrations of PN could also be observed in some Hoxb2−/− ( Figure 2J–2L; n = 2 out of 6 ) . Moreover , Hoxa2−/−;Hoxb2−/− mutant specimen ( n = 3; Figure 2M–2O ) appeared to display more ectopic PN as compared to single mutants ( compare Figure 2D–2L and 2M–2O ) , thus indicating some degree of functional redundancy and genetic interaction among Hox PG2 genes . In summary , these results indicated that Hoxa2 , and to a lesser extent Hoxb2 , may regulate the response of subsets of PN to environmental cues to precisely maintain their rostral migratory route , thus ultimately contributing to control the final location of pontine nuclei along the AP axis . Hox PG2 may be intrinsically required in PN throughout migration . Alternatively , or in addition , they may be required nonautonomously to pattern the environment through which PN migrate and to which PN respond in order to direct their rostral migration . To address this question , we focused on Hoxa2 function . To achieve Hoxa2 inactivation in PN , we mated a mouse carrying a Hoxa2 floxed allele , Hoxa2flox [33] with the Wnt1::Cre transgenic mouse line that allows Cre-mediated deletion in rhombic lip progenitors [6 , 9 , 25 , 34] . In whole-mount brains from Wnt1::Cre;Hoxa2flox/flox fetuses , we found scattered ectopic neurons that appeared to migrate prematurely from the PN stream ( Figure 3E–3H ) . At E15 . 5 , the ectopic neurons had reached the ventral midline as assessed by Tag1 expression ( Figure 3G ) . These results supported an intrinsic requirement of Hoxa2 expression in PN to maintain rostral migration ( see also below ) . However , the phenotype was less pronounced than in Hoxa2 null mutants ( compare Figure 3E–3H with Figure 2D–2I ) . Such a difference may not be explained by an incomplete deletion of Hoxa2 in PN precursors , as we have previously shown that the Wnt1::Cre driver is able to induce a complete excision of Hoxa2 [35] . Thus , these results may rather indicate that Hoxa2 is required both in a cell autonomous and a nonautonomous manner to regulate the response of PN to guidance cues during their rostral migration ( see below; see also Figure 4 ) . We next determined whether the PN migration defects detected in Hoxa2−/− mutants could be explained by a perturbed expression of ligands or receptors known to control their migration . Chemoattraction of tangentially migrating PN along the DV axis involves the Netrin-1 ligand/Dcc receptor guidance system [13–15 , 36] . Netrin-1 is secreted by the floor plate , whereas its receptor Dcc is expressed in PN throughout their migration ( Figure 4A ) . In Hoxa2−/− mutant brains , we found that Netrin-1 expression in the floor plate was unaffected ( unpublished data ) . Dcc was also normally detected both in the PN stream and the ectopically migrating neurons , as shown both by in situ hybridization and antibody staining ( Figure 4E and 4S; and unpublished data ) . It is noteworthy that PN are not immediately attracted towards the floor plate , but undertake their long rostral migration before finally turning ventrally towards the floor plate . This suggests that , in wild-type mice , the Netrin-1/Dcc attractive signaling system may be antagonized during the rostral phase of PN migration ( phase 2; Figure 1C ) . The chemotropic molecules of the Slit family and their Robo receptors are major repellents for developing neurons and have been shown to antagonize Netrin-1 activity on axonal growth in a dose-dependent manner [37 , 38] . Thus , we first investigated the spatial distribution of the Robo1–3 receptors and Slit1–3 ligands during PN migration . In addition to Robo3/Rig1 ( Figure 4B; [16] ) , Robo1 and Robo2 were also found to be expressed during PN migration as shown by Robo2 in situ hybridization and by anti-Robo1 whole-mount immunostaining on E14 . 5 hindbrains ( Figures 4K , 4Q , S1A , and S1B ) . The presence of Robo receptors in migrating PN was further supported by the binding on whole-mount hindbrains of a Slit2 fragment genetically fused to alkaline phosphatase ( LRR-hSlit2-AP; [39] ) ( Figure S1C ) . Slit1–3 were all expressed in the floor plate and rhombic lip , though not in PN ( Figures 4C , 4D , 4U , S1D , and S1E; and unpublished data ) . Interestingly , from E13 . 0 , Slit2 and Slit3 , but not Slit1 , were also expressed in neurons of the FMN ( Figures 4C , 4U , and S1D; and unpublished data; see below ) . We next asked whether Hoxa2 may regulate Robo and/or Slit expression during PN migration . In E14 . 5 Hoxa2−/− fetuses , Robo3 and Dcc were expressed at normal levels in migrating PN ( Figure 4E , 4F , and 4M; see also Figure S3D and S3H; and unpublished data ) . In contrast , Robo2 transcript levels were significantly lower in migrating PN of Hoxa2−/− single and Hoxa2−/−;Hoxb2−/− compound null mutants than in control mice ( Figure 4N and 4T ) . Interestingly , down-regulation of Robo2 expression was particularly evident in ventrally migrating ectopic cells that nonetheless maintained normal expression of Dcc , Robo3 , and Barhl1 as assessed by in situ hybridization on adjacent sections in both single Hoxa2−/− and compound Hoxa2−/−;Hoxb2−/− null mutants ( compare Figure 4I–4K with 4L–4N , and 4O–4Q with 4R–4T ) . Moreover , we found a notable down-regulation of Slit2 expression in the FMN of Hoxa2−/− mutants ( n = 6 ) , whereas normal Slit2 expression levels were detected in rhombic lip and floor plate ( compare Figure 4C and 4D with 4G and 4H ) . Thus , the PN migratory defects observed in the absence of PG2 Hox function may be mediated , at least partially , through decreased Slit-Robo signaling due to defective maintenance of Robo2- and Slit2-sustained expression during PN migration along the AP axis . In Robo3-deficient mice , PN can still migrate along the rostral pathway ( phase 2 ) but are unable to undergo their final ventral migration ( phase 3 ) to reach the floor plate ( Figure S2I; [16] ) . To address the potential involvement of Robo1–2 and their ligands Slit1–2 in PN migration , we next analyzed single and compound knockout mice . None of the single or compound heterozygous mutants showed significant PN migratory abnormalities ( Figures 5A , 5B , and S2A–S2E ) . In contrast , similar migration defects of PN were observed at E15 . 5 in all Robo1−/−;Robo2−/− ( n = 19 ) and Slit1−/−;Slit2−/− ( n = 18 ) compound mutant mice , following whole-mount labeling with antisense Barhl1 or Robo3 riboprobes or immunostaining with anti-Robo3 ( Figures 5E , 5F , 5I , 5J , S2F , and S2G; and unpublished data ) . Although PN normally left the rhombic lip ( phase 1 ) , during phase 2 , cohorts of PN left the stream and prematurely migrated ventrally , condensing in small ectopic clusters adjacent to the midline . The leading processes of Robo3-expressing PN neurons were still oriented toward the floor plate and crossed it normally ( unpublished data ) . Moreover , DiI tracing performed on E18–postnatal day ( P ) 0 mutants showed that PN axon projections to the cerebellum were normal ( unpublished data ) , thereby suggesting that migration , but not axon guidance , was selectively affected in these mutants . The ectopic nuclei expressed PN markers including Barlh1 , Pax6 , Robo3 , and Tag1 , and were observed at least until E17 . 5 ( Figures 5E , 5F , 5I , 5J , S2F , and S2G; and unpublished data ) , as double knockouts were not viable and died a few hours after birth . The ectopic neurons were also immunoreactive for Dcc ( unpublished data ) . However , many PN still reached a normal location in ventral r4 ( Figure 5E , 5F , 5I , and 5J ) . Such phenotypes were fully penetrant although the size and position of the ectopic PN clusters varied between mutant embryos and brain stem side ( Figures 5 , S2F , and S2G ) . Such PN migratory defects strikingly phenocopied the abnormalities observed in Hox PG2 knockout animals ( compare Figure 5E , 5F , 5I , and 5J with Figure 2D , 2E , 2G , and 2H ) . Strikingly , similar ectopic PN migrations were also observed in compound Robo2+/−;Slit2+/− fetuses in which only one dose of Robo2 and Slit2 was simultaneously deleted ( Figure 5M ) , thus providing strong genetic evidence supporting their dosage-dependent interaction in regulating PN migration . This latter result further supports the idea that the abnormal neuronal migration observed in Hoxa2 knockout mice may be at least partly due to the simultaneous reduction of both Robo2 and Slit2 levels ( Figure 4 ) , further underscoring the intrinsic and extrinsic requirements of Hoxa2 in regulating rostral PN migration . In summary , Robo1–2 and Slit1–2 molecules control in a redundant manner the horizontal , rostrally oriented migration of PN ( phase 2 ) , similar to Hox PG2 genes ( summary in Figure 5N ) . However , as Slits are expressed in the floor plate throughout the AP extent of the hindbrain , the lack of expression at this location was unlikely to explain the rostrocaudal specificity of the migration defects . The FMN , which is located in ventrolateral r6 , expresses high levels of Slit2 , and to a lesser extent Slit3 ( Figures 4C and S1D ) . Double immunostaining for Islet-1 and Robo3 ( Figure 4V ) further revealed that the rostral turn between phase 1 and 2 of PN migration coincides with the AP level of the FMN , and that PN initiate their final movement toward the floor plate ( phase 3 ) only after they have migrated over the FMN ( Figure 4U–4W ) . These observations , together with the results of the Slit knockouts and the reduced expression of Slit2 in the FMN of Hoxa2-deficient mice , strongly suggested that the FMN may play a major role in maintaining normal horizontal migration of PN through expression of Slits . To further support the potential involvement of the FMN in PN horizontal migration , we analyzed a mouse mutant devoid of cranial branchiomotor nuclei . The paired homeodomain-containing Phox2b gene is required for the generation of all branchiomotor neurons [40] . Phox2b inactivation resulted in the lack of all cranial branchiomotor nuclei , including the absence of the FMN as confirmed by the lack of Islet1 and Slit2 staining on whole-mount E14 . 5 Phox2b−/− specimen ( Figure 5L; [40]; and unpublished data ) . In contrast , Slit2 was normally expressed in the rhombic lip and floor plate of Phox2b−/− mutants ( Figure 5L; and unpublished data ) . In E14 . 5 Phox2b−/− embryos , cohorts of PN prematurely migrated ventrally and generated small supernumerary nuclei in ectopic posterior locations ( Figures 5G , 5H , and S2H; n = 4 ) as in Hox PG2 , Robo1−/−;Robo2−/− , Slit1−/−;Slit2−/− , and Robo2+/−;Slit2+/− mutant mice ( compare Figures 2 and 5 ) . Overall , these results strongly indicated that the FMN is an important extrinsic source of Slit signaling for rostrally migrating PN to prevent their premature ventral migration ( summary model in Figure 5O ) . In Drosophila embryo , Robo2 expression in the mesoderm is likely to be controlled by Hox cofactor genes such as homothorax [41] . In addition , a putative Hox binding site has been described in the Drosophila Robo2 locus , although evidence of direct regulation is lacking [41] . To investigate whether Hox PG2 factors may directly regulate some aspects of the Slit-Robo signaling system in the mouse , we tested the ability of Hoxa2 to bind the Robo2 regulatory genomic region in vivo . As Hox proteins preferentially bind their target genes through heterodimerization with Pbx cofactors ( e . g . , [42 , 43] ) , we screened in silico about 500 kb of genomic sequence containing the entire Robo2 locus for the presence of potential NGATNNATNN Pbx/Hox consensus binding sites ( Figure 6E; [44–47] ) . We only considered potential sites that were embedded within 150–500 base pair–long DNA stretches , displaying more than 90% nucleotide conservation at the nucleotide level in mammals and other vertebrates ( Figure 6B; and unpublished data ) . By applying such constraints , we selected four putative Pbx/Hox binding sites located within 10 kb upstream of the Robo2 transcription start site , as well as within the first and second introns ( Figure 6A and 6B; and unpublished data ) . To test the potential of Hoxa2 to bind Robo2 in vivo , we performed chromatin immunoprecipitation ( ChIP ) analysis [48] on the selected sites from P19 teratocarcinoma cells , a suitable cell culture system to study Hox-regulated targets ( e . g . , [43] ) . Indeed , P19 cells expressed significant levels of Robo2 and Hoxa2 , as detected by reverse transcriptase PCR ( RT-PCR ) ( Figure 6C and 6D ) . To perform ChIP on putative Hoxa2 binding sites , we generated a specific polyclonal antibody raised against a unique peptide of the Hoxa2 protein ( see Materials and Methods ) . Nonquantitative PCR amplification of DNA fragments containing the four putative binding sites was carried out on anti-Hoxa2 immunoprecipitated chromatin . As shown in Figure 6F , the immunoprecipitated chromatin showed a substantial enrichment selective for the sequence , including the site located in the second intron . No enrichment was detected for the remaining sites , as well as for the control Gapdh gene ( Figure 6H; and unpublished data ) . To further support these data , we carried out real-time quantitative PCR ( qPCR ) assays on immunoprecipitated chromatin with the anti-Hoxa2 antibody . A strong enrichment of the fragment containing the putative Pbx/Hox binding site in the second intron was confirmed , as compared to controls ( Figure 6G ) . Altogether , these results demonstrated that Hoxa2 can directly bind Robo2 genomic sequences in vivo and , together with the results in knockout animals , strongly suggested that Hoxa2 may directly regulate sustained Robo2 expression during PN migration .
The LRN , ECN , RTN , and PGN constitute the major sources of cerebellar mossy fibers [3] . These nuclei originate from the same stripe of rhombic lip neuroepithelium in the posterior hindbrain , and their generation periods partially overlap [4 , 10 , 49 , 50] . Neurons of these nuclei also express a similar set of transcription factors before leaving the rhombic lip , including Pax6 and Math1 [51] , and during their migration and settling , such as Pax6 and Barhl1 [21 , 32] . Expression of such transcription factors may provide cells with information about their specification as mossy fiber precerebellar neurons and/or to acquire a general migratory behavior upon exiting the rhombic lip . Accordingly , precerebellar neurons migrate abnormally in Pax6 and Barhl1 knockout mice [21 , 32] . Yet , neurons contributing to distinct nuclei migrate following specific pathways and settle at stereotypic AP and DV positional coordinates in the brain stem . Thus , other sets of transcription factors must regulate the responsiveness of migrating precerebellar neurons to environmental guidance cues and drive their distinct migratory routes . Hox genes are prime candidates to regulate the directionality of cell migration along the AP axis , although so far little evidence has been available in the mammalian central nervous system . We found that PN contributing to RTN and PGN expressed Hox PG2–5 genes throughout their AP migration and settling , thus expressing a code characteristic of their axial origin posterior to r5 . Segmental specification and the Hox expression program of precerebellar neurons may select which migratory direction to take upon leaving the rhombic lip . In Caenorhabditis elegans , the rostral or caudal migratory choices of the QR or QL neuroblasts are regulated by the expression of the lin-39 and mab-5 Hox genes , respectively [52] . In the chick embryo , overexpression of Hoxa2 in the r1 rhombic lip , normally devoid of Hox expression , induced neuronal derivatives to migrate ventrally instead of rostrally , thereby adopting a migratory route reminiscent of more-posterior rhombic lip derivatives [53] . Here , we show that mouse PG2 Hox genes are involved in the maintenance of the rostral migration of PN by preventing premature ventral migration and settling of PN at posterior locations . Similarly , specific Hox programs might control the directionality of migration of other precerebellar nuclei along the AP axis . Analysis of the specific Hox expression codes of all precerebellar nuclei will be required to support such an hypothesis . An interesting finding is the variability in the penetrance and/or severity of the migratory phenotypes in Hoxa2 or Hoxb2 mutants . In Hoxa2 mutants , the fraction of PN displaying migration errors varied both in spatial distribution and number among individuals , whereas the bulk of PN followed a normal rostral migration pathway ( Figure 2 ) . We also often observed asymmetric phenotypes between the two sides of the same brain stem . Furthermore , in Hoxb2 mutants , only one third of the specimen displayed an abnormal phenotype . The lack of specific molecular markers did not allow us to distinguish between reticulotegmental or pontine gray neuron identities within the ectopic PN subpopulation . Nonetheless , overall , our results strongly suggested that the ectopic neurons belonged to a random subpopulation due to insufficient redundant functional gene effects . On the one hand , such differences in phenotypes indicate locally random variations of threshold levels of guidance cues and/or of responses of PN in mutants . On the other hand , the limited extent of the abnormal migratory phenotype indicates that such a molecular guidance system is quite robust and buffered against a certain degree of variation , such as loss of function of one or two Hox genes . In fact , even in double PG2 Hox mutants , many PN still migrated normally ( Figure 2 ) . Thus , the loss of PG2 Hox function may be stochastically compensated by other members of the Hox family during PN migration through rhombomeric domains . In a strict interpretation of the “posterior prevalence” model of Hox function [54] , only the most “posterior” ( i . e . , 5′ located in the cluster ) PG Hox gene expressed , i . e . , PG5 , would be expected to select the migratory behavior of PN . The results of PG2 Hox gene inactivations indicated that such a strict model is unlikely to be operating in migrating PN . Rather , a partially quantitative aspect may be added in which local guidance responses of PN may also rely on overall Hox protein distribution and/or levels . Alternatively , a “dynamic” posterior prevalence model might be at work in which the preponderant role of PG5 to PG2 may be sequentially switched while PN are progressing rostrally across inter-rhombomeric domains . The program of Hox gene expression in migrating neurons may continuously integrate extrinsic segment-specific cues with intrinsic regulation of relevant target gene expression , instructing guidance information to PN about the progression of their positional coordinates along the AP axis . Discriminating between such possibilities will need to await the analysis of PN migration in single and compound knockouts for PG3–5 Hox genes . We show that the integrity of the Hox expression program of PN during migration is required to regulate their responsiveness to guidance cues and that Hox PG2 genes are important components of such a molecular guidance system . Specifically , the analysis of Hoxa2 knockout animals supported that Hoxa2 is required both intrinsically in PN and extrinsically to define a local environment permissive to their rostral migration . This assumption is based on the following observations: ( 1 ) Hoxa2 is expressed throughout migration and settling of PN ( Figure 1 ) ; ( 2 ) in Hoxa2 mutants , early steps in migration appear unaffected; this suggests that migration itself , rather than an early event such as the generation of precursors fated to form the pontine nucleus , is being affected; ( 3 ) the inactivation of Hoxa2 in Wnt1+ precerebellar precursors resulted in migration errors of PN ( Figure 3 ) , although at a lower penetrance and severity than in the null mutants , suggesting that Hoxa2 may be additionally required to pattern the environment through which PN migrate; and ( 4 ) in Hoxa2 mutants , the expression of Robo2 in migrating PN and Slit2 in FMN are down-regulated , and compound Robo2+/−;Slit2+/− mutants showed that normal expression levels of such molecules are required for PN guidance ( Figures 4 and 5 ) . Thus , the PN migratory phenotype observed in Hoxa2−/− mutants is likely to result from an impairment of Hoxa2 function both in migrating neurons and in the local environment through which PN migrate . Little is known about how signaling mediated by distinct guidance molecules distributed along the DV and AP axes is integrated in PN during their tangential migration . Our data suggest that specific molecules control PN migration behavior at precisely defined choice points and that Slit/Robo signaling plays a key role in this process . Although numerous studies have involved Slit-Robo ligand–receptor interaction in axon guidance and branching [2 , 39 , 55–61] , much less is known about their involvement in neuronal migration , in particular in vivo ( e . g . , [59 , 62–64] ) . For instance , it was previously shown in mice that the Robo3 receptor is required for the last phase of ventral PN migration ( Figure S2; [16] ) . In mice lacking Robo3 , PN still leave the rhombic lip and migrate rostrally , but are unable to turn towards the ventral midline , despite normal expression of Netrin-1 and Dcc ( Figure S2; [16]; and unpublished data ) . Robo3 was proposed to function as a negative regulator of Slit responsiveness , somehow repressing Slit repulsive activity from the floor plate and thus interfering with Robo1/Robo2 receptor activation [65] . Robo3 is coexpressed with Robo1 and Robo2 during PN migration until they reach the floor plate ( Figures 4B and S1 ) , when Robo3 expression is down-regulated . Thus , one possibility is that in Robo3-deficient mice , Robo1/Robo2 repulsive activity would be activated too early , unmasking Slit repulsive activity from the floor plate and forcing PN to remain in the dorsal hindbrain . According to this model , midline-derived Slit would be the main repulsive source for PN neurons ( but see below ) . In addition to the floor plate source , Slits are expressed at the rhombic lip . Although migrants from the rhombic lip have been shown to be repelled by exogenous sources of Slit2 in coculture [66] , our present data indicated that Slit-mediated repulsion may not control the phase 1 of PN migration from the rhombic lip , as PN still leave the rhombic lip in compound Slit1;Slit2- , Robo1;Robo2- , and Robo2;Slit2-deficient mice . However , Slit3 might still compensate for the loss of Slit1/Slit2 expression at the rhombic lip . Instead , our analysis revealed a major role for Slit1/Slit2 and Robo1/Robo2 during the phase 2 of PN migration along the AP axis and identified the FMN as another important source of Slits for migrating PN , in addition to the floor plate ( see also below ) . Impaired Slit/Robo signaling resulted in strands of PN migrating out from the stream along the DV pathway in ectopic posterior positions above the FMN ( Figure 5 ) . Thus , Slit2-mediated signaling from the FMN and Slit1/Slit2 from the floor plate are among the main driving forces that prevent PN from reaching the ventral midline upon leaving the rhombic lip , forcing them to migrate rostrally towards r3 . The fact that in compound Slit1;Slit2 and Robo1;Robo2 mutants , the leading process of PN still cross the floor plate and then project into the cerebellum ( unpublished data ) strongly suggests that in this system , Slit/Robo signaling primarily controls cell migration and not axon guidance . The DV PN migration requires floor plate-derived Netrin-1 and its receptor Dcc [14 , 15] . However , even in the absence of Netrin-1 or Dcc , PN manage to undertake phase 1 and phase 2 of migration , leaving the rhombic lip and navigating rostrally before aggregating in an ectopic dorsal position [15] . Thus , attraction by Netrin-1 towards the midline appears to be essential only during phase 3 of migration , once PN turn ventrally towards the floor plate . As a corollary , PN must be partially insensitive to the Netrin-1/Dcc attraction before reaching ventral r4 , despite their continued expression of Dcc ( Figure 4A ) and the presence of Netrin-1 all along the floor plate [15] . Our results show that in the absence of Slit1–2- or Robo1–2-mediated signaling , many PN migrate prematurely towards the midline . This suggests that Slit repulsive activity may counterbalance and prevail over Netrin-1/Dcc–mediated attraction . Slit-Robo signaling may negatively regulate the responsiveness of PN to Netrin-1/Dcc through several possible mechanism ( s ) . For instance , the activation of Robo1/Robo2 receptors in migrating PN by secreted Slit ligands might lead to dimerization of the intracellular domains of Robo and Dcc , resulting in a partial silencing of Netrin-1 attraction on PN , as described in Xenopus spinal axons [38] . Thus , in such a scenario , the activity of Slit-activated Robo receptors would be required to inhibit Dcc activity in target neurons . However , Slit/Robo and Netrin-1/Dcc guidance systems could also be acting independently , and PN migratory behavior could result from a balanced integration of attractive and repulsive responses within target neurons . Such possibilities are not mutually exclusive and remain speculative at this point . It is noteworthy that single Robo1 , Robo2 , Slit1 , or Slit2 homozygous mutant mice did not show significant PN migration defects ( Figure S2 ) . This argues for redundant functional roles among Robo receptors or Slit ligands . However , the PN migratory defects in compound Robo1;Robo2 mutants phenocopied those of compound Slit1;Slit2 mutant mice , showing that both Robos and Slits are required for PN migration . This strongly favors a direct Slit/Robo interaction in migrating PN . Strong support for this idea also came from the analysis of compound Robo2+/−;Slit2+/− heterozygotes in which the deletion of only one dose of Robo2 or Slit2 was sufficient to induce migration defects similar to those observed in double Robo1–2- or Slit1–2-deficient mice , demonstrating Slit/Robo dose-dependent interactions . Moreover , from the results of Slit1;Slit2 compound mutants and the analysis of Phox2b knockout mice lacking the FMN ( Figure 5 ) , it can be inferred that the Slit2 source diffusing from the FMN is necessary to maintain the normal rostral migration of PN neurons ( models in Figures 5O and 7A ) . At r3 level , PN neurons may turn ventrally because Slits diffusing from the FMN in ventral r6 may become limiting and fall below a threshold level . Notably , it is also at this axial level that Robo3 function becomes preponderant and interferes with Slit repulsive activity . Finally , our results indicate that neither Robos nor Slits are essential for the anterior progression towards r3 of PN , but rather that they prevent migrating neurons from entering the wrong territories . Additional signals must be involved in attracting PN anteriorly and/or repelling them from the posterior brain stem . Attractive signals from trigeminal branchiomotor neurons ( MN ) can be ruled out because PN still migrate in Phox2b knockout animals , which lack all branchiomotor nuclei , including trigeminal MN ( Figure 5G and 5H ) . Another possibility is that PN might use the adjacent trigeminal nerve tract as a migration substrate to orientate themselves . Neurons from the posterior migratory stream have been shown to adopt such an axonophilic migration [67] . Chemoattraction of PN could also be provided by the meninges that overlay the migrating stream . Signaling from the meninges has been implicated in the tangential migration of cortical hem-derived Cajal-Retzius cells in the cerebral cortex . The meninges secrete the chemokine CXCL12 and enhance the migration of CXCR4 expressing Cajal-Retzius cells [68] . Interestingly , migrating PN express CXCR4 , and their migration is disrupted in CXCR4-deficient animals [69] . Moreover , CXCR4/CXCL12 signaling can be modulated by Slit upon Robo binding to CXCR4 [70] . Lastly , the meninges over the migrating PN have been shown to be a localized source of retinoic acid , and treatment of the fetus with exogenous sources of retinoic acid has been shown to result in migratory abnormalities of precerebellar neurons [71] . Our data indicate a novel role for the FMN in maintaining the rostral migration of PN . In mammals , facial motor neurons migrate tangentially from the ventricular region of r4 across r5 to colonize r6 , where they undergo a radial migration to finally condense into the FMN , next to the pial side in ventral r6 [72–74] . Such a stereotyped migration takes place between E10 . 0 and E14 . 0 , at which stage most of the facial motor neurons have reached their final destination in ventral r6 [73] . In addition to PN , the r6–r8 rhombic lip neuroepithelium generates the ECN and LRN neurons that undertake a ventrally oriented extramural pathway to finally settle in the caudal brain stem . ECN/LRN start migrating earlier than PN , approximately between E13 . 0 and E15 . 0 , whereas PN migration occurs between approximately E13 . 5 and E17 . 5 [10 , 50] . PN and ECN/LRN neurons express similar molecular markers , including Robo2 and Dcc receptors , yet they display distinct migratory pathways and final locations [9 , 10 , 36 , 49 , 51] . There is a temporal correlation between the end of the posterior FMN migration and the beginning of the PN rostral migration ( phase 2 ) . Thus , one possible mechanism to maintain the distinct migratory behavior of these populations could depend on the timing of Slit repulsive signaling from the FMN . Specifically , by the time most of the facial motor neurons have reached their final location in ventral r6 , i . e . , around E14 . 0 , the amount of Slit secreted from the FMN could become quantitatively sufficient to antagonize the Netrin-1/Dcc–mediated attraction . This may prevent further ventral progression of neurons along the ECN/LRN pathway and allow later-born neurons , at least those closer to the source , to maintain a rostral PN pathway . An additional role might be played by the Slit-expressing facial motor axons exiting the hindbrain at the level of r4 . Indeed , the PN stream has to navigate in a “corridor” delimited dorsally by the VIIIth and ventrally by the VIIth nerve roots ( Figure 1; [25] ) . Thus , decrease of Slit-mediated repulsion at the level of the VIIth nerve root might also contribute to the PN navigation errors observed in the various mutants . Notably , the migratory behavior of the FMN varies in different vertebrate species due to specific signaling cues in r5 and r6 ( e . g . , [74] ) . Sharks , lizards , or salamanders have similar organization and location of FMN to mammals , whereas in zebrafish , facial motor neurons migrate into r6 and r7 [75] . Chick embryos are peculiar in that facial motor neurons migrate dorsally within r4 , similarly to trigeminal branchiomotor neurons in r2 . Interestingly , rhombomere contributions and migratory pathways of pontine nuclei also vary among vertebrates ( e . g . , [8] ) . Thus , one possibility is that the distinct migratory behavior of the FMN in different vertebrates may in turn contribute to explain distinct species-specific migratory routes of PN . Finally , based on our results , it is likely that structure ( s ) other than the FMN are involved in signaling to PN . Indeed , in Phox2b mutants , the migration errors induced by the lack of FMN are only partial , whereas the bulk of PN follow their normal pathway of migration ( Figure 5G and 5H ) , thus indicating the influence of additional structure ( s ) . Also , in compound Slit1;Slit2 mutant mice , the expression of Slit3 in FMN is not sufficient to rescue the absence of Slit2 , also resulting in a partially penetrant migration phenotype similar to the Phox2b mutant phenotype ( Figure 5I and 5J ) . Hence , whereas our analysis does not allow us to determine the exact location of the Slit1 source , a combinatorial activity of Slit molecules from the FMN and other sources such as the floor plate appears necessary to maintain the rostral migration of PN . To date , only a handful of direct targets of Hox genes have been identified in vertebrates [42 , 43] . In particular , despite the increasing evidence for Hox gene expression during late phases of mammalian nervous system development ( e . g . , [20] ) , the nature of Hox direct targets remains largely elusive . Thus , it is unclear how Hox genes may contribute to the molecular regulation of complex aspects of neural circuit assembly , such as neuronal migration and/or topographic axon pathfinding . In the case of Hox PG2 genes , we show that their main role is to maintain the migration of the PN along a defined AP pathway . Interestingly , this is achieved not by direct negative regulation of Netrin-1/Dcc , but via the local modulation of Slit-Robo signaling levels while PN migrate through distinct rhombomeric territories . Another important finding is that the regulation of genes encoding for transcription factors involved in general aspects of PN differentiation or migration such as Barhl1 or Pax6 was not affected by the lack of Hox PG2 genes , indicating that regulation of PN directional migration by Hox genes act in parallel with or downstream from such transcription factor activities ( Figure 2; and unpublished data ) . As for Hoxa2 , our ChIP assays in P19 cells and in situ hybridization data on mutant fetuses strongly support direct regulation of Robo2 expression levels in migrating PN , whereas regulation of Slit2 expression in the FMN appears to be indirect . In fact , at the time when Slit2 down-regulation is observed , Hoxa2 is not expressed in facial branchiomotor neurons ( unpublished data ) . Generation , migration , and initial differentiation of facial branchiomotor neurons appear to take place normally in Hoxa2 mutants ( unpublished data ) , although an early patterning defect of r4- and/or r5-derived motor neurons cannot be formally ruled out . Down-regulation of Slit2 in the absence of Hoxa2 might be more likely due to abnormalities in r4 neural crest-derived glia resulting in a late impairment of FMN maintenance and connectivity . Indeed , lack of Hoxb1 in r4 neural crest-derived glia resulted in late FMN defects similar to in Hoxa2 knockout animals [76] . In addition , in Hoxa2−/− mutants , the identity of second branchial arch-derived muscles targeted by the facial nerve is altered , potentially resulting in a late degeneration of the FMN [30] . In conclusion , we provide for the first time , to the best of our knowledge , evidence for the implication of Hox PG2 genes in tangential migration of PN and identify the guidance receptor Robo2 as a direct target gene of Hoxa2 . We further show that Slit-Robo signaling involving sources of Slit2 from the facial branchiomotor nucleus and Robo2 expression in PN are key components of the molecular guidance system maintaining PN rostral migration . Our data provide a conceptual framework to understand how the PN response to multiple guidance cues along the AP and DV axes is regulated at the transcriptional level and in turn translated into coherent neuronal migratory behavior ( see model in Figure 7 ) .
Slit- and Robo-deficient mice were previously described and genotyped by PCR [39 , 56 , 60] . Hoxa2 null and Hoxa2flox lines were previously described and genotyped by PCR [30 , 33] . The day of the vaginal plug was counted as E0 . 5 . All animal studies were done in accordance with the guidelines issued by the French Ministry of Agriculture . Phox2b−/− homozygous mutant embryos die at E10 . 5–E13 . 5 [40] . However , treatment with noradrenergic agonists allows the rescue of embryo lethality through later stages . To obtain Phox2b−/− fetuses , drinking water of pregnant Phox2b+/− females was supplemented with 1 mg/ml of l-phenylephrine , 1 mg/ml of isoproterenol , and 2 mg/ml of ascorbic acid , from E8 . 5 onwards . Mutant fetuses were genotyped as described in [40] . To generate the human Leucine Rich Repeat ( LRR ) Slit2-alkaline phosphatase fusion protein ( LRR2-hSlit2-AP ) , the second LRR of Slit2 ( amino acids 341–505 ) was amplified by PCR and cloned between the XhoI and XbaI sites of AP-Tag5 vector ( Genhunter ) . Binding was performed as described in [39] . After dissection , brains were fixed by immersion in 4% paraformaldehyde in 0 . 12 M phosphate buffer ( pH 7 . 4 ) ( PFA ) . Brains were blocked in 0 . 2% gelatin in PBS containing 0 . 25% Triton-X100 and incubated overnight at room temperature with goat anti-rat Robo1 ( R&D Systems ) , goat anti-human DCC ( Santa Cruz Biotechnology ) , goat anti-human ROBO3 ( R&D Systems ) , and rabbit anti-Islet 1 ( Abcam ) , followed by species-specific biotin-coupled secondary antibodies ( Jackson Laboratories ) or fluorochrome-coupled secondary antibodies donkey anti-goat CY3 ( Jackson Laboratories ) and donkey anti-goat A488 ( Invitrogen ) . Detection was performed using Vectastain Elite ABC Kit following manufacturer instructions . In situ hybridization was performed as described in [16] . Briefly , brains were dissected , fixed in 4% paraformaldehyde ( PFA ) overnight , cryoprotected in 20% sucrose , and then embedded in Shandon Cryomatrix ( Thermo Electron Corperation ) before freezing at −80 °C . The 20-μm cryostat sections were cut in a coronal plane . For whole-mount staining , brains were fixed in 4% PFA overnight , dehydrated , and then stored at −20 °C in 100% methanol . For double in situ hybridization analyses , antisense riboprobes labeled with fluorescein-11-d-UTP were additionally used . Whole-mount hindbrains were processed as for single in situ hybridization [16] , but the two antisense riboprobes labeled with digoxigenin-11-d-UTP ( Barhl1 ) or fluorescein-11-d-UTP ( Slit2 ) were mixed ( 200 ng/ml ) in the hybridization buffer . The Dig-UTP probes recognized by an anti-DIG antibody conjugated to alkaline phosphatase , was detected first by NBT-BCIP reaction . Next , hindbrains were rinsed in a solution of glycine ( 0 . 1 M [pH 2 . 2] ) during 15 min and extensively washed with MABT ( pH 7 . 5 ) ( NaOH 200 mM , maleate 100 mM , NaCl 150 mM , Tween 20 0 . 1% ) . They were blocked in a solution of Tris ( pH 7 . 6 ) 100 mM , NaCl 150 mM , Tween 0 . 1% , complemented with 20% normal goat serum ( NGS ) , for 1 h at room temperature . Hindbrains were incubated overnight at 4 °C with anti-fluorescein antibody conjugated to alkaline phosphatase ( 1/5 , 000; Roche Diagnostics ) . After several washes , the alkaline phosphatase activity was detected using INT ( 2-[4-iodophenyl]-3-[4-nitrophenyl]-5-phenyltetrazolium chloride ) ( 2 . 48 mg/ml ) and BCIP ( 5-bromo-4-chloro-3-indolyl phosphate , toluidine salt in DMSO ) ( 2 . 48 mg/ml; Roche Diagnostics ) diluted in a solution of Tris ( pH 9 . 5 ) 100 mM , NaCl 100 mM , MgCl2 50 mM , Tween 0 . 1% , levamisole 2 mM . Whole-mount hindbrains were fixed overnight in 4% PFA and stored at 4 °C in PBS/70% glycerol . The following probes were used: Hoxa2 [30]; Hoxb2 [77]; Hoxa3 [78]; Hoxb3 [79]; Hoxb4 [80]; Hoxd4 [81]; Hoxb5 [82]; Barhl1 [21] , Pax6 [83] , and Tag1 [31] . For alkaline phosphatase staining , brain sections were incubated in PBS at 65 °C for 90 min to inactivate the endogenous alkaline phosphatase . After the inactivation , sections were washed in a solution of NTMT ( NaCl 5 M , Tris HCL 1 M [pH 9 . 5] , MgCl2 1M , and Tween 20 50% ) . Finally , the sections were overlaid in a solution consisting of NTMT with NBT and BCIP . The sections were rinsed first in PBS and next in EtOH 100% , and then mounted onto slides . Whole embryos were processed for β-galactosidase histochemistry by two washes in 0 . 1 M phosphate buffer ( pH 7 . 4 ) , fixed in 4% PFA at 4 °C for 1 h . After two washes in 0 . 1 M phosphate buffer , brains were put into the staining solution ( 2 mM MgCl2 , 5 mM K3Fe ( CN ) 6 , 5 mM K4Fe ( CN ) 6 , Tween 20 0 . 1% , PFA 0 . 2% , and 1 mg/ml X-gal ) at 37 °C for 2 h . Stained brains were then dehydrated in methanol 100% , and stored at −20 °C for further analysis by in situ hybridization . Total RNA from P19 cells was collected using Trizol reagent following manufacturer protocol ( GibcoBRL ) . cDNA synthesis was performed using SuperScriptII Reverse Transcriptase ( GibcoBRL ) following the manufacturer's instructions . Primers: Hoxa2: PCR1: 5′-TCGACGCTTTCACACTCGACACTGAT-3′ ( forward ) and 5′-CCGGTTCTGAAACCACACTT-3′ ( reverse ) . PCR2 ( nested ) : 5′-AGTCACCCTCGCCACGGCGCT-3′ ( forward ) and 5′-TCTGCAAAGGTACTTGTTGA-3′ ( reverse ) . Robo2: 5′-ATATCTGATACTGGCACTTATAC-3′ ( forward ) and 5′-CTGAAAGCCTCAATGATATACGC-3′ ( reverse ) . An anti-Hoxa2 rabbit polyclonal antibody was raised by immunization against the following peptide: KFKNLEDSDKVEEDEEEKSLC , encompassing amino acids 209–228 of the Hoxa2 murine protein . Such a unique peptide was chosen to avoid potential cross-reactivity of the antibody with other Hox proteins . Before injection , the peptide was coupled to activated ovalbumin according to the manufacturer's protocol ( Pierce ) . Antibody specificity was tested on Western blot from extracts of COS cells transfected with Hoxa2 or other Hox proteins ( unpublished data ) . ChIP assays were performed using the Upstate EZ ChIP Chromatin immunoprecipitation Kit , following the manufacturer's protocol . P19 cells were cultured to confluence , fixed in 1% formaldehyde , and sonicated 24 times for 10 s in 50-s intervals using a Fischer Bioblock Scientific Sonicator ( Vibracell ) . Sonicated DNA was immunoprecipitated using the generated polyclonal anti-Hoxa2 antibody and control antibodies . The immunoprecipitated Robo2 , Gapdh , and Hprt sequences were first amplified and detected by nonquantitative PCR ( 31 cycles ) followed by visualization on agarose gel . In addition , real-time quantitative PCR ( qPCR ) was carried out using the Quantitech SYBR Green PCR Kit ( QIAGEN ) with a Roche LightCycler . The ChIP primers for nonquantitative PCR are as follows: Robo2: 5′-CTATGGGTTTTGCTTTATCTGTCCC-3′ ( forward ) and 5′-GGTAGCTGAGCATGTTATTGTCC-3′ ( reverse ) ; Gapdh: 5′-TCTGCGCCCTTGAGCTAGGACTGG-3′ ( forward ) and 5′-TTCGCACCAGCATCCCTAGACC-3′ ( reverse ) . For real-time qPCR , the following oligonucleotide primers were used: Robo2: 5′-TGATAAGTTGACCAGTCAGTG-3′ ( forward ) and 5′-TGTGTTATGAGTCCTCAGATG-3′ ( reverse ) ; Hprt: 5'-TTATCTGGGAATCCTCTGGG-3′ ( forward ) and 5′-AAAGGCAGTTCCGGAACTCT-3′ ( reverse ) . | In the developing central nervous system , neurons migrate sometimes over long distances from their birthplace to their final location , where they condense in specific nuclei . The precise positioning of migrating neurons is critical to the building of ordered connectivity with their target partners . Little is known about how exposure of migrating neurons to simultaneous attractive and repulsive guidance cues may be integrated at the transcriptional level and in turn translated into directional migratory responses specific for each neuronal population . Here , we focus on the molecular mechanisms regulating the directionality of long-distance migration of pontine neurons in the mouse brainstem . Such neurons belong to the so-called precerebellar system , which is essential for coordinated motor activity , and provide the principal input to the cerebellum . We provide evidence for the implication of homeodomain transcription factors of the Hox gene family in the control of pontine neuron migration along the brain rostrocaudal axis . We identify the guidance receptor Robo2 as a direct target gene of the Hoxa2 gene . We further show that repulsive signaling mediated through the Robo2 receptor expressed in migrating neurons and its ligand Slit2 secreted from the facial motor nucleus are key components of the molecular guidance system that maintains caudorostral migration and prevents premature attraction towards the brainstem ventral midline . Our data provide a conceptual framework to understand how transcriptional regulation of the response to environmental guidance cues controls stereotyped neuronal migratory behavior in the developing mammalian brain . | [
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| 2008 | Hox Paralog Group 2 Genes Control the Migration of Mouse Pontine Neurons through Slit-Robo Signaling |
Wolbachia are maternally inherited bacteria that commonly spread through host populations by causing cytoplasmic incompatibility , often expressed as reduced egg hatch when uninfected females mate with infected males . Infected females are frequently less fecund as a consequence of Wolbachia infection . However , theory predicts that because of maternal transmission , these “parasites” will tend to evolve towards a more mutualistic association with their hosts . Drosophila simulans in California provided the classic case of a Wolbachia infection spreading in nature . Cytoplasmic incompatibility allowed the infection to spread through individual populations within a few years and from southern to northern California ( more than 700 km ) within a decade , despite reducing the fecundity of infected females by 15%–20% under laboratory conditions . Here we show that the Wolbachia in California D . simulans have changed over the last 20 y so that infected females now exhibit an average 10% fecundity advantage over uninfected females in the laboratory . Our data suggest smaller but qualitatively similar changes in relative fecundity in nature and demonstrate that fecundity-increasing Wolbachia variants are currently polymorphic in natural populations .
When microbes that live within animal cells are transmitted only maternally , their reproductive success is directly tied to that of the matrilines they inhabit . Both intuition and mathematics suggest that such endosymbionts will be selected towards mutualism , if possible , increasing the fecundity of their female hosts [1] . The expectation that vertical transmission favours evolution towards mutualism is supported by both laboratory co-evolution experiments between viruses and bacteria and comparative data from a wide range of natural associations [1 , 2] . Mutualisms generally have long evolutionary histories , but given the potentially explosive rate of bacterial evolution [3] , rapid evolution of mutualisms in nature might also be expected . Here we report such evolution by bacteria ( Wolbachia ) associated with a dipteran host ( Drosophila simulans ) in natural California populations . In less than 20 y , the Wolbachia in California D . simulans have changed so that infected females now produce more eggs than uninfected females under laboratory conditions , whereas infected females previously suffered a significant fecundity deficit . Cytoplasmic incompatibility ( CI ) in insects is normally caused when Wolbachia-infected males mate with uninfected females or females that carry a different Wolbachia strain [4] . Because CI causes embryo lethality , infected females , who are protected from CI , often have a reproductive advantage over uninfected females . This results in the rapid spread of the infection through host populations when the Wolbachia are faithfully transmitted from mother to offspring and produce relatively minor fitness costs . Hoffmann et al . [5] discovered Wolbachia-induced CI in California populations of D . simulans . Initially , the California D . simulans Wolbachia ( wRi ) infection was found only south of the Tehachapi transverse range in the southern quarter of the state . From 1985 to 1994 , we monitored the infection's spread north [6 , 7] . We showed that the dynamics and equilibrium infection frequencies in nature could be described accurately by a discrete-generation model with only three parameters: μ , the average frequency of uninfected ova produced by an infected female; H , the relative hatch rate from “incompatible” fertilisations of uninfected eggs by sperm from infected males ( the other three possible fertilisations produce indistinguishable hatch rates ) ; and F , the relative fecundity of infected females [8–10] . Using replicated field assays in the early 1990s , we found the following: μ ≈ 0 . 045 , H ≈ 0 . 55 , and F ≈ 1 . 0 . In contrast , in laboratory populations , the infection showed perfect maternal transmission ( μ = 0 ) , and infected females were 10%–20% less fecund than uninfected females ( F ≈ 0 . 8–0 . 9 ) . Our field-based parameter estimates produce a predicted equilibrium infection frequency , p̂ ≈ 0 . 94 , consistent with data from several locations , including three populations where we monitored the infection's introduction and spread over about 2 y , with dynamics that roughly matched our simple predictions [7] . The wRi infection quickly spread northward through California and is now pervasive throughout most North American populations of D . simulans . Models suggest that both Wolbachia infections and the host nuclear background should evolve to reduce deleterious effects associated with the infection and to increase the transmission fidelity of the microbe [11 , 12] . Despite the fact that CI allows Wolbachia to spread within populations , intrapopulation selection of Wolbachia is not expected to directly affect the level of CI [11 , 12] , unless host populations are structured so that kin selection favours more intense CI [13] ( e . g . , when infected males can reduce the larval competition experienced by the progeny of their female siblings , a condition that is likely to be rare for D . simulans ) . In contrast , host evolution is expected to reduce the intensity of CI ( i . e . , increase embryo viability from incompatible crosses ) [12] . Both Wolbachia and host evolution affecting transmission ( μ ) , level of incompatibility ( H ) , and fecundity of infected hosts ( F ) are plausible , because both Wolbachia- and host-related effects influence CI , transmission , and fitness [14–16] . Indeed , some Wolbachia infections do not induce detectable levels of CI and have no known deleterious effects on host fitness [17 , 18] . Positive fitness effects from Wolbachia infections have been suggested [19–22] , and indirect comparative evidence from different Wolbachia infections in Drosophila indicates that Wolbachia–host interactions may become more benign and potentially mutualistic over time [4 , 23] . Southern California populations of D . simulans are natural candidates for observing such evolution , because they have been stably infected for at least two decades , the host populations produce on the order of 10–15 generations per year [7] , and the parameter values describing transmission , fecundity , and CI level are such that both the infection and its host should experience significant pressure to evolve [12] . We have informally monitored these populations for evolutionary changes in CI and other Wolbachia effects for about a decade . Here we provide our accumulated evidence that the Wolbachia infection in D . simulans has changed to become more mutualistic , while no evolution by either the Wolbachia or D . simulans has been detected affecting CI levels or the fidelity of maternal transmission .
Apart from the viability effects associated with CI , female fecundity is the only fitness component known to be affected by the wRi infection in California D . simulans [9] . To test whether fecundity effects associated with this Wolbachia infection have evolved , we re-examined female fecundity in California D . simulans after 20 y [5] . In the late 1980s , fecundity costs were evident in the laboratory when lines treated with the antibiotic tetracycline , which cures Wolbachia infections , were compared to untreated lines , or when naturally uninfected and infected lines were compared [9] . Flies were collected in 2002 and uninfected lines were generated by treatment with tetracycline . Infected and uninfected lines were then scored for fecundity every day over 5 d . Overall , there was a fecundity advantage associated with the infection; in some lines no fecundity advantage was detected , while in other lines the total egg output was significantly greater in infected individuals ( Figure 1A ) . In contrast , the wRi line collected from Riverside in 1988 and maintained in the laboratory still produced a fecundity deficit , comparable to the deficits found in infected lines previously ( see Table 10 of [8] and Table 7 of [9] ) . We repeated this fecundity assay , again curing many of the same lines after 10 mo of laboratory culture . To test the sensitivity of the fecundity effect to culture conditions , we restricted access to yeast . Overall , the infected lines still showed greater fecundity than the uninfected lines derived from them . Although one line showed a decrease in fecundity when infected , there was a significant increase overall , and no significant interaction effect ( Figure 1B ) . When individual lines were considered , there appeared to be a shift in fecundity for line R3 and R24 ( Figure 1A and 1B ) . However , when we computed confidence intervals on these data for the difference in fecundity relative to the uninfected lines , the bootstrapped confidence intervals ( bCIs ) overlapped between , for example , the yeasted R3 line ( mean difference 42% , 95% bCI 3 . 7% to 85% ) and non-yeasted R3 line assays ( mean difference 0 . 4% , 95% bCI −6 . 8% to 20 . 4% ) . Hence , the lack of repeatability for the statistical significance of individual lines is likely to reflect primarily the large inherent stochasticity of fecundity data . In contrast , the positive effect of the Wolbachia infection on mean fecundity across lines is evident in both of the experimental treatments reported in Figure 1A and 1B ( the mean effect is 23% in Figure 1A and 10% in Figure 1B ) . Because Wolbachia effects on fitness may be temporarily or permanently induced by tetracycline treatment [14 , 24] , we re-collected isofemale lines of D . simulans from Irvine , California , in 2004 . We then generated uninfected lines from these and reciprocally crossed infected and uninfected sublines two generations after tetracycline treatment ( using old males in the incompatible cross ) to homogenise nuclear backgrounds and remove the effects of the antibiotic treatment . Fecundity was then assayed each day over 10 d , controlling for body size . Again , a significant overall fecundity advantage of approximately 10% was associated with Wolbachia infection ( F1 , 185 = 8 . 03 , p = 0 . 005 ) , with three lines out of eleven ( IR1 , IR2 , and IR28 ) showing a significant fecundity advantage ( Figure 1C ) . Therefore , the fecundity effect associated with the wRi infection in the laboratory has changed from negative to positive . The apparent lack of consistency of this fecundity advantage across individual lines suggests that there may be polymorphism among the Wolbachia strains infecting the populations near Riverside and Irvine . These data do not demonstrate polymorphism because there is no significant line-by-infection interaction in our fecundity assays . However , polymorphism is expected from our theoretical analyses ( see “Mathematical Analyses” ) and is directly demonstrated from additional data presented below . Our data indicate that the fecundity deficit initially associated with the wRi infection in laboratory assays [7 , 9] has disappeared . We computed 95% CIs of the mean difference between infected and uninfected lines following Sokal and Rohlf ( p . 444 of [25] ) for the 2004 Irvine data and earlier published data ( Table 10 of [9]; Table 7 of [7] ) . The mean difference for infected minus uninfected lines from the 2004 data was 10 . 4% ( 95% bCI 2 . 6% to 18 . 2% ) and for the earlier data was −19 . 4% ( 95% bCI −24 . 5% to −13 . 6% ) . Thus , the infection has changed from causing a significant fecundity deficit to causing a significant fecundity advantage in the laboratory ( an overall shift of 30% ) . Evolutionary changes may also influence CI levels and maternal transmission . To test for changes in CI , we reciprocally mated infected and uninfected individuals derived from isofemale lines of D . simulans collected in 1999 and 2002 and compared these with the old wRi line collected at Riverside in 1988 . We also mated females to males that were 5 , 10 , and 15 d post-eclosion , as male age can affect CI [7 , 9 , 26] . When males were 5 d old , levels of CI were high , with an average of 92% egg mortality for all lines in crosses between uninfected females and infected males ( Figure 4 ) . CI levels did drop off when males were 10 and 15 d old , as described previously [7 , 9] . However , there was no difference in the level of CI between the Riverside collections in 1999 and 2002 and the old 1988 wRi line , and the power of our CI tests indicated that we could have detected a difference of 8% or greater . Hence , the level of CI has certainly evolved significantly less than the fecundity effects observed in our laboratory assays . The levels of CI for matings involving males in the three age classes are also similar to those found previously [7 , 9] . We assume that the lack of change of CI in our laboratory assays implies relative constancy of CI levels in nature . Hence , we expect that H , the average relative hatch rate from an incompatible fertilisation in nature ( sperm from an infected male fertilising an uninfected ovum ) , remains approximately 0 . 55 , as estimated previously [7] . We did not directly re-measure field maternal transmission rates of the Wolbachia infection . Instead , we used field infection frequencies and our assumption that H remains approximately 0 . 55 to indirectly estimate transmission efficiency , as any significant increase or decrease in maternal Wolbachia transmission would lead to an observable change in the equilibrium infection frequency . For instance , with H = 0 . 55 , explaining an equilibrium frequency of 0 . 94 requires a transmission efficiency of 0 . 964 to 0 . 953 if the relative fecundity of infected females , F , is between 0 . 9 and 1 . 1 . Our previous estimates of transmission frequency in nature , denoted 1 − μ , averaged 0 . 96 , consistent with the observed infection frequency and field estimates of CI intensity and relative fecundity [7] . Because selection among mutually compatible Wolbachia variants acts to increase the parameter combination F ( 1 − μ ) [12] , we expect μ to decrease . If the failure rate of maternal transmission , μ , was halved to 0 . 02 ( which would have roughly the same fitness impact on Wolbachia as increasing F by only 2% ) , the expected equilibrium infection frequencies would rise to 0 . 97 , with H = 0 . 55 and F between 1 . 0 and 1 . 1 ( see “Mathematical Analyses” ) . We collected 654 D . simulans females from four locations in California ( Irvine , Ivanhoe , Riverside , and Winters ) , and screened F1 females from each field-collected female for Wolbachia using PCR . We found that infection frequencies did not differ significantly between the locations , with an overall infection frequency of 93% ( Table 1; G test for homogeneity , G = 3 . 09 , df = 3 , p = 0 . 38 ) . These infection frequencies do not differ from those found in earlier studies [7 , 9] , suggesting that transmission of Wolbachia from mother to offspring ( 1 − μ ) has not changed appreciably . To determine if the observed fecundity advantage of wRi infection is associated with evolution of the wRi strain and not merely a new strain of Wolbachia that has invaded California , we tested for compatibility between the old wRi line collected in 1988 and the IR2 line collected from Irvine in 2004 . If Wolbachia strains are incompatible ( i . e . , cause CI in matings between lines ) , then it is likely that a new strain has invaded California populations of D . simulans . However , we found no difference between the hatch rates of crosses between and within lines ( F3 , 72 = 0 . 08 , p = 0 . 97 ) , indicating complete compatibility between the Wolbachia strains . In addition , we sequenced part of the Wolbachia wsp gene ( 611 bp ) for the old wRi strain collected in 1988 and strains from 25 isofemale lines collected at Irvine in 2004 and found no differences at the nucleotide level . We also sequenced part of the Wolbachia ftsZ gene ( 718 bp ) for five of these strains and found no differences in nucleotide sequence compared to the 1988 wRi strain . Therefore , it is likely that the change in fecundity has involved evolution of the wRi strain rather than invasion by a new strain . Essentially all studies of Wolbachia effects have been limited to laboratory populations . The wRi infection of D . simulans is one of very few whose effects have been studied in nature ( cf . [27–29] ) . We have shown that wRi has apparently evolved in nature over the past 15 y so that laboratory isofemale lines with the infection tend to show a fecundity advantage on the order of 10% rather than a fecundity disadvantage on the order of 20% , as was the case when the wRi infection was new in California D . simulans . We do not yet have new fecundity data from wild-collected females to directly compare fecundities of infected versus uninfected females in nature . However , we previously reported such data while we tracked the northward spread of wRi through California . During this spread , we were able to study populations in which wRi was at intermediate frequencies , allowing us to make comparisons between the fecundities of wild-collected infected versus uninfected females . We performed nine comparisons , four in the Tehachapi mountains of southern California in 1988–1989 ( Table 4 of [9] ) , then five more near Davis in northern California in 1992–1993 ( Tables 6 and 7 of [7] ) ( all of these data are summarised in Table S1 ) . Collectively these studies assayed the fecundity of more than 1 , 000 females from nature , and each of our nine comparisons was based on 45–203 females . Only the first of these nine studies ( 104 females ) found a statistically significant fecundity deficit for infected females ( F = 0 . 82 , p < 0 . 05 ) . More relevant to our new laboratory fecundity data is that the four 1988–1989 studies produced an average relative fecundity of F = 0 . 92 , whereas the five 1992–1993 studies produced an average of F = 1 . 03 ( with three of the last five point estimates for F above one ) . Although the difference between these two sets of estimates is marginally non-significant in a one-tailed test ( t-test , t = 1 . 76 , df = 7 , p = 0 . 06 ) , it suggests that the changes in relative fecundity we have documented in the laboratory reflect similar changes in nature . This is discussed further below in light of our theoretical analyses .
In less than 20 y , the wRi strain that invaded and spread throughout California in the 1980s has evolved from inducing a fecundity deficit in the laboratory to providing a fecundity benefit to its host , as theory predicts [12] . There has been no detectable change in the level of CI , indicating that the genes controlling fecundity have at most minor pleiotropic effects on CI . Rapid evolutionary change within this system has resulted in a parasitic Wolbachia evolving towards a more mutualistic interaction with its host . Interspecific comparisons ( e . g . , [30–32] ) and laboratory experimental evolution systems ( e . g . , [33] ) provide many examples supporting the theoretical prediction that vertical transmission , as opposed to horizontal transmission or a mixed mode of transmission , tends to promote mutualism [34–36] . There are well-known examples in which viruses have rapidly evolved to become more benign in nature [37 , 38] . However , these are best interpreted as evolution towards an “optimal” level of virulence , rather than evolution towards mutualism [34] . We know of no previous examples in which an evolutionary shift towards mutualism has been observed over a period of decades in nature . To understand the evolutionary dynamics in nature that have so rapidly produced the new fecundity effects , we assume—consistent with our laboratory CI data—that the relevant Wolbachia variants are fully compatible with each other . This implies that within the population of infected individuals , the frequencies of alternative Wolbachia types follow haploid selection dynamics with fitness determined by the parameter combination F ( 1 − μ ) , irrespective of whether the variants cause different levels of CI with uninfected individuals [12] . Between 1988 and 2002 , the California populations of D . simulans have produced about 200 generations [7] . The observed fecundity variation produced by different Wolbachia on a common genetic background ( Figure 3 ) demonstrates polymorphism for the fecundity-increasing Wolbachia variant ( s ) . Irrespective of within-host dynamics , we can use discrete-generation haploid selection theory to explore the selective pressures responsible for the spread of fecundity-enhancing variants among hosts and their likely evolutionary trajectory ( see “Mathematical Analyses” ) . Assuming that the observed changes are attributable to increased frequency of variants initially present , but extremely rare , in the 1988 southern California wRi population , our analysis suggests that selective advantages in the field are likely to be on the order of 5% ( whereas 1% or 15% are unlikely ) . Theory also indicates that the current polymorphism should be transient and that the fecundity-enhancing variants should reach very high frequencies in these populations over the next 5–10 y . Hence , we predict that the continuing evolution of these Wolbachia populations will be easily documented . Our data on compatibility of the “new” versus “old” Wolbachia variants and their DNA sequence similarity indicate that Wolbachia effects on its host evolve readily in natural populations by selection among closely related Wolbachia variants . Such rapid evolution helps to explain the diversity of effects of Wolbachia on host fitness noted in the literature: these effects range from negative [9 , 15 , 39] to positive [19 , 21 , 22] to the extreme where Wolbachia becomes essential for host survival [20] or host fertility [40 , 41] . It also helps to explain the inconsistent effects of Wolbachia on host fitness detected in previous experiments [22 , 42]; changes in the apparent host effects of Wolbachia over time or between experiments may well reflect selection among Wolbachia variants rather than residual effects of antibiotics or changes in Wolbachia density . The rapid evolution of wRi , as well as rapid evolution of Wolbachia hosts [43] , suggests a dynamic interaction between parasitic and mutualistic life modes and rapidly changing effects of endosymbionts in host insect evolution .
The CI assays used D . simulans collected from Riverside , California , in 1998 and 2002 and maintained in the lab as isofemale lines until testing . Fecundity assays included the 2002 isofemale lines and those established from females collected at Irvine , California , in 2004 . A California wRi-infected line from 1988 was included in some assays . To determine the infection frequencies in California populations , approximately 200 female D . simulans were collected at each of four localities ( Irvine , Ivanhoe , Riverside , and Winters ) in 2004 , and F1 individuals scored for infection status by PCR assay ( described below ) . To produce uninfected sublines for each line , larvae were treated with 0 . 03% tetracycline [5] for two generations . Lines were reared for at least two generations without tetracycline before the CI and fecundity experiments . Level of CI was determined by mating virgin 5- , 10- , and 15-d-old Wolbachia-infected males to uninfected virgin females ( >5 d old ) from the same 1998 and 2002 collected lines . Reciprocal crosses acted as controls . Males were mated once , and females were placed after mating in a vial with a spoon containing 5 ml of agar-treacle-yeast medium and left for 24 h at 25 °C . The number of unhatched eggs was counted >24 h later . CI data ( egg hatch rates ) were angular transformed prior to analysis . Model I ANOVA ( analysis of variance ) and t-tests were used to compare CI levels between the Riverside collections from 1998 and 2002 and the wRi line from 1988 . Five fecundity experiments were done . In the first two ( Figure 1A and 1B ) , lines from the 2002 Riverside collection were cured , and infected and uninfected females from each line were mated to uninfected males from the same line . In the first experiment , the 1988 wRi line was included to re-test the previously described fecundity deficit [9] . Flies were reared at low densities by placing 20 eggs per vial on 15 ml of medium . To measure fecundity of emerging flies , pairs of 1-d-old virgin females and males were placed in vials with spoons as for the CI tests . Spoons were replaced every 24 h for 5 d and eggs counted . Between ten and 15 females were assayed for each line . Yeast paste was added to the medium surface in the first experiment , but not in the second experiment , to see if the same fecundity-enhancing Wolbachia effect could be detected when egg output was suppressed due to the absence of live yeast . In the third experiment ( Figure 1C ) , lines from the 2004 Irvine collection were cured as above . To control for nuclear background , we crossed uninfected and infected flies from the same line reciprocally and scored F1 offspring for fecundity ( with live yeast ) after they had been reared and set up as above . Fecundity scoring was extended from 5 to 10 d to increase the likelihood of detecting small differences . Between 15 and 20 replicates were assayed per infected/uninfected treatment of each isofemale line . Wing size ( measured as centroid size based on landmarks [44] ) was also measured for each female and used as a covariate in analyses to control for body size . To assign the effects on fecundity to either Wolbachia or a host–Wolbachia interaction , we backcrossed the nuclear background of one Irvine line showing the greatest fecundity advantage ( Figure 1C; IR2 ) into the 1988 wRi strain , and the 1988 wRi line nuclear background into the IR2 strain , both for five generations ( Figure 2 ) . Ten-day fecundity was measured on 20 replicate pairs of males and females per backcross line as above . Wolbachia strain and nuclear background were treated as fixed effects in the ANOVA for fecundity . Finally , to determine whether the Wolbachia fecundity effect was polymorphic within the 2004 Irvine lines , we backcrossed the 1988 wRi line nuclear background into 20 strains ( isofemale lines ) from the 2004 Irvine collection for two generations ( Figure 3 ) . Ten-day fecundity was measured on 20 replicate pairs of males and females as above . Model I ANOVA was used to determine Wolbachia strain differences for fecundity . We also determined the coefficient of variation [25] for the lines in this experiment and the infected and uninfected lines from the third fecundity experiment ( 2004 Irvine lines ) to see if they fitted the patterns expected ( fecundity of infected > fecundity of uninfected > fecundity of infected with a homogenised background ) . We determined the infection status of all lines collected from the field or after treatment with tetracycline using extracted DNA from females in a PCR with the Wolbachia-specific primers 76–99 forward and 1012–994 reverse which amplify a ~ 950-bp fragment of bacterial 16S rDNA [45] . The D . melanogaster primers su ( s ) forward 724–753 and su ( s ) reverse 1113–1092 were included in each reaction as a control [7] . To determine the infection frequency in the four populations collected from California in 2004 , we first assayed DNA as above from a single F1 female from each field-collected female . In addition , another PCR with the Wolbachia-specific primers wsp81F and wsp619R [46] was performed with the same DNA to minimise the chance of false positives . If either or both of these assays were negative , DNA was extracted from a second F1 fly from the same isofemale line and subjected to the two PCRs . This second fly was used to control for PCR artefacts and imperfect maternal transmission of Wolbachia [7 , 9] . To determine compatibility between the 1988 wRi line and the IR2 line collected at Irvine in 2004 , we crossed virgin males and females ( >5 d old ) between and within each line in a reciprocal design . Males were mated once , and females were placed after mating in a vial with a spoon containing medium as above for the CI assays . The number of unhatched eggs was counted after 48 h . The analysis was as above for the CI assays . To compare the similarity between the 1988 wRi strain and the Irvine strains collected in 2004 , we sequenced 611 bp of the highly variable Wolbachia wsp cell-surface gene [46] and 718 bp of the Wolbachia ftsZ cell-cycle gene . DNA was extracted from a single female from each of 25 isofemale lines from the 2004 Irvine collection and the laboratory line of the 1988 wRi strain . The partial wsp gene fragment was amplified from all lines using the primers and protocol found in [46]; the partial ftsZ gene was amplified from only five isofemale lines from the 2004 Irvine collection and the 1988 wRi line as in [47] . Amplified fragments were sequenced using the BigDye Terminator cycle sequencing kit ( v3 . 1 , Applied Biosystems , http://www . appliedbiosystems . com ) . Sequences were aligned using the CLUSTAL W algorithm [48] . We also included in the analysis the original wsp and ftsZ sequences of the wRi strain found in GenBank ( http://www . ncbi . nlm . nih . gov/Genbank; accession numbers AF020070 and U28178 , respectively ) . We analysed various mathematical models to address three issues discussed in the text: ( 1 ) inferences concerning transmission-rate evolution based on the dependence of equilibrium infection frequencies on the three parameters that are sufficient to explain dynamics and equilibria in nature [7] , ( 2 ) the intensity of selection responsible for the observed evolution , and ( 3 ) predicted future frequency changes in the fecundity-enhancing Wolbachia variant ( s ) . Our methods and analyses leading to our conclusions are described below . Regarding dependence of equilibria on parameter values , to make inferences concerning whether Wolbachia's maternal transmission rate has evolved , we first considered how the stable equilibrium infection frequency , denoted p̂ , changes with the parameters H , the relative hatch rate from incompatible crosses , F , the relative fecundity of infected versus uninfected females , and μ , the fraction of uninfected ova produced by an infected female . Based on field estimates of infection frequencies , we concluded that the equilibrium frequency throughout central and southern California in 1992 was approximately p̂ ≈ 0 . 94 ( with 95% confidence interval 0 . 92 to 0 . 96 ) [7] . This was consistent with our theoretical prediction for p̂ from field-based parameter estimates [7] . Our new laboratory data suggest that F has evolved significantly , while H has remained relatively constant . Given the change in F , it may seem surprising that our new estimate of the infection frequency in central and southern California , approximately 93% ( with 95% confidence interval 0 . 90 to 0 . 94 ) , does not differ significantly from the frequency estimated previously . Our formula for p̂ allows us to examine the consistency of these observations . Evolutionary theory suggests that if Wolbachia variants remain fully compatible , F ( 1 − μ ) should tend to increase [12] . Thus , we are particularly interested in determining whether μ has decreased . However , because fitness is proportional to F ( 1 − μ ) , changing F from 0 . 9 to 1 . 0 or from 1 . 0 to 1 . 1 involves a selection coefficient on the order of s = 0 . 1 ( which should produce significant changes in polymorphic Wolbachia variant frequencies over tens of generations ) . In contrast , halving μ from 0 . 04 to 0 . 02 involves much weaker selection , on the order of s = 0 . 02 , so that hundreds of generations would be required for significant evolution . Hence , we expect that detectable evolutionary changes since the mid-1980s in μ are much less likely than detectable changes in F . Figure 5A and 5B explore how varying F changes the values of H and μ necessary to explain equilibrium population frequencies of 0 . 90 or 0 . 94 . Figure 5 assumes μ = 0 . 04 and shows the values of H needed to produce p̂ = 0 . 90 ( solid line ) versus 0 . 94 ( dashed line ) as F varies from 0 . 9 to 1 . 1 . As shown , varying F over this range requires very little change in H to preserve p̂ . Similarly , Figure 5B assumes H = 0 . 55 and shows the values of μ needed to produce p̂ = 0 . 90 ( solid line ) versus 0 . 94 ( dashed line ) as F varies from 0 . 9 to 1 . 1 . Again , changing F has little effect . Both graphs indicate that changes in F are likely to have little impact on p̂ . This is shown directly in Figure 5C , which assumes H = 0 . 55 and μ = 0 . 045 ( or μ = 0 . 0225 ) and plots p̂ as F varies from 0 . 9 to 1 . 1 . Clearly , changes in F over the range suggested by our laboratory and field data have little impact on p̂ . In contrast , a change in μ that would have a much smaller impact on Wolbachia fitness would produce changes in p̂ that our samples would have detected . Regarding selection intensity , within the population of infected individuals , the frequencies of mutually compatible Wolbachia variants follow haploid selection dynamics with the fitness of each variant proportional to F ( 1 − μ ) , irrespective of the level of CI they produce with uninfected females [12] . All else being equal , two conclusions follow: ( 1 ) the level of CI is not subject to direct selection based on between-host frequency dynamics , and ( 2 ) for values of F near one and μ near zero ( as suggested by our data ) , selection for modifying F is much stronger than selection for modifying μ . These inferences are consistent with our data , which suggest that H and μ have remained relatively constant , while F has increased . To make quantitative inferences , we assumed discrete generations . If we consider two Wolbachia variants such that F1 ( 1 − μ1 ) /[F2 ( 1 − μ2 ) ] = 1 + s , the frequency of variant 1 in generation t , denoted pt , changes according to Our inferences about plausible selection intensities follow from equation 1 , assuming that the observed changes have occurred over roughly 200 generations . Figure 6 illustrates the selection intensity needed to explain a transient polymorphism , assuming that the fecundity-enhancing variant was rare in the population 200 generations ago . It shows that for low initial frequencies ( say , between 10−3 and 10−6 ) , selection coefficients , s , on the order of 0 . 04–0 . 08 would produce polymorphic frequencies ( between 0 . 1 and 0 . 8 , for instance ) that are consistent with our data . In contrast , for s on the order of 0 . 01 , a significantly longer time would be necessary to produce the polymorphism observed ( see Figure 7 ) , whereas if s were as large as 0 . 15 , polymorphism for the fecundity-enhancing variant would tend to be very short-lived ( on the order of 2 y ) . Regarding future evolution , our analysis suggests that the currently inferred polymorphism for the fecundity-enhancing variant ( s ) is likely to be transient . We can use equation 1 to understand the time scale over which near-fixation is expected . Figure 7 plots the time required for a favoured variant to increase from an initial frequency of 0 . 001 up to a frequency of between 0 . 1 and 0 . 9 . The difference between the highest and lowest lines indicates the time required for the frequency to increase from 0 . 1 to 0 . 9 . Note that for s as large as 0 . 15 , this time is only about 31 generations , or approximately 2 y in these populations . Given that samples collected in 2002 and 2004 both showed an apparent polymorphism , it seems unlikely that selection was this intense . This inference is consistent with our conjecture that the current Wolbachia-induced fecundity advantage in nature is likely to be less than the 10% effect observed in the laboratory , just as the fecundity deficit of roughly 15% found in the laboratory in 1989 [9] corresponded to a fecundity deficit for infected field-collected females that was probably less than 10% in 1989 [9] and less than 8% in 1992 [7] . Conversely , if the fecundity advantage was as small as 1% ( corresponding to s = 0 . 01 ) , as Figure 7 shows , the inferred polymorphism would be unlikely to arise in only 15 y ( about 200 generations ) . Hence , for plausible levels of selection , we are likely to be able to observe significant frequency increases of the fecundity-enhancing Wolbachia variant ( s ) in nature over the next few years .
Sequences for the wsp and ftsZ genes sequenced in this study have been deposited in GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) under the accession numbers EF423730–EF423735 for ftsZ and EF423736–EF423761 for wsp . | Wolbachia are endosymbiotic bacteria that live inside the cells of their invertebrate hosts . They are transmitted directly from mother to offspring , and spread through populations by manipulating the reproduction of their hosts . The most common reproductive manipulation responsible for the spread of these bacteria , called “cytoplasmic incompatibility , ” arises when infected males mate with uninfected females , resulting in fewer offspring than normal . There are fitness costs for the hosts associated with Wolbachia infections , most commonly involving a reduction in egg production . Theory predicts that this detrimental effect of Wolbachia on its host should result in selection for the bacteria to evolve a more benign lifestyle , changing the bacterium from being parasitic to more mutualistic . We document such a shift in a Wolbachia infection of fruit flies ( Drosophila simulans ) from California . The shift occurred extremely rapidly , over 20 years . Consequently , Wolbachia-infected hosts now have higher rates of egg production than their uninfected counterparts . Changes in the genome of Wolbachia seem to be responsible for this , rather than changes in the host genome . Our study reveals that bacteria and their hosts represent components of a dynamic interacting system that can evolve rapidly over time . | [
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| 2007 | From Parasite to Mutualist: Rapid Evolution of Wolbachia in Natural Populations of Drosophila |
Epidemics of infectious diseases often occur in predictable limit cycles . Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates , resulting in deterministic chaos . However , persistent deterministic chaos has never been observed , in part because sufficiently large oscillations in transmission rates are uncommon . Where they do occur , the resulting deep epidemic troughs break the chain of transmission , leading to epidemic extinction , even in large cities . Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission , rather than through high-amplitude fluctuations in transmission rates . We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom . Unlike the regular limit cycles seen in the UK , measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns . We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission . This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics .
Acute immunizing infections remain a leading cause of death worldwide , and have accounted for a significant portion of all morbidity and mortality throughout human history , especially among children and in countries without adequate vaccination coverage [1–4] . Understanding the processes that determine epidemic patterns in these diseases can aid in forecasting and improve the efficacy of public health interventions . Studying the epidemiological dynamics of these diseases also provides a unique window on population-level predictability and its limitations , in an important applied context . Epidemics of acute immunizing infections often occur in predictable cycles[5–10] . The underlying drivers of measles epidemics are particularly well understood , consisting of the basic demographic clockwork of repeated depletion of the susceptible population by infection or vaccination , followed by susceptible recruitment through birth . Cycles of human aggregation from school holidays or the migration of workers and their families cause seasonal fluctuations in transmission to sustain recurrent epidemics [2 , 11] . This overall clockwork is modulated by secular variation in susceptible recruitment caused by changes in birth rate and vaccination uptake [12] , and by demographic stochasticity and local extinction in small populations , which predisposes smaller towns and cities to be entrained to the dynamics of larger metropolitan centers [13 , 14] . Simple mathematical models that incorporate these drivers have in many cases successfully predicted incidence patterns , making measles a canonical system in the study of non-linear population dynamics and prime target for elimination [5 , 6 , 11 , 12 , 14] . The most intensively studied incidence patterns for measles are from Europe—notably the UK—during the prevaccination era and are characterized by stable limit cycles ( regularly occurring seasonal epidemics ) with annual or biennial periods [5 , 11 , 15 , 16] . In the biennial cycles , susceptible depletion in the major epidemic years is replenished by births throughout the following year , during which minor epidemics may occur[17] . Increasing birth rate in this context causes the susceptible population to replenish more rapidly , leading to a collapse from biennial to regular annual cycles , as observed during the post-World War II “baby boom” [5 , 12 , 18] . In contrast , recent analyses of measles in western Africa—notably Niger—have revealed complex dynamics , featuring episodic epidemics with highly variable amplitudes . These are primarily caused by sharp seasonal increases in population density driven by collective migration [2] resulting in deep epidemic troughs , through nonlinear resonant feedbacks [6 , 19] . Owing to the intense seasonality , the equations describing measles dynamics in Niger produce deterministically chaotic trajectories , as conjectured by previous theory [6 , 18] . However , the deep post-epidemic troughs invariably break the chains of transmission , precluding local persistence . Previous case studies , therefore , suggest an impossible tension with respect to chaos in real world epidemics . Despite its mathematical plausibility [20–23] , the large amplitude seasonal fluctuations in transmission rates that have been presumed a prerequisite for chaos [12 , 17 , 19] , in practice result in so deep epidemic troughs that frequent stochastic extinctions are inevitable[19] . Thus exotic nonlinear dynamics and local persistence have been thought to be in opposition in nature [24] . We refute that hypothesis here by showing evidence of widespread persistent chaos in the epidemic dynamics of prevaccination measles in the United States , which emerged via a new route to chaos that is less prone to stochastic extinction .
We take a comparative approach , analyzing 20-year biweekly time series data on measles incidence in 80 major cities , 40 in the US and 40 in England and Wales ( UK ) . To compare these contexts , we fit a Time-series Susceptible Infected Removed ( TSIR ) model to the measles incidence data for each of the 80 cities [5 , 6 , 11] . The TSIR model describes macroscale properties of the stochastic branching process of measles spread , focusing on the expectation for number of secondary cases arising from the current population of infected individuals ( hereafter the “deterministic skeleton” ) and the probability distribution describing variation around that expectation due to the stochastic nature of infectious disease spread . Representing the number of infected individuals in generation t by It , the concurrent number of susceptible individuals by St , and the population size by Nt , the TSIR model is given by E[It+1]=βtItαStNt−1 ( 1 ) It+1∼Neg . Bin . ( E[It+1] , It ) ( 2 ) where E[ . ] is the expected value and βt represents seasonally fluctuating transmission rate in each city . The mixing parameter α , usually set at slightly less than unity , accounts for latent inhomogeneity in contact patterns between susceptible and infected individuals [23] , as well as compensating for instabilities arising from discretizing the underlying continuous-time process [25] . Following previous work [11 , 16] , we use α = 0 . 975 for all cities , which leads to good performance under forward simulation of the model . The TSIR model for measles operates at the characteristic two-week serial interval of infection . Eq 2 represents the birth-and-death stochasticity inherent in transmission dynamics resulting in a negative binomial distribution of new cases with mean E[It+1] and dispersion parameter It , so that the variance in It+1 is given by E[It+1] + E[It+1]2/It . To study the deterministic skeleton of the dynamics , we model It+1 = E[It+1] in place of Eq ( 2 ) . Susceptible dynamics are modeled as St+1=St+Bt−It+1 ( 3 ) where Bt is the observed time-varying birth rate in a given city ( see below ) . Secular variation in susceptible recruitment is a well-known driver of variation in measles periodicity [12] that we account for by using data on birth rates for each city when fitting and doing forward simulations . The full procedure for fitting the TSIR model to data follows well established techniques [5 , 11] that also included here as Supporting Information ( S1 Text ) . We assembled biweekly time series of measles incidence in US cities using the Project Tycho database [26] and took biweekly measles incidence and demographic data for cities in the UK from previous work [11 , 27] . For US cities we took estimates of population size for each city over the period of the study from census data [28] and estimated effective birth rates by differencing biweekly time series of the number of children under one year old [29] , adjusting for the rate at which children age out of this class . For total and infant population sizes in the US , biweekly time series were obtained by evaluating at each biweek a spline function fitted to the decennial data ( see S1 Text ) . Variations in the approach to reconstructing US recruitment rate , including varying background infant mortality , and changing the degrees of freedom in spline fitting , did not affect the results . We used data for the 40 US cities in the Project Tycho database with the most records of measles incidence , which included most major US cities . While the Project Tycho database has measles incidence data from 1903 to 1953 , data coverage was uniformly high for these 40 cities between 1920 and 1940 , so we used that period in the analysis . For the England and Wales measles data we used the city of London plus the largest 39 cities that were more than 50km from London to prevent a “borough effect” where UK cities in the greater London area are entrained to its dynamics . Due to limitations on data availability , the US measles data we used extends from 1920–1940 , whereas the England and Wales measles data extends from 1944 to 1964 . Our analysis accounts for demographic differences associated with the changing time window between the US and the England and Wales data , including differences in birth rates over time among cities and countries . Consequently , the temporal mismatch between the US and UK data does not drive the observed epidemic patterns—evidence from other sources clearly shows that measles epidemics in London , UK and other major UK cities remained predominantly biennial and non-chaotic in the period covered by the US data ( 1920–1940; see S1 Text ) [15 , 30] .
Measles dynamics varied systematically among cities and countries in the prevaccination era , with US cities exhibiting more diverse and episodic epidemics than cities in the UK . Whereas measles dynamics in the UK were predominantly locked on a biennial cycle , as previously reported [5 , 6 , 11 , 31] , a majority of US cities showed lower frequency , higher amplitude oscillations ( Fig 1 ) . Consequently , the mean periodicity of a city’s measles incidence ( see S1 Text ) varied more widely among US cities , and was higher on average , compared to cities in the UK . We found a comparable systematic variation in the shapes of the underlying seasonal transmission patterns in each country , particularly a systematically lengthening of the summer period of low transmission in US relative to the UK ( Fig 2A ) . Given the biology and demography of measles transmission , it is likely that this lengthening is associated with historical differences in timing and duration of school summer holidays between the two countries [11 , 32] . Corroborating historical data on the timing of school holidays in US cities is not currently available . Whatever their origins , we show that these systematic differences in transmission rates caused measles dynamics in the US to diverge from the stable annual or biennial limit cycles previously characterized in measles epidemics for the UK . US measles cycles exhibit higher and more variable mean periodicity ( Fig 2B–2E ) and are more sensitive to initial conditions ( Fig 2E–2G ) , which are hallmarks of complex population dynamics[33] . This conclusion is supported by several lines of evidence , as follows . First , twenty-year forward simulations of the deterministic skeleton of the TSIR model parameterized with the fitted seasonal transmission function for each city yielded close fits to the times series of measles incidence ( Fig 2B–2D and Fig C in S1 Text ) , confirming that observed differences among cities and countries in epidemic complexity can be explained by systematic variation in seasonal transmission patterns . Occasional discrepancies between the data and the forward simulations ( such as for London in 1964 , where the epidemic was smaller than predicted ) are due in part to the accumulation over many biweekly timesteps of measurement errors in the data on birth rates and case counts , which were imperfectly reported . In addition , latent processes not included in the TSIR model , such as variation in age structure , may cause discrepancies between the forward simulations and the data . However , previous work [12] has shown that the long-term impact of such latent structure may be encoded in the shape of the seasonal transmission function , which could explain how simple models can successfully capture the key features of epidemics in complex populations , as is the case with the measles periodicity described here . As a second line of evidence for transmission-driven differential complexity in US measles epidemics , the deterministic TSIR simulations of measles in US cities were much more sensitive to initial conditions relative to UK cities . That is , slight changes to the initial proportion of the population that was susceptible or infected in US cities produced large differences in epidemic periodicity , but this was not the case for UK cities ( Fig 2E–2G ) . The fine-scale dependence on initial conditions in US cities precludes long-range historical forecasts of measles epidemics in the prevaccination US because the outcome of such simulations depends on precise estimates of the initial proportion of the population that is susceptible and infected , which cannot be estimated without significant statistical uncertainty ( Fig 2F and 2G ) . This is in contrast to the UK , where accurate forecasts of the prevaccination era incidence time series can be achieved from a wide range of starting conditions , making forecasts in the UK resilient to statistical uncertainty in the initial susceptibility of the population ( Fig 2E ) . Third , the stochastic model ( Eq 2 ) , which continually pushes epidemic trajectories away from the deterministic skeleton , provides further evidence of how cities with subtly different seasonal transmission patterns respond to perturbations in the number susceptible and infected . The stochastic simulations also show good correspondences between the model and the data , as measured by assessing whether the distribution of periodograms generated under repeated forward simulation of the stochastic model qualitatively matched the periodogram of the data . The distributions of periodograms for UK cities ( Fig 2H ) were less dispersed than in the US ( Fig 2I and 2J ) . For US cities , stochastic simulations revealed multiple distinct periodic patterns . These distinct patterns coexist in the same parameter space , emerging as a result of stochastic variation in the simulation process alone ( Fig 2I and 2J ) . In this case the data match a subset of the possible periodograms , while other periodograms suggested by the model for a given parameterization were not observed ( Fig 2J ) . In the parlance of dynamical systems theory this suggests the presence of coexisting attractors[12] or important unstable manifolds [34 , 35] . In practical terms , the stochastic simulations imply that if time could be repeatedly wound back and played again from a similar starting point , biennial measles dynamics in UK cities would still be biennial , whereas US cities would display a diversity of possible trajectories . Where it happens that measles cycles in a US city are predominantly biennial ( e . g . New York ) , the regular periodicity belies a sensitivity to initial conditions . As further evidence for chaotic measles dynamics in the US we calculated dominant Lyapunov Exponents ( LE; see S1 Text ) for each city in the data . LEs measure the rate at which similar epidemic trajectories converge ( LE < 0 ) or diverge ( LE >0 ) [6 , 36] , quantifying the sensitivity of a dynamical system to small changes in state . While all LEs for cities in England and Wales were negative , the majority of US cities had positive LEs ( Fig 3A ) . This corroborates the results of the stochastic simulations , providing another line of evidence for sensitive dependence on initial conditions across US cities , due to a slight change in the shape of the seasonal transmission function relative to cities in the UK . Surprisingly , however , the resulting complex dynamics in the US were as stable as those in the UK in terms of the risk of local extinction , with local extinction rare in all cities above around 300 , 000 inhabitants in both countries ( Fig 3B ) . The highly irregular measles dynamics of the US are , thus , as robust to stochastic extinction as the clock-like regularity of the epidemics in the UK . This is surprising because previous models of chaotic epidemic dynamics for seasonally immunizing infections predicted that increased complexity is accompanied by increased risk of stochastic extinction , apparently precluding persistent deterministic chaos in real-world scenarios [12 , 19 , 24] . Analysis of the clockwork underlying these epidemic dynamics reveals two distinct routes to deterministic chaos for seasonally modulated immunizing infections ( Fig 4 ) . To demonstrate these routes we began with the TSIR model for Los Angeles , US , which has a mean periodicity of ~3 years and a positive LE , and systematically varied the amplitude of the seasonal transmission function , and/or the duration of the period of low transmission ( see S1 Text ) , while holding susceptible recruitment constant . On one hand , increasing the amplitude of seasonal oscillations in transmission leads to chaotic dynamics through the previously well-characterized route [6] , corresponding to the extinction-prone measles dynamics observed in Niger , where deep epidemic troughs frequently break local chains of transmission [19] . On the other , increasing the duration of the seasonal period of low transmission , while holding seasonal amplitude constant , also leads to chaos . In contrast to the chaotic measles epidemics previously described in Niger , the new route to chaos revealed in the prevaccination US is associated with local persistent chains of transmission ( Fig 3B ) . Therefore , although these distinct routes to chaos yield equivalent levels of deterministic complexity , they are associated with contrasting properties of local persistence: only the new low-amplitude route to chaos exemplified by measles in US cities can sustain true chaotic fluctuations for a significant period of time ( Fig 4B ) . The existence of distinct routes to chaos with contrasting probabilities of local extinction explains both the complexity and persistence of measles epidemics in the prevaccination United States , as well as the systematic differences between measles epidemics in the prevaccination US , the prevaccination UK and in present-day sub-Saharan Africa . In particular , we found transitions to complex epidemic dynamics do not necessitate high-amplitude fluctuations in transmission rates nor broad secular changes in susceptible recruitment , as previously thought [12] . The US analysis shows that subtle shifts in seasonal transmission patterns can also lead to chaos . But the origins of dynamic complexity—whether through the canonical routes or the newly described low-amplitude route operating in the US—have important implications for the local persistence of the resulting epidemics . Finally , we note that systematic differences in dynamics at the city level may have propagated to affect countrywide patterns in the spatiotemporal coherence of disease incidence patterns . While the annual or biennial predictable measles cycles in UK cities represented synchronous phase locked oscillations across the entire island[14 , 37] , the locally persistent chaotic dynamics in the US appeared to break the phase-lock in US measles epidemics , both over the same spatial scale as the UK , and overall ( Fig B in S1 Text ) . However , further , more detailed , spatial analyses are necessary to tackle systematic variation in the strength of these correlations at inter-city and regional scales . This may be an interesting avenue for future work .
The realization in the 1970s that simple models of population growth can have complex dynamics [20] , spurred several decades of effort in ecology and epidemiology to explain highly variable time series using a few general equations [22 , 38] , with hopes of emulating the success of Newtonian physics [24] . Although controlled laboratory experiments supported the hypothesis that complex dynamics in living populations can emerge from simple rules [39] applications to real-world scenarios were often stymied by the role of stochasticity—chance events play a significant role in the growth trajectories of many live populations , but such variation is minimized in deterministic models and controlled experiments . A particular challenge was the fact that canonical chaotic models of seasonally forced epidemics carried a high risk of stochastic extinction . Specifically , the route to chaos described by these models involves broad-scale changes in susceptible recruitment , such as changes in birth rate or vaccination coverage , or significant structural changes in the seasonal pattern of transmission , such as changes in the amplitude of seasonal fluctuations in transmission rate [12 , 19] . But these structural changes result in deep epidemic troughs , where the chain of transmission is maintained by only a few individuals . This greatly increases the likelihood that an epidemic will fade out , due to random variation in the timing of infection and removal events [12 , 19 , 40] . This tradeoff , where achieving a realistic level complexity requires an unrealistic rate of stochastic fadeouts , apparently precluded persistent deterministic chaos as an explanation for capricious incidence time series . In contrast , we have shown that small shifts in the seasonal pattern of disease transmission can offer a new , more stable , route to persistent deterministic chaos ( Fig 4 ) . The relative importance of noise and determinism in population dynamics varies with context: for instance , stochasticity appears to generate proportionally more of the amplitude in rubella cycles ( as well as driving patterns in local extinction ) , because the deterministic skeleton of these dynamics falls on an attractor that is globally less stable [41] . Similarly , the intermittent 3–4 year periodic pertussis dynamics is thought to emerge from stochastic resonance around a deterministic skeleton with a dominant annual period [42] . For US measles dynamics in the prevaccination era , the effects of stochasticity and determinism are inextricably intertwined through highly nonlinear sensitive dependence on initial conditions . Although not conclusive , analysis of cross correlation in measles incidence across cities suggests that US cities may have had less synchronized epidemics at regional and country-wide scales ( Fig B in S1 Text ) . The level of synchrony among connected populations has been shown to influence patterns of disease persistence per se , but predictions for the impact of chaos on metapopulation dynamics have been somewhat equivocal , as epidemic chaos has its own complex relationship with persistence . Specifically , spatial decorrelation , such as that seen among US cities , can improve disease persistence in a metapopulation context , as subpopulations that experience local extinctions may be more likely to be rescued by connected subpopulations that have not experienced a fadeout—the metapopulation “rescue effect” [43–45] . However , spatial decorrelation specifically linked to chaos was previously thought to be an unlikely source of pathogen persistence , because deep seasonal troughs in transmission rates , thought to be a prerequisite to chaos , tend to synchronize the timing of fadeouts across the metapopulation , diminishing the rescue effect [40] . The new route to locally persistent but decorrelated dynamics may change this perspective . Our analysis demonstrates the impacts of chaos on the metapopulation dynamics of cities , showing that the network consequences of complex epidemic patterns depend on the origins of the complexity . On one hand , if complex cycles emerge via high amplitude fluctuations in transmission rates , then local populations will be more likely to experience synchronous fadeouts , and metapopulation rescue effects will not considerably improve local persistence[19 , 40] . On the other hand , if complex epidemic cycles emerge from slight changes in the duration of the seasonal period of low transmission—as shown here for measles in the prevaccination era US—local populations will experience relatively higher rates of disease persistence , in addition to a plausibility of significant metapopulation rescue effects . The enhanced persistence is consistent with the data presented here , where the observed probability of measles fadeouts across US cities ( Fig 3B ) was still lower than that predicted in single-city simulations under the more stable route to chaos operating in the US , suggesting the presence of a rescue effect[44] ( Fig 4B ) . In conclusion , the emergence of persistent chaotic epidemics in the prevaccination US from small shifts in seasonal transmission patterns reveals a novel and potentially widespread route to chaos in population dynamics[24 , 46 , 47] . Moreover , these results show empirically that the viability of chaotic populations depends subtly on the route to chaos . In practice , this means that small perturbations in transmission rates , such as those caused by shifts in host behavior or the imposition of epidemic control measures , can lead to a rapid erosion of the capacity to forecast epidemic patterns , which can in turn reduce the efficacy of control strategies such as reactive vaccination[2 , 19 , 48 , 49] . Generally , population dynamics are deterministically more sensitive to perturbations than previously thought . | Measles epidemics continue to pose a significant public health risk wherever vaccination coverage is low . In such populations transmission rates tend to fluctuate seasonally , mirroring patterns of human aggregation , due to the timing of school terms , and/or the migration of workers and their families . Here we show empirically that slight changes in the seasonal pattern of measles transmission can lead to massive shifts in the complexity of measles dynamics , in some cases driving epidemic patterns that resemble deterministic chaos . Our analysis is based on a comparison of 20-year biweekly measles incidence time series in 80 major cities in the prevaccination era United States and United Kingdom . The results are important in two ways: first , in contrast to previous theory , we show that subtle shifts in seasonal patterns of transmission can cause deterministic chaos in the epidemic dynamics of acute immunizing infections; second , we demonstrate that this new route to deterministic chaos is significantly more robust to stochastic extinction compared with previous chaotic models , suggesting chaotic dynamics may be more common in natural populations than previously thought . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2016 | Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns |
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